20180402Annual Report.pdf

AEwsra
- 13.o(
Avista Corp.
1411 East Mission P.O.Box3727
Spokane. Washington 99220-0500
Telephone [phone]
ToflFree 800-727-9170
March 30, 2018
Diane Hanian, Secretary
Idaho Public Utilities Commission
Statehouse Mail
W. 472 Washington Street
Boise,Idaho 83720
RE:Avista Utilities 2018 Annual Report Regarding Selected Research and Development
Efficiency Projects
Dear Ms. Hanian:
Enclosed for filing with the Commission is an original andT copies of Avista Corporation's dba
Avista Utilities ("Avista or the Company") Report on the Company's selected electric energy
efficiency research and development (R&D) projects, implemented by the state of ldaho's four-
year Universities.
Please direct any questions regarding this report to Randy Gnaedinger at [phone] or myself
at 509-495-4975.
Senior Manager, Regulatory Policy
Avista Utilities
509-495-497s
linda. gervai [e-mail]
.': t\3.{
.=--.- &_it=%n
,-fi* f, fTl,,E., r CT,;): h, m
\J/* I
:rH - lll;EE o
U): ff'l(J cnV
Enclosure
Aiwsra
AVISTA UTILITIES
SELECTED RESEARCH AND DEVELOPMENT
EFFICENCY PROIECTS . IDAHO
Annual Report
March 30,2018
Avista Research and Development Projects Annual Report
IVlarch 3O 20'18
THE FOLLOWING REPORT WAS
PREPARED IN CONFORMANCE WITH
IDAHO PUBLTC UTTLTTTES COMMTSSTON (IPUC)
CASE NO. AVU-E.13.08
oRDER NO. 32918
March 30,2018
Page | 1
Avista Research and Development Projects Annual Reportlrar.h ?n 2n1A
ANNUAL REPORT
SELECTED RESEARCH AND DEVELOPMENT EFFIGENGY PROJECTS
IPUC CASE NO.32918
TABLE OF CONTENTS
I. SCOPE OF WORK 3
3A. lntroductionB. BackgroundII. KEY EVENTSA. Request for lnterest.
B. Selection of Projects
C. Description of Selected Projects
D. Project Manager and Related Communications;................
E. Agreements
F. Project Milestones...III. ACCOUNTINGA. Schedule 91 Available Funds
B. Funds Authorized for R&D Projects in 201612017
C. Funds Expended and Remaining Balance
IV. PROJECT BENEFITS..A. Residential Static VAR Compensator (RSVC) Year 3
C. CAES: Water/Energy Conservation Analysis..
D. IDL Energy Management Phase 2V. RESEARCHIN-PROGRESS....A. Summary of Research ln-Progress
B. Other Relevant Activity
LIST OF APPENDICES
,,4
,.4
,.4
5
5
7
..8
..8
10
10
10
10\t
12
t2
12
13
13
13
15
APPENDIX A
APPENDIX B
APPENDIX C
APPENDIX D
APPENDIX E
APPENDIX F
APPENDIX G
APPENDIX H
APPENDIX I
APPENDIX J
APPENDIX K
APPENDIX L
Two-Page Reports
Request for lnterest
Boise State University Agreement
University of ldaho Agreements
Final Report: RSVC Year 3
Final Report: Micro Grid Phase 2
Final Report: CAES Water Energy Conservation
Final Report: IDL Energy Management Phase 2
lnterim Report: RSVC Year 4
lnterim Report: Energy Storage
lnterim Report: IDL Temperature Efficiency
lnterim Report: Aerogel
Page | 2
Avista Research and Development Projects Annual Report
March 30, 2018
I. SCOPE OF WORK
A. lntroduction
This report is prepared in conformance with ldaho Public Utilities Commission
(IPUC) order No 32918. This includes key events during the reporting period and
accounting for related expenditures.
Avista Corporation, doing business as Avista Utilities (hereinafter Avista or
Company), at 1411 East Mission Avenue, Spokane, Washington, is an energy
company involved in the production, transmission and distribution of energy as well
as other energy-related businesses. Avista Utilities is the operating division that
provides electric service to more than 600,000 electric and natural gas customers.
Their service territory covers 30,000 square miles in eastern Washington, northern
ldaho and parts of southern and eastern Oregon, with a population of 1.5 million.
Avista also provides retail electric service in Juneau, AK through a subsidiary called
Alaska Electric Light and Power Company.
-
SEntVffirmfiofiY
a Bocrri€ rnd tlltural G.r
Q [atudGrs
Page | 3
Avista Service Territory
l(ettlo Falh O ONTANA$r
SpokrneO
othello o Pulman
Clarkstori
o
\r-{
b Gnndc $v /
I
OREGON tDAt{o
Avista Research and Development Projects Annual Report
March 30,2018
On August 30, 2013 Avista applied for an order authorizing it to accumulate and
account for customer revenues that will provide funding for selected electric energy
efficiency research and development (R&D) projects, proposed and implemented
by the state of ldaho's four-year Universities. On October 31, 2013 Order No.
32918 was granted to Avista. Avista now recovers up to $300,000 per year of
revenue to research from the Company's Schedule 91 Energy Efficiency Rider
tariff.
This program provides a stable base of research and development funding that
allows research institutions to sustain quality research programs that benefit
customers. lt is also consistent with ldaho Governor Butch Otter's ldaho Global
Entrepreneurial Mission "iGem" initiative in which industry would provide R&D
funding to supplement funding provided by the State of ldaho.
B. Background
ln the 1990s, with the prospect of electric deregulation, utilities reduced or
eliminated budgets that would increase costs not included by third-party marketers
for sales of power to end-users. Research and development was one of those
costs. This has led to the utility industry having the lowest R&D share of net sales
among all US industries.
Research and development is defined as applied R&D that could yield benefits to
customers in the next one to four years.
ln 2010, Governor Otter announced ldaho would support university research as a
policy initiative with some funding provided by the state and supplemental funding
expected from other sources. This project provides additional funding to selected
research.
!I. KEY EVENTS
A. Request for lnterest
The request for interest for projects funded in 201612017 academic year was
prepared and distributed to all three ldaho Universities on March 21,2016. A full
copy of the request for lnterest is included in Appendix B.
On April 21, 2016, Avista received 9 proposals from the University of ldaho and 1
proposal from Boise State University. Following is a list of the proposals received:
Universitv of ldaho
1. Grid Defender Utility Pole lnstallation and Protection System2. Case Studies of lmpact LED Lighting Upgrades on Overall Energy Use to
Better Structure Avista !ncentives
3. Developing Practical Energy Saving Recommendations for the North West
lndustries and Assessing of Environmental Protection
Page | 4
Avista Research and Development Projects Annual Report
March 30,2018
4. Electric School Buses with Secondary Vehicle to Grid Energy Utilization
5. Downtown Spokane Micro Grid
6. CAES Water/Energy Conservation Analysis with Avista
7. Assessment of Potential Energy Savings Through Solar Roadways
lnstallation on Ul Campus
8. Energy Trading System
9. Using Reduced-Order-Models for Simulation-Based Commissioning of
Buildings
Boise State Universitv
1. Operation and Control of Distributed Residential Static VAR Compensator
(RSVC) Phase lV
B. Selection of Projects
Avista prepared an evaluation matrix for the 10 proposed projects. A team of
individuals representing Distribution, Transmission Planning, Generation and
Demand Side Management, co-filled out the matrix to rank each of the projects.
The following factors in no particular order were considered in the ranking process.
. Research Areas Already Being Done (EPRI, WSU, AVA)
Com plemenURed undanUNew. PotentialValue to Customers kwh/K/V/$ (1-10). COz Emission Reduction (Y/N). Market Potential (1-10). Are Results Measurable (Y/N). Aligned with Avista Business Functions (Y/N). New or Novel (Y/N). Ranking (1 -10)
C. Description of Selected Projects
Following is a brief description of each of the four selected projects. Project teams
compiled "Two Page Reports" which summarize and highlight project details. These
Two Page Reports are included in Appendix A. Additional details are included in
the final project reports in Appendix E, Appendix F, Appendix G, and Appendix
H.
RSVC Year 3: Operation and Control of Distributed Residentia Static VAR
Compensators (RSVC) Phase lV
Phase I of this project was funded in Year 1 and consisted of a study model of a
Residential Static VAR Compensator (RSVC) for regulating residential voltages.
Phase !l of this project was completed without funding assistance from Avista. This
phase consisted of building an open loop control prototype of the RSVC device.
Page | 5
Avista Research and Development Projects Annual Report
March 30,2018
Phase lll of this project was funded in Year 2 and consisted of performing a time-
series simulation over multiple months using a model developed in OpenDSS of
downtown Spokane and a ruralfeeder near Lake Pend Oreille.
The objectives of the research during Phase lV were to develop the framework and
simulation platforms to allow for distributed control algorithm tests of the Residential
Static VAR Compensator. The research included simulation of 1) voltage control, 2)
power factor control, 3) multi-RSVC interaction, 4) RSCV interaction with voltage
regulators, battery energy storage systems, photovoltaic generation, and pre-
existing capacitor banks, 5) conservation by voltage reduction, and 6) RSVC
simulation comparison to field measurement data.
Micro Grid Phase 2: Downtown Spokane Micro-qrid
The scope of this research was to perform studies on how to establish a microgrid
under emergency conditions. The goal was to create a practical plan to investigate
and employ a master controller to establish the microgrid and to manage the
inclusion of generation, energy storage, and critical loads in a set of anticipated
scenarios. Advantage to the ratepayers was measured through maintaining critical
loads for public safety and system reliabili$ and security and improved recovery
from emergency situations.
A unified model of the microgrid was developed in Powerworld simulation software.
This model was then used to validate a model of the system built in RSCAD. Once
this was completed the control systems for load shedding and battery control were
added. A study was then performed to ensure the control systems behaved as
expected. This is demonstrated in the analysis and key results section below. ln
addition, a study was performed to determine the feasibility of adding battery
storage, followed by determination of optimal locations, and rating of the potential
battery storage systems. The microgrid has a very good potential to improve the
resilience of the system since it is predominantly supplied by hydroelectric
generation that is very close to the critical loads.
CAES: Water/Enerov Conservation Analvsis with Avista
The University of ldaho and ldaho National Laboratory through their partnership
with the Center for Advanced Energy Studies (CAES) worked collaboratively with
one carefully selected member of the Northwest Food Processors Association
(Litehouse Dressing in Sandpoint, lD) to do a combination of process modeling and
system-dynamic modeling of their operation. The goal was to identify and create
plans for implementation of new technologies that would significantly reduce their
energy and water consumption. Because of the nature of the energy use in food
processing, new technologies have the potential to substantially reduce the energy
and water use by some customers.
Five models were developed to simulate the Litehouse process energy balance.
Models 1 and 2 were run using HYSYS, and they represent the two refrigeration
Page | 6
Avista Research and Development Projects Annual Report
March 30,2018
cycles responsible for maintaining the walk-in refrigerator temperature at 38'F.
Models 3 and 4 were run using HYSYS, and they were used to help determine the
current cooling load on the floorplan and the energy load on the individual systems.
Model 5 was a mathematical model run using Python, and it was used to help
determine the approximate size of refrigeration equipment needed to maintain a
room at a specified temperature.
IDL Enerqy Manaqement Phase 2: Usinq Reduced-Order-Models for Simulation-
Based Commissionino of Buildinos
Phase 1of this project was funded in Year 1 and completed by the University of
ldaho lntegrated Design Lab (lDL). During Phase 1, the group performed a virtual
control commissioning for part of the HVAC system at a building on the University
of ldaho campus. A detailed energy model was combined with communication
pathways to the building control hardware. The research on the economizer setting
at the College of Business and Economics (COBE) building revealed areas for
optimization.
The continuation of this research in Phase 2 included moving this technology
forward in two important ways: 1) by performing a physical demonstration at the
site, and 2) by simplifying the modeling process. A physical implementation of
virtual commissioning would greatly add to the commercial case for this technology.
Additionally, the research aimed to simplify the fully detailed building models to
reduced-order thermal models that could be used to tune building controls.
The research focused again on the Albertson's College of Business and Economics
building (COBE) at the University of ldaho Moscow Campus. The reduced order
model was composed of sets of differential equations with system parameters
which describe the dynamic nature of heat transfer though a building. These
parameters were determined through software optimization in order for the model to
best predict the zone temperature of the building when compared to the zone
temperature as predicted by EnergyPlus. The reduced order model was coupled
with an HVAC model to predict the total annual energy consumption of the building
which was then used to determined potential energy savings measures. lt was
found that the COBE building lacked thermostat setbacks during periods of
unoccupancy, and the ROM model was used to predict the energy savings
associated with updating the controller. lt was found that approximately 104,000
kWh of potential energy savings could be realized if the thermostat had properly
programed temperature setbacks during times the building is unoccupied.
D. Project Manager and Related Communications;
Avista set out to find an independent third pafi project manager based in ldaho.
On September 26, 2014 Avista entered into an agreement with T-O Engineers as
this independent third party project manager. T-O Engineers is an ldaho company
based in Boise, Idaho with offices in Boise, Meridian, Coeur d'Alene, and Nampa,
ldaho, as well as Spokane, Washington.
Page | 7
Avista Research and Development Projects Annual Report
March 30.20't8
T-O is tasked with providing project management, organizational structure,
milestone setup, milestone tracking, and incidental administrative services. The
project manager for T-O Engineers is JR Norvell, PE. The deputy project managers
are Natasha Jostad, PE, and Tyson Schlect, ElT. JR and Natasha are both based
out of the Spokane office, and Tyson is based out of the Boise office.
E. Agreements
On Augusl25,2016 Avista entered into an agreement with Boise State University
The full agreement is included as Appendix C.
By July 18, 2016 Avista executed individual task orders which were assigned for
each of the University of Idaho research projects selected, and those agreements
are included in Appendix D.
F. Project Milestones
The following graphic identifies each project's specific tasks as well as the overall
research and development schedule and milestones. Final reports from each
Principle lnvestigatorwere submitted in the fall of 2017.|n addition to the written
report, each research team presented their findings in person to Avista. IDL and the
CAES group both presented their findings to Avista on August 15,2017, and the
RSVC group and Microgrid group presented their findings on August 29,2017.
Page | 8
Annual ReportAvista Research and Development Projects ,.r"n aO. rO.,,
l. Prcject ltlanagament
2. Develop Follow{n Propo3al rTask 1: Project KickOff
3. Prepare Final Report I I
Task 2: Oata Acquisition
Task 3: RSVC Dynamic Simulation
Task 4: Effect of RSVC on Existing Caps
Task 5: RSVC sizo 8nd Voltago Ctil Alg.
Task 6: HW Prototype Dev.
Preliminary Testing
Task 7: Oraft Report
I I I I I I I III .rLTask 8: Final Report and Presentation
Task 1: Mastercontroller
lMain Oirective)
Task 2: RTDS Model
Task 3: Small Scale Example Model
Task 4: Small Scale Example Model:
Trip Breaker
Task 5: Small Scale Exampls Model:
Trip Broaker with Rel.y
Task 6: lsland Detection Setting3
I
Task 7: Load Shedding with VipoB
.-T---
I
I
Task 8: Protection Settings
Task l: Gather Basoline Data
Task 2: Establish on Site Communication
fask 3: Simplify Detailed Energy ltlodel
Task 4: Analyze Effectiveness of Reduced
Order Models
F I I art
fask 5: Develop Workflow for Practitione6
fask 1: Project Kickoff
Task 2: Meet with Lighthouse
Iask 3: Receive Requested Process
lnformation
I I
fask 4: Provide Working Simulalion of
Proce3a
1. Project Kickoff
I
FL I
2. Follow on Proposal to Avista
3. Final Report and Presentation to Avista
4. IPUC Oeliverablo3
Page | 9
Task Description 5sp/r6 ocqL6 Nd/16 De.l16 tanltT FebllT Ma.l17 Ap.l17 MaylLT !!nl17 tolltT k eJlT
lDt
CAES Water
All
Avista Research and Development Projects Annual Report
March 30,2018
III. ACCOUNTING
A. Schedule 9'l Available Funds
Effective November 1, 2013 Avista can fund up to $300,000 per year of R&D from
revenue collected through Avista's Schedule 91, Energy Efficiency Rider
Adjustment. At the end of each year, any monies not allocated toward payment on
R&D projects roll over as available resources for the next year. A summary of the
balance for Schedule 91 from the beginning of Order No. 32918 is shown in the
table below.
B. Funds Authorized for R&D Projects in 201612A17
Contracts lor 201612017 are as follows:
C. Funds Expended and Remaining Balance
Following is the final budget summary tor 201612017 FY R&D Projects.
Academic
Year
New
Funding
Balance
from
Previous
Year
Total
Funds
Available
Contracted
Amount
Actual
Expenditures Balance
2014t2015 $300,000.00 $0.00 $300,000.00 $287,941.00 $243,467.32 $56,532.68
2015t2016 $300,000.00 $56,532.68 $356,532.68 $252,493.00 $235,809.03 $120,723.65
2016t2017 $300,000.00 $120,723.65 $420,723.65 $372,665.16 $358,641.82 $62,081.83
2017t2018 $300,000.00 $62,081.83 $362,081.83 $326,755.89
Agency Description Contract
Amount Point of Contact
Boise State University RSVC Phase 3 $ 98,901.00 Dr. Said Ahmed-
Zaid
University of ldaho Microgrid Phase 2 $ 86,179.61 Dr. Herbert L. Hess
University of ldaho CAES Water Energy
Conservation $ 93,354.55 Dr. Richard N
Christensen
University of ldaho IDL Energy Management Phase
2 $ 64,230.00 Elizabeth Cooper
T-O Engineers Project Manager $ 30,000.00 James R. Norvell
Total $ 372,665.16
Page | 10
Avista Research and Development Projects Annual Report
lvlarch 3O 2OlR
Description Contract
Amount Total Expended Budget
Remaininq
RSVC Phase 3 $ 98,901.00 $ 95,934.99 $ 2,966.01
Microgrid Phase 2 $ 86,179.61 $ 86,179.6't $ 0.00
CAES Water Energy Conservation $ 93,354.55 $ 82,332.95 $ 1 1,021.60
IDL Energy Management Phase 2 $ 64,230.00 $ 64,230.00 $ 0.00
Project Manager $ 30,000.00 $ 29,964.27 $ 35.73
Totals $ 372,665.16 $ 358,641.82 $ 14,023.34
D. Cost-Recovery
The costs associated with R&D are funded from revenue collected through Avista's
Schedule 91 - Energy Efficiency Rider Adjustment. The outstanding balance was
rolled over to the current year's R&D budget, as seen in the table in Section A. All
R&D projects are invoiced on a time and materials basis with an amount not to
exceed. The costs would be included in the Company's annual tariff filing in June if
the rider balance requires a true-up.
Page | 11
Avista Research and Development Projects Annual Report
March 30.2018
!V. PROJECT BENEFITS
A. Residential Static VAR Compensator (RSVC) Year 3
RSVCs offer potentially significant energy savings. The RSVC device increases
cost effectiveness by voltage regulation and is a valuable tool in energy efficiency.
RSVCs offer distinct advantages over traditional shunt capacitor solutions. RSVCs
can operate in capacitive or inductive modes without generating substantial
harmonics. This device can be used in smart applications such as:
1. Continuous voltage control at load point
2. Power factor control
3. Mitigation of power quality issues
Electrical companies use conservation by voltage reduction (CVR) as a solution for
managing power distribution networks. ln order to keep end voltages within
acceptable standards, electrical companies should be able to regulate residential
voltages. The RSVC provides this solution on a local level. The device has the
potential to overtake conventional static var compensator solutions because of its
smaller footprint, power controllability and its realization as a single-phase device.
B. Micro Grid Phase 2: Downtown Spokane Micro-grid
ln recent years the electric industry has focused on increasing reliability by
interconnecting between different network systems. This has also helped increase
range of service. The biggest drawback of the interconnected system is the
sensitivity to rolling blackouts. Microgrids were developed to isolate sections of the
system to protect customers.
Development of a microgrid will overall increase the reliability of power. Energy
banks supplied with the grid can also help supply the peak load. The current
microgrid suffers from a significant power quality problem when local generation is
insufficient to handle grid mode level loads. The microgrid will include a controller to
receive data from multiple points in the system and shed non-essential loads.
G. CAES: Water/Energy Conservation Analysis
Food production is essential to the infrastructure of the United States. As production
increases so do energy and water demands. This study addresses these needs and
suggests that production facilities work in conjunction with their utility providers to
improve plant systems and operations. Decreased energy consumption has
significant financial benefits for the plant under consideration (Litehouse). Avista will
also benefit long-term by having energy demands reduced. The study focuses on
reviewing Litehouse Foods' current system and determining the where the biggest
decrease in energy consumption can be applied.
Page | 12
Avista Research and Development Projects Annual Report
Itlarch 1O 2OlR
D. IDL Energy Management Phase 2
Because every building and control system is unique, it can be difficult to tune these
controls for optimum efficiency on a large scale. Virtual controller commissioning
using energy models can identify errors and correct controls. This project aims to
streamline this technology by moving it from specific software to a mathematical
model that can be generated for any building based on climate, loads and
construction. This will help broaden the appeal of the virtual commissioning process
and many industry partners may market this as an energy-savings service to their
clients. Virtually commissioning the system using a thermal model holds great
promise in being able to test for missed energy savings or occupant discomfort as
compared to the design intent.
V. RESEARCH IN.PROGRESS
A. Summary of Research ln-Progress
There are currently four projects in progress for the 201712018 academic year.
lnterim Reports are included in Appendices I through L. Milestones for each current
IPUC funded project are listed in the table below.
Page | 13
Avista Research and Development Projects Annual Report[rrrnh 30 2O'lR
'1. Projoct Menagemont
2. Dovolop Follow.on Proposal
E I I I
Task 1: RSVC Control Systom Dcaign:
Voltage Rogulation Loop
3. Prcpare Final R.porl
Task 2: RSVC Control Systom Dosign: Powor
Rogul.tion Loop
Ta3k 3: Do3ign ol a tunctaonel labor.tory
prototyp€ ot en RSVC
Task 4: Tosting ot an RSVC prototypo in a
laboratory or homa environmont
f.sk 5: Final dr.tt Rcport I
''l I
TaEk 8: Dellycr flnal r.port .nd pr.sant
ro3ult8 at Avlste hcadquartcE
fask 'l: ldontity potontial appliaations for
9nergy storagc
I I I I I I
fask 2: Dotomlna pow.r ratings, ramp ratos
fa3k 3: Categorlzc th..ppllcetlons bascd on
tha anaillary scrylc.3 thoy provlda
fask 4: Roduce th. set ot potontial
rn0ineeE
fr3k 5: Davclop rn approach tor 3lzlng
tnargy atorag.
f.3k 5: Sonior deslgn t.am: lmplamcnt
modols and for bettary onorgy storago
Task 1: Projoct Plenning and Rcportlng
T.3k 2: Establish Currcnt Bas.lina Setpoinb
Tesk 3: Soloct a Slto
Task 4: Colloct Oporational Data I
Task 5: Dovolop Energy Modol
Task 6: Test Altorn.tivo Controls
Task 7: Estimato S.yings
I F I IT.Bk 8: D3valop Workflow for Practltlonors
Tesk l: Latoretura Survay
..t
T.3k 2: As.ogal Ch.mat.rizatlon .nd
Acqulrlng Comm.ralal Producta
Task 3: Fiold Data Colloclion
T.sk 4: Cost Analysis
TaBk 5: Environmcntal lmpact
l. Projoat Kiakott
I I I I L
2. Follow on Proposal to Avista
3. Flnal Roport end Prosontation to Avista
4. IPUC Daliverabl.s
Page | 14
Task Dercription sepll'1 a&ifl Nov/l?Dsc/17 Janl18 Feb/18 Mar/18 Aer/1a May/18 Jun/18 tdl18 AuClrC
Avista Research and Development Projects Annual Report
March 30.2018
B. Other Relevant Activity
Project kick-off meetings were held on-site at each University of ldaho location and
at Boise State University.
A progress meeting is held bi-monthly for each project. These meetings typically
take 0.5 hours and include a review of schedule, monthly progress reporting,
invoicing, Avista comments, and action items for the next month. The meetings are
organized and led by the lndependent Program Manager, T-O Engineers.
Attendees for each meeting include the Principal lnvestigator, Co-lnvestigators,
Student Researchers, Avista personnel, and the lndependent Program Manager.
There are currently four projects in progress for the 201712018 academic year.
Funds were rolled over from previous years to fund an additional proyect. Contracts
for these projects total $326,755.89. Budget details and funds expended will be
summarized in the 2019 Annual Report.
Page | 15
lllytsra
APPENDIX A
Two-Page Reports
B AH-rETEBOISE STATE UNIVERSITY
OPERATION AND CONTROL OF DISTRIBUTED
RESTDENTTAL STATTC VAR COMPENSATORS (RSVC)
Project Duration: September 20t6 - August 2017 Project Cost: Total Funding 998,901
OBJEGTIVE
The overall goal of the RSVC project during
Year III is to demonstrate the RSVC ability to
boost low voltage points to the minimum
allowable level while reducing the voltage
level at the head of the feeder substation.
This way, the entire distribution network can
take part in conservation by voltage reduction(CVR). Additional benefits include rapid
voltage response to distribution feeder eventsat the residential level to improve power
quality.
BUSINESS VALUE
The deployment of multiple RSVCs offers a
significant potential for energy savings as well
as cost effectiveness by voltage regulation. It
can be a valuable tool in a utility energyefficiency and demand-side management
programs.
INDUSTRY NEED
The new single-phase RSVC device has the
distinct advantage over a conventional shunt
capacitor of being able to operate in acapacitive or inductive mode without
generating substantial harmonics. This RSVC
employs a novel pulse-width-modulation
(PWM) technique applied to two specially-
designed bidirectional switches controlling the
variable reactive power output. This smart
device can be used in multiple applications
such as continuous voltage control at a loadpoint, power factor control, mitigation of
power quality issues, etc. The new RSVC
device has the potential to disrupt current
competitor devices on several fronts includingcost, power quality, and smart-grid
applicability or compatibility.
BACKGROUND
Year I of the project consisted of a study
model of an RSVC for regulating residential
voltages. Year II of the project consisted of
developing a hardware prototype of the
RSVC. Year III of the project is to investigate
the potential benefits of deploying multiple
RSVCs on the residential side of the
distribution network.
PROJEGT TASKS - Year lll
Task 1: Project Management
Internal weekly project coordination meetings
take place every Wednesday morning. Bi-
weekly status meetings with Avista take place
on Thursdays.
Task 2: Data Acquisition
Measured residential and substation feeder
data has been received from Avista.
Task 3: RSVC Dynamic Simulation
This task will build upon the open-loop control
of the current RSVC prototype by adding a
more sophisticated control scheme for voltageand power factor control. This task is
ongoing.
Task 4: Effect of RSVC on Existing Capacitor
Transient Switching Study
This task has been assigned as a senior
design project at Boise State University. The
situation involves the modeling of a 10-mile
long distribution feeder with five uniformly
distributed loads every two miles. The
conductors are made of a/O ACSR wires. Atfull load, each load consumes 300 kVA at0.85 power factor. A fixed 1200-kVAR
switched capacitor bank is located at 2/3 of
the distance from the main substation and its
switching in and out creates an overvoltage
affecting the power quality of nearby
customers. An RSVC control system is being
designed in MATLAB/S|mulink to regulate the
voltage level of a sensitive customer and to
mitigate the effect of an overvoltage. This
task is ongoing.
Task 5: RSVC kVAr Size and Voltage Contro!
Algorithm Development
The RSVC is designed to be installed at the
residential service entrance across the 240V
bus. Figure 1 shows the proposed installation
with a fixed capacitor and a variable inductor.
h
LOAD
Residential Load Bus
RSVC
240V
Figure 1: Installation Model for RSVC
Simulation studies show that the size of the
RSVC will be dependent on the lowest voltage
point in the distribution system. An analysis
of the results indicates that the RSVC can
boost the residential service voltage by
approximately 5.3 V for each 5 kVAr of shunt
capacitance. It is recommended that
additional residential and distribution feeder
data be simulated to increase the sample set
and provide a more accurate estimate than
5.3 V. Because the overall distribution feeder
may have short periods of time where the
voltage is higher than typical, it is
recommended to match the inductive element
VAr size to the capacitive element VAr size.This will prevent an overvoltage at the
residential service due to the fixed capacitor.This task is complete pending Avista
approval.
Task 6: Hardware Prototype Developmenti. Hardware Prototype
The RSVC prototype is being developed in a
power laboratory at Boise State University.
This prototype is based on the simulation
model shown in Figure 2, This device is a
reactive power compensator that can regulatea residential load voltage with a fixed
capacitor in shunt with a reactor controlled by
two bidirectional switches. The two switches
are turned on and off in a complementary
manner using a pulse-width modulation
(PWM) technique that allows the reactor to
function as a continuously-variable inductor.
The current RSVC prototype consists of a
190-pF capacitor and a 30-mH inductor
choke. The This device can operate in a
capacitive or inductive mode without
generating substantial harmonics. The RSVC
uses a novel pulse-width-modulation (PWM)
technique to create the variable reactive
compensation.
Transformer Output
Voltage
Figure 2: Simulation Model for RSVC
ii. RSVC Hardware Prototype Testing Results
Preliminary test results for the hardware
prototype were performed at 60 VAC with a20-ohm resistive load. The switching
frequency (Fsw) of the two bidirectional
switches was varied between 3 k1z and 12
kHz. Table 1 shows the RSVC output voltage
(Vo) and RSVC power losses (PL) as a resultof varying the duty cycle at discrete
frequencies.
Table l: Hardware Results
Task 7 & 8: Report and Presentation
These tasks are ongoing.
PROJEGT TEAM
I
PRINCIPAL INVESTIGATORS
Name Dr. Said Ahmed-Zaid
Orqanization Boise State University
Email sa hmedzaid (dboisestate.edu
Name Dr. John Stubban
Oroanization Boise State University
Email ioh nstu [e-mail]
RESEARCH ASSISTANTS
Name Muhammad Kamran Latif
Orqanization Boise State University
Email [e-mail]
Name Zivano Liano
Orqanization Boise State University
Email ziya no lia [q@u. boisestate.edu
TASK
TIME
ALLOCATEO
START
DATE
FINISH
DATE
Dvnamic Sim 3 month ADr'17 luly'17
Existinq Cao SW 4 months Oct'16 May'17
RSVC Sizinq 1 month Oct'16 Mar '17
HW PrototyDe 2 months Oct'16 Auo'17
Final Reoort 2 months lulv'1 7 Auq'17
The information contained in this documenl is proprietary and confidential.
.]
Distribution
Feeder
Voltage
Lsvc
Switch
Fixed
Cs'c
swl
Based
Bidirectional
Switch
Switched
Inductor
DUTY CYCLE OF THE SUYI (D)
o=O D = O.5O D = O.7'l D=tFsw
kHz Vo
(v)
PL
(rY)
Vo
(v)
PL
0u
Vo
(v)
PL
(YY)
Vo
(v)
PL
(w)
3 75.4 9 72.4 17 1
6 75.4 9 57.7 19
9 75.4 1g 70.6 )a 57.7 19
12 72.4 22 70.8 29 57.7 19
SGHEDULE
I
t - - - -'tI
i
,
i
I
!L
The information contained in this documenl is proprietary and confidential
Mic
Project Duration: 9 months
Universityorldaho AHrETtr
College of Engineering
rogrid Development in Downtown Spokah€, WA
Project Cost: $86,179.62
OBJECTIVE
In the event of a disaster caused blackout, cities
such as Spokane, WA, can suffer substantial
economic losses, and potential danger to the
public. The implementation of a microgrid system
in downtown Spokane will allow the economic
losses and public endangerment to be lessened.
The microgrid will also help to improve the
reliability of the downtown network and allow for
critical loads to be kept intact during an extended
power outage. The design of the microgrid
system utilizes four main energy sources:
photovoltaic, gas/diesel generators, battery
banks, and two local hydroelectric power plants.
The power grid has limitations the system will
produce, the microgrid will be designed to feedthe most important facilities only. When
generation doesn't meet the demand the least
important load must be shed.
BUSINESS VALUE
The implementation of a microgrid in downtown
Spokane allows the reduction of economic losses
(due to the sudden shutdown of equipment).
Computer shut downs, locks malfunctioning,
hospital equipment failures, industry equipment
failures, and more; all of these failures contributeto economic losses. The microgrid will allow
Avista to keep some power on in the case of a
blackout. The microgrid will also alleviate issues
when restoring the grid after a blackout.
INDUSTRY NEED
In the last few decades, the industry has focused
on interconnectivity between different network
systems in order to increase reliability and rangeof service. A drawback of the current
interconnectivity is sensitivity to rolling
blackouts. Thus, in order to provide increased
reliability, microgrids were developed to
temporarily isolate a part of the system to
protect those customers from the major events
impacting the macrorid.
Developing the microgrid will increase the
reliability of power in Downtown Spokane. The
addition of renewable distributed generation and
energy banks to support the microgrid can also
be used to help supply the peak load.
BAGKGROUND
While the Spokane Microgrid is in the "island"
mode, it suffers from a significant power quality
problem as local generation is insufficient to
handle grid mode level loads. There are two
efforts to alleviate the energy shortage. One
method is load control in the form of a master
controller. This controller will receive data from
multiple points in the system and shed the non-
essential loads.
Another method is increasing the amount of
available local generation and energy storage.
The new generation will be in the form of existing
diesel generators at the hospitals, solar panels,
and megawatt batteries.
SGOPE
The scope of this project to create a microgrid for
the Downtown Spokane area. In order to do this
there are several pieces that need to come
together. The following paragraphs discuss how
this project is progressing and the next steps to
reach completion.
Learning RTDS
RTDS stands for real time digital simulator. The
first semester was spent learning how to use this
program. A test case was created using model
loads and transmission lines. The parameters of
this model power system were all known. By
comparing the known values of the power system
to the test case values generated from the RTDS
program, it was determined that the program
was being utilized correctly. By knowing how to
use this program alternate models can be built in
the future, that will reflect the loads to be looked
at in the microgrid.
Fault Implementation
The next step is to add circuit breakers and faults
into the model. Once this is complete the
program can run a simulation with a fault present
so the circuit breaker would be tripped. Knowing
how to trip breakers in RTDS will be key once the
load shedding scheme is implemented.
Load Shedding
In an ideal situation the generation would always
meet load conditions and every load could always
be served. This is not the case for the downtown
microgrid. When the demand is higher than the
generating capacity, load must be shed in order
to keep the system from crashing. For this task a
priority list for the critical loads was provided.
The load shedding scheme will first be
implemented on the RTDS. This will allow the
system to cut the lowest priority load when the
demand exceeds the generation.
The next step is to implement the load shedding
scheme onto the RTAC which stands for Real
Time Automation Controller.
Human Machine Interface
The human machine interface also known as an
HMI, will be used to control the RTDS model that
is provided. For the development of the HMI a
program from SEL, known as the AcSElerator,
will be used. Using the AcSElerator will develop
the software that will be implemented on the
Real Time Automated Controller.
The first step to creating the controller is to
become familiar with the software being used.
Using example projects that are provided by SEL,the AcSElerator program can be better
understood.
After learning the AcSElerator program, the
RTDS model can be uploaded. This will allow the
AcSElerator to test the different settings usingthe RTDS model provided. The AcSElerator
software will be used to create a master
controller that will be used to manage the
microgrid. The controller will be able to report the
status of the microgrid to the control center
where an engineer will be able to determine the
best action for islanding.
Detailed Specifications for Equipment and
Equipment Locations
This task includes detailed specifications for
equipment that will be used in order to
implement our design. This includes cost
estimation. Some equipment might be energystorage devices, inverters, transformers,
switches, auto-synchronizers, natural gas
conversion kits, and much more.
This task also includes choosing the best
locations to install the equipment listed above.
The main concentration being efficient solar panel
locations and options for where to place energy
storage devices
PowerWorld Simulation
This task includes integrating our team's design,
which will include the location of the solar panels
and batteries, with the existing PowerWorld
model representing the downtown network.
DELIVERABLES
The deliverables for this project will be:o Load shedding scheme in RTDSo HMI using AcSEleratoro Photovoltaic locations and power output
availableo Battery locations and detailed specificso Options for converting hospital generators
to partially natural gaso Options for pairing hospital generators
with the grido Detailed cost estimation for design
installation
PROJECT TEAM
SGHEDULE
.J
grL
PRINCIPAL INVESTIGATOR
Name Dr. Herbert Hess
Orqanization University of Idaho
Contact #[phone]
Email [e-mail]
Name Dr. Brian lohnson
Orqanization University of Idaho
Contact #[phone]
Email bioh nson @u ida ho.ed u
RESEARCH ASSISTANTS
Name Jordan Scott
Orqanization University of Idaho
Email scoto330(ovandals.uidaho.edu
Name Lyn Enqland
Orqanization Universitv of Idaho
Email Duff4880@vandals. uidaho.edu
Name Lexi Turkenburq
Orqanization Universitv of Idaho
Email Turkg655@vandals. uidaho.edu
Name Christine Paoe
Oroanization University of Idaho
Email Paqe31 14@vandals. uidaho.edu
TASK START
DATE
FINISH
DATE Gompletion
Small Scale
Example Model 9/26/2Ot6 t2/7/2016 100o/o
Trip Breaker with
Model 9/26/20L6 t2/t/2076 100o/o
Trip Breaker with
Relay in Model 9/26/20L6 t2/1/2076 100%
Mastercontroller 8/22/ZOt6 8lU20t7 25o/o
RTDS Model 9/8/2076 6/U20t7 50o/o
Load Shedding 9/9/2076 4/t/20t7 25o/o
Protection Settings 9/L/2O76 Continuous 50o/o
Universityotldaho AHvtsrts
College of Engineering
CAES Water lEnergy Conseruation Analysis
Project Duration: 12 months Project Cost: Total Funding: $90,645.06
OBJECTIVE
Food processing is one of the largest
consumers of water and energy in the Pacific
Northwest. Reducing this consumption is
necessary to improve efficiency of systems
and is of interest to Avista and its customers
in food production. We propose to assist in
decreasing water and energy consumption of
food processing plants by examining current
technologies and suggesting improved
systems to better align with consumption
needs. We will focus on one customer,
Litehouse Foods, and determine where a
change in their current systems will provide
the largest decrease in energy consumption.
These systems will be modeled by two
simulation programs, Aspen-HYSYS and
Flownex Simulation Environment. The results
from both programs will be compared
extensively to ensure the accuracy of the
results and the effects of any proposed
changes to current systems, Based on the
simulation results upgrades will be proposed
to Litehouse Foods to decrease overall energy
usage while improving the efficiency of their
systems. They will be encouraged to
implement these upgrades in their planned
renovations,
BUSINESS VALUE
Decreased energy consumption has financial
benefits for the plant under consideration. In
addition, newer technologies will provide
them with a longer operation while meeting
the demands of their customers and their
energy provider. Avista will also benefit in the
long-term as their energy demand will
decrease, benefitting them financially as well.
These changes will also allow them to meet
their regulations and demands easier.
INDUSTRY NEED
Food production is essential to the
infrastructure of the United States. As the
population continually grows, the amount of
food produced also increases. There is also a
demand for energy and water efficiency due
to the growing population. In order for food
production to continue, measures must be
taken to improve individually plant efficiencies
not only in the Nofthwest, but nationwide.
This study addresses these needs and
suggests that production facilities work in
conjunction with their utilities providers to
improve plant systems and operations.
BAGKGROUND
The growing demand on energy and water
supplies jeopardize the delicate balance
between food production and, energy and
water use. Because of this high water and
energy use, the Northwest Food Processors
Association (NWFPA) has a goal for its
members to reduce energy consumption by
5Oo/o by 2030. Improvements on this goal
have declined recently as it becomes more
difficult to identify additional savings. In
addition, the nature of food production
presents obstacles in the heating and cooling
within processes. These are often required on
a continuous basis to maintain the integrity of
the food produced. Any proposed changes to
production plants will need to take this into
account during the design phase.
$*lfa
ffi
SGOPE
Task 1: Prepare Information (COMPLETE)
The first task involves making contact with
Litehouse Foods in Sandpoint Idaho, touringtheir production facility, and organizing
and/or creating modules necessary to
perform simulation of energy and water
consumption.
Task 2: Create Models (IN PROGRESS)
Once the sufficient modules have been
organized to represent process units in the
refrigeration systems, the simulation models
in both Aspen-HYSYS and Flownex simulation
environment will be created in the following
steps:
1. Basic process system with designed
operating parameters
2. Incorporation of humidification for all
condensers3. Simulation of one summer and one
winter month
Task 3: Optimize and Upgrade
With the working models of the refrigeration
system, a performance evaluation will take
place considering the required load vs.
installed system capabilities. There will be an
optimization study to combine currentsystems and remove unnecessary
components to maintain required load and
significantly decrease energy consumption.
Improvements will be proposed to the plant
for incorporation into planned renovation.
Task 4: Instruct Avista
Complete models with modules will be
presented to Avista engineers along with
detailed instructions on how the model and
system operate in conjunction. This will give
Avista the tools for further implementation
with Litehouse systems as well as additional
customers.
Task 5: Final Report
The final report will include all findings of the
study. Information on current plant process
systems and constructed models will be
included.
DELIVERABLES
The deliverables for this project will be
o Project report and documentationo HYSYS and Flownex models with all
modules that would enable Avista
engineers to model current plant
configurations. Models provided to partnering
companyo Training on how to use model and
implement for use in other plants
PROJEGT TEAM
SGHEDULE
PRINGIPAL INVESTIGATOR
Name Dr. Richard Christensen
Organ ization University of ldaho
Contact #208-533-8102
Email rch ristensen @ uida ho.edu
Name Dr. Karen Humes
Organization University of ldaho
Contact #208-885-6506
Email kh umes@uida ho.edu
RESEARCH ADVISORS
Name Dr. Michael McKellar
Organization ldaho National Lab
Email [e-mail]
Name Dr. Dennis Keiser
Organization University of ldaho
Email denn isk@uida ho.edu
RESEARGH ASSISTANTS
Name Jivan Khatry
Organization University of ldaho
Email Kh at6738 @va n da ls. u ida ho.ed u
Name Stephen Hancock
Organization University of ldaho
Email hanc8362 @va nda ls. uida ho.ed u
TASK TIME START
DATE
FINISH
DATE
Prepare lnformation 4 month Sep 2016 Dec 2016
Create Models 4 month Jan 2017 Apr 2077
Optimize and Upgrade 2 month Apr 2017 Jun 2017
lnstruct Avista 1 month Jun2OL-1 Jul 2Ol7
Final Report 1 month Jul2OtT Aup,2OL7
t
I I\lTTG RATE D
D ESIG N LAB
Universityolldaho
Simulation-Based Commissioning of Energy
Management Control Systems
Project Duration: 13 months Project Cost: Total Funding $64,230
2016 Funding $34,723
2Qg tqlding $29,507
A,Vvtsrt
OBJEGTIVE
Optimal controls are essential for building
efficiency. However, because every building
and control system is unique, it can be a
challenge to analyze and tune these controls
on a large scale, Using energy models to
commission a building is one way to identify
errors and correct controls. This virtual
controller commissioning can save significant
amounts of energy and money. Due to the
lengthy period of time required to construct
energy models, this approach is rarely
utilized. The research proposed herein would
streamline the virtual commissioning method
and increase its market potential.
BUSINESS VALUE
The many benefits of this technology have
been limited by its tether to specific software
platforms (e.9., EnergyPlus and BCWB). The
research proposed will help to move this
service beyond a specific modeling softwareto a mathematical model that can be
generated for any building based on climate,
internal loads and construction. This will
vastly broaden the appeal of the virtual
commissioning process to industry partners
who may offer this as an energy-savingsservice to their clients. Controller
manufacturers may advertise their products
as pre-commissioned. Co-simulation will allow
verification of control logic and has been
shown to detect faults which were otherwise
seen as impossible to predict in the design
phase. They may also use the technology to
virtually test new and innovative control
systems without risks to building owners fromunproven control logics. Virtually
commissioning the system using a thermal
model holds great promise in being able to
test for missed energy savings or occupant
discomfort as compared to the design intent.
INDUSTRY NEED
As the co-simulation adoption and
applications grow, the technology allows
either building managers or utility companies
to provide short-term forecasts of the building
behavior and load based on the thermalmodel and weather information.
Commissioning based on an EnergyPlus
building model has been shown to correctly
predict energy demand and forecast shortfalls
in the cooling or heating capacity. Utilities,building managers, and/or third-party-
providers may embrace this approach as a
curtailment strategy to predict and mitigate
peak building loads automatically. Once the
thermal model is created, and communication
is established with the EMS, the economic
benefits of automation, fault detection, and
prediction are immense.
BACKGROUND
The COBE (College of Business and
Economics) building at the University of Idaho
main campus was selected for a physical
demonstration of this technology's capabilities
in this project. In the previous study, theteam had developed an energy model
calibrated to the building's utilities usages
and acquired a duplicate of the building's air-
handling controller. These were used to
explore the EMS logic and test new settings.
The process offered insight into the barriers
and potential benefits of using simulation-
based commissioning. However, the time
required to calibrate an EnergyPlus model is
extensive, and thus is not utilized by
r
practitioners. We will develop a methodology
of modeling the building using reduced order
model techniques, and compare the result tothe previous modeling and commissioning
study. This will be accomplished through
using a Grey-box uses building attributes to
estimate thermal behavior and energy use.
SGOPE
Task 1: Project Planning and Reporting
Conduct team meetings and ongoing project
updates, reports and deliverables as requiredby Avista staff, project management
contractor and the PUC.
Task 2: Gather Baseline Data
The next step was to gather historical EMS
and weather data from the site location to
benchmark savings and to verify the previous
study's results.
Task 3: Establish Communication at the Site
Based upon work completed in 20t4-2OL5,the team would perform a physical
implementation of the co-simulation at the
site to establish live-data transfer between
the EMS and the energy model. This step was
rescheduled to the spring in order to capture
the proper weather conditions to realize the
correct energy savings. During the winter
months, the economizer high-temperature
lockout is rarely used, and in order to verify
the models prediction this task had to be
pushed back to a time when the outdoor air
temperatures push the economizer is
operations to where the controls settings
have a significant impact.
Task 4: Simplify Energy Model
This task involves converting the detailed
EnergyPlus models into reduced-order
thermal models and using them to tune
building controls. This work is being
performed in parallel with Task 3. Targeted
sub-system monitoring to isolate impact of
Task 3 will be conducted as needed and as
budget allows.
Task 5: Analyze Effectiveness of Reduced Order
Model
The team will compare the accuracy of the
reduced-order model to the EnergyPlus
model. The analysis will also include a studyof sensitivity, variable requirements, and
complexity of signal communication relativeto the EnergyPlus and the reduced order
models.ii[,t
Task 6: Develop Workflow for Practitioners
The final tasks will be a study of the process
that will add to the current literature and
promote this technology service to furthercommercialized techniques. This
documentation will be part of a master
student's academic work and potential
publications in a conference or a journal will
be sought.
DELIVERABLES
At the conclusion of the project, the research
team should be able to conclude the
following:. Reduced Order Model Methodology. Case Study Utilizing Methodology
PROJEGT TEAM
PRINGIPAL INVESTIGATORS
Name Elizabeth Cooper
Orqanization University of Idaho
Contact #(208\ 40t-o642
Email ecooper@uida ho. ed u
Name linchao Yuan
Oroanization University of Idaho
Contact #[phone]
Email icvuan @u ida ho.ed u
RESEiARCH ASSISTANTS
Name Damon Woods
Oroanization University of Idaho
Email [e-mail]
Name Sean Rosin
Orqanization University of Idaho
Email [e-mail]
SCHEDULE
IP-In progress
TASK TIME
ALLOCATED
START
DATE
FINISH
DATE
STATUS
Proiect Plannino 3 months Auo'16 Nov'16 C
Gather Baseline
Data 2 months Au9'16 Oct'16 C
Establish site
Communication 1 month Apt't7 MaY'17 IP
Reduced Order
Model 6 months Dec'16 May'17 IP
Effectiveness of
Model 4 months Apt'17 Aug'u IP
Workflow
DeveloDment 2 months Jul'L7 Aug'77 IP
The information contained in this document is proprietary and confidenttal
Airrtsra
APPENDIX B
Request for lnterest
Avista Corporation
East 141I Mission Ave.
Spokane, WA99202
A)EvtsrA
Request for Proposal @FP)
Contract No. R-40239
Avista Energy Research (AER) Initiative
INSTRUCTIONS AND REQUIREMENTS
Proposals are due by 4:00 p.m. Pacific Prevailing Time (PPT), April 21,2016 (the "Due Date")
Avista Corporation is an energy company involved in the production, transmission and distribution of
energy as well as other energy-related businesses. Avista Utilities is the operating division that provides
electric service to approximately 362,000 customers and natural gas to approximately 323,000
customers. Avista's service territory covers 301000 square miles in eastern Washington, northern Idaho
and parts of southern and eastern Oregon, with a population of 1.5 million. Avista's primary, non-
regulated subsidiary is Ecova. Avista's stock is traded under the ticker symbol *AVA'. For more
for
information about Avista,visit www.avistautilities.com.
Avista Corporation
East 1411 Mission Ave.
Spokane, WA99202
AiFvtsrt
Avista Corporation ("Avista")
RFP Confidentiality Notice
This Request for Proposal ("RFP") may contain information that is marked as confidential and proprietary to
Avista ("Confidential Information" or "Information"). Under no circumstances may the potential Bidder
receiving this RFP use the Confidential Information for any purpose other than to evaluate the requirements of
this RFP and prepare a responsive proposal ("Proposal"). Further, Bidder must limit distribution of the
Information to only those people involved in preparing Bidder's Proposal.
If Bidder determines that they do not wish to submit a Proposal, Bidder must provide a letter to Avista
certifying that they have destroyed the Confidential Information, or return such Information to Avista and
certify in writing that they have not retained any copies or made any unauthorized use or disclosure of such
information.
If Bidder submits a Proposal, a copy of the RFP documents may be retained until Bidder has received notice
of Avista's decision regarding this RFP. If Bidder has not been selected by Avista, Bidder must either return
the Information or destroy such Information and provide a letter to Avista certifuing such destruction.
Avista and Bidder will employ the same degree of care with each other's Confidential Information as they use
to protect their own Information and inform their employees of such confidentiality obligations.
RFP No. R-40239 Page 2 of 9
Avista Corporation
East 1411 Mission Ave.
Spokane, WA99202
Alfivtstfr
lnstructions and Requirements
1.0 PURPOSE
in response to the Idaho Public Utilities Commission Order No. 32918, Avista Corporation will fund up to
$300,000 per year of applied research that will further promote broad conservation goals of energy efficiency
and curtailment. Specifically, Avista is seeking a qualified four year institution in the state of Idaho to
provide such applied research (the "Services"). In light of the rapidly changing utility landscape, Avista
would be interested in funding research projects which are forward thinking and would assist the utility in the
development of product and services which provide an energy efficient commodity to its customers. The
applied research and development projects can be one or multiple years and can be used to support university
research programs, facility and studies.
The following institutions are eligible to submit Avista Energy Research (AER) initiative proposals.
1. University of Idaho 2. Boise State University 3. Idaho State University
Persons or institutions submitting a Proposal will be referred to as "Bidder" in this RFP; after execution of a
contract, the Bidder to whom a contract is awarded, if any, will be the name of the university ("Institution").
2.0 STATEMENT OF'WORK
The attached Statement of Work ("SOW") specifies the activities, deliverables and/or services sought by
Avista. This SOW will be the primary basis for the final SOW to be included under a formal contract, if a
contract is awarded.
3.0 RFP DOCUMBNTS
Attached are the following RFP Documents:
. Appendix A - Proposal Cover Sheet
. Appendix B - Sponsored Research and Development Project Agreement
4.0 CONTACTS / SUBMITTALS / SCHEDULE
4.1 All communications with Avista, including questions (see Section 5.1), regarding this RFP must be
directed to Avista's Sole Point of Contact ("SPC"):
Russ Feist
Avista Corporation
1411 East Mission Avenue
PO Box 3727,M5C-33
Spokane, WA99220-3727
Telephone: [phone]
Fax: [phone]
E-Mail: [e-mail]
4.2 Proposals must be received no later than 4:00 PM Pacific Prevailing Time ("PPT"), on April2l,
2016 ("Due Date"). Bidders should submit an electronic copy of their Proposal to
[e-mail]. In addition to an electronic copy, Bidders may also fax their Proposal to 509-
495-8033, or submit a hard copy to the following address:
Avista Corporation
Attn: Greg Yedinak Supply Chain Management (MSC 33)
1411 E. Mission Ave
POBox3727
Spokane, WA 99220-3727
RFP No. R-40239 Page 3 of9
Avista Corporation
East t4l I Mission Ave.
Spokane, WA99202
Alivtsrfr
No verbal or telephone Proposals will be considered and Proposals received after the Due Date may
not be evaluated.
4.3 RFP Proposed Project Schedule
March2l.2016
April4.20l6
April 11,20r6
April2l,2016
April28.2016
Avista issues RFP
Bidder's Questions/Requests for Clarification Due
Avista's Responses to Clarifications Due Date
Proposals Due
Successful Bidder selection and announcement
Contract and Statement of Work ExecutedMay 6. 2016
5.0 RFP PROCESS
5.1 Pre-proposal Questions Relating to this RFP
Questions about the RFP documents (including without limitation, specifications, contract terms or
the RFP process) must be submitted to the SPC (see Section 4.1), in writing (e-mailed, faxed, or
addressed in accordance with Section 4.2, by the Due Date. Notification of any substantive
clarifications provided in response to questions will be provided via email to all Bidders.
5.2 Requests for Exceptions
Bidder must comply with all of the requirements set forth in the documents provided by Avista as
part of this RFP (including all submittals, contract documents, exhibits or attachments). Any
exceptions to these requirements must be: (i) stated separately, (ii) clearly identify the exceptions
(including the document name and section), and (iii) include any proposed altemate language, etc.
Failure by Bidder to provide any exceptions in its Proposal will constitute full acceptance of all
documents provided by Avista as part of this RFP. While Avista will not consider alternate language,
etc. that materially conflicts with the intent of this RFP, Avista may consider and negotiate the
inclusion of terms that would be supplemental to the specific document if such terms reasonably
relate to the scope of this RFP.
5.3 Modification and/or Withdrawal of Proposal
5.3.1 ByBidder: BiddermaywithdrawitsProposal atany time. Biddermaymodiffasubmitted
Proposal by written request provided that such request is received by Avista prior to the Due
Date. Following withdrawal or modification of its Proposal, Bidder may submit a new
Proposal provided that such new Proposal is received by Avista prior to the Due Date and
includes a statement that Bidder's new Proposal amends and supersedes the prior Proposal.
5.3.2 By Avista: Avista may modifu any of the RFP documents at any time prior to the Due Date.
Such modifications will be issued simultaneously to all participating Bidders.
5.4 ProposalProcessing
5.4.1 Confidentiality: It is Avista's policy to maintain the confidentiality of all Proposals
received in response to an RFP and the basis for the selection of a Bidder to negotiate a
definitive agreement.
5.4.2 Basis of Any Award: This RFP is not an offer to enter into an agreement with any party.
The contract, if awarded, will be awarded on the basis of Proposals received after
consideration of Bidder's ability to provide the services/work, quality of personnel, extent
and quality of relevant experience, price and/or any other factors deemed pertinent by
Avista, including Bidder's ability to meet any schedules specified in the Statement of Work.
5.4.3 Pre-award Expenses: A11 expenses incurred by Bidder to prepare its Proposal and
participate in any required pre-bid and/or pre-award meetings, visits and/or interviews will
be Bidder's responsibility.
RFP No. R-40239 Page 4 of9
Avista Corporation
East l41l Mission Ave.
Spokane, WA99202
AiTr-nsrr
5.4.4 Proposal Acceptance Term: Bidder acknowledges that its Proposal will remain valid for
a period of 60 days following the Due Date unless otherwise extended by Avista.
5.5 Contract Execution
The successful Bidder shall enter into a contract that is substantially the same as the Sponsored
Research and Development Project Agreement governing the performance of the Services/Work
applicable under this RFP included as Appendix B. However. those Universities that have prior
written agreements with Avista" may mutuall), agree to utilize those agreements with an extension
and some modifications to the documents.
6.0 PROPOSAL REQUIREMENTS AND SUBMITTALS
Bidder's Proposal must conform to the following outline and address all of the specified content to facilitate
Avista's evaluation of Bidder's qualifications; approach to performing the requested Services/Work; and other
requirements in the SOW. Proposals will be evaluated on overall quality of content and responsiveness to the
purpose and specifications of this RFP, including the information set forth in Section 6.5 below.
6.1 Proposal Process
Each eligible institution will be limited to TEN specific proposal submittals. One representative of the
eligible institutions will be responsible for submitting all of the proposals.
The proposal must not exceed 6 pages not including the appendices. The proposal shall be in I I point
font, 1.5 spaced and one inch margins. The original and one electronic copy of the proposal (PDF -
Form) must be provided to Avista's point of contact listed herein.
6.2 Proposal Submittals The following items are required with Bidder's Proposal. Each proposal
shall contain the following project elements.
1. Name of Idaho public institution;
2. Name of principal investigator directing the project;
3. Project objective and total amount requested (A general narrative summarizing the approach
to be utilized to provide the required services);
4. Resource commitments, (number of individuals and possible hours for services);
5. Specific project plan (An outline of work procedures, technical comments, clarifications
and any additional information deemed necessary to perform the services);
6. Potential market path;
7. Criteria for measuring success;
8. Budget Price Sheet / Rate Schedule;
9. Proposal Exceptions to this RFP (if any);
10. Appendix A - Proposal Cover Sheet (last 2 pages of this document)
11. Appendix C: Facilities and Equipment
12. Appendix D: Biographical Sketches and Experience of the principle investigators and / or
primary research personnel for each project (ifdifferent individuals for each project
submitted)
RFP No. R-40239 Page 5 of9
Avista Corporation
East 141I Mission Ave.
Spokane, WA99202
A)Evtsrt
6.3 Proposal Cover Sheet
Bidder must fiIl out, sign and date the attached Proposal Cover Sheet. The signatory must be a
person authorized to legally bind Bidder's company to a contractual relationship (e.g. an officer of
the company).
6.4 Institution Information
o Institution Oualilications
Bidder shall provide information on projects of similar size and scope that Bidder has
undertaken and completed within the last five years. Please include a list of references on
Appendix A that could be contacted to discuss Bidders involvement in these projects.
Institution Resources
Identify any unique or special equipment, intellect, hardware, and software or personnel
resources relevant to the proposed Services that Bidder's firm possesses(list in Appendix D).
o Project Personnel Qualifications
Provide a proposed organization chart or staffing list for a project ofthis size and scope and
identiff the personnel who will fill these positions. If applicable, identiff project managers who
will be overseeing the Services and submit their resume identifring their work history, (please
see Section 6.2, question f4).
o Approach to Subcontracting
If Bidder's approach to performing the Services will require the use of subcontractors, include
for each subcontractor: (a) a description oftheir areas ofresponsibility, (b) identification ofthe
assigned subcontractor personnel, (c) resumes of key subcontractor personnel, (d) a summary
of the experience and qualifications of the proposed subcontracting firms in work similar to
that proposed, and (e) a list ofreferences for such work.
6.5 Evaluation Criteria
Avista will evaluate each proposal based upon the following criteria:
6.5.1 Project Requirements. Strength ofProposal
o Responsiveness to the RFP
r Creativity in Leveraging Resources
o Samples of Work Products
o Overall Proposal (Complete, Clear, Professional)
6.5.2 Strength & Cohesiveness ofthe Project Team
Overall ability to manage the project
Technical ability to execute the Services
Research/analysis ability
Project milestones with clear stage and gates (annually)
Overall team cohesiveness
Qualifications and Bxperience
Experience working with electric utilities
Proj ect management and multi-disciplined approaches
Experience working with organizations in a team atmosphere
a
a
a
a
a
6.5.3
o
a
O
RFP No. R-40239 Page 6 of9
Avista Corporation
East l4ll MissionAve.
Spokane, WA99202
A)Evtsrfr
7.0 RESERVATION OF AVISTA RIGHTS:
Avista may, in its sole discretion, exercise one or more of the following rights and options with respect
to this RFP:
. Modiff, extend, or cancel this RFP at any time to obtain additional proposals or for any other reason
Avista determines to be in its best interest;
o Issue a new RFP with terms and conditions that are the same, similar or substantially different as
those set forth in this or a previous RFP in order to obtain additional proposals or for any other reason
Avista determines to be in its best interest;
o Waive any defect or deficiency in any proposal, if in Avista's sole judgment, the defect or deficiency
is not material in response to this RFP;
o Evaluate and reject proposals at any time, for any reason including without limitation, whether or not
Bidder's proposal contains Requested Exceptions to Contract Terms;
o Negotiate with one or more Bidders regarding price, or any other term of Bidders' proposals, and
such other contractual terms as Avista may require, at any time prior to execution of a final contract,
whether or not a notice of intent to contract has been issued to any Bidder and without reissuing this
RFP;
o Discontinue negotiations with any Bidder at any time prior to execution of a final contract, whether
or not a notice of intent to contract has been issued to Bidder, and to enter into negotiations with any
other Bidder, if Avista, in its sole discretion, determines it is in Avista's best interest to do so;
o Rescind, at any time prior to the execution of a final contract, any notice of intent to contract issued
to Bidder.
[BND OF RBQUEST FOR PROPOSAL INSTRUCTIONS AND REQUIREMENTS]
RI'P No. R-40239 PageT of9
Avista Corporation
East 14ll MissionAve.
Spokane, WA99202
A)Evtsrt
APPEIIDIX A - Proposal Cover Sheet
Bidder Information
Organization
Organization Form:
(sole proprietorship, p".t*.thtpfr.@rporation, etc.)
Primary Contact Person:Title:
City. State. Zip:
Telephone:_ Fax Federal Tax ID#
E-mail Address
Name and title of the person(s) authorized to represent Bidder in any negotiations and sign any contract that may
result ("Authorized Representative") :
Name: Title:
If classified as a contractor, provide contractor registration/license number applicable to the state in which
Services are to be performed.
Provide at least three references with telephone numbers (please verifu numbers) that Avista may contact to
verifr the quality of Bidder's previous work in the proposed area of Work.
REFERENCE No. I
Organization Name:
Contact Person
Project Title:
Telephone
Fax:
Email
REFERENCE No. 2:
Organization Name:
Contact Person
Project Title
Telephone:
Fax:
Email:
REFERENCE No. 3
Organization Name:Telephone:
Fax:
RFP No. R-40239 Page 8 of9
Avista Corporation
East l4l I Mission Ave.
Spokane, WA99202
AFrstsrx
Contact Person
Project Title:
Email:
By signing this page and submitting a Proposal, the Authorized Representative certifies that the following
statements are true:
1. They are authorized to bind Bidder's organization.
2. No attempt has been made or will be made by Bidder to induce any other person or organization to submit
or not submit a Proposal.
3. Bidder does not discriminate in its employment practices with regard to race, creed, age, religious
affiliation, sex, disability, sexual orientation or national origin.
4. Bidder has not discriminated and will not discriminate against any minority, women or emerging small
business enterprise in obtaining any subcontracts, ifrequired.
5. Bidder will enter into a contract with Avista and understands that the final Agreement and General
Conditions applicable to the Scope of Work under this RFP will be sent for signature under separate cover.
6. The statements contained in this Proposal are true and complete to the best of the Authorized
Representative' s knowledge.
7 . If awarded a contract under this RFP, Bidder:
(i) Accepts the obligation to comply with all applicable state and federal requirements, policies,
standards and regulations including appropriate invoicing of state and local sales/use taxes (if any) as
separate line items;
(ii) Acknowledges its responsibility for transmittal of such sales tax payments to the taxing authority;
(iii) Agrees to provide at least the minimum liability insurance coverage specified in Avista's attached
sample Agreement, if awarded a contract under this RFP.
8. If there are any exceptions to Avista's RFP requirements or the conditions set forth in any of the RFP
documents, such exceptions have been described in detail in Bidder's Proposal.
9. Bidder has read the "Confidentiality Notice" set forth on the second page of these "NSTRUCTIONS
AND REQUIREMENTS" and agrees to be bound by the terms of same.
Signature Date:
*** THIS PAGE MUST BE THE TOP PAGE OF BIDDER'S PROPOSAL ***
RFP No. R-40239 Page 9 of 9
Airrlsra
APPENDIX C
Boise State University Agreement
I. TARTIES
SI'ONSORID RESIARCH AND DE\/ULOPNTENT PRO.IECT AC]rll|iNIENI'
'fHlS Agrcement is made and entered into by and benveen Boise State Universit-v. an
ldaho state institution of higher education (Universiry). and Avista Corporalion. a
Washington corporation (Sponsor;. In this Agreement, the ahove entities are somc-times
re{'ened to as a Parr.v and jointly reterred to as Parties.
II. PURPOSE
2.1 'Ilris Agrcement provides the terms and conditions lor an Avista-sponsorcrJ cncrgy-
efficiency applied research and developmenl project wlrich is ol' mutusl interest and
benefit to University and Sponsor. and which has been approved by the ldaho Public
Utilities Commission under Order 12918.
The pertbrmance of such sponsored research and development projcct is consistent rvith
Universitv's status as a non-profit, tax-exempt, educational institution. antl ma1, dr.'rive
benetlts for Sponsor, University. and sociery" by the advancenrent uf knuwledge irr the'
field of study identified. The perlbrnrance of such sponsored research and developnrettl
projects may also derive beneflts for Sponsor through thc dcvclopnlenl of encrgy
el'ficiency products and/or services thut could be ofl'ered to Avista cuslonlers in ldalro and
other jurisdictions and/or licensed or sold to other utilities or their custonrenr b-l' Avista.
t.l
a 1
1.3 Llniversitv's capabilities retlect a substantial public investment. which tJnivcrsity, a.s a
pan of its mission as a state higher education instirution. rvishes to utilize in a cooperative
and collaborative efforl rvith Sponsor, including substantinl finirncinl investment in
sponsored research and development projects, as described below.
III. DEFINITIONS
l. I "Budget" shall mean the Project Budget contained in .4tteclmtenr ..1- Builgat. rvhich is
he'reh,v" i nc orporatc-d by reference.
t.2 "Project Director(s)" shall be as described in each Scope o[ Work. rvho shall lre the
principal investigator for the R&D Project.
f,-l "Sponsor Liaison" shall be as described in each Scope of'Work, a Sponsor representative
tlesignated by Sponsor to be thc prinrar-v contact rvith the Project Director.
.1.1 "Sponsored R&D Project" shall mean the Avista-sponsr.rred research and development
project covered by this Agreement tbr the perfonnance by Universiry ol'the Scope of
Work under the direction of the Project Director.
j._i "Scopr- oI Work" shall mean each scope of'rvork for thc Sponsored R&D l'rojecl. utrder
lhe direction of the Project Director, and any other attachrnents that nray provide
additional informalion on the Sponsored project to bc perlbmrcd.
3.6 "Conlldential Information" shall mean any information, experience or data regarding a
disclosing Party's plans, prograrns. plants. processes, pnrducts. cost.s. equipme nl
operations or cuslomers, including u'ithout limitation algorithms, formulae. techniqtrcs.
I
Avisra Connacr lt-4 1002 (OSP n"o.7278)
improvenrents. technical drawings and data, and computer sohrvart. rvhetlrer in written,
-eraphic, oral or otlrer tangible form, designated in writing as conlidential bl' thr.
disclosing Party at tlie time of disclosure to lhe receiving Party.
"lntelleclual Property" shalI mean any lnvention. Copl,right. 'l'rademark, Iltask Work,
Trade Secret, and/or Proprietary lnformation producetl under the Scopc of Wtlrk.
l. ti "Invenlion" shall mean cerlain inventions and/or discoveries conceived and redtrced to
practicc during the period of performance of the Sponsored R&D Project and through
performance of the Scope of Work. and rcsulting patenls. divisionals. continuations. or
substitutions of such applications. all reissues and foreign counter?arts thcrcof. uprrrr
which a Universiry or SPONSOR employ'ee or agent is or ma1' be a nanred inventor.
3.9 "lnvention Disclosure(s)" shall mean a rvrifien disclosure of a potentiallv pttentable
lnvention(s) provided to Sponsor and/or the Universiry's Technologv l'ransfer C)lfics.
Ll0 "Copyrighted Material" shall mean any rvork developed under the Sc'ope of \\/ork lhat is
subject to copyright under copyright larv whether or not registered undcr ltderal
copyright law. and including any arrd all moral rights tht-reto.
i.l I "Trademark" shall mean any trade or service marks developed under tlre Scope ol Work
rvhether or not registered under either state or federal lrademark larv, and includirrg all
relaled goodwill.
3.12 "Mask Work" shall mean any t\yo or three dimensional layout or topologv of an
integrated circuit developed in the Sponsored R&D Project under the Scope ol Work.
i.13 "Equipment" shall mean tangible personal property (including inlbrnration technologr
systems) hrving a useful lit'e of rnore than one ),ear and a per-unit acquisition cosl
exceeding $-i.000.00.
J,ll "Supplies" shall mean all tangible personal propertv other lhan Iirluipmcnl
lV. SCOPE OF WORK: NO WARRANTY
4,1 Unir,ersit-v shall fi,rmish the labor, materials. arid equiprncnl necessary to provide the
services applicable under this Agreement in accordance rvith written Scopes of Work,
mutually agreed to by the Parties. Such Scopes of Work will be inct:rporated into this
Agreement by this reference r.r,hen executed by both Parties. a sample o[ which is
irrcluded in this Agreement as .-lttathment .4-Budget, Attuchnrcnt A Sutpe o.l lllork.
;1.:Modit'ications to a Scope of Work requested by Sponsor will be perlbrmed in accortltncc
rvith a wrilten Change Ordcr. mutually agreed to b-y the Parties. Changc Orders will be
ilrcorporated into this Agreement by this reference upon executir-rn by both Perlies. I:or
Sponsor. a Change Order may be signed by eitlrer Sponsor or b1, Sponsor's 1'hird Party
Pro.iect Manager.
4.1 Urriversit-v agrccs to usc its reasonable c.llbrts [o perfonl thc'serviccs outlined irr anv
Scope of Work in accordance with the terms and conditions ol' this Agreement.
UNIVERSITY DOES NO'f REPRESENT, WN RRANT. OR CL'ARANI-ET.] 1'IIA'I-'TIIE
DESIRED RESULTS WIT.L BE OI]1'AINF,D UNDIR TI-IIS ACREEMENT.
ADD:1'IONALLY. L'NIVERSITY MAKES NO RI,PRISEN'IATION AS TO TI'IT
2
Avista Ci,ntmct R-41002 (OSP No. 7278t
PA1'I]N1'ABII.ITY OR PROTECI'ABILII'Y OF ANY IN'I'ELL bC' I'LIAI. PROPER-ry
CREA'TED LTNDER THIS AGREEMENI"
-l.i KickOlTMeeting/ReportingRequiremens.
Kic'k-off Meeting. Within thirty (30) da1,s ol'executing this Agret'ment and/or an
associatsd Scope of Work, the Universir.v rvill anend (eitlrer in person or
telephonically) a kick-off meeting with the Sponsor.
Progress Reports. Universiry -shall provide .r lwo pagc vvrinen report orr tlrc
progress of the Scope of Work every six (6) nronths lirllorving the excculion of
such Scope of Work.
1.i.3 Final Technical Repon, Universit-v shall furnish a tinal writr!'n repon withirr
thirtv (30) days of corrlrletion ol'the Period ol'Perlbrmancc as del"inrd in Section5.l. This report will include al a minimum: a suntman, ol' pro-iect
accomplishments. a summary of budget e.xpcnditures. stage and gates status,
tuumber of thculry utilized, studenl participation, ancl a status ol the projcct antl
complelion timelines. Sponsor and Llniversiry" will identify whelher such report
will bc presenled in person or clectronicall.v in each Scopc. of W'errk.
4.3.4 Final Financial Report. A t'inal t'inancial report shall be furnished rvithin sixty
(60) days of completion of thc Period of Perfonnanct. as dcfinr..d in Section 5 . I .
4.4 Third Party Project Manager. Sponsor rvill retain an independerrt third party to assist
Sponsor with monitoring nrilestones and deliverables fcrr each Scope of Work. Iiniversitl,
agrees to cooperate with such third part-v and provide any requested intbrmation in a
tinrely manner.
V. (;ENERAL-I'ERMS AND CONDITIONS
ln consideration of the mutual premises and covenants contained herein. the Partics a_qree to the
lbllorving ternrs and conditions.
5.1 Pc'riod of Pertbrmance. The speeific period o1'pcrformancc lbr euch project will be
detined in each Scope ol'Work. and any changes rvill be nrutually agreed upon in writing
be'hveen the Parties in acctlrdance with the Change Ordcr process set ,'orth in Sectir:n {"2.
_i.2 Funding, Sponsor agrees to reimburse University for services performed under this
Agreemenl on a time and materials basis in accordance n,ith each Project Budget (as
described in Section 5.3 below). including any not to excecd amounts. Any unspent
funding remaining upon Sponsor's acceptance of University's Final 'l'echnical Report
under Section .1.3.3, above, and its Final Financ'ial Report under Section -1.3.4. above- the
expiration or lerm of the Agreement shall be returned to Sponsor.
5.3 Proiect Budget. Each Scope of Work u'ill set fo(h a Project Budget (see..lllrrr'lrntent .-l-
Sutpe oflVork und Budgef . Deviations from this Project Budget may be nrade to and
fronr any expenditure line item within the University systL'nr. as long as such deviation is
reasonable and necessary in the pursuit of the Scope of Work and pre-approvcd by
Sponsor. provided however that University shall nol be requirecl to receivc prior ruriflen
approval for amounts less than $500. The total amount identit'ied in each Scope of Work
rnay not be exceedc'd witltoul prior written agreement through a Change Order.
j
Avista Contract R;[1002 (OSP No. 7278)
4.3. r
4,3.1
5l lnvoices. Payments are due to University rvithirr thirty 1301 dals tiom the lJniversitl,
inverice date.
lnvoices should be sent to
Namc/Title: Randv Cnar'dinqer Phone: 509-495-?047
Addrcss: l,lll E.Ave.E-m a i I : Randy. C naed i n gc'r ftjrav i stacorp. cont
CityiState/Zip: Spokane. WA 99220
5._i Equipment and Supplies. University shall retain iitle to ani l:quipnrcnt and Supplies
purclrased rvith l'unds provided b-v Sponsor under this Agreenrr.rrt.
56 Kcy Persorurel. The Pro.ject Director may select and supervise other Sponsored R&L)
Projecl staff as needed to perform the Scope of Work. No othr..r person rvrll be
substituted for tlte Project Director, e.\cepl rvith Sponsor's approval. Sponsor may
exercise Termination for Convenierrce provisions of this Agreenrent if a satislactory
subslitute- is not identified.
5.7 Cr:nuol of Scope of Work, The control of the Scope of Work rests entirely u,itlr Sponsor.
but control of the perfonrrance of the [Jniversity and the Sponsored R&D Projcct staff in
execuling the Scope of Work rvithin the Sptrnsored R&D Projer.-t shall rc.st entirell, u'ith
Univcrsit-\'. The Parties agree that Universit-v, through its Prnject Dircetor. shall mainlairr
regular communicalion with the designated liaison f,or Sponsor and tl:e Llniversity'ri
Project Direclor and Sponsor's Liaison shall mururll1.' deline thc fieclucncy and nature ol'
such comrrrunications.
i.8@
5.8.1 I'o the extent allowed by larv. and subject to the publication provisions sel lbnh
in Scction 5.9 belorv, University and Sponsor agree to usc reasonable care to
avoid unauthorized disclosure of Cont'idential lnfornration. including u,ithout
limitation taking mcasures to prevent crcaling a prenaturr' bar to a Llnited States
or loreign palent application. Each Party will limit access to. und any publication
or disclosure of. Confidential Inlormation received fronr anolher Party hr,'rr"'to
and/or created and reduced to practice as a part of the Sponsored R&D Projurct. to
those persons having a need to know. Each Par-ty shall cnrplrry the sanrc-
reasonable sat'eguards in receiving, storing. transmitting. and using Cont'idential
lnformalion that each l'arry normally exercises with respr'ct to its orvn potentially
patentable inventions and other confidential infonnation of signilicanl value .
5.8.2 Cont'idcntial Information shall not be disclosed by thc receiving Party to a third
party: (i) for a period ol'three (3) years from receipt ol such Conlidential
Information; or (ii) until a patent is published or the Conlldential Irttbrmation o1-
a Parry is published by the disclosing Parr-r': or (iii) tlniversity and Sponsor
mutually agree to such release in a rvriting signc'd b-v hoth Panies.
Notwithstanding thc above. any lntellectual Properq' arising out ol. created or
reduced to praciice as a pan of the Sponsored R&D Project shall bc' subjecl to the
requirements set lbrth belorv in Section 5.9
4
Avista Conracl R-41002 (OSI'] No. 7378)
5.8.3 Ths terms of conl'identiality set forth in this Agreement shrll not bc conslrucd to
limit the partics' right to independently develop products s,itlrout thc usc ol'
rnother Party's Conl'ldential I nformation.
5.8.4
i.
ii,
iii,
iv,
vi.
5
.{r'ista Contract R-4 l00l (OSt' No. 7178 )
Confidential Information shall not include information which:
was in the receiving Part-v's possession prior to receipt ol' thc disclosed
information:
is or becomes a matler ot' public knowledge through no lauh of the receiving
P"rry;
is received from a thirtl party without a dury ol'confidentiality;
is indepcndently developed by the receiving Party:
is required to be disclosed under operation of larv. including but not linrited to tlre
Idaho Public Records Act. l.C. $$ 9-337 through 9-350;
is reasonably ascertained bv LINIVERSITY or SPOn\SOR to crcatL' a risk ttr a
person involved in a clinical trial or to general public health and satbty.
5.9 Publisation. Sponsor and University acknow'ledge the necd to baknce Sponsor's ncctl to
protect comnrercially leasiltle teclrnolo-eies, prodtrct-s. nnd prouesses. including the
prescrvation of lhe patentability of Invr..ntions arising out ot. created in or retJuccd to
practice as a part of the Sponsored R&D Project that fall within the Scopc ol'Work. with
Universiry's public rcsponsibilitv to freely disseminate scientific findings frrr the
advancement ol' knorvledge. lJniversity recognizes that the public disscmination of
infonnation based upon the Scope of Work perforrned under tlris Agreemcnt cannot
contain Confidential lnformation (unless au{horizsd tbr disclosure per subseclion 5.8.?
abqve). nor should it jeopardize Sponsor's or University's abilih, to commercialize
Intellectual Property developed hereunder. Similarly. Sponsor recognizes that thc
scientilic results of the' Sponsored R&D Project rnay be publishablc after Sponsor's
interests and patent rights arr,' protected and, subject to the contidentiality provisions of
tltis Agreernent. may be presentable in fonrnrs such as symposia or international, national
or regional professional meetings. or published in vehicles such as books, journals.
rvebsites. theses. or dissertations.
University and Sponsor r-:och agree not to publish or othenvise disclosc Sponsor
Confidential lntbrnration or L)nivcrsity Confidential lnformation. unless authorizcd in
writing by the disclosing Party. Sponsor agrecs that Llniversit-r,, subject to revierv by
Sponsor, shall have the right to publish results of the Sponsored R&D Project. excluding
Sponsor Confidential Information that is not euthorized in u'riting to be disclosed by
Sponsor. Sponsor shall be fumished copies of any proposed publication or prcsenlation
it leest thirt-y (30) days before subrnission ol'such proposed publieatir:n or presenlation.
During that time, Sponsor shall have the right to revierv the material lbr Sporuor
Confidential lnformation and to assess the patentability of any lnvention describcd in the
malerial, lf Sponsor decides that a patent application [or an lnvcntion should he llled or
other Intcllectual Property t'iling should be pursued, the publication or presentation shall
bc delayed an additional sixty 160) da1's or until a patent application or other application
lbr protection ollntellectual Propc'rty is filed. rvhichever is sooner. At Sponsor's ruquest,
Sponsor Conlldential lnlbrmation -shull hc delcted lo the extent permissible' by and in
compliance rvith University's record retention obligations. provided. howL'vcr thrrt during
suclr retention periods. Universitv shall maintain the Sponsor Conl'idential lnltrmration in
accordance with Section 5.S.
5.l(") Publicitr-. Neither Paqv shall use the name of the other Part1,, nor any nrember of thc
other Part_v's employees, nor either Parry's Tradenrarks in any publicity. advertising.
-i, t I
sf,les promotion. news release. nor other publicit,r" matter withoul tlre prior writtcn
approval ofan autltorized representative ofthat Party.
Tcnnination fbr Canyenjsnce, This Agreement or any individual Scopc of Work rnay be
terminated by either Parry hereto upon writlen notice delivered to the other Parry at least
sixt).(60) days prior to lhe date oltermination. By such lerminalion. ncither Parly nray
nullity obligations already incurred prior to the date of lennination. llpon receipt of any
such notice of tcrmination. Universitl' shall, except as otherwise tlirccted by Spo:rsor-
irnmediately stop performance of $e services or Work {o the extcnt spee ified in such
notice. Sponsor shall pay all reasonable costs and non-cancelable obligations incurred by
University as of the date of termination. Universiry shall use its reasonahlc ellbrts to
minimize the compensation payable under this Agreement in thc event of such
termination.
5.12 Ternrination tbr Cause. Either Party rnay ternrinate this AgreernL'nt or an individual
Scope of Work at any time upon thirty (30) days' prior written noticc in the evcnt of a
material breach by the other Parry. provided the breaching Party has not cured sur":h
brcach during suclr 30-da1' period. A material breach inchrdes. rvithoul lirnitaliort.
insolvency. bankruptcy. general assignment [or lhe bencfit ol creditors. or becoming the
subject of any proceeding commenced under any statute or lau, tbr the rclicloldebtors.
or if a receiver. truslce or liquidator of any property or iuctrnte tll' eithcr Party is
appointed, or il' Univenity is not perfonning the services in accordance with this
Agreement or an individual Scope i:f Work.
5. li Temination Oblisations. ln addition to those obligations set out in 5.ll and -5.12. any
tcrmination of this Agreement sr an individual Scope ol' Work shall not relieve either
Parh' of any obligations incurred prior to thg datc of tcrmination including. but not
limiteiJ to, Sponsor's responsibility to pa)' Universiry, lbr all rrrrrk pcrlornred through
the date of termination, calculaled on a pro-rata basis given lhc. pr,'rcr-'ntage ol'cr.rn:pletion
of the Sponsored R&D Project on the elfective date o[ tlrc tr'rnrination. and tirr
reimbursenrent to University ot'all non-cancelable conrnrilnrents alre'ad1, incurrcd tilr thc
terminaied Sponsored R&D Project. Upon termination, Univcrsity shall prornptl-v dcliver
to Sponsorall Sponsored R&D Projsct deliverables. whether conrplete or still in progress.
and all Sponsor Confidential lnfonnation disclosed to University in connection with thc'
Sponsored R&D Project. Additionally. in the event lntellectual Propertv was created &\ a
result of the Sponsored R&D Project. Sponsors' rights to negotiate a license to such
lntellectual Proper"ty shall apply pursuant to Seetion 5.16 belorv, imd thr-'paflies agree to
execute any documents evidencing joint ownership. if applicable. Thc' riglrts and
obligations of Article 5.8 ol'this Agreenrcnt shallsurvive lermination.
5.14 Dispute Resolution. Any and all claims, disputes or controvrrsiss nrising under, ou1 ol.
or in connection rvith this Agreement. which the Parties hereto shall he unablc ttl rcsolvc
rvithin sixty (60) days, shall be rnediated in good thith by the Panies' rr'spective Vice
l'residents for Research or equivaL'nt.
|rl6thing in this Agreenrent shsll be construed to lirnit the Parties' choicc of a nrutually
acceptable dispute resolution mqthod in additisn to the dispute rcsolulion procedure
outlined above, or to limit the Parties' rights to any remedy al larv or in equity tor breach
of the terms ol this Agreement and the right to receive reasonablc attorney's fiees and
costs incurred in enforcing the terms of tlris Agreemcnt.
6
Avista Conract R-41002 (OSP No.7278)
5. l5
7
Avists Contract R-11002 (OSP No. 7278t
Disclaimer. LINIVERSITY MAKES NO EXPRESS OR lMPLltiD W.,\RltAN'fY AS
TO THE CONDITIONS OF' THE SCOPE OI-' WORK. SPONSORF.D PROJECT OR
ANY INTELLECTUAL PROPERTY, CENER TED INFORMNTION, OR PRODUC'I
MADE OR DEVELOPED UNDER TIIIS AGRIiEMENI', OR lHE
MERCHANTABILITY. OR FITNESS FOR A PARTICULAR PLJRPOSI] OF 'TFIIl
SPONSORED PROJEC'I-. SCOPE OF WORK. OR RESULTING PRODLICT.
-i. l6 lntellectual Property.
5.16.1 Universitl' lntellectual Property. University shall orvu ull riglrts and titlc to
Intellectual Property created solely b1, Llniversity employees,
5.16.2 Sponsor Intellectral Propert.v. Sponsor shall or.vn all rights and title to
Intellecrual Property created solely by Sponsor and without use of University
resources under this Agreement.
5.16.3 .lOlNT lntellectua] Property. University and Sponsor will jointly own anv and all
lntellecrual Properry developed jointly (e.9., t<-r the extent the partic's rvould bc'
considered joint inventors and/or .ioint copyright holdcrs. as applicable- unrler
relevan( U.S. intellectual property laws) undcr this Agrcr.'mcnt.
5.16.4 Either Parry may tile tbr and maintain lrrtellectual Propen-v protections lbr Joint
lntellectual Properry dcveloped under this Agreement. ln the evenl that a Parq,
wants to obtain or maintain any lntellectual Propeny proteclions conccrnin-si Joint
lntellectual Property. the non-filing Parry agrees to cxL'cute any associated
documentation reasonably requested.
5.16.5 Joint lntellectual Propertl,shall be ortned equalll,b1,the panies. Exccpt as
provided below. the parties acLnowledgc:1i1 to share cqually all cxpcnscs
incurred in obtaining and nraintaining Intellectual Propertv prolections on Joint
lntellectual Properry, and (ii) that each Pany shall have the right to license such
Joint lnventions to third partie's (uith the riglrt to sublicensel u'ithout acctruutirrg
to the other and without the consenl of tlre other.
5,16.6 Reserved.
-5.16.7 Nonvithstanding the tbregoing. a Parry rnay decidc al any tirne that il does not
wanl to financially support Intellectual Propcrry protcctions for certain Joint
Intellectual Property (a 'Non-Sunnortins PsrM'). ln that case, tlrc' other Party
is tiee ts seeh and obtain such lntellectual Properly protectiol'ls al its own
expense ta '!g.Apg4!Sg_Eartv"), providcd that titlc to any such lntellectual
Properly proteclions shall still be held jointly by thc parties.
.i.16.8 Universiry and Sponsor rvill promptly disclose to the other Party in rvriting any
lntellectual Property made durin-e the scrvices purformcd herc'under, Such
written disclosure by University shall be sufJlcicntly detailed ltrr Sponsor tu
assess the commercial viabiliry of the technology and shall be provided arrd
maintained by Sponsor in confidencc pursuant to the ternrs of Article 5.8.
Sponsor shall have up to ninety (90) days trom thr' reccipt ol'thc disclosure tL'r
inform University whelher it elects to have University tilt- a patent application or
otherwise scek Intellectual Property prolection pursuant to the procedures set
forth below.
5. 16.()University hereby grsnts to Sponsor an option to negotiale an exclusive license
under any University lntellectual Properry rights thal Sponsor rvishcs to pursue
(the "Negotiation Right"). Llniversit-v agrees to negotiate in eood laith to attRrrpt
to establish the terms ol a license agreement granting the Sponsor tlre exclusive
rights to malie. have made. usc. sell. offer to sell. expon and import products in
the applicable tleld ol'use under thc applicable lntellectual Property rights. Such
license agreement shall be in accordance with policies, procrrdurcs and guide lines
set out by the ldaho State Board of Etlucation. and shall includc al leflsl the
follorving provisions: a license t'ee. annual maintenance payments/nrinimum
royalties, milestone pa)'ments lrvhere applicable) and royaltl, paynrents. payrnent
of'all past and future costs incuned by Universiry associateil u'illr thc protc.ction.
prorecution and maintenance of the University lntellecrual []ropcny- rights" the
limited right to grant sublicenses. sublicense tbes. a commitment hy thg Sponsor
and any approved sub-licensccs to exert best cllbrts to introduce liccnsed
producb into public use as rapidly as practicelble. the right ol' Universitv to
terminate the license agreement should the Sponsor nol meet on), negotialed due
diligence milestones. a comnritmenl to maintain the conlldentiality o!" my
l-lniversiry Confidential Inlormation under lntellcclual Propcrtv Rigals. and
indemnity and insurance provisiorrs salisfactory to tlniversir!. Additionalll', any
license will include a reservalion ol.rights lbr University to use the lntellectual
Property Rights fbr research. tcaching and other larvful purposes ol' the
tlniversity, Notrvilhstanding anything in this Agreenrent to the contrary. this
Agreemenl shall only require the Parties lo negotiale in good laith to anempl t()
enter into a license, and shall not require either Party to enter into such a liccnsc
unless the tenns and conditions for such license art' salislactury to such Pirrly in
its sole discretion. Sponsor's Negotiation Right shall. for lntcllcctual Properw
disclosed by University to Sponsor under Section 5.16.8. es.tend t'or one lrundrcd
eighty days (lE0) days after such disclosure (the "Ncgotiation Period"), Sponsor
shall lrave upon exercise of its Negotiation Right, one hundred eighry ( 180) da-ys
to negotiate the terms of thc liccnse. rvhich period can be extendc.d by nrutual
written agreement of the Parties. ln the event lhat Sponsor does not esercise its
Negotiation Right as to any disclosed lnvention or lntellt'clual Prtrpcrty within
the Ncgotiation Period or the parties fail to reach a rrrutuallv acct'ptable license
agreement within the above specitied time period: (i) Sponsor's Negotiation
Ri-eht shall end: and (ii) University shall be entitled Io negotiatr-' in good laith
with one or morc third parties an exclusive or nonexclusive licr.nse 1o the
lntellectual Property in its sole discretion.
5. 16. l0 University. afler due consultation rvith Sponsor. slrall plornptll, file and prosecutc
patent applicalions on University lntellectual Propcrry to rvhich Sponsor
exercised its Negotiation Right during the Negotialion Pr'riod. using counsel of
Llniversity's choice. Because Universiq, and Sponsor have a common lL'gal
interesl in the prosecution of such applications. University shall kecp Sponsor
advised as to all developments rvith respecl to application(s) and shall prornptly
supply copies of all papers received and flled in c:onnection *'ith thr.'prosecution
in sufficient tinre tbr Sponsor to comment. Sponsor underslands and agrccs tlrat
such exchange of informatlon may include privileged inlorrnation and that h-v
such an exchange in lurtherance of the colnmon interests of thc panies, the
Llniversity does not intend to rvaive the attorney/client privilege. attorney \ryork
product immunity. common interest privilcgc. andlol any ollrer applicable
privilege. protection, or imnruniry. Sponsor's comrnenls shall bc tilhcn inlo
consideration. Sponsor shall rcimburse University for all reiuonable tlut-ol'-
pocket costs incurred in conneclion with such preparation, filing. and prosecution
8
Avista Conrrao R-a l00l (OSP No. 7l7E)
of patent(s). Sponsor shall be re-sponsible for all such costs under this Scctiun
until Sponsor notitles Llniversity in rvriting that Sponsor desires to discontinue its
financial support; provided, hou,ever. Sponsor shall also lre responsible [or all
costs incurred by Universig,aflcrthe tlete of notice under this Section rnd which
are reasonabl,v related to Sponsor's prior guitlance lo Unir,ersit1,,
5.l6.ll Within nine (9) months of the liling date of a U.S. patent application. Sponsor
shall provide tu University a written list of forr,.ign countries in rvhich
applications should be l'iled. Sponsor slrall provide University advance furrding
for all lbreign applications/tilings. If Sponsor elects to discontinuc financial
suppofl of any patent proseculion, in anv country. Universiry shall be lice to
continue prosecution at t.lniversity's expense. ln such event. Llniversity shall
have no l-urther obligation to Sponsor in regard to such patent applications or
pBtenls.
-i.l6.ll University. subjc'ct to its Copyright policy. hereby grants to Sponsor a non-
exclusive. royalty-free, non-sub-licenseable license to usc' Copyrighted Material
to which University holds the Copyright. with the exception of copyrighted
software (which shall be licensed in accordance with Section 5.16.9 above). lbr
its intemal. non-commercial use.
5. l6.ll Sponsor understands that Universit-y must comply with the provisions of LiS
Patenl larv. including the Bayh-Dole Act.
5.16.l4 No Pany shall invoke the CREATE ACT (Cooperative Reseirrch antl 'l"echnology
Enhancement Act ol' 200.1 and subsequenl anrendments and irlplcnrenling
regulations) without written consent of the other Parry.
-i. l7 lndemniw and Hold Harmless. Sponsor shall fully irrdcnrnit-v and hold harmless the state
of ldaho. Universit,v and its governing board, officers. employees. and agents from and
against any and all costs. losses, danrages. liabilities, expenses. demands. and judgments.
including court costs and reasonablc aftorney's lees, rvhich may arise out of Sponsor's
activities under or related to this Agreement and Sponsor's negligcnt corrduct.
Additionalty. Sponsor shall tully indemnifl, and hold harmless thc state of ldaho,
Universitl and its goveming board. oftrcers. employees, and agents lrorrr and against any
nnd all costs. losses. damages. liabilities, expenses. demands. and .f udgments, including
court costs and reasonablc attomey's fees, which rnay arise out ol Sponsor's use,
conrmr'rcialization. or distribution of information. materials or products which result in
rvhole or in part from the research performed pu$uant to this Agreernent. provided.
holvever. that Sponsor shall not indemnifu University for any claims resulting directly
lrom University's lack of orvnership or inliingement of 6 third-parry's intellcctual
propertv rights.
ln tlre event that any such loss is caused by the negligence of both Partics, including their
cmployecs. agents. suppliers and subcontractors. the lcss shall be borne by tlre Parties in
tltc proportion that their respective negligence bears to the total nc'gligencc causing thc
loss: provided. horvever, that any loss bome by the University shall in an.v cvent onll' be
to the extent allowed by ldaho larv, including, rvithout linritation. the lirnits of liabiliry
specilied in ldaho Code 6-901 through 6-929. knorvn as thc" Idaho Tort Clainrs Act,
5.18 Amendments. This Agreenrent may be amended by mutual aqreement of the l'anies.
Such amendments shall not be binding unless they are in writing and signed by personnel
authorized to bind each ofthe Parties.
I
Arista Contract R-4 1002 (OSP No. 7378)
5.lS Assiqnment. The rvork to be provided under this Agreemr'nt. and uny clainr arising
hr..rcunder, is not assignable or delegable by either Pany in rvholc or in part. rlithout the
express prir:r rvritten consenl of the other Parry. except as required by'ldaho lalv. policy
r:r regulatiott.
5.10 Notices. Aly notice or conrnrunication required or pernritted under this Agreement shall
be delivered in person. by overnight courier. or by registerr"'d or ce'rril'ied mail, postage
prcpaid and addressed to the Party to receive such nolice at the address given below or
such other address as muy herealter be designated by notice in rvriting. Nr:tice given
hercundcr shall be elfective as ofthe date ofreceipt ofsuch notice:
University:
Namel'fith:: Contract Otllcer
Phone: (208) 426-l+15
Address: l9l0 Llniversity Drive
Ciry/State/Zip: Boisc, ID 837?5- I I i5
h,-mai I : sponsoredagreenrents(Dboisestate.edu
Sponsor:
Name/Title: Randy Cnaedinger
Phone: [phone]
Address: l4l I E. Mission Ave.
Citv/State/Zip: Spokane, WA 99?20
E-mai I : [e-mail]
j.l r Coverning Law: Jurisdiction and Venue: Atlorneys' Fees. This Agreemenl shall be
constmed and interpreted in accordance lvith the larvs of tlrc statr' o[ ldalro. rvitlrout
regard to its choice of law provisions. Any legal proceeding instituted betrvec'n thc
panies slrall be in the courts of the Counry of Ada. State of ldaho. and r.raclr of'the parlies
agrces to submit to the jurisdiction of such courts. ln the evr'nt zury lcgal aclion is
comnrenced to construe, interpret or enlorce this Agrer.'ment. the prevalling Party shall be
cntitled to an arvard against the non-prevailing Party for all of'thc prevailing Party's
reasonable attonreys'fees. costs and expenses incurred in such actioll. including anv
appeals.
5.ll Courpliance tvith Larvs. Sponsor understands that Urtiversity and Sponsor arc subject to
Llnited States laws and federal rcgulations. including the export ol'technology (i.c'..
technical data and technical assistance). computer softrvare. laboraton, prototypes and
oilrgr commodities (including the Anns Expon Control ,.\ct. ns amcncled, the Esport
r\dnrinistration Act of 1979 and associaled implu'mentirrg rr.'gulatiorts and exr-'cutivc
orders). and that Sponsor's and University's otrligations hereunder are conlingent upon
compliance with applicable United Statcs larvs and regulations. including thosc lbr c"\port
ct-rntrol. The transl'er of cerrain tcchnology and commodities. even rvithin the bordcrs ol'
the Llnited Slates. nray require a license from a cognizant agencv of thc' Llnited States
Govemrnent and/or a wrinen assurance by Sponsor tlrat Sponsor shall nol translcr
technology. soft'rvare or commodities to certain foreign p€rsons or couutries lvithout prior
approvul of an appropriate agency of the United Statr.'s Govl'rnrnent. Neither Univc:rsity
nor Sponsor represert thal a lir:ense shall not be required. nor that. if rr'quired. it will be
issued.
5.li S.qve:abilitv. lf any provision of this Agreement or any provision ol any docutnenl
incorporated by reference shall be held invalid, such invalidiry shall not affb{t thc other
l0
r\vista Contract R-.1 I 002 ( OSP No. 7278 )
provisions of this Agreement which can begiven effect without the invalid provision, if
such remainder conforms to the requirements of applicable law and the fundamental
purpose of this Agreement, and to this end the provisions of this Agreement are declared
to be severable.
5.24 No Joint Ventwe. Nothing @ntained in this Agreement shall be construcd as creating a
joint venture, pannership, or agency relationship between the panies,
5.25 Force Maieure. Any prevention, delay or stoppage due to strikes, lockouts, labor
disputes, acts of God, inability to obtain labor or materials or reasonable substrtutes
therefore, governmental reslrictions, govefilmental regulations, governmental controls,
enemy or hostile governmental action, civil commotion, fire or other casualry, and other
causes beyond the reasonable control of the Party obligated to perform (except for
financial ability), shall excuse the performance, sxcept for the payment of money, by
such Party for a period equal to any such prevention, delay or stoppage.
5.2'7 Delegation and Subcontracting. University shall not (by contract, operation of law or
otherwise) delegate or subcontract performanca of any seryices to any other person or
entity without the prior wril:eo consettt of Sponsor. Any such delegation or
subconlractlng without Sponsor's prior written consent will be voidable at Sponsor's
option. No delegation or subcontracting of perforrrance of any of the services, with or
without Sponsor's prior written consent, will relieve University of its responsibiliry ro
perform the services in accordance with this Ag:eement.
5.26 Entire Aereement: Order qf Slecedencc. Tlus Agreement contairrs all tlre terms and
conditions agreed upon by the Parties. No other understandings, oral or othenwisq
regarding the subject matter of this Agreement shall be deemed to exrst or to bind any of
the Parties herelo. ln the event of an incorcisiency in this Agreement, the inconsistency
shall be resolved by giving precedence in the following order:
IN WITNESS WHEREOF, the Parties hereto have caused this Agreement to be executed as of the date
set forth herein by their duly authorized representatives.
1.
2.
3.
4.
University
BOISE STATE LINWERSITY
Sponsor
AVISTA CORPOP.ATION
By:By
Name:
Title:
Date:
Ex tve
Name:
?itle:
Date;
r(rrcst rlt n ]f
tor ,r
" -?s-\b
1l
Avista Contrafl R-41002 (OSP No. 7278)
Applicable statutes and regulations;
Terms and Conditions contained in the Agreement;
Any attachments or addendums; and
fuiy other provisions rncorporated by relerence or otherwise into th.is Agreement.
Budget Categgries
ATTACHMENT r\ - BUDCE'I'
UNIV I]RSIl'Y PROPOS AL # 72'78
Mths Total
Salaries
Pl Dr. Said Ahmed-Zaid Summer
Co-Pl John Stubban lAsst Rsrch ProQ
Fringe Benefits
Pl Dr. Said Ahmed-Zaid Summer
Co-PlJohn Stubban
o&E
Desktop Computer
Parts & supplies for hardware prototype
Total O&E
Travel
Domestic Travel
Total Travel
Total Direct Costs
Base for lndirect Calculation
lndirect Costs (F&A) 40,5% MTDC
On-Campus Research
Total Costs
1.00
1?
otlo
35o/o
32o/o
5
c
9,917
40,003
$49,920
3,47't
1?.801
$16,272
1.000
$1,000
70.392
70,392
38,509
98.901
q
(
$ 1,900s 1,300-5------160-
$
S
$
$
t2
Avista Contract R-.{ 1002 (OSP No. 7278)
ATTACHMENT A - SCOPE OF WORK
TINI\IERSITT' PROPOSAL # 7278
lDorumenl Conutenc'et rnt lhe N*t Pugc.J
l3
:\v isla Contract R-4 1002 (OSP No. 727E )
OPERATION AND CONTROL OF
DIS'TRIBUTED RESIDENTIAL STATIC VAR COMPENSA'TORS
Attnchment A
INSTITIITION Boise State University
PRINCIPAL INVESTIGATORS Said Ahmed-Zaid. PhD, PE and John Stubban. PhD. l'l:
PROJECT 0B"LECTIVES
Develop the tianrework and simulation platfornrs to allorv tbr distributed control algorithnr tests
ol'the Resitlcntial Static VAR Compensator (RSVC). Specitic simulation objectivcs are :
o Voltage control.
o Porver tactor control.
e Multi-RSVC interaction.
r RSVC interaction with voltagc regulators. battery energy storege systerns. photl'rvoltaic
generation. and pre-existing capacitor banks.
r Conscrvation by voltage reduction (CVR).
o RSVC simulation comparison to field n'reasurement data.
PERIOD OF PIRfOIWIA|{CE etlt20l6 to 8/311?017
RESOURCE COMMITMENTS
PITOJECT PLAN
'fhis purpose ol' this proposal is lor the Boise State University team to provide engineering
research and services for Phase lV o[ the ongoing project of a Residential Static VAR
Compensator (RSVC).
l4
Avista Contract R-41002 (OSP No. 7278)
Pcrsornel Yo Effort Nlonths Comments
Said Ahmed-Zaid |%I Project oversight.
John Stubban 409,1 t2 Project ovcrsight and production stall-.
A ndrr"is Valdepefi a Delgado As
needed
PhD student antl production statl'.
Ta.s/t l.' l'nlec't Management (Pe$brned throughout projct't)
Proiect managcment rvill be pertbrmed b1,the principal investigator team ol'Dr. r\hmed-Zaid and
Dr. Stuhban.'[hey will lead the project tsanl to efficient and timely conrpletion ol'work through
the provisions of rvell planned. rvell-scheduled and nranaged research and scrvices such irs:
ldenti$ risks. anticipate problenrs. and quickly implement plan rcsolutions to minirnize impacts
on thc project, Nlonitor the effort expended in the devetopment of project delivcrables. Define
and implcnrr-nt specil'ic requirements lor project scheduling. staffing. and quality. Monitor the
pro.iect for deviatit'rns tronr the established scopc: identifi,and quantif-r" changes requested by
Avista be lare proceeding with the rvork.
Other :a.sks: Conduct weekly internal project coordinatit'rn mectings to producc good
conrmunications and clcar undcrstanding of scope. schedules. budgets. technical issues,
coordination of interrelated tasks and near term deliverables. Participate in biweekly conference
calls with Avista to update study and research progress. Prepare an agenda lor the confcrence
call using thc tenrplate provided by Avista. Address work conrpleted during dre cunent period
and work expected to be performed during the next period. Highlight items rcquiring resolution
by Avista and the BSU research project team.
Tusk 2: Otttu.'lcqui.sitiun ancl Datahsse Preparation (hy Oc'tober l, J0l6)
The following data will bc requested from Avista to perfomr the studies tbr thc RSVC rcscarch
project:
. Suitabl,' distrihution capacilor ancl surrounding connectivity to cquivalencc for a transie nt
sn,itching study.
o PowerWorld simulation database snd corresponding dynarnic parameter files of the
Avista area.
o Distribution fbeder aux files of don,ntown Spokane and other suitable study areas to
augment the Avista area case for dynamic simulations. Il- dynamic parameters are not
available for distribution feeders. generic models will be used with oversight by Avista.
o Fk:urly load prolile data of requested stud-v area.
r5
Avista Contract R-41002 (OSP lio.7278)
Isslr -1.' R.SIt' D),nuntic Simulation (b), Decernher l, 2016)
'l'hc RSVC dynamic simulation will consist of threc steps.'l'hc'frrst w'ill be to dcvclop the closed
It'rop dynamic control for the RSVC using MATLABlSimulink rvith thc Sirnscape Porver
Sl,stems'l'oolbox. This task rvill build upon the open loop control of the currcnt RSV(' prototypc
by adding a rnorL- sophisticated control scheme for voltage and power factor control.
'Ihe second step will consist of dcveloping a simulation capable generic SVC nxrdel using the
parameters developed in step one. Thc SVC nrodel will usc as reference the llPRl SVSM0I or
similar rnodel to allow fbr compatihility across difterent simulators. This rnodel rvill bc tcsted
using the PowerWorld dynamic simulator. Tests will include voltage and power thctor control.
and any debug required for qualiry* essurance. A comparison will be made benvc'en the dvnnrnic
perlbrnrance of the RSVC in PorverWorld and MAl'l.AB, taking into considcration that
PowerWorld is a positive seguence only sirnulator.
'l'he third step will consist of testing the RSVC in a simulation environment nrodeling its
intcndcd deployment. This step will simulate the dynamic intr.'raction bctween the RSVC and the
tollorving powcr systr'm elements using PovverWorld rvith a suitable dynamic datalrase such as
the SAG 741 Avista distribution feeder. The power svstL'nr r-'lemcnts tested lor intr.'raction with
the RSVC may include:
: Multiple RSVCs.
r Voltage rcgulators.
. Automatically switched shunt dcvices.
r Banery energy storage system (BESS).
r Photovoltaic distributed generation at the residential or light commercial lcvcl.
r Translormer LTC switching (if simulation tinre scale applicable).
Tusk4: Ellbct ol'li.t'l'(r on E"ristingCctpctcilorTrunsient SrlrTcAixg Study {hv !l.lu1, l" 30171
'[his analysis will evaluate thc effect of the RSVC on existing capacitor slvitclring transients.
This pro.iect task will include rcsearch on the RSVC's ability to reduce harmonics and voltage
tlicker on existing capacitor switching events using ATP or MATLAB/Simulink and Simscape
Power Systems transient rnalysis softwaru-. Simulations will includc'an inrush analysis witl":
l6
Avistq Contract R-l1002 (OSP No. 727E)
cilpacitor switching and prc'viously energized capacitor switching. outrush due to a lhult cvcnt.
and transient rccovery volta-ee.
Ias/t 5; ftSl/i|,tl'.-lr Sr:u und Voltuge (lontol algtrithm tlevelopmetrt (h1' lllcry l, 3Ul7)
Devclop a set ol'algorithnrs for RSVC placement and sizing for residcntial and light commercial
RSVC deplol'ment. Develop a strategy tor both voltage and porver l'actor operation modr to be
implenrented in the deploycd RSVCs. Test the devr,'loped algorithrns for residential and Iight
commercial u.sing OpenDSS to quantity the lrencfrts by using conservation hy voltagc reduction
(cvR).
Tusk 6: Hurtlv,ure Prototvpe Developmettt (hy Augutt 31. 2017)
Continue developing and testing the RSVC hardware prototypc. Test a first-generation RSVC
prototype in an open-loop configuration in the laboratory first andlor in a residential setting.
-Iims-perrnitting. add and tune an automatic control loop for voltage regulation.
Tusk 7: Drqft Report (h1t,4ugust l, 2017)
Compile a draft rcporl to include an exccutive summary. methodology. clata. assumption.
analysis and conclusions. Prepare and submit the drafi report to Avista by enrail or a sccurc llle
shnrr.' sitc. l'he draft report r,vill be delivered to Avista for their revicrv and commcnts,
IasA ll.' Deli'rer.linal report tnd presenl resu//,r at Avi.\to heac{quarters (hl,Augtst 31.l0l7)
lrepare and subrnit thc hnal report to incorporate Avista comments ox the drall reporl. Prepare a
presentation to be given onsite at Avista headquarters. Deliver thr' final report to Avista via cmail
0r fl secure file shure sitr'.
Background
ln distribution nelworks. when the voltage at a bus falls below a reference value. reactivc power
is injected into the s)'stcm by the RSVC to raise thc voltage. Wlren the bus voltage heconres
higher than the relerencc value, reactive powsr is absorbed by thc RSVC tu lorver thc' bus
voltagc. lnstead of using conventional methods of shunt compensation. such as static banks r-rfl
shrnt inductors and shunt capacitors connected to the secondary of the distribulion transfbrnrcr.
RSVCs are smart devices rvlrich can be more flexiblc and more powerful tools lor load
t7
Avisla Contrast R-41002 (OSP No. 727E)
manegement, These devices are built based on the concept of a single-phasc Statlc VAR
compensator (SVC) and they implement conservation by voltage reduction (CVR) to regulate
and reduce thc energy consuntption during peak demand hor.rrs. l'herefore, the RSVC dcvice
helps to oprimize the voltage level dl,namically via a sophisticatc'd snrart grid technology to
continuously reduce the energy consumption during pcak hours. The abilitl' to control thc ['low
of reactive porver continuously in both directions gives the RSVCs tlre potential to f'latten thL-
voltagc and hence increase the benefits of CVR by lorvering the voltage unittrnnly in the t'ct'der.
Previous Work
Phase t of this proje'ct consisted of a study model of a residential static VAR Conrpensator
(RSVC) for regulating residential voltages. The srudies from Plrase I showed that a singlc-plrase
RSVC oflers a signiticant potential tbr energy savings by v,rltage regulation and can lreconre a
valuable tool to assist with energy efliciency, especially during peak demand hours.
Phase ll olthis project consisted of building an opcn loop control prototype ol'thc RSVC device.
1'hc implem€ntation strategy involved a soltware centcred approach that used an FP(iA in
conjunction rvith bidirectional switchcs. The bidirectional srvitches were madl' ol'l!{OSFEl's and
controlled using a state machine to provide a snrooth transition between states.
Phase III of this project consisted of perlbrming a tinte-series simulation over nrultiple months
using a model developed in OpenDSS of downtown Spokane and a rural t'eeder near l.ake Pend
Oreille. The simulations showed that the rieployrnent ol'RSVC could enharrce CVR h1, llattcning
the voltage profi!e along the feeder. This allorved the voltage at the feeder head to be reduccd.
which maximized the benefits of CVR. A cost-benefit analysis was alst'r perlbnned showing that
deplol,nrcnt of the RSVC could be econonrically leasible.
POTENTIAL MARKET PATH
'l'hc' potential market path oI Lhe residential static var compensator (RSVC) heing built and
simulated in this project is similar to that of other shunt-connected. reaclive-injection-based
devices currently being deployed b.v somc utilities, An RSVC prototypc is heing tesled in
hardware at Boise State Universitv for a voltage control application {Consr,'rvation by Voltage
Reduction) on the consumer sidc ol'the distribution feeder. 'l'he single-phasc RSVC dcvice has
l8
:\vista f-'ontnrct R-{1002 (OSP No.7?78)
the advantage over conventional shunt capacitors ol'being able to operate in a capacitive or
inductivc mode without generating large undesirable harmonics. The RSVC's harmonic lootprint
are not rypical of nrost thyristor-based SVCs currently deployed. The RSVC uses a novcl pulse
rvidth modulation (PWM) scheme to create the variable VAR conrpensation. which pushcs the
RSVC harmonics into a higher frequency band. 'l-his snlart device can be used in multiple
applientions suclr as continuous voltagc ccntro! at a load point. polver tactor eontrol. nritigation
cl porvcr quality issues. etc. The benefits of the nrass deployment of RSVCs on thr- consumer
side ol' distribution networks will be demonstrated througlr a nuntber of sinrulation studies
proposed in this project. 'Ihe nerv RSVC device has the potential to disrupt other competitor'
devices on three fronts: cost, power quality, and smart-grid applicability or compatibility.
CRITERIA FOR MEASURING SUCCESS
'lirere arc tlrrec major criteria for success:
l. '[hc dynamic simulations will show that the control algorithrns will not destabilize the
power system. and will validalc the control algorithms devc"loped for the RSVC.
Dcpending on the research results. the RSVC may also be capable ol activc'ly reducing
the voltage srvings during dl,namic cvents. and improve the power systcm perlbrmance.
Dc'liverables for the dynarnic analysis will be the SVSM0I generic model parameters.
and sirnulation results o[ the control svstem interaction befr,"een the RSVC and other
power system elenrents.
2. Thc nrathematical algorithnrs lor RSVC placenrent and control will show incrcased
conservation by'voltage reduction rvith a number of aggregate devices. Dcliverables fbr
the placement and control rvill be a methodology on horv to apply the algorithnr to any
{beder and simulation results of the algorithm applied to one of Avista's keder.
3. T'he capacitor transient slvitching study will show that the RSVC does not produce
unw'antcd transients and is able to darnp out voltage transients. Depending on the
research, the RSVC may also be capable of improving the existing s,v-stcm pr)wer quality.
Deliverables will include tlre results of the inrush. outrush and transient recovery voltage
simulutions.
4. A rvorking RSVC prototype tested in an open-loop configuration (and possibly in an
automatic closed-loop configuration) in the laboratory will be demonstratetl to Avista
('orporation.
t9
Avista Contraqt R-4 1002 (OSP No. 7?78)
APPUNDIX A: Proposal Covcr Shect
;\PP}INDIX C: Facilitics untl Equipment
Boise State []niversity is one of thrce state-supported research active universities in the state nf
ldaho. Foundetl in I9l2 as Boise Collcgc'. it has experienced rapid gronth. particularly in thc last
l5 years. ln that tinre it has establishecl itsell as an acconrplishcd rcsearch universily. norv
oftering ! doetoral dcgrees and research expenditures on the order ol$35Miyear. Boise Statc's
Olficc of Sponsored Programs has a well-trained support staff to handle contracting. accounting,
compliance and reporting related tasks.
20
Avista Contract R-41002 (OSP No. 72781
APPENDIX D
University of ldaho Agreements
frwsta
PROJECT TASK ORDER for SERVICES
Master Agreement No.Task Order No.Modification No.Modification Date
MA, Ul/Aw.sfa R-39872 166/.7
This Task Order is made and entered into this 14s day of July 2016, by and between
Avista Corporation, herein called SPONSOR, and the Regents of the University of ldaho,
herein called UNIVERSITY. The Task Order describes aclivates to be conducted by
UNIVERSIry for SPONSOR. Any deviation from the work outlined in this Task Order
and Attachment A must first be approved in writing by SPONSOR. ln addition. work
performed under this Task Order is subject to the provisions of the Master Services
Agreement. The Master Agreement, and this Task Order and Attachment A constitute
the entire agreement for the WorU Services applicable under this Task Order. The
terms and conditions of this Task Order may not be modified or amended without the
express written agreement of both parties.
Title of Seruices: Downtown Spokane Micro-Grid
Start Date:
08/15t2016
Duraton (number of months)
12 months
Estimated completion date:
08114t2017
UI PI:
Herbert Hess
SPONSOR Representative
Consideration and Payment:
Ul agrees to perform the Services set forth in Attachment A, Scope of Services, and SPONSOR
agrees to pay for said Services in accordance listed as budgeted amounts upon performance by
Ul. The obligation and rights of the parties to this Task Order shall be subject to and govemed by
terms and conditions of this Task Order and the Master Agreement.
Fundlng Amount (3): (Per Attachment A, Budget)
$86,179.61
Progress Report Date:
Final Report Date: 0813112017
Deliverables:
lN WITNESS WHEREOF, the parties hereto have set their hands on the day and year
first written above:
Ul Representative Signature Agency Representative Signature
fuah,t&r*/
Deborah Shaver, Director
Date:
Heather Rosentrater VP
Date:
vR 07l18/16
Attachment A
Dow ntown Spokane Micro-grid
Universiry of ldaho
Principal lnvestigalor: Herbert L. Hess
Co-Principal Investigator: Brian K. Johnson
Total Amount Requested: 586. I 79.62
Objective
The scope of this research is to perform studies to determine how to establish a microgrid under
emergency conditions in downtown Spokane. Our goal is to create a practical plan to invesligate and
employ a master controller to establish the microgrid, to manage the inclusion of generation. enerry
storage, and critical loads in a set of anticipated scenarios. Advantage to the ratepayers will be measured
through maintaining critical loads for public safety and system reliability and security and improved
recovery from emergency situations.
Resource Commitment and Student Involvement
Resource commitments for this project include the following:o Pl and co-Pl research time, up to25o/o commitment as normal academic duties.r One graduate student as a funded Research Assistant to analyze existing data from
Avista. develop models and conduct simulations uing them. and determine the best
course of action to take.o One undergraduate student as a funded Research Assistant to work on a subset of the
model development and to perform simulalions.r Laptop computer equipment and software. This is a simulation study.
Details are contained in the Budget and Budget Justification.
This project involves students in every part of the research: project definition. modeling of
the system and its components. creating the simuladon studies, analyzing the results of those
simulations. and making recommendations for means to further investigate and implement
discoveries. We have employed a team like this on a number of projects of similar scope and
purpose in the past. Studens are all rnajoring in Electrical Engineering with an expectation to
concentrate in Electrical Power Engineering. Having a sponsor like Avista as the recipient of
deliverables enhances student performance greatly. We have realized a great deal of success
from teams of this composition and its combination of technical competence and multiple levels
of seniority and experience.
Technical Approach
When the balance between the total generation and load is disturbed. some loads may not be served
and outages occur. This imbalance can be caused due to severe weather conditions, failure of the
generating equipment, or failure of transmission facility. While outages are not desirable and jeopardize
system reliability. some have more advene consequences than others. To minimize these adverse impacts
on customers and to prevent these outages causing further chaos, critical /oads. such as hospitals and
prisons. must be supplied unintemrpted power during system outages.
One solution to this problem is to design and operate a local ,l,/icrosrrd based on Avista's downtown
hydroelectric generation capabilities and upon proposed energy storage. A microgrid is a small-scale
power grid that can serve local loads with or without connection to the utility grid. Over the past year.
Avista has acquired a robust model of downtown Spokane through this university partnership. It now
remains to use this model to understand how to create a microgrid and to activate and manage its
operation. Important tasks in this research include idartifying sinrations in which establishing a
downtown microgrid is the best option, creating an organizing such a microgrid, allocating resources, and
managing the microgrid for unintem.rpted operation.
l. ldentify conditions appropriate to establishing a downtown microgrid. Verifr loss of
transmission feeders. Establish conditions that require activation of plans for a microgrid, When
these conditions appear, activate the planned microgrid.2. Create and organize such a microgrid. Bring down the downtown grid and set priorities for
bringing up generation, storage. and critical loads. Bring up these resources in a logical
sequence. loading down the system appropriate, Establish and veriff protection methods of
resources and loads.3. Allocate resources. The available generation and load profile is in the model that was created this
year. Propose appropriate energ/ storage and verifu its effectiveness, Establish load prioritization
within the microgrid. Create algorithms that can reallocate resources to maintain mariimum
service and guarantee stability.
4. Ensure unintemrpted operation. Develop strategies for actively managing the microgrid.
including load shedding and load pickup. engaging generation and energy storage, and
terminating the microgrid when emergency conditions end. Use Avista's demand response
system to track the microgrid's performance and to optimize use of resources in real time.
Avista@ has expressed interest in making this happen among many of their customers. This project
follows on from the creation of the models necessary to understand how to create, assemble. and rnanage
a downtown microgrid. With the acquisition of these models, Avista wants to research possible microgrid
scenarios as a planning measure. Continuing the project for a second year to create and test these
scenarios has been encouraged by Avista engineers when we contacted them. With the windstorm of
November 2015, Avista's management is all the more interested in lhis concept and has said so in our
discussions with them.
Task l: Design and Test a Downtown Micmsrid with its Master Control Unit
The incumbent microgrid project under this same program provides a model of the proposed
downtown microgrid. This proposed second year of this project will address several issues encountered
when organizing, implementing, and operating such a microgrid. Reliability, security, and simplicity of
setup are key in small scale power systems, such as this proposed microgrid.
To create and operate the microgrid, computer-based control system must be incorporued. It would
provide the means to quickly communicate necessary setup and operating controlto the microgrid's
elements. Such a master controller would consist of a central processing unit and several extemal
monitoring units. The monitoring units would be positioned at each critical location feeding the master
with data about current conditions. Tluough these monitoring units. the master would constantly monitor
lhe external grid and determine if tlre microgrid should be implemented. Once implemented, the master
controller would detennine which loads will be included based on the limited amount of generation.
Then the master controller would rcgulate the microgrid. controlling entry and exit of loads and operating
the microgrid until a decision is made to terminate the microgrid when Avista's grid is restored. the
microgrid no longer being needed-
Many Microgrid Control Units (MCUs) to serve as master controller are available as microgrid
technologt and research advances. These systems vary in amount of computing power, available UO
interfaces, functional subsystems and commurication systems. An entire microgrid control system will
require several subsystems based on the microgrid's generation souncesr storage capabilities, require load
management, and protection. To determine the optimal control system for the microgrid several products
will be compared, incorporating appropriate research advances on microgrid control in the course of the
project.
To establish the downtown microgrid, the MCU identifies the existence of a situation wherein
Avista's grid may not provide sufficient power for the downtown load. To assemble the microgrid, there
are two options: either to shed all load except the loads that become the downtown microgrid or to shed
all load and then immediately restore those loads that will comprise the downtown microgrid. Issues such
as establishing and maintaining stability, maximizing the load that becomes part of the microgrid given
available generation, and when to pick up or shed load in response to generation changing due to water
flow or the availability of any available renewable enerry. This research will determine which option is
best, how to implement that option, and why that proposed path is best.
Management ofthe microgrid will proceed as per established procedures on how a grid should be
managed. Priority of loads will be established by Avista policy and implemented by the microgrid
controller. Priorities will be based on historical data and its known seasonal and periodic variations and
on possible renewable energy additions to the downtown generation mix and on proposed additions to
enerry storage. This research will provide input to establishing and maintaining those load priority
policies in light of available generation and enerry storage. Typical scenarios will be developed to refine
the policies and to program them into the microgrid master controller. These scenarios will then be
simulated to engage the microgrid and to determine its performance under anticipated conditions and
inputs. Recommendations for refining the microgrid will be made from the results of these simulations
and tests.
Task ?: lnterface to distributed eeneration and to storage (.Proposed senior desim project)
Distributed generation (DG) can enhance a downtown microgrid, easing generation requirements.
DC peneration with additional generation sources and energr storage provides the possibility of more
enerry rcsources. ln reality there are technical limits on the degree to which distributed generation can be
connected, especially for some intermittent forms of renewable generation within the unknowns of a
downtown microgrid. Investigating the possibility of incorporating such resources will be the subject of a
senior design project
Increasing DG penetration consists of a combination of generation sources, loads and eners/ storage,
interfaced tlrough fast acting power electronics. This combination of units is connected to the microgrid
at appropriate points to enhance the capability to provide electrical power near point of use. Key issues
for the micro grid are those of system stability (power flow, voltage control, and system prCItection)
during establishment, operation, and termination of the microgrid.
The question to be solved is what reasonable combination of renewables and enerry storage would
enhance the feasibility and the capability of a downtown microgrid under emergency conditions. Senior
desigrt students will be presented with this problem and asked to propose such a system and then prove
that the desired advantages are present They will assess the costs and issues that must be addressed in
further detail as well.
Task 3: Automatic generator control (Ongoing senior desiBn project)
In an electric power system, automatic generation control (ACC) is a system for adjusting the power
output of multiple generators at different power plants. in response to conditions within the microgrid.
This automatic generator control would be a key part for the Spokane microgrid because a large share of
their generation is hydroelectric.
There are tluee primary aspects of this project:r Creating a model for the generators at hand. Currently. such models are incomplete. This
will require that the students develop a framework for such a model and identiff
shortcomings. They will then find appropriate ways to remedy the shortcomings, whether
through finding missing or inadequate parameter specification for the generators, for
example, or for making reasonable estimates based on good judgment. Creation of model for
generatorsr Control of reactive power: From the generator and system models, the students will develop
algorithms to control reactive power. Voltage stability within the microgrid will be the goal.
A set of scenarios will test the algorithms and provide recommendations for improvement.
r Control of real power: this is frequency stability within the microgrid. Again. students will
develop algorithms to control generation and load. Frequency will be the indicator. A set of
scenarios will test the algorithms and provide recommendations for improvement.
This is an ongoing senior design project, started at the halfuay point of the cunent (and fint) year of the
microgrid project.
Project Planl. ln coordination with Avista engineers. identifr conditions appropriate to establishing a downtorvn
microgrid. Select a master controller to organize, establish and monitor such a downtown
microgrid.
2- Develop strategi€s for actively managing the microgrid. Set priorities for establishing the
downtown microgrid: bringing up generation, storage. and critical loads. Establish and verify
security and protection methods of resources and loads.
3. Simulate the microgrid under several anticipated scenarios. Create algorithms that can reallocate
resources to maintain maximum service and guarantee stability. Simulate Avista's demand
response system to track the microgrid's performance and to optimize use of resources in real
time.
4. Test generationn storage, and load prioritization within the microgrid. Report the results of these
tests and provide recommendations tbr establishing and operating such a microgrid and on how to
further advance plans for a downtown microgrid.
5. Incorporate results from two senior design projects into the generation, storage, and load
management of the microgrid. Senior design projects enhance the number of students who are
involved with the project while incurring minimal or no additional cost.
Potentisl Market Path
This project develops the underlying engineering of a study of a downtown microgrid solution that
provides an organized response to emergency conditions within downlown Spokane. Results from this
project rvill determine where to proceed with this technologr. lf the technologl appears to become
feasible and in what time horizon, then recommendations for developing it further will be tnade. Reduced
scale hardware studies will follow, creating and analyzing performance of such a microgrid on the Analog
Model Power System at the University of ldaho. lf that shows promise. engaging energy developrnent
resources. for example through US Depafiment of Enerry or State innovation and enrepreneurial funding
would follow. Creating a business plan would be appropriate at that point in the development process.
when nearing completion of such follow-on projects.
Deliverables to Measure Project Success
The deliverables for success in this project will include the following documentation of prcdictions of
microgrid performance:o Organize a downtown microgrid based upon Avista priorities
o Select and program a master controller to establish and operate such a microgrido Determine response in simulation to appropriate scenarios that would be presented to such a
microgrid.o Develop further plans for advancing a microgrid concept in downtown Spokane within slr years.
r ldenti& the cost advantages to ratepayers of implementing this microgrid within a six-year time
rvindow.
Proposal Exceptions
Per section 5.2 of the RFP, the Universiry has described exceptions to RFP requirements and conditions
in the letter dated 4/15/16 and included with Appendix A.
Expense Year I
A PUFaculty Salaries $4,362.15
B
PUFaculty Benelits
(http://www.uidaho.edu/osp/fr ingebenefi tstabIe)$1,356.63
c StudenU Post Doc / Grad / Undergrad Salaries $37,424.00
D
Student /Post Doc /Grad / Undergrad Benefits
(ZVo=academ ic year; 7 .4o/o= summer)$ r.556.32
E
Student /Post Doc lGrad /Undergrad
Tuition & Insurance s10,429.78
F lH Salaries
G
lH Benefits
(<20 hrs week/9%)
H Equipment/Computers $5,000.00
I Supplies - Background check s300.00
)Travel $400.00
K Evaluation Costs s0.00
L Modilied Total Direct Cost (A, B, C, D. F-K)s50,399.09
50.30%
F&A / Overhead
(https:/iwww.uidaho.edu/research/facu lty/resources/f-and-a-rates)$2s,3s0.74
M Direct Cost + F&A 525,2a9.83
Total amount of Request (M + f)s86,179.61
Budget Justification
Pl/Faculty Salaries: 2% of salary to administer project and mentor students
Pl/Faculty Benefits: University faculty fringe benefit rate is 3 l.l0%. Student fringe benefit rate is 2% in
the academic year and 7u.40Yo summer.
Student Salaries: One graduate student paid at the rate of $21.00 per hou for 780 hours (nominal 20
hours per week) during the academic year and 440 hours (nominal 40 hours per week) during the summer.
One undergraduate student paid at the rate of $ 12.00 per hour for 468 hours (nominal I 2 hours per week)
during the academic year and 440 hours (nominal 40 hours per week) during the summer.
Student benefits: %. Student fringe benefit rate is 2% in the academic year and 7u.40o/o summer.
Tuition and insurance: Academic year graduate student in-state tuition per student at the rate of $8468.66
and health insurance at the rate of $ I 96 I . I 2. Summer health insurance is included in the payment for
spring semester.
EquipmenVcomputers: Computer hardware and software to perform simulations as required to obtain the
project's del iverables.
Supplies: Background check of each student and professor as required by univenity policy. Any money
not spent on background checks may be spent as student or faculty salaries and benefis.
Travel: Two trips to Avista in Spokane to present results and to consult with Avista engineers and
management.
F&A / Overhead: Assessed at the rate o150.3% of project direct costs except tuition.
PROJECT TASK ORDER for SERVIGES
Master Agreement No.Task Order No.Modification No.Modification Date
MA, Ul/Avrsta R-39872 16636
This Task Order is made and entered into this 14th day of July 2016, by and between
Avista Corporation, herein called SPONSOR, and the Regents of the Universig of
ldaho, herein called UNIVERSIW. The Task Order describes activates to be
conducted by UNIVERSITY for SPONSOR. Any deviation from the work outlined in
this Task Order and Attachment A must first be approved in writing by SPONSOR. ln
addition, work performed under this Task Order is subject to the provisions of the
Master Services Agreement. The Master Agreement, and this Task Order and
Attachment A constitute the entire agreement for the WorU Services applicable under
this Task Order. The terms and conditions of this Task Order may not be modified or
amended without the express written agreement of both Darties.
Title of Services: "CAES Water/Energy Conservation Analysis with Avista"
Start Date:
08-1s-2016
Duration (number of monthr)
12
Estimated completion date:
o8-14-2017
UI PI:
Richard Christensen
SPONSOR Representative:
Heather Rosentrater
Consideration and Payment:
Ul agrees to perform the Services set forth in Attachment A, Scope of Services, and SPONSOR
agrees to pay for said Services listed as budgeted amounts upon performance by Ul. The
obligation and rights of the parties to this Task Order shall be subject to and govemed by terms
and conditions of this Task Order and the Master Agreement.
Funding Amount ($): (PerAttachmantA, Budget)
$93,354.55
Deliverables:
Progress Report Date:
Final Report Date: 0813112017
Other:
TX
lN WTNESS \ ftlEREOF, the parties hereto have set their hands on the day and year
first written above:
U I Representative Signature
tb*,th tlr*tDeborah Shaver, Diiector OSP
Date:
Heather Rosentrater VP
Date:
vR 07.18.16
Attachment A
CAES Water/Energy Conservation Analysis with Avista
l. Name of ldaho Institution: The University of ldaho (Ul), Idaho Falls Campus. 1776 Science
Center Drive. Idaho Falls, Idaho 83402
2. Name of nrincioal investieator: Dr. Richard N. Christensen
3. Proiect Obiective and Totsl Amount Reouested: The University of Idaho (Ul) and ldaho
National Laboratory through their partnership with the Center for Advanced Enerry Studies (CAES) rvill
work collaboratively with one carefully selected member of the Northwest Food Processors Association
(NWFPA) to do a combination of process modeling and systemdynamic modeling of their operation. The
goal is to identifl and create plars for implementation of new technologies that would significantly
reduce their energr and water consumption. Because of the nature of the enerry use in food processing
(heating. often followed by cooling), new technologies have the potential to substantially reduce the
energy and water use by some customers. This will be accomplished by meeting the following objectives:
o Analyze the enerry water footprint of the plant within a food processing plant using Aspen
HYSYS process modeling soflware.
o Characterize the energr and water consumption, identifying entering and exiting streams and
lhose areas where improvements can be made.
. Apply new and innovative techniques for water and enerry conservation at the specific points in
the plant where the potential for savings is the greatest.
r Evaluate the impact of these new technologies on enerry and water use within the plant.
r Perform an economic and sociaal evaluation of the impact of these new technologies, and
e Develop an integrated modeling approach that will allow stakeholders to collaboratively develop
and analyze nerv water and energy strategies for food processing
plants. The amount requested for this proposal is $93J5.1.34.
{. Resource Commitments:
Dr. Richard N. Cluistensen: 6! hrs for project management and student mentoring
Dr. Karen Humes: 20 hrs for project numagement and review
Dr. Michael McKellar: Through a separate collaboration with CAES. Dr. McKellar will mentor a student
for 40 hrs in the use of Aspen HYSYS at no cost to the sponsor
Mr. Jacob Jacobson: 65 hts for student mentoring in societal and economic evaluations
1
Two gradtrate students (GRAs): 1032 hrs each for a total of 2064 hrs. Ul rvill provide ruition and fees
(-Sl E,000) for these two students from other sources. One GRA on Aspen HYSYS modeling, and one
GRA on engineering, societal and economic evaluations of potential solutions
5. Specilic Proiect Plan
lntroduction
The growing demand on enerry and water supplies jeopardize the delicate balance between food
production and, eneryy and water use (FEW). Food processing is one of the largest consumers of water
and energl in the Pacific Northwest. Because of this, the Northwest Food Processors Association
(NWFPA) has a goal for its 450 members to reduce enerry consumption 50% by 2030. Savings to date
has been accomplished by making simple and obvious changes. However, improvements have
significantly fallen offin the last few years as it becomes more difficult to identi$ additional savings. The
NWFPA recognizes that in order to reach this goal. they will need to employ special technologies such as
combined heat and power (CHP), renewable enerry methods, new methods for heating and cooling.
However, it is not well understood how those technologies will impact other areas of the enerry-water
spectrum. Therefore, a preliminary analysis will be done to evaluate the engineering, as well as the
societal and economic consequences of any proposed methods. We hypothesize that active management
through advanced modeling and visualization techniques, will lead to better oulcomes in the face of
growing water scarcity and the recognized need to reduce enerry consumption. These techniques will
enhance decision maliers' ability to make better and more informed decisions related to managing energy
and water use in these complex systems.
Approach
We will work with Avista and NWFPA to identif, one NWFPA member company most suitable for
investigation, based on a number offactors, such as:
a) Their energy use and type of operations, with emphasis placed on companies with operations
represenlative of other NWFPA members (in order that this demonstration be relevant for as
many NWFPA members as possible )
b) Location in geographic areas in which Avista may have specific targets for demand reduction,
due to transmission capacity or other issues
The outcome will be a mathematical model and process or methodolog that will allow users to test
different development strategies for a particular plant or set of plants and visualize the short-term as well
as long-term impacts to energ/ and water consumption, GHG emissions and socio-economic impacs.
The overall goal of this study is to examine the integrated management options for long-tenn water and
energy resource management through the integration of new technologies and practices within the food
2
processing industry to reduce overall dernand and impact on eners/ and water. The approach will be to
couple detailed process modeling of food processing facilities with a systems model thal can assess both
short-term and long-term impacts to the overall system perflormance and include socio-economic
components in the analysis. Tltis initial project will select one food processing company within the
Avista geographic transmission area. However, the end goal is to develop a model and methodolory that
can be used in a large number of the NWFPA membership companies.
Modeling Effort
A process model for one NWFPA member company will be developed using ASPEN HYSYS process
modeling software. The first stage of modeling will be the given design of the companies' processes
which will include pressures, temperaturcs, flows, and fluid compositions throughout the process.
lncluded in this stage will be sizing of the equipment throughout the plant or plants.
The second stage is to analyze the existing model to identify areas where the energy could be more
efficiently used. To do this. an enerry analyses is performed to identiff available work throughout the
processes and to find areas where larye losses of this available work occur. This will focus efforts on
components that have the largest lost work to find \vays to improve energi utilization, Examples of
improvements in enerry usage could include more ellicient heat exchangers, better piping and component
insulation, process heat recuperation, exergy boosting, enerry use in a different order or new substitute
technologies. The models may be used to reduce water usage through simulation of better water recovery
syslems, water puri$ing technologies. and process heating or cooling using alternative methuis.
A critical part of the modeling effort is to analyze the economics of modified prouesses. By considering
the economics of the modifications, the processes may be optimized to economically reduce ener5/,
increase production, and reduce water usage.
Prcccss Modeling Sofhryare
APSEN HYSYS process modeling software provides basic components such as pumps, compressors.
turbines, piping, chemical columns. chemical reactors, valves, heat exchangers and others to simulate
thermal and/or chemical processes. The software uses a large database of thermodynamic, fluid. and
thermal properties for a wide variety of gases, liquids. and solids. I-IYSYS ensures correct mass and
energy balances as well as ways to simulate components that are not a standard part of the software. The
component simulations are detailed enough to size and provide initial design parameters.
Discussion of Possible Process Modifications
tn 2015. Bonneville Power Authority published the Enerry Efliciency Technolory Roadmap (James V.
Hillegas-Eiting 2015).' We will quote widely from this document in the following section, specifically
from Volume 7: Industrial Food Processing and from Volume 8: Combined Heat and Power.
3
Areas we would evaluate with respect to actual food processing include: sterilization and pasteurization
based on microwave or ultrasonics (V7, p4), equipment needed to upgrade heat content in waste stream to
higher more useful temperature ranges (V7. p6), use of multistage CO2NH3 cascaded refrigeration for
low temperature freezing (less than -25C) (V7. p8), use of more energy intensive mechanical cooling
rather than absorption cooling when waste heat is available at an appropriate temperature (V7, pl0), an
increased focus on HVAC within the plants (Y7, p26), use of real time energr monitoring hardware. Also,
where it would be placed and what equipment would be used (V7. p28), determine when and where it
might be possible to use smart electriciry management systems tbr food processing and self-correcting
energy management systems based on known inputs(V7. p34).
Areas we would evaluate with respect to combined heat and power are taken from Volume E and include:
use of organic Rankine cycle and Kalina cycle to recover ener6/ from low and moderate temperature
waste heat streams (V8. p2), use of Stirling Engines for very high temperature waste enerry streams (V8.
p4), use of industrial mechanical and absorption devices to recover latent and sensible heat from exhausts
up to 400F, (V8, p8), use of the dynamic models developed to determine the optimal combination of CHP
generators, combined cooling, heating and electric power generators, thermal storage, cooling storage and
electrical storage (VE, pl4), microwave drying of wet biomass and higher temperature gasifie(V8, p l8),
application of higher Coefficient of Performance (COP) absorption chillers and chillers that are able to
convert process heat to freezing (V8, p22). investigate possibility of using low grade heat ( 140-200F) gas
trom boiler or water from the cooling tower to make electricity (V8, p36), use a thermally driven heat
pump to raise lower temperature water lo a useable temperature (V8,p36), use a cunent program to
integrate CHP. process. and low heat temperalure recovery (V8. p36)
ln summary, we find the following quote from V8. p32 most interesting: "Find analysts capable to
perform full analysis of an operating plant for optimum effective energy use: integrate CHP, maximize
other process heat recovery, utilize low temperature heat by new technologies to enable CHP by
improving economics and to avoid elecricity use by direct thermal drive from waste heat." This is
*actly u,hat we propose lo do herein.
Socio-Economic Analysis
There is an economic advantage to reducing enerry consumption within a processing plant. However, it
is important lo recognize that there are more than economic measures forjudging improvements within a
complex system. Improvements in energy consumptions cannot be taken al the sake of the environment.
If so, any improvemenls will most likely be shortJived as the environmenlal impacts in the long term
may reduce economic retums. Addifionally. it is important for any solutions to be socially acceptable.
4
'Ihis project will develop a dynamic analysis of key system performance indicators similar to the
Balanced Score Card (BSC) approach used by many organizations (Kaplan and Norton. 1992)2. The
concept of balance central to this system specifically relates to (Niveu 2002)3:
r Balance between financial and non-financial measures.
r Balance between internal and extemal constituents of the organizations.
r Balance between lag and lead indicators.
However, the BSC approach has a number of deficiencies: one way cause and effea. does not consider
tirne varying impacts, and no meehanism for validating the pertbrmance indicators.
This project will go beyond the BSC approach and includes a system dynamics approach that looks at
short-term versus long-term impacts while considering feedback rvithin the system, System dynamics also
enables us to build formal computer simulations of complex systems and use them to leam about and
design more effective management strategies (Sterman. 2000)3. This approach will analyze impacs of
management decisions across a set of key metrics that take into account not only financial indicators but
also intangibles (e.g.. customer satisfaction, business image. environmental impact.-.). The approach will
be to worh with management to determine a key set of indicators that are deemed impactful, develop a
visual mapping of the process and interactions, identifr time lags and feedback pnocesses, develop a
dynamic balanced scorecard (DBSC) simulation model of the processes, validate strategies through the
dynamic simulations, and deploy the dynamic balanced scorecard framework. Food processors in the
Northwest are interested in active management solutions to water scarcity, setting production goals for
enerry and water conservation, developing technolory to reduce greenhouse gas emissions. and
improving resource efficiency. The DBSC work above focuses on in-plant analysis and efficiency
improvements across a wide balance of key indicaton to assess technologies that would provide long-
term and impacting improvements.
6. Potential Market Path:
This project will result in two models that will allorv the quantification of enerry and water savings
potential and the evaluation of any risks or deleterious effects of the recommended enerry and water
saving measures. In a second year. if funding is available, the collaboration will verifu the methodologt
by application to two otlrer NWFPA members. If the verification is successful, the collaboration will
establish a fee schedule for the evaluation pnocess and market the methodologr to all NWFPA members.
Once established in the Northwest region, the collaboration would make these services available to the
country nationwide.
7. Criteria for measurins succ6s:
This project will be a success if the tbllowing occur:
5
. An ASPEN-HYSYS model on one NWFPA plant is completed and used to evaluate enerry and
water usage. This model will be a deliverable included in the final report.
r Viable altematives are suggesled and modeled that save l0% to 20 % of energy and water usage.
e No extemal deleterious effects are identified for the alternative suggested above.
o A methodolory is delineated that allows the application of the models used to other plants within
the NWFPA. This methodolog will be a deliverable included in the final report.
r A Final Report is generated that documents all findings.
Budeet
Anticinated Exoenditures #hrs $/hr
8.
Total
PUStudents
Faculty - Christensen-Richard
Faculty - Summer, Christensen
Temp Help - Jacobson
Faculty - Humes, Karen
Grad Student MS (AY) 2 Students
Grad Student PhD - Summer 2 Students
Fringe Benefit Cost and o/o
PUFaculry (AY)
PI/Faculty (Summer)
Staff
Grad Studenr - AY
Grad Student - Summer
Equipment- I computer
Travel
Humes to ldaho Falls from Moscow 3-days 2 nighs
Hurnes, Christensen, Jacobson, Portland 3{ays, 2 nights
42
20
65
20
I 560
s04
3l.lo/o
31.1%
40.9Yo
2.0%
2.0%
$80. r 3
$80. r 3
$45.00
$75.94
$19.6r
sl9.6t
$3,365
$ I,603
s2.92s
$1,519
$30.s92
$9,883
sl,sre
$498
sr,r96
s6l2
$198
s2.300
s1.302
s4,600
0.503
$62,1 l2
$3t,242.34
TOTAL $93,35.1.3,1
9, Proposal Exceptions Per section 5.2 of the RFP. The University has described exceptions to RFP
requirements and conditions in the letter dated 4/l5l16 and included with Appendix A. The letter rvill
be included by the Ul Office of Corporate and Foundation Relations for the submission.
References:
l. James V. Hillegas-Eiting, Ed., Energt EficiencyTechnologt Roadmap,BPA Technolory lnnovation,
2015.2. Robert S. Kaplan and David P. Norton, Harvard Business Review. January-February 1992.J. Paul R. Niven, Balanced Scorecard Step-by-Step: Maximizing Performance and Maintaining Results.
Wiley. October 2002.
5
Total Direct Costs
lndirect Cost 50.3%
4. J.D. Sterman, Business Dynamics System Thinkng and Modellingfor a Complex l{orld, McGraw-Hill
Higher Education, Boston, Mass, USA, 2000.
Letters of support and a flow chart for the proposed work can be found at:
http : //www. u idaho.edu/i dahofal I s/caes/logic-diagram
7
PROJECT TASK ORDER for SERVICES
Master Aqreement No.Task Order No.Modification No.Modification Date
MA, Ul/Avisfa R-39872 1 6643
This Task Order is made and entered into this 15 day of July, by and between Avista
Corporation, herein called SPONSOR, and the Regents of the University of ldaho,
herein called UNIVERSITY. The Task Order describes activates to be conducted by
UNIVERSITY for SPONSOR. Any deviation from the work outlined in this Task Order
and Attachment A must first be approved in writing by SPONSOR. ln addition, work
performed under this Task Order is subject to the provisions of the Master Services
Agreement. The Master Agreement, and this Task Order and Attachment A constitute
the entire agreement for the Work/ Services applicable under this Task Order. The
terms and conditions of this Task Order may not be modified or amended without the
express written aqreement of both parties.
Title of Services:
Using Reduced-Order-Models for Simulation-Based Commissioning of Buildings
Start Date:
08-01-2016
Duration (number of months)
13
Estimated completion date:
08-31-2017
UI P!:
Elizabeth Cooper
SPO NSOR Representative:
Heather Rosentrater
Consideration and Payment:
Ul agrees to perform the Services set forth in Attachment A, Scope of Services, and SPONSOR
agrees to pay for said Services listed as budgeted amounts upon performance by Ul. The
obligation and rights of the parties to this Task'Order shall be subject to and governed by terms
and conditions of this Task Order and the Master Agreement.
Funding Amount ($): (Per Attachment A, Budget)
$64,230.00
Deliverables:trN
Progress Report Date:
Final Report Date: 8-31-2017l-l other:
lN WITNESS WHEREOF, the parties hereto have set their hands on the day and year
first written above:
U I Representative Signature Age n cv Representative Si g natu re
hl.:201607.15 I5i6S{7m
Deborah Shaver, Director OSP
Date:
Ogit ltsEnd t hbah N.SB
Deborah N. Shave f ffirdsPndP,qnc,turn;bv@&b.do
Heather Rosentrater VP
Date:
vR 07.15.16
Attachment A
Name of Idaho public institution:
University of Idaho
Name of principal investigator directing the project;
PI: Elizabeth Cooper
Co-PI: Dr. Jinchao Yuan
Project Title - Objective - Amount Requested
Using Reduced-Order-Models for Simulation-Based Commissioning of Buildings
Project objective and total amount requested
Amount Requested: $64,230
Objective
Optimal controls are essential for building efficiency. However, because every building and control
system is unique, it can be a challenge to analyze and tune these controls on a large scale. Using energy
models to commission a building is one way to quickly identify errors and correct controls. This virtual
controller commissioning can save significant amounts of energy and money. The research proposed herein
would streamline this method and increase its market potential.
In 2015, through funding from Avista's Energy Research (AER) Initiative, the IDL performed a
virrual control commissioning for part of the HVAC system at a building on the University of Idaho campus.
The research on the economizer setting at the College of Business and Economics (COBE) building revealed
areas for optimization. Simulation results indicated a potential savings of 240 MWh per year for this one
building alone. Were this approach expanded to the campus level, it could result in an annual savings of
7,200 MWh. This proposal will build upon this past research on advisement from Avista and try to adapt the
method so that it is more amenable to commercial applications. Under past funding from Avista, the team has
already created a detailed energy model of the COBE building and established communication pathways
between the energy modeling software and the building control hardware. This enabled the team to run
remote hardware-in-theJoop simulations to identify savings opportunities and estimate energy reductions.
The continuation of this research would move this technology forward in two important ways: by
performing a physical demonstration at the site, and by simplifl,ing the modeling process. A physical
implementation of virtual commissioning would greatly add to the commercial case for this technology. The
second component of this research is in response to feedback from Avista from the 2015 work. The aim is to
simpliff fully detailed building models to reduced-order thermal models that could then be used to tune the
building controls. The past research relied on specific building models in EnergyPlus. While these models
are suitable for academic study, they are less practical for commercial applications because of the time and
expense of building a detailed energy model of the building, and are difficult to scale up. Moving this
method towards a more general software platform and modeling method would widen its appeal to the
controls industry.
The objectives of the overall project are to lower total building energy use, and reduce building start-
up duration by employing a streamlined, simulation-based method for building commissioning. This service
promotes efficiency and has the potential to be used in future curtailment strategies by enabling building
controls to predict and respond to peak loads. Performing a physical implementation of virtual controller
commissioning and pursuing research into simplification and scalability of this technique will advance this
service towards commercialization and widespread adoption.
Resource commitments, (number of individuals and possible hours for services):
Personnel Hours estimate Description
Dr. Jinchao Yuan, P.E.312 Provide overall management of the project
and technical expertise
Elizabeth Cooper 56 Provide project guidance, support
publication plan and market path approach
Post-doctoral Fellow 218 Provide technical support and technical
expertise and execute daily tasks
L. Damon Woods - PhD ME student 160 Provide technical support and execute daily
tasks ofthis project
TBD MS ME student 320 Execute daily tasks of this project
Specific project plan
This work will pick up from the 2015 analysis performed on the COBE building. The research team
will begin by verifying the control sequence current operation of the building's HVAC system. Past work
relied on "dummy" inputs to the remote controller. These were compared to data recorded by the building's
Energy Management System (EMS). In order to bring the work up to the current building situation, the team
will confer with the building facilities team and gather additional historical data from the EMS. This
information will enable the researchers to verif! the current controls and determine a current baseline of the
building's energy consumption prior to commissioning.
IDL will work closely with the operators at the site to establish a communication pathway from the
current controller at the site to the EnergyPlus software model that was created during the 2015 work. This
physical implementation of the co-simulation at the site will feed a live stream of building conditions to the
energy model allowing the research team to compare control decisions on a real-time basis instead of using
RFP No. R-40884 Page 2 of 6
historical records and simulation estimates. Communication between the EMS and the energy model will be
achieved by routing the EMS signals directly to a laptop with the energy model or by replicating the
information on a stand-alone network to enable signal transfer. Work will involve syncing data transfer rates
and sending analog outputs from the laptop containing the energy model.
The project will then turn to breaking down the full EnergyPlus model into a reduced-order thermal
model in a state-space format. A software from the Swiss University ETH called OpenBuild is designed
specifically for this task. The team at IDL will try this method to convert the EnergyPlus specific model into
a general mathematical model. While this method may be effective for structures that have current energy
models, the main goal of the research is to make the modeling method faster and more general. Thus, the
team will also pursue the parallel development of a state-space dynamic model of the COBE building simply
through knowledge of the building's structure and an analysis of the field data. The final step will be to
contrast the state-space model with the EnergyPlus model. Both models will be tested against historical and
live data to ensure their responses are consistent. The team will then attempt to substitute the simpler
mathematical model can be substituted for the complex EnergyPlus model and also be used for tuning the
HVAC controls.
The research team will document the process and establish a pathway for industry to develop and
commercialize the service. The documentation will detail the methods and be pursued for publication as part
of the thesis work of a master's student at the IDL. The project will be carried out in the following phases:
Task 1 Proiect Planning and Reporting - Conduct team meetings and ongoing project updates, reports and
deliverables as required by Avista staff, project management contractor and the PUC. Ongoing throughout
the project.
Task 2 Gather Baseline Data - The team will collect and process current and historical EMS and weather
data at the site to establish a baseline in order to benchmark savings and veriff the current controls; I -2
months duration.
Task 3 Establish Communication at the Site - Based upon work completed in2014-2015, the team would
perform a physical implementation of the co-simulation at the site to establish live-data transfer between the
EMS and the energy model; 3-4 months duration.
Task 4 Simplify Detailed Enerev Models - This task involves converting the detailed EnergyPlus models
into reduced-order thermal models and using these to tune building controls. This work can be performed in
parallel with Task 3. Targeted sub-system monitoring to isolate impact of Task 3 will be conducted as
needed and as budget allows; duration ofthe project.
Task 5 Analyze Effectiveness of Reduced-Order Models - The team will contrast the accuracy of the
reduced-order model compared to the EnergyPlus model. The analysis will include a study of sensitivity,
variable requirements, and ease of signal communication relative to the EnergyPlus model.
Task 6 Develop Workllow for Practitioners - The final tasks will be a study of the process that will add to
the current literature and promote this technology service to further commercializationtechniques. This
RFP No. R-40884 Page 3 of6
documentation will be part of a master's student's academic work and publication in a conference or journal
will be sought.
Potential market path
The service of pre-commissioning building energy management systems could save substantial
energy, increase occupant comfort, and greatly reduce the time between building start-up and proper
operation. The building commissioning process has been shown to be highly cost-effective while also
improving comfort and productivity (Mills, 2009). HVAC controls commissioning is very important because
commercial buildings operated in an unintended manner have been shown to increase energy consumption
by 20% compared to the intended design (Westphalen and Koszalinski, 1999). A recent baseline study of
buildings in the Pacific Northwest found average office building annual energy use to be ll2 kBtu/ft2 with an
average office building size of 20,000 ft2. (Baylon, et al., 2008). Given these assumptions, controls
commissioning could save 131,200 kWh/yr for an average office building. The simulation results from the
2015 work showed a potential savings of 7,200 MWh for the University of Idaho campus. In addition to
significant energy savings, simulation-based pre-commissioning of control systems can enhance existing
commissioning efforts, reduce risk to owners, increase occupant comfort, and increase realization rates for
energy savings from EMS.
The many benefits of this technology have been limited by its tether to specific software platforms
(EnergyPlus and BCVTB). The research proposed will help to move this service beyond a specific modeling
software to a mathematical model that can be generated for any building based on climate, intemal loads and
construction. This will vastly broaden the appeal of the virtual commissioning process to industry partners
who may offer this as an energy-savings service to their clients. Controller manufacturers may advertise their
products as pre-commissioned. Co-simulation will allow verification of control logic and has been shown to
detect faults which were otherwise seen as impossible to predict in the design phase (Haves, Xu, 2007). They
may also use the technology to virtually test new and innovative control systems without risks to building
owners from unproven control logics. Virtually commissioning the system using a thermal model holds great
promise in being able to test for missed energy savings or occupant discomfort as compared to the design
intent.
As the co-simulation adoption and applications grow, the technology allows either building
managers or utility companies to provide short-term forecasts of the building behavior and load based on the
thermal model and weather information. Commissioning based on an EnergyPlus building model has been
shown to predict energy demand and correctly forecast shortfalls in the cooling capacity (Harmer, Henze,
2015). Utilities, building managers, or third-party-providers may embrace this approach as a curtailment
strategy to predict and mitigate peak building loads automatically. Once the thermal model is created, and
communication established with the EMS, the economic benefits of automation, fault detection, and
prediction are immense.
RFP No. R-40884 Page 4 of 6
It is the intent of this project team to develop a work-flow around this technology which will enable
incentive programs, or other value-added energy services for utilities, or a third-party provider, to be
developed which will improve the effectiveness of new building commissioning, existing building retro-
commissioning, and promote new and innovative designs for high performance buildings. The market path
may include a new utility incentive program or as a value-added energy service in a utility portfolio.
Criteria for measuring success
o Project team has coordinated with the building facilities team to gather relevant historical data at the
site and established a method of weather data collection.
o Project team has established a communication framework between the COBE building EMS and the
energy model.
o Project team has adapted the full EnergyPlus model into a reduced-order thermal model.
o Project team has completed a comparative analysis of the two models and demonstrated a means to
substitute the mathematical model for the EnergyPlus model.
o Project team is able to present a replicable work flow which can be implemented into a pilot project
incentive program or value-added energy service by a utility or third-party provider.
Budget Price Sheet / Rate Schedule
Expense Year I
Pl/Faculty Salaries (Cooper and Yuan)
Pl/Faculty Benefits (Cooper and Yuan)
Postdoctoral Fellow Salaries
Postdoctoral Fellow Benefi ts
Graduate Student Salaries
Graduate/Undergraduate Student Wages
Student Benefits
Graduate Student Tuition Remission (partial)
Equipment/BAS Controller
Travel
F&A lOverhead (Excludes Tuition)
Total
$ 15,398
4,799
5,718
2,338
5,120
3,360
212
3,080
1,500
2,250
20.46s
$ 64,230
RFP No. R-40884 Page 5 of6
Budget Justification
***All Hourly Rates are averages over FY16 and FY17
Salaries -Support Requested from Elizabeth Cooper at 56 hours ($51.28lhr), Jinchao Yuan at 312 hours
($40.15/hr), Post-Doctoral fellow at2l8 hours ($26.23lhr) and one graduate student at320 hours ($16.00/hr),
and one graduate student at 160 hours ($21.00/hr)
Fringe - Estimated based on the following rates: 3l.l% Faculty, 40.9% Post-doctoral Fellow, 2.5% Students
(estimate since some hours will be complete in FY20l6)
Tuition Remission - Estimated tuition costs for one graduate student for Fall of 2016 and Spring of 2017
$3,080
Equipment/Computers - Estimated for project period is $1,500 for new BAS controller
Travel- Estimated travel expenses for project period $2,250, to include: (1) trip to Spokane for project kick-
off; (2) trips to University of Idaho in Moscow for equipment installation and retrieval; (1) trip to Spokane
for final presentation to Avista for PI's and graduate students.
F&A/Overhead - IDL is considered an on-campus unit of the University of Idaho with a federally negotiated
rute of 50.3Yo.
Proposal exceptions: Per section 5.2 of the RIP, the University has described exceptions to RFP
requirements and conditions in the letter dated 4115116 and included with Appendix A.
References:
Baylon, David., Robison, David., Kennedy, Mike. (2008)."Baseline Energ,t Use Index of the 2002-2004
Nonresidential Sector: Idaho, Montana, Oregon, and Washington." Ecotope
Haves, Philip., Xu, Peng,. (2007)."The Building Controls Virtual Test Bed - A Simulqtion Environmentfor
Developing and Testing Control Algorithms, Strategies, and Systems." Proceedings: Building Simulation
2007.
Harmer, L.C., Henze, G.P. (2015). "Using Calibrated Energy Models for Building Commissioning and Load
Prediction." , Energ/ and Buildings 2015.
Mills, Evan Ph.D. (2009). "Building Commissioning: A Golden Opportunity for Reducing Energy Costs and
Greenhouse Gas Emissiorrs." Lawrence Berkeley National Laboratory. Report Prepared for: The Califurnia
Energ,, Commission and Public Interest Energt Research (PIER).
Westphalen, Detlef., Koszalinski, Scott. (2001). "Energy Consumption Characteristics of Commercial
Building HVAC System, Volume I: Chillers, Refrigerant Compressors, and Heating Systems." Arthur D.
Little, Inc. Report No. 36922-00
Xu, Peng., Haves, Philip., Dringer, Joe. (2004). "A Simulation-Based Testing and Training Environment for
Building Controls" Published in the proceedings of SimBuild 2004.Lawrence Berkeley National Laboratory,
LBNL-s580I
RFP No. R-40884 Page 6 of6
lYg/tsta
APPENDIX E
Final Report: RSVC Year 3
Confidential
APPENDIX F
Final Report: Micro Grid Phase 2
*ylsta :
Universityotldaho AHwsrt
College of Engineering
Robust Microgrid for Downtown Spokane
Project Duration: 12 months Project Cost: Total award: $86,179.61
OBJEGTIVE
The main objective of this study is to
continue the feasibility study for forming a
microgrid in the downtown area of the city of
Spokane, Washington, and consider how the
design approach can be extended to other
locations. The objective of creating a
microgrid is to reduce the impact of major
transmission outages on high priority loads
without the need for major transmission
upgrades to create an equivalent level of
support. The main source of power for the
microgrid is from the two existing
hydroelectric generators located near the
critical loads. As the power generated from
the two run-of-the-river hydroelectric plants
is not sufficient to supply the critical loads at
all times of the year, the project evaluated
options for supplementing this with
photovoltaic aeneration, energy storage and
for converting diesel backup generators to
natural gas allowing them to run for longer
periods. The project also developed and
tested preliminary control schemes for load
shedding and for charging/discharging the
battery.
BUSINESS VALUE
The implementation of a microgrid in
downtown Spokane potentially allows for a
reduction of economic losses due to the
sudden shutdown of equipment. For example,a sudden power shutdown for an industry
could lead to the damage of production lines
being developed. The microgrid will allow
Avista to keep power on for critical loads in
case of a blackout. The microgrid will also
alleviate issues when restoring the grid after
a blackout.
The plan makes use of the existing
infrastructure while adding minimal additional
controls in order for forming a microgrid to
create potential benefits. The costs associated
with the failure to supply critical loads are
significant, The microgrid has the potential to
pay back costs in its operation. This is one ofthe reasons that microgrids are gaining
importance in the power industry. The
microgrids will provide an opportunity to
reduce the outage related expenses. They
include the demand side managed, distributed
generation, and energy storage, each of
which have benefits during normal operation.
Additional operational benefits when the
microgrid grid-connected during normal
conditions include potential for improved
energy efficiency, energy surety, greenhouse
gasses emission reduction, and avoid cost of
power interruptions for critical facilities in the
microgrid.
The results of the study on microgrid
development for downtown Spokane can be
applied in other areas of the Avista system or
other utilities in the region or nation. The
proximity of the generation resource close to
the load centers has huge benefit in the
reduction of transmission and distribution
losses.
BACKGROUND
While the Spokane Microgrid is in the
"island" mode, it suffers from a significant
stability risk, as local generation is insufficient
to supply all of the critical loads, let alone the
entire load in the microgrid footprint. The
previous year of this project evaluated this
microgrid and determined its potential
boundaries, identified critical loads and
performed a preliminary study on potential
addition of photovoltaic generation and
energy storage. The project also identified
areas that needed further study.
Since the islanded microgrid will be
supplied solely by renewable sources with
seasonal or daily variations in availability
(hydroelectric and solar photovoltaic), the
variations in the generation can be
compensated by installing electrical energy
storage in the microgrid. This project
estimated the power and energy ratings of an
electrical energy storage system and
identified potentials locations to place it based
on electrical performance and available sites.
After determining the available energy
resources, a model of the Spokane downtown
microgrid was developed and implemented in
a real time digital simulator by acquiring the
data from distribution and transmission
system models available from Avista. An
initial design for a microgrid controller we
proposed and studied through simulation.
Once the models were completed the next
step was create a control system for loading
shedding and battery control. During certain
times of year, the power supply will equal
demand, but for most of the year, there will
be a deficit, and as a result, the lowest
priority load will need to be shed.
There are two efforts to alleviate the
energy shortage. One method is load controlin the form of a master controller. This
controller will receive data from multiple
points in the system and shed the lower
priority loads.
Another method is increasing the amountof available local generation and adding
energy storage. The new generation will be in
the form of existing diesel generators at the
hospitals, solar panels, and megawatt
batteries.
PROJECT SGOPE AND ANALYSIS
Task 1: Microgrid location characterization
This is an essential first step. It involves
defining the electrical boundaries of the
microgrid, identifying energy resources and
critical loads, fetching the network data, and
collection of historic load and energy resource
data from Avista.
Task 2: Creating a simulation model in a real
time digitalsimulator
From the information given by Avista a model
of the microgrid was created in RSCAD, the
graphical user interface for the real time
digital simulator. The data given included:
generator parameters, load profiles,
transmission and distribution line parameters,
and capacitor modeling. Then this data was
used to create an accurate model of the
microgrid, This task included identifying the
feeders connecting the critical loads and
identifying the best possible ways of
supplying them with minimal loss. Also
included in this task was model validation to
ensure accurate results during simulation.
Task 3: Creating a load shedding scheme
Once the model was created in RSCAD the
next step was to create a load shedding
scheme. The purpose of this scheme is to
shed a load when the available generation
can't meet the demand of the microgrid area.
The first step in creating the load shedding
scheme was to determine a priority list for the
loads. Determining this is important because
scheme will shed the lowest priority load first
in order to keep the system stable.
The next step was to implement the load
shedding scheme in RSCAD. This was done
using a subset program of RSCAD called
component builder, which utilizes C-code built
into a component. The final step was to
perform real time simulations to ensure the
load shedding scheme performed the way it
was intended to.
Task 4: Battery control
Depending on the time on day, generation
may exceed demand or vice versa. Knowing
this batteries can be installed on the system
and used to optimize the system operation
and possibly keep a load from having to be
shed. UET batteries were selected because
Avista already has one installed.
A control system was designed and tested
created to optimize the use of the batteries.
The control for this was created using
component builder in RSCAD as well. The
first step was to get the battery to charge or
discharge depending on voltage conditions
across the system.
The next step is to fine tune the discharge
portion. The UET batteries have the capability
to discharge max power or a short period of
time or a lower power level over a longer
period of time. The control will sense if
batteries are needed and then make a
decision how much power is needed and
supply that amount.
Task 4: Automation controller
The next step was to begin implement of the
control schemes using a commercialautomation controller. An automation
controller can be built by combining
automating logic and a Human-Machine
Interface (HMI). A HMI allows the user to
interact with a system. By using relays,
detecting faults and tripping breakers can be
done efficiently, Data can be transmitted and
received throughout the microgrid systemusing a communication protocol called
General Object-Oriented Substation Events
(GOOSE).
Task 6: Final Report
Compile final report with the results from
the studies as well as the models, proposed
solutions and any upcoming technologies.
DELIVERABLES
The deliverables for this project will be:
. Completed RSCAD model with analysis ofthe load shedding and battery control
schemes.
. Proposed work for future projects.
o Analysis results and proposed ideas for
the control and operation of the microgrid
PROJEGT TEAM
PRINCIPAL INVESTIGATOR
Name Dr. Herbert Hess
Organization University of Idaho
Contact #(208) 88s-4341
Email hhess@uida ho.ed u
GO.PRINGIPAL INVESTIGATOR
Name Dr. Brian Johnson
Organization University of Idaho
Contact #(208) 88s-6902
Email bjoh nson@uida ho.edu
RESEARGH ASSISTANTS
Name Jordan Benjamin Scott
Organization University of Idaho
SGHEDULE
Email Scot0330@va nda ls. u idaho. edu
Name Lexi Turkenburg
Organization University of Idaho
Email Tu rk9655@vandals. uida ho.ed u
Name Jacquelyn Dufau England
Organization University of Idaho
Email [e-mail]
Name Nathan Bliesner
Organization University of Idaho
Email Blie3945@vandals. uidaho.edu
Name Maximilian Schnitker
Organization University of Idaho
Email Schn6884@vandals. uidaho.edu
Name Christine Page
Organization University of Idaho
Email Page3114@vandals. uida ho.ed u
Name Keegan Miley-Hunter
Organization University of Idaho
Email Mile7150@vandals. uidaho.edu
TASK NAME Base Iine
schedule
Actual
completion
Master Controller
Directive)
(Main 8/22/2076 8/1/20t7
Small Scale
Sub Project: Build a small scale
model from a previous exampleto understand how to use and
how a system operates in RTDS.
9/26/20t6 L2/7/20L6
Small Scale Example Model:
Trip Breaker
Learn how to trip breakers in
RTDS and to understand the
protection is developed for the
Microgrid
9/26/2016 u30/20L7
Small Scale Example Model:
Trip Breaker With Relay
Understand how to create an
interface with the microgrid in
built in RTDS and the physical
relays. As well as understand
how to set the relay to open the
breakers.
9/26/2076 7/7/2Ot7
RTDS Model
Main Project
9/8/20L6 6/L/2Ot7
Load Shedding Scheme
Control system using component
builder in RSCAD to determine
when the power system should
shed and which load to shed first.
9/9/20t6 7 /30/2077
Battery Control 5/22/20t7 7/30/2077
The information contained in this document is proprietary and confidential
Implement control system using
component builder in RSCAD to
determine when the batteries
should charge and discharge, aswell as how much power to
output when it discharges.
I. EXEGUTIVE SUMMARY
This study reports on the results from a
feasibility study on establishing a microgrid in
the city of Spokane. A microgrid is a portion
of the main power grid that can be isolated
from the rest of the grid under abnormal
conditions. It can operate in both grid
connected and islanded modes of operation.
The study identified the available generation
resources for the microgrid and identified
critical loads based on their priority and the
system topology. The potential amount solar
energy generation within the footprint of the
microgrid based available locations was
estimated.
A unified model of the microgrid was
developed in Powerworld simulation software.
This model was then used to validate a model
of the system built in RSCAD.
Once this was completed the control
systems for load shedding and battery control
were added. A study was then performed to
ensure the control systems behaved as
expected. This is demonstrated in the
analysis and key results section below.
In addition, a study was pertormed to
determine the feasibility of adding battery
storage, followed by determination of optimal
locations, and rating of the potential battery
storage systems.
The microgrid has a very good potential to
improve the resilience of the system since it
is predominantly supplied by hydroelectric
generation that is very close to the critical
loads.
!I. TECHNOLOGYUTILIZED
. Powerworld Simulator version 19.
. RSCAD 5.001.1
. SEL-451 Protection, Automation, and Bay
Control Systems
. SEL-3530 Real-Time Automation controller
(RrAC)
SEL-5033 AcSELerator RTAC software:
used to configure the SEL-3530 Real-Time
Automation Controller
SEL-5032 AcSElerator Architect software:
used to configure GOOSE communications
SEL-5030 AcSElerator Quickset software:
used to configure and commission the
SEL-451 Protection, Automation, and Bay
Control System
III. ANALYSIS AND KEY RESULTS
a. Electrical boundaries of the
microgrid
The substations that are closest to each of
the critical loads were identified. The main
substations to best supply the critical loads
are: Post Street, Metro, College & Walnut and
Third & Hatch. There are four main points of
common coupling (PCCs) identified at these
substations where breakers would be openedto form a microgrid. Figure 1 shows the
transmission network with electrical
boundaries of the microgrid identified.
a
a
a
I
t.-j'.r..1 I -tl:l
il
Figure 1 Transmission network within the
area of microgrid
b. Renewable Energy Resources
Within the region of the microgrid thereare two important renewable energy
resources that can be utilized. Those are
solar and hydroelectric energy. Figures 2 and3 below show the both the hydro and solar
energy potential. It is observed that waterflow is low in: August, September, and
October which is where the average solar
energy is high.
-6n
-SII-_I"'*t
rcBID^u
600,m
:@.oo
46.@
tm.00
26.m
!m 00
o.m
E
f
Summer day
IIIIIIINT:.
,.n f.b Ma. Apr Mrl tun tul Alt 5!p O<t iov DlL
,46
tt,A
&m
tuoMC)N T,l
Figure 2 Monthly average river discharge from
1891 - 2016s: illlIIT.i:r.all
I r"n Frb vi. Ap. Mry ,",l.rrl,, aut slp Oc tov [re(
Figure 3 Solar radiation average in the city for
30 years
c. Load and Generation profile
Figure 4 shows the combined averagegeneration output profile over the
representative 24-hour periods in each of the
seasons. Figure 5 shows the comparison
between the total load and total generationon a typical summer day. As the total
generation is not sufficient to supply all of thecritical loads load shedding needs to be
performed. The plot also shows the battery
discharge profile.
.,-t$i-$**$$S.$"i-i-t-*$1"$*t-$$$,$.
Figure 5 Total load vs total generation on a
summer day including load shedding and
battery addition
d. Load Shedding and battery
control analysis
From the analysis above it is observed
that that demand exceeds generation during
several parts of the day. Possible solutions
include shedding additional loads or adding
energy storage. Figure 6 shows the control
logic for how the load shedding and battery
control operates.
t ! !. l t r a I t0uuattEIu!uuu]IE:a
Hirotdt day
-rdtu6 -rd&ndH
+b6 _kuy
Stan
I0ft
5
r5
s
],.a"
l0
Hoe cf $r dry
-!{.6' -k
crJt
-BG
Figure 4 Combined average generation output
profile over 24-hour period in 4 seasons Figure 6 load shedding and battery control
logic.
e. Battery Size and potential
Iocation for placement
From the analysis it was shown that the
difference between the power supply and
demand can be stored or supplied with a
properly sided and controlled battery energy
The information contained in lhis document is propnetary and confidential
D€la,Shed6n0
LoEL-
lG;ffiF*
Lo0rq9lo!
Slate.
storage system. This difference was taken
into account when sizing the batteries. The
battery was sized to have 2MW peak charge
or discharge capacity. The battery needs to
discharge for up to 6 hours and there needs a
total energy rating of l2MWhr in the worst
case. Table 1 summarizes the load profile
analysis for each of the seasons.
The voltage sensitivity at each bus in this
microgrid was calculated to determine the
best location for the battery. The bus that
experienced the largest voltage sensitivity to
changes in load conditions was chosen as the
best location for battery in this study. Figure6 shows the percentage change of voltage
taken on the ordinate and size of the step
changes of load taken on abscissa. Based on
these results, load 3 bus is most sensitive bus
and an appropriate candidate location for a
battery.
Table 1 Summary of battery peak power
requirement study
IV. GONGLUSION
The study identified the available generation
resources, the critical loads based on Avista's
priorities, and the microgrid system topology.
Potential for solar energy generation within
the area encompassed by the microgrid was
estimated. A model was developed using a
combination of Powerworld simulations
software then transitioned into a real time
simulator using RSCAD. As observed in the
results, demand is often higher than the
available generation. There are several
options that can be applied to remedy the
situation. Battery energy storage systems canbe added along with the corresponding
control logic. At times this will be enough but
in the cases where it isn't, a load shedding
scheme can be implemented and will shed a
specific order of loads when the correct
conditions are met. The system performance
can be improved by adding capacitors for
voltage support and energy storage devicesfor real power support. The proposed
microgrid has a very good potential to
improve the overall resilience of the system.
V. FUTURE WORK
1. A portion of the microgrid can be isolated
as a nanogrid. The critical loads around
substation 3 are rarely supplied within the
microgrid due to their lower priority and
lack of energy resources. If an additional
solar installation is added, the area can
potentially disconnect from microgrid to
form a nanogrid.
6
s
{
m
+a,qr.rB I
-cnBM.a-c|@d, -ordda
Figure 6 Percentage voltage change with
respect to power
As part of this project a senior design team
explored options for converting some of the
diesel backup generators at the hospitals to
operate on natural gas. Since the natural gas
supply to the Avista system is self-powered, a
regional blackout will not interrupt power
supply. The natural gas conversion will allow
the hospitals to generate power for longer
periods of time in the case of an extended
outage. This also reduces the potential
pollution from the diesel generators units.
Having the hospitals able to supply more of
their own critical load using these generatorswill enable the hydrogeneration and
photovoltaic aeneration to supply other
critical loads instead.
The information contained in this document is proprietary and confidential
Season
Total
critica!
loads
supplied
Peak
Deficit
value
Deficit duration
MW Sta rt
time
End
time
Spring 7 1.78 2:00PM 7:00PM
Summer 4 1.45 5:00PM 9:00PM
Fall 6 0.6 4:00PM 4:59PM
Winter 6 0 NA NA
2. Further analysis on the microgrid model
can be performed such as, the economic
benefit analysis, transient stability study
using transient simulation software where
modeling includes more realistic governor
and exciter models to study potential
control structures for the hydro
generators.
3. The load shedding scheme currently in
place will shed the same list of loads
regardless of the time of day. This isn't
the most efficient since depending on the
time of day different loads have moreimpoftance. Further study into load
shedding to implement this would be
beneficial. The implementation of the load
shedding scheme in the automation
controller needs to be completed and
tested.
4. The bi-directional power flow in the
microgrid will be a challenge for
protection. Potential modifications to allowa protection scheme to transition fromgrid connected to microgrid operation
need to be developed and tested.
5. Right outside of the microgrid area is a
waste energy plant that produces several
MW of power that could be supplied to the
downtown area during the operation of a
microgrid. Utilizing this power could make
it so very little load shedding would haveto be performed. A study to determine
the feasibility of this would be beneficial.
Note: Please refer to the Master's thesis
document in Appendices A results.
APPENDICES
Appendix A: Master's Thesis
Jordan B, Scott, Master's Thesis,
Microgrid Operation with Load Shedding
and Battery Control. University of Idaho,
December 2OL7.
The information contained in this document is proprielary and confidential
Microgrid Operation With Load Shedding
and Baffery Control
A Thesis
Presented in Partial Fulfillment of the Requirements for the
Degree of Master of Science
with a
Major in Electrical Engineering
in the
College of Graduate Studies
University of Idaho
by
Jordan Benjamin Scott
Major Professor: Herbert L. Hess, Ph.D.
Committee Members: Brian K. Johnson, Ph.D., John Crepeau, Ph.D.
Department Administrator: Mohsen Guizani, Ph.D
December 2017
ll
Authorization to Submit Thesis
This thesis of Jordan Scott, submitted for the degree of Master of Science with a major in
Electrical Engineering and titled "Microgrid Operation With Load Shedding and Battery
Control," has been reviewed in final form. Permission, as indicated by the signatures and
dates given below, is now granted to submit f,rnal copies for approval to the College of
Graduate Studies for approval.
Major Professor:Date:
Herbert L. Hess, Ph.D
Brian K. Johnson Ph.D
Date:
John Crepeau, Ph.D
Department Administrator:
Mohsen Guizani, Ph.D
Committee Members: Date:
lu
Abstract
One of the goals of this thesis is to implement a load shedding scheme within a
microgrid to drop the lowest priority load when demand is anticipated to become greater
than the supply. Another goal is to implement a battery controller that communicates to the
battery when to charge, when to discharge, and how much to charge or discharge. By doing
this, critical loads could be kept online and will not have to be shed.
This research project successfully designed and tested a load shedding scheme in a
real time digital simulator to keep critical loads online, as well as adding a battery controller
to help keep loads from being shed when possible. This controller includes commands such
as when to charge, when to discharge, and how much to discharge. By doing these things a
significant portion of the microgrid studied has been shown to continue to have power in the
event of a blackout that could last anywhere from minutes to days, no matter what time of
year the blackout occurs.
IV
Acknowledgements
While working on my thesis I have had the great honor of working with some very
amazingpeople whom I would like to thanks.
I would like to thank both my advisor Dr. Herbert Hess and Dr. Brian Johnson for
helping me with this project. Both of whom went above and beyond what was required.
They spent many hours providing valuable knowledge and making sure I had the resources
needed to make this project a success. They made working on this project enjoyable and for
that I thank them.
I had the honor of working with several undergraduate students during the course of
my research, and without them I would not have been able to accomplish as much work as I
did. Those students include: Jacquelyn England, Lexi Turkerburg, Christine Page,
Maximilian Schnitker, Nathan Bliesner, and Keegan Miley-Hunter.
Each of those students helped me accomplish various aspects of the project.
Jacquelyn and Lexi helped me get the project going and did whatever task was asked of
them. Both of them helped work on the load shedding and the HMI. Christine and Keegan
ended up taking over the HMI and spent many hours getting it set up. Maximilian worked
on the load shedding and helped develop a model for testing. Nathan took on a new task
designing a controller for the batteries.
Last of all I would like to thank my high school math teacher Mr. Keith Price. He
taught engineering is more than math. He taught me learning math is just a stepping stone
for building and designing various things, and to look for the fun in it. As a result he is one
of the main reasons I am an engineer today.
v
Dedication
q araqH hlep A dd,acare ilb uail to orq cM,akp lanlq,
eu1 /atuuto fu dr4d /4,4dred dt ctlcll & ?/4q ohlata"a&l
ar,rd?il/k,
v1
Authorization to Submit Thesis ll
Abstract llt
Acknowledgements lv
Dedication .........v
Table of Contents vl
List of Tables xilt
Chapter 1 Load Shedding Introduction I
1.1 Background ... 1
1.2 Problem Statement ...1
1.3 Proposed Solution.....a...J
Chapter 2 Liter ature Review
2.0 Micogrid Overview......5
..5
..5
..7
2.1 Benehts of a Microgrid
2.2 Load Shedding..
2.3 Battery Control
2.4 Diesel to Natural Gas Converters.........,7
..8
..9
2.5 Chapter 2 Summary
Chapter 3 Design and Model Development
Table of Contents
vll
3.0 Load Shedding Definition.....9
3. I Undervoltage Parameters 9
3.2Initial Design l0
3.3 Chapter 3 Summary t2
Chapter 4 Software Development for Load Shedding Scheme 13
4.0 Software 13
4.1 RSCAD Implementation ....14
4.2Load Shedding Scheme in RSCAD.,..,14
4.3 Cbuilder in RSCAD ....14
4.4 Chapter 4 Summary ...16
Chapter 5 Controller Implementation in RSCAD t7
5.0 YO Ports of the Component...
5.1 Parameters of Component....1 8
5.2 Chapter 5 Summary ...20
Chapter 6 Simulink and RTDS Simulation Results of Load Shedding Scheme...................21
6.0 Simulation in Simulink .21
6.1 Simulating the Load Shedding Scheme in RSCAD .....22
6.2 Chapter 6 Summary ...27
Chapter 7 z Battery Controller .....29
7.1 Introduction........29
7 .2 Battery Model Introduction
7.3 Battery Controller in RSCAD.....
7 .4 Battery Controller Implementation in RSCAD
7 .5 Battery Power System Model Configuration in RSCAD
7.7 Implementation in Simulink
7.8 Simulation of Battery Controller in Simulink
7.9 Chapter 7 Summary.....
Chapter 8: Diesel to Natural Gas Conversion Kits..
8.0 Introduction to Diesel to Natural Gas Conversion .
8.1 Diesel to Natural Gas Conversion Kit.....
8.2 Conversion Kit Specifications.
8.3 Chapter 8 Summary........
Chapter 9: Automation Controller
9.0 Automation Controller Introduction.
9. 1 Hardware Development....
9.2 Software Configuration .....
9.3 Protection Relay
9.4 GOOSE Protocol Communications ....
9.5 The Human Machine lnterface....
vill
.......3 I
32
......45
.,,45
35
45
52
.46
46
...48
...48
...48
...49
...49
51
9.6 Chapter 9 Summary 54
?A
lx
Chapter 10 Summary of Accomplished Work and Future Work........55
10.0 Summary of Accomplished Work .55
References ......62
Appendix A: Truth Tables for Finite State Machine ...66
Appendix B: Next State and Output Logic Diagrams ...68
Appendix C: RSCAD Files....77
.h and .c fiIes....77
Appendix D: Battery Controller Coding....94
Appendix E: Battery Power System Coding.....99
Appendix F: Software and Hardware Used in Automation controller Development.103
10.1 Future Work: Transfer of Automation Logic to RTAC... ..............56
10.2 Future Work Time Based Load Shedding ...................56
10.3 Future Work Fully Implementing Load Shedding in RTDS Hardware in Loop
Simulation ......................57
10.4 Battery Control Future Work........ .............57
10.5 Future Work on Diesel to Natural Gas Conversion......... ..............58
10.6 Future Work Communication Between an RTDS and Automation ControlIer.................58
10.7 Future Work Establishing GOOSE Protocol Communications for the RTAC..................60
x
List of Figures
Figure 1.1: Microgrid Area....2
Figure 1.2: Monthly Average Hydro Electric Generator Flow Rates [1]aJ
Figure 1.3: Monthly Solar Cell Power Generation Potential [].J
Figure 3.1: Load Shedding Scheme CFD.........
Figure 3.2:Data Flow Diagram for Main Undervoltage System ........
Figure 4.1: Graphical Representation of Load Shedding Controller...
.. 10
.. 11
.15
Figure 5.1: List of Inputs .. l8
Figure 5.2: List of Outputs
Figure 5.3: List of Parameters .19
Figure 5.4: Overview of Available System Configurations
Figure 6.1: Simulink Simulations All Loads Triggering....
20
...,.....,.2|
Figure 6.2: Simulink Simulations Load Logic Resetting.....22
24
25
26
27
30
32
aaJJ
34
35
35
Figure 6.3: Load Shedding Interface
Figure 6.4:Load Shedding Simulation 2 Bus Voltages
Figure 6.5: Load Shedding Simulation 3 2 Loads Tripped.....
Figure 6.6: Load Shedding Simulation 4 All but Highest Trip
Figure 7.1: Battery Control Flow Diagram.............
Figure 7 .2: Battery Controller
Figure 7.3: Controller Inputs ........
Figure 7.4: Controller Outputs......
Figure 7.5 : Controller Parameters.
Figure 7 .6: 2 Controller System....
xl
Figure 7.7: Battery Model 36
Figure 7 .8: Battery Inputs 5t
Figure 7 .9 : Battery Nodes.......
Figure 7.10: Battery Parameters ............
Figure 7 .ll Battery Currents
Figure 7 .12: Battery Monitored Values
Figure 7.13: State Transition Diagram
Figure 7 .14: Battery Controller in Simulink.....
Figure 9.1: Hardware Implementation Scheme
5t
38
38
....39
....40
...42
..48
Figure 9.2 : Software Implementation Scheme ......49
Figure 9.3: Example of SEL Relay LED Alarm Light Protection. .... 50
Figure 9.4: HMI for One Breaker .................. 53
Figure 9.5: Bus Voltages and Line Currents for One Re1ay........ ...... 53
Figure 10.1: Combined Average Generation Output Profile Over a 24HrPeriod in 4 Seasons
......... 56
Figure 10.2: Editing Outputs in the SCD file 59
Figure 10.3: GTNET-GSE Output Parameters 60
Figure 10.4: Establishing GOOSE Protocol for the Relay
Figure B.1: 52 Logic........
6t
68
Figure B.2: Sl Logic.........69
Figure B.3: S0 Logic.........
Figure B.4: Output Logic Load I ....
Figure B.5: Output Logic Load2 ....
70
7l
72
x11
Figure 8.6: Output Logic Load 3
Figure B.7: Output Logic Load 4
Figure B.8: Output Logic Load 5
..73
..7 4
75
Figure C.1: Cbuilder.def File 77
xlll
List of Tables
Table 7.1: Output Transition Table 4t
Table 7.2:Battery Controller Simulation Low Frequency Input. ...-..42
Table 7.3:Battery Controller Simulation High Frequency Input. .....43
Table 9.1 : Example of SEL Relay Breaker Trip Tag ...... 50
Table 9.2:Example of Establishing Alias Relay Ta9........... ............. 51
Table 9. 3: Example of HMI Relay Trigger Tag........... .................... 51
Table A. 1: State Transition Table With Encoding.......... ................. 66
Table A. 2: Output Table With Encoding 67
1
Chapter 1 Load Shedding Introduction
1.1 Background
In the instant that a local or regional blackout occurs, power is lost. The duration of
this loss can range from minutes to several days. To mitigate the effects of such a blackout
on a regional utility, a microgrid has been designed to provide power and stability to critical
facilities such as hospitals, jails, and government buildings. In an ideal microgrid, available
generation, would always be greater than demand. In a situation where load exceeds
generation load would have to be shed. As a result of this, in order to keep the microgrid
operational, a scheme is needed to implement prioritized load shedding.
1.2 Problem Statement
The proposed microgrid shown in Figure 1.1 will utilize two hydro-electric
generation sites and solar power from within the micogrid. The available generation is often
less than the total load in the microgrid footprint. These power sources are influenced by
season variations and daily weather pattems and could fluctuate greatly in their generated
power, voltage magnitude, and frequency (Figure 1.2 and 1.3) [1]. These figures only show
the seasonal variation, not daily. Due to this potential for instability and due to mismatch
between generation and loads, it is necessary to have a plan for immediate reductions in
power consumption to maintain operational voltage to critical loads.
MICROGRID AREA
Srb6trtpn 4
t:.r.
ltltt rirtv
2
2
ul lv tro
5001V lne
tu
5-f
tuhdirnS
\i/e!t J[re
81,
9rb6trtbn 6 urv
llw llrY
Figure 1.1: Microgrid Area
Another problem can occur if load reduction is not enough to maintain operational
voltage and frequency to the critical loads. If the load is too small, generation can be
reduced. If load is too large, generation can't supply all the loads. The next step from there
is to shed load to keep the microgrid from becoming unstable and having a voltage collapse.
The first load to be shed is the lowest priority load in the system. Once this first load has
been shed, the load shedding scheme will determine if another load needs to be shed in turn
in order to maintain stability.
One of the other questions that arises from shedding the load is how long should the
system wait before it sheds another load? If the load shedding scheme is initiated too soon,
frequency and voltage oscillations could cause a false shedding command to be sent to one
of the loads. The solution to this is to set sufficient time delay between load shedding
commands so there is negligible chance of transients causing an error. A backup to this
would be to set the load shedding controller such that a load has to be under a set voltage
]E
grhetion 9
!H DS
9rhdbn5
!!r?
t Jn,
ltJtr
$yiro !!enUqr I
Itdro !!Enti)o
fuhatron I
grbstrlion 3
Arbstarton 70l{
ullr,
t rll
JF,<
t -l
I
I
I
J
threshold for a set amount of time before it sheds. The timing has to be set so that a load
isn't shed if any of the bus voltages in the system merely fall below the set voltage
momentarily, but also has to be set fast enough so that the system doesn't become unstable
and suffer a blackout.
Figure 1.2: Monthly Average Hydro Electric Generator Flow Rates []
Figure 1.3: Monthly Solar Cell Power Generation Potential [1]
1.3 Proposed Solution
When the microgrid is in an island state, it will be responsible for providing power to
the critical loads in the local area. The goal of this work is to develop a load shedding
scheme when it isn't possible to supply all the loads in the microgrid. The load shedding
1.,lr,l/l
&,
Lr,a
d,lr,FtL,:(J
co:)(J
600.00
500.00
400.00
300.00
200.00
100.00
0.00
I DISCHARGE
I r I I :T
Nov Dec
I. I:.7
AuB Sep OctJan Feb Mar Apr May Jun .lul
MONTH
12
l0
I
6
4
2
0
Jan Feb Mar Apr May .lun Jul Aut Sep Oct Nov
d d
Oec
I FI.AT PI.ATE
t I-AX|S TRACT(II{G
Iz.AXIS IRACKING
_l
_aa-
t l-rn I t-
4
scheme will act to disconnect the lowest level priority loads to maintain the voltage
magnitude. This load shedding scheme constantly measures voltage, and will trigger when a
critical load's voltage magnitude drops below a preselected level for a preselected time, such
as 0.90 per-unit for 5 seconds. Once an undervoltage event occurs, the system will identiff
the prioritized critical load based on the information found from a past research project [].
Once this identification has been made, the lowest priority load will be shed. Shedding load
provides voltage, and frequency stability to the microgrid. As a result of the load shedding
scheme constantly monitoring voltage, if shedding the first load is not enough to maintain
acceptable voltage levels across the system, the load shedding scheme will initiate another
load shed. This process will continue until voltages across the system are within an
acceptable range. Once voltage is in this range, the load shedding scheme reverts to only
monitoring voltages.
This thesis covers a few main points. In detail, it defines what load shedding is and
why it is needed. Following this is the the development of a load shedding scheme which
includes logic diagrams and software development. Finally it shows the results of the load
shedding scheme. In this microgrid load shedding is the last resort. One of the options to be
checked before load is shed utilize the batteries that can supply power to the microgrid, and
keep load from being shed.
The objective of the energy storage model is to add the batteries and their energy
storage control in the present microgrid model. The purpose of the battery controller is to
evaluate the frequency of the system and send a signal to the battery to correct the
frequency, as well as help with real power management.
5
Chapter 2 Literature Review
2.0 Micogrid Overview
Microgrids are power system configurations that give both economic and
environmental benefits when compared to expanding legacy modern power systems. [15].
In other words, microgrids are power systems with power consumption located near
generation such that the microgrid can become an independent entity, even controlled and
dispatched separately [7]. Another useful aspect of a microgrid is that it can generate in
both islanded and grid connected mode. In order to be able to do this, concepts such as load
shedding and battery control need to be looked at in order to keep the system stable.
2.1 Benefits of a Microgrid
Microgrids offer several potential benefits power utilities:
1. Reduce the carbon footprint by reduction in the use of conventional sources of
energy if they are based on renewable energy generation [6].
2. Lower operational costs by reducing the number and length of transmission and
distribution lines [ 1 I ].
3. Encouraging utilization of renewable energy sources [3].
4. Supplemental power for peak loading, which in turn postpones upgrading the system
[3 l].
5. Improve system reliability [17].
2.2Load, Shedding
An advantage of local control is that it relies on local measurements, thus avoiding
certain communication dependencies leading to jitter or delay. The communication
architecture within the microgrid needs to be well established, so that it can be utilized
6
within the demand control applications. It is advisable to combine the central and local
control schemes where fast and coordinated response is required [1]. Load shedding is
performed in order to keep the frequency and voltage of the power system stable. The most
economical way of improving system stability is to equalizethe generation to load (via load
shedding or generator control), thereby minimizing the disturbance impact to the power
system [12].
A simplified model of the microgrid in Figure 1.1 is implemented in Real Time
Digital Simulator (RTDS) [1]. RTDS provides opportunity to perform hardware in loop
simulations. It provides low level output signals, which resemble the real system data such
as current transformer outputs or voltage transformer outputs. These outputs can be directed
to intelligent electronic devices to perform protection or automation operations [ 1]. One of
those operations is load shedding, which will shed load based on undervoltage conditions.
A historical method for detecting power unbalances is to detect a decrease in power
system voltages [3]. Once this detection has been made, and load shedding has been
decided, a load shedding scheme will determine which load to shed. By doing this, voltage
levels will be regulated to improve local reliability and stability [4].
One of the popular load shedding methods for a microgrid is implementing an
undervoltage scheme. The Western Region of the Entergy System is located within Entergy
Texas service area,utilizes this type of scheme 122]. Through design and testing it was
found that this method would be sufficient. This load shedding scheme was put into service
in June 2014 and as of mid 2015 no operational issues have been reportedl22].
7
2.3 Battery Control
By installing a battery energy storage system within a microgrid, power can be
supplied to critical loads. There are also other pros of using batteries in a power system. A
few of the advantages are savings in terms of reduced power bought during the peak hours,
improving power quality in cases where variable renewable power sources are connected to
the grid, and providing emergency power to critical loads []. In order to be efficient, battery
sizing needs to be looked at.
As stated in the previous paragraph, it is important to size the battery appropriately.
Batteries can improve the balance between generation and demand, and thus, the baffery size
will have signif,rcant impact on the grid's economic operation [1]. When sizing batteries for
this system, the following rules were followed. Those rules include: operating
considerations, design and aging margins, voltage window design, and charging and
discharging limitations [18]. Once the size of the batteries has been calculated, the next
thing to look at is placement.
Optimally placed batteries can regulate the voltage throughout the power system,
thus reducing the need to expand the current power system or to shed load. [19]. This thesis
uses what was described in the previous paragraphs and implement a battery model and
battery controller in Matlab Simulink.
2.4 Diesel to Natural Gas Converters
Throughout the microgrid area, there are several diesel generators that can produce
approximately 14.75 MW of power. Due to emission limits, these aren't a viable source of
power for this microgrid. A dual fuel diesel engine is a diesel engine fitted with a dual fuel
conversion kit to enable use of cleaner burning alternative fuel like compressed natural gas
8
in addition to oil [20]. Performance tests have shown that the emissions are significantly
less when compared to the standard diesel generator [21]. This thesis will look into
applying diesel to natural gas conversions within the microgrid area.
One minor issue that can be encountered with diesel to natural gas converters are
diesel is still needed to ignition of the engine [23]. Another issue is that once the engine is
modified the warranty can be voided [23]. These issues were overcome by keeping a supply
of diesel ready, and by using a United States law that says warranties are not voided after
aftermarket accessories have been added [23].
2.5 Chapter 2 Summary
Chapter 2 is a very brief literature review on work pertaining to microgrids. This
chapter discusses what a microgrid is and the benefits that come from it. This is followed
by how load shedding is an option to keep the system up and running when generation can't
meet demand. There are several methods to achieve load shedding with one of the more
common ways to accomplish this is to use an undervoltage scheme. Load shedding is
considered the last resort option. Before load is shed within the microgrid batteries can be
added to keep the system stable. To best utilize the batteries a battery model and a battery
controller will be implemented to best serve the microgrid.
9
Chapter 3 Design and Model Development
3.0 Load Shedding Definition
Load shedding is the intentional reduction of load on a power system, normally to
increase, but possibly to decrease the voltage magnitude and frequency to appropriate levels.
In other words, load shedding is the act of balancing available generation and load when
load exceeds generation. Controlling the voltage magnitude of the microgrid will help
maintain the overall stability of the system. By shedding load the microgrid controller will
be able to maintain power to the highest priority loads without intemrption.
Load shedding helps voltage stability by balancing reactive power to help keep the
microgrid operational. Frequency stability is also important factor when looking at a power
system. When applying reactive power, voltage stability is the indicator of success, while
working with real power, frequency stability is the indicator of success. For this project, the
load shedding scheme maintains both voltage and frequency stability. It is being done this
way because the method has been demonstrated in literature review. [n later chapters of this
thesis, frequency measurements determine how the batteries operate.
3.1 Undervoltage Parameters
There are many methods for detecting conditions to trigger load shedding in power
systems, yet one of the most reliable is the monitoring of the system voltage [1]. Voltage
monitoring will give a clear snapshot of the system's stability with the capacity to react
quickly. This project will be monitoring the microgrid's voltage magnitudes to detect a pre-
selected voltage value of below 0.9 per-unit. These parameters were chosen to allow the
control systems of the hydro and solar power generation to maintain power to the critical
loads for as long as possible before another load needs to be shed.
10
3.2 Initial Design
For developing a load shedding scheme, there were a few problems to keep in mind.
The first problem is that the load shedding scheme would shed loads based of a priority list.
The second problem is that it would shed load only after bus voltages had fallen below a
certain tolerance level. The third problem is that once a load has been shed, a delay needs to
be implemented so a false load shed signal is not sent and a load is taken offline. The control
flow and data flow diagrams, shown in Figures 3.1 and 3.2,were created to address the
problems according to these points. Figure 3.1 and Figure 3.2 are the control flow and data
flow diagrams, respectively. . Figure 3.2 is a more general control flow diagram, which
shows how the load shedding scheme will operate in conjunction with the battery controller.
Start
M€asure Load
Vonage
lf vollage <0.9
0
.Automation conboller ('l or 0)
l0 wrll do nothingl
Signal From
Pick Next load to
shed. lout of eiqht]
False
And Gate
tlil unsigned char representing
loads
Shed selecled load
Oalay to
elimrnale
transienls
Load Shedding ControlFlow Diagram
False
1
True
Figure 3.1: Load Shedding Scheme Control Flow Diagram
11
Figure 3.2:Data Flow Diagram for Main Undervoltage System
The first stage was creating the logic inside Simulink that would wait until the
program sends a shed-load signal. This will occur if the batteries cannot aid the microgrid's
voltage levels further. Once the load shedding controller receives this signal it will measure
the incoming voltage from the microgrid loads and compare the magnitudes with the
preselected value of 0.9. The load shedding scheme will check if an undervoltage event has
occurred and output a Boolean value. This Boolean value will meet the load-shed signal at a
logic block that will assure this conditions are met before a load is shed. If the signal to shed
a load and an undervoltage event have occurred, the load-shedding controller will output the
selected load that should be shed to the main program as an eight bit, unsigned character.
The purpose of Figure 3.2 is to show a high level overview of how the battery controller will
work with the load shedding scheme. The results in Chapter 6 will demonstrate successful
lvlainBattery Logic
(Charge oO
Measure
Voltage
Load
Sheclding
Logic
Battery Status
(Supply)
Battery Status
(Chargel
Battery Status
(0tr)
otc
EJ
go$
o
f0
rEr,a
€tt
?
Data Flow Diagram
Charging \bltage
6
T2
solution to problems discussed in the first paragraph of this section come from Figures 3.1
and 3.2 respectively.
3.3 Chapter 3 Summary
The main points in the chapter are describing what a load shedding scheme is and
why it is needed. Load shedding is the dropping a load from the power system in order to
keep a power system stable. This chapter also discusses how the load shedding scheme will
be created, as well as how it will work with the battery controller. Chapters 4-6 go into
detail on the loading shedding scheme, and chapters 7-9 discuss the battery controller.
l3
Chapter 4 Software Development for Load Shedding Scheme
4.0 Software
As stated in Chapter 3, the initial modeling and testing of the load shedding scheme
was performed in the MATLAB graphical control flow simulation software, Simulink. This
platform was chosen due to the vast control system libraries and the dynamic simulation
capabilities the software provides. Simulink has the capacity to graphically model and
test/remove elrors from a control system inside the lntegrated Development Environment
(IDE), without using extemal resources. This process allows for a quick error removal and
boosts the progress towards the final goal. Simulink code also has the potential to be
converted to RSCAD code and implemented into the microgrid model to test the load
shedding scheme with the RTDS hardware [24].
The load shedding model working in Simulink was used as a reference while the
same load shedding scheme was built in RSCAD. This was done due to the fact RSCAD
doesn't have a lot of tutorials to help build a control system, so Simulink was used as a
stepping stone to ensure the load shedding would react correctly in RSCAD. A Finite State
Machine, (FSM), was chosen over using unsigned characters and bit-masking due to the
compiler requirements for real time operation of the RSCAD software when transferring c-
code between the two programs. A Moore FSM was selected and designed with two inputs
and five outputs. The first input comes from either the main system as an undervoltage
signal or from the energy storage system when the backup batteries are unable to meet the
demand of the microgrid. The second input is a reset signal that will set all the outputs to
their default position. The software design were created based on the truth table and can be
found in Appendix A. Following this, the FSM was created inside a program Simulink.
Figures B1-B9 in Appendix B are the next state and output logic respectively. The current
t4
state and next state logic connect via a resettable flip-flop that was designed for this system.
The flip-flop operates on an external clock/pulse-generator to ease the portability into future
systems. Figures B1-B9 in Appendix B also show the equations used for each logic section
for the loads.
4.1 RSCAD Implementation
The final test model for the microgrid was built in RSCAD. This software uses a
special purpose computing system to simulate in real time. Inside RSCAD, a new
component was built specifically for the load shedding scheme using c-code. The
component was then implemented into the microgrid model and setup to measure the per-
unit voltage of the load buses to ensure power system stability.
4.2Load Shedding Scheme in RSCAI)
The RSCAD simulation software uses premade functions as well as a component
builder to design and program the user's control or power blocks to use in a circuit or power
system. For this project the Cbuilder was used to draw and code a block that will be added
into the RSCAD draft circuit of the Microgird built by a previous graduate student [1]. The
use of this software should give accurate feedback on the microgrid's ability to operate with
a load-shedding scheme.
4.3 Cbuilder in RSCAI)
Cbuilder allows users to design, draw, code, and parameterize control and power
components for use in a system models. The block design of the load shedding controller
was created with the appropriate inputs and outputs needed entered into the header and c-
file. Figure 4.1 illustrates the design of the load shedding controller based off Figure 3.1.
15
The colors of the VO ports signify the value type; Green are real numbers (doubles data type
64 bits long) and Blue are integers (32 bits long). A more in-depth view on how the
controller was designed and the C code implemented is found in Appendices A-C.
Figure 4.1: Graphical Representation of Load Shedding Controller
The tools in Cbuilder described above were used to address the following problems
introduced in Chapter 3. The first problem is that the load shedding scheme would shed
loads based of a priority list. The second problem is that it would shed load only after bus
voltages had fallen below a certain tolerance level. The third problem is that once a load has
been shed, a delay needs to be implemented so a false load shed signal is not sent and a load
Glosr-BRK
CLN
Rmrl
LoedShcd-Enrblc
Load l
rignels
Load2
Load3
Load4
Loed5
Load*Shed
loadS
rpr€t
LOADSHEB
U-te
B_q
u
u
U_\A
cloql
t6
is taken offline. Once a load has been shed a delay is asserted before bus voltages are
measured again.
4.4 Chapter 4 Summary
The main point of this chapter was to discuss the software development for the load
shedding scheme. It was f,rrst built and tested in Simulink to use the available resources
available in the program. Once the controller was behaving correctly in Simulink it was
then built in RSCAD using Cbuilder. Chapter 5 will further discuss the development of the
load shedding scheme, and Chapter 6 will go over the results.
t7
Chapter 5 Controller Implementation in RSCAD
5.0 VO Ports of the Component
. This chapter discusses how the load shedding controller in Figure 4.1 was
developed and implemented. We want this pseudo hardware to measure voltage, and if the
bus voltages fall below a set tolerance level then shed the lowest priority load on the list of
loads is shed. Once a load has been shed a delay is implemented before measures bus
voltages are processed so a false trip signal is not sent.
The six U_V inputs, Figure 5.1, are the per unit values from bus nodes in the power
system that the user desires to monitor for undervoltage protection. The "Close_BRK",
(B_C as seen below in Figure 5.2), input is the signal that tells the load-shedding controller
to compare the bus voltages with the value that was chosen as the limit at which the loads
should be brought back on. The "CLK" input is the clock signal that determines when the
load-shedding controller will process the programmed code. When the "CLK" signal is
asserted the load-shedding controller will execute the program and will determine whether a
load will be shed or reconnected to the power system. The reset input has been put into the
design as a precaution for the unlikely event that the FSM goes into a state that it was not
designed for. When a reset is asserted the load-shedding controller software state will be
forced into state zero, (idle state). The "LOADSHED" input is the signal that tells the load-
shedding controller to shed load when the undervoltage conditions are met.
18
V If,Ft'TS
reset
clock
u_v2
uv3
u_v4
u_vs
u_v5
t_vl
B_C
INAIX'HEI}
Figure 5.1 : List of Inputs For the Load Shedding Scheme
The outputs are the signals that go to the breaker controllers inside the power system.
As seen from Figure 5.4, each output is an integer and can be changed inside the parameter
window in the draft file to whatever value is needed for a specific breaker controller. The
number of loads can be readily adjusted in the C code. An operating scenario with these
parameters is tested in chapter 6.
ffi
U
trr
EN
ffi
U
ET
E[4
llrl
E[
-&
E
-&
-&
v tIIrF0?s
1oad1
loEd2
1oaal3
load4
load5
rt'l-r V I0 Point: load1 tllff)v
ilfi v IO Poinr: load2 (I$I)v
ffiI v IO Point: 1oad3 (Iml v
nl-I IO PoinE: 1oad4 (IIfi)v
nII v IO Point: loads (IlcI)v
Effi-effi
Effi
-&trr
-effi
Figure 5.2: List of Outputs for the Load Shedding Scheme
5.1 Parameters of Component
The parameters of the load shedding component were designed to make the coding
of the component intuitive. Each of the parameters can be seen in both Figure 5.3 and Figure
5.4. The value of the per-unit voltage that will trigger an undervoltage event is controlled by
the "U_Vrange" parameter. The value at which the breakers will be allowed is controlled by
ilfiI f IO PoinE: reset (Iltll
REAL IO Point: clock (RElI,l
REAL v IO PoinE: L V2 (REAII v
REAL v IO Point: U V3 (RF}I)v
REAI v IO Poior: U V4 (REAII v
REAL v IO PoiDr: U V5 (REALI
REAL v IO Poj.Er: U v6 (REAI)v
REAL V IO Point: t Vl (REAL)v
REAL v IOPoint:BC(REA.I)v
ffi V IO PoinE: I0ADSIiED (Iml v
v
t9
the "Close_BRK_Rg" parameter. Both the "U_Vrange" and "Close BRK_Rg" are both real
numbers that are represented by 64 bits. In the instance the system that runs the load-
shedding controller has memory registers, both values for load action can be altered to an
integer (32 bits), or a char (8 bits), to meet the conditions of the system. The controller will
delay load shedding as well as the reclosing of breakers with the "FaultDelay" and the
"Close_BRK_1", both of which are a time delay, with the code shown in Appendix C. Prtyp
is the solve model card type used in RSCAD. Proc is the assigned controls processor. Pri is
the priority level of the loads. These parameters control the number of iterations the program
goes through, effectively delaying the program. The last of the parameters are
"OpenLOADl-5" and "CloseLOADl-5" and are used to the set the values that output to the
breaker controllers.
V PIBAICTBi/CIilP('TAIIIE
U_Vrurge
pltl?
EaultDeI6y
Close_Enl(_E
CIose_ERK_fu
ClosefllAl)3
CIoSeIIIAD{
CloseIInDS
op€DI0AD1
opeDmAD2
OpedOAD3
OpeDmAD{
OpenJIAD5
CloseIOADl
CloseLOlD2
REAL Para.Deter: U Vrange
IIII v ParaDeter: prtlp v
II{I v Parameter: faultDelay v
I}IT V PareqeEer: Close BRK t
REAL v PararDeter: Close BRK Rg v
IIilI v Parameter: CloseI"OlD3 v
fiff v ParaneEer: CloseloAD4 V
rIrI Fararoeter: CloseLOABs
IIII v Paraneter: oIxnLOlDl V
ilff Parameter: Op€nIrA.Dz v
Il[I v Pararneter: C!€DIOAD3
$IT Paraneter: o1renl"oAD4
IHl v Parrneter: openl,oADs v
I}rI V PararDeter : CloseloADl V
IIII v ParaDeter: CloseLoAD2 V
_t-&ml
-effil
EffiI
-eEil
EtrrI
-effil
-&Eil
BEII
-effil
-effil
ETf,I
-sffil
-effil
-effil
-eul
Figure 5.3: List of Parameters for the Load Shedding Scheme
20
Proc Assj.qned Controls ProcessoE
Pri Prioricy Level
g_vrange Range of underroltage Cetectioa
plEyp So1ve Uodel oa card tlXle:
Close_BRK_Rg Range oi volEage Eo c].ose bftakers
v sEctllol:
Clogel.oADl Value of output iot to close Breaker
CloseICAD2 Value of output int io close Breaker
CloselOAD3 Value of outpuE int to clcee Eleaker
CloseLOlD4 Value of output int to close Brealrer
CIoseLOADS Va1ue of output int to clcae Blea].er
o,penLOAlI Value of outpuc inE to OPE$ Brea!.er
OpenIOA.D2 Value of ouEtlut inE Ec OPEN Breater
CEenI,CAD3 Value of ouEput inE to OFEI{ Breater
OpenLOAD4 Value of output iat to OPEN ErEake!
Open],gAos Value of output inE to OPEH Ereaker
'7 SECrIttII: oDEIAYil
faultDeLay Delay io rule out faulca
Close_BRK_t Delay until breal.er(st close
1
1
1
1
36
;RFC;GPC/FBs
0.92 0
t
1
1
1
1
a
I
B
I
0
0
0
0
0
0
0
0
0
0
5
I
0
Figure 5.4: Overview of Available System Configurations for the Load Shedding Scheme
5.2 Chapter 5 Summary
Chapter 5 describes how the load shedding controller in Figure 4.1 was developed.
We want this controller program to measure voltage and if the bus voltages fall below a set
tolerance level then shed the lowest priority load on the list of loads. It should then delay
before it measures bus voltages again so a false trip signal is not sent. In short this
chapter discusses how the system operates. An example of using this software is shown in
Chapter 6.
IIfIEGER
Ir{TEGER
REALY}B
IffiLE
RL4I
1TIIEGER
ISIEGEF.
II{TE€F8
INlEGER
1}IIEGER
1],ITEGER
II{"IEGER
I}I'JEGER
lSIEGER
II{IEGEA
IHNEB
i}IIIAR
2l
Chapter 6 Simulink and RTDS Simulation Results of Load Shedding Scheme
6.0 Simulation in Simulink
The Simulink simulation software was first to be used to test the load-shedding
scheme. During the simulation a fixed step size was used to observe the potential time frame
of the decision to shed load. Figure 6.1 shows the results of a simulation using a pulse
generator with a period of 50 seconds to enable signal to shed load, which was initiated at
32 seconds. These signals are triggered to overlap each other. Once the first load has been
shed and 50 seconds has passed load2 will shed, which will then overlap the first load shed.
Figure 6.1: Simulink Simulations All Loads Shed in Succession
The decision to shed load shown in Figure 6.1 was based on the undervoltage logic
scheme and the timing for shedding loads was based off the period of the pulse generator.
The period of the pulses can then be adjusted to shed loads slower or faster based off of
what works best for the system. The program may also be reset by asserting the reset input
t
Ld-9.drnl._Lo$d I
Lsd- &.dniq,Lors2
Lcd Shlum Ladj
kd-Sr'cddrl{_Lqda
[d_Snd$& LEEi
22
at any time. Upon asserting the reset, at the rising edge of the pulse generator all five outputs
will be pulled to zero. This can be observed in Figure 6.2.
Figure 6.2: Simulink Simulation All Loads Shed in Succession Then Reset
In Figure 6.2 fwe outputs have sent the signal to shed load (refer to the legend on the
top right). The reset input is then assertedat2S5 seconds on Figure 6.2 and all load outputs
are pulled to zero at 300 seconds. This 15 second delay is to ensure all the loads will be reset
before the load shedding process starts again. After two pulse generator periods the load
shed signal is once again asserted to show that the first load will be triggered after a reset
has occurred.
6.1 Simulating the Load Shedding Scheme in RSCAD
The Real Time Digital Simulator (RTDS), was used to simulate the microgrid model
with the load-shedding controller implemented into the model with the needed per-unit
measurements attached at the correct positions. Each of the bus voltage measurements are
L6-Mn{ Logut
rdd-slnddrlt- Lqd2!
t
t
_r
Lord. Sfidnold_Srqbifl-Ld_Shdm
I
I
I
i
I
s l5?N :!$4!O
1
F
n ls 5fl,
r'500.000
z3
represented meters and green lights when each load is connected (breaker closed), and red
lights when the load is disconnected from the microgrid (breaker open). This is opposite to
industry practice where red represents a live circuit and green an open circuit. It should be
noted that these meters are on the source side of the breaker, and as a result when load is
shed the voltage meter values will increase. The meter readings for the loads that are shed
in Figures 6.3 , 6.5 , and 6.6 will go to zero because those meters are on the load side of the
breaker. The most critical load, Meter U_Vl, does not have an indicator light because it will
never be shed from the microgrid. This will allow the most critical loads to stay active for
the longest time with little reduction in voltage. The load-shedding controller was set to
trigger an undervoltage event at a per-unit voltage of below 0.90 and reconnect a load to the
microgrid at 0.93 per-unit voltage. These settings should allow the microgrid to remain
stable, but further study is required to verify this. The goal of simulating the load-shedding
scheme is to make sure the loads are shed when there is a threat of undervoltage and yet
delayed enough to keep any transients from triggering unnecessary load shedding.
The RTDS Runtime simulation window can be seen in Figures 6.3 through 6.6 in
multiple stages of the simulation. Figure 6.3 is the setup of the Runtime program. The top
left meters and the lights directly below them correspond with each other as most critical
load on the far left ("U_V1"), and least critical load in the middle, ("U_V6" and "Loadl").
The switches to the right of these meters are the integer inputs that tell the load-shedding
controller to shed or reconnect loads if the proper voltage condition is present. To the right
of the switches are the sliders that control the excitation of the hydroelectric generators one
and two, along with the "Clock" of the load-shedding controller between them. The reset
switch is the last control component and is used to set the FSM back to an idle state in case
24
of program failure. Below the control components are the meters and graphs that monitor the
bus voltages and power of specific loads.
In order to run this simulation and test the load shedding scheme as shown in the
f,rgures below the excitation of the hydroelectric dam generators was lowered in small
increments. As the simulation continues one will be able to see the load voltages drop to
nearly zero as each of the loads are shed. The two graphs from the middle to the right-hand
side are the three-phase RMS voltage and the power of the most critical load, respectively.
Figure 6.3: Load Shedding Interface in RTDS Runtime
Once the simulation was running to demonstrate the load shedding scheme the next
step in the test was turning down the output voltage of hydroelectric generator one to
simulate excess load on the system. Transitioning between Figure 6.3 and Figure 6.4 one
can observe that as the voltage of the system drops the monitors will light up blue to indicate
<<tlc\'!I
5
Shed-able Loads
LEdl RUS (ku tdd 2 RMS (rvl [r.d 3 RMS (rV]
,-.r-.-% ffi EE
Lod 4 Rtts (lill tord 5 RMS (rvl
llin --1r:r-1Er;6rj----
Highest Priority Load
ffi u-unswr Power of Highesl Pnonty Load
;<tl
Ar'..
t
l
:
ll-Ejl ll4Ji rlgJl l4JiHUnJI Ur
25
the voltage the bus voltage has dropped below 0.9 per unit. Once the controller detects this
undervoltage event a delay counter starts and waits for any transients to clear to keep loads
on as long as possible.
Figure 6.4:Load Shedding Simulation 2 Bus Voltages
As the delay period was completed the first load was shed. Then, a second delay
period passed while the voltage was under the designed limit. Figure 6.5 shows the lights
monitoring U_V5 and U_V6 have turned red and the bus voltages of the first two loads have
dropped to almost zero. The reduction in power and voltage of the highest priority load is
also visible in Figure 6.5 as well.
0. 9038 E.8969 0. 890 8.89 0.8922 o.89?"
f,,A r't'
.,
,drl
Ao*'ot-L__.+_
L$#F
t.tr lJ{
26
Shed-able Loads
Lodl RMSi (kv) Lold 2 RMSi (kV) Load 3 RirS (tV)
a od!4Mo&rD@EraE !,
Lord 5 RMS {kV}Lud 4 RMS (kV)
ffiffi
Highest Priority Load
@l l v,nrsnvrI F,6qS I -
Power of Highest Pnority Load
<<qA'.
L--:{
a aa
Figure 6.5: Load Shedding Simulation 3 2 Loads Tripped
The last step in testing the load-shedding was lowering the per-unit voltage of
generator low enough so that the only load left online is U_Vl which is the highest priority
load. This test was performed to keep the most critical load at or above 0.9 volts per-unit.
With a voltage level at or above 0.9 per-unit the systems connected to that bus would still be
able to operate. Noted in Figure 6.5 is the power of the highest priority load, this value
appears to vary a lot because the vertical scale is small, when in reality it varies by
approximately .lMW. Figure 6.6 shows all the loads except the last load as shed. The most
critical load, (U_V1), has a per-unit value of above 0.9.
I
Eitrr_]lt,-;'l
27
Figure 6.6:Load Shedding Simulation 4 All but Highest Trip
In Figure 6.6 above, the power and voltage is stable at 09079 volts per-unit.
Although there are some buses that are undervoltage the most critical remains connected
within voltage limits.
6.2 Chapter 6 Summary
Chapter 6 of this thesis centered around the implementation of a load shedding
scheme. One of the issues of the microgrid discussed in chapter was actions to take in
certain instances when not being able to supply power to every load in the system. This
chapter takes the list of prioritized loads from phase 1 of this project. It then discussed how
the load shedding scheme was implemented in Simulink, which was in turn used as a base to
implement in RSCAD. This was done for ease of development and to ensure accuracy. The
tests results from simulations for Simulink and RSCAD prove that the load shedding scheme
Highest Prionty Load
@l uvrnrsrwrtE6i?t -
Shed'able Loads
Lo.dl Rl,ls (l(v) Lo..t 2 RMS (rV) Lold 3 RMS (tY)
ffiffiffi
L..d 4 Rlrs (kv) L6d i RMS (wl
6ritra:-] iEnE:-]f:rr-tJ.j&-i I rl rlid
Power of Highest Priority Load
-.;_llrlLi
'I
=--ltr
28
worked in simulation. Figure 4.1 shows a block diagram ofjust the load shedding controller.
A more in depth view on how the controller was designed and the C code implemented is
found in Appendices A-C. Specifically the content of the appendices is as follows:
Appendix A shows the truth tables for the finite state machine, Appendix B show the next
state and output logic diagrams and equations, and Appendix C shows the RSCAD Cbuilder
files and the Ccode that goes along with them.
In this microgrid, the load shedding is not the first option to take action. Before any
load is shed the system checks to see if there are any batteries that can supply power to the
microgrid. In order to use these batteries efficiently there needs to be a battery controller to
tell the batteries whether they need to supply power to the microgrid or to charge. To
implement this a battery model built and tested. This is discussed more in greater detail in
the next few chapters.
29
Chapter 7: Battery Controller
7.1 Introduction
The purpose of the battery controller is to evaluate the frequency of the system and
send a signal to the battery to correct the frequency, as well as help with real power
management. If the batteries can't maintain all of the bus voltages at .9 per-unit, then the
battery controller sends a signal to the load shedding scheme. The controller is a stackable
unit that will communicate to other controllers to ensure they have done everything
necessary before changing the state of other batteries [3]. Appendix D shows the coding for
the battery controller shown in Figure 7.1. The battery controller needs to be able to
perform several tasks. Those tasks include supplying power to the system at the appropriate
time or using power from the system to charge. A flow diagram is shown in Figure 7.1.
30
Start
Read
Frequency
Tne
TrL e
OpiisEl Freo
H€h F.eq
Fage
TruE
Reduce
State
.L:
True
Start
Supplying/Absorbing Stop
Supplying/Absorbing
Clear Wait
Command
Previous Device
Lcil Frea
FEISE
F9 Ee
E9 se
Set Wait
Command
Previous Device
Device
Next
lncrease
State
True
FElse
Figure 7 .l: Battery Control Flow Diagram
31
The batteries will react to changes in frequency of the system make a decision based
off that. The controller is set up such that it can increase or decrease the amount of power
transfer to or from the batteries. The first step with the controller is that it takes frequency
measurements from across the system and compares those frequencies to a reference. Based
off of those comparisons the battery controller will make one of a few decisions. The f,rrst is
the frequency higher or lower than the system normal. Then the controller will decide to
either start charging the batteries or using the batteries to supply power. Once a battery is in
supply mode, the controller will measure voltages across the system to check if one battery
is adequate to maintain a stable system. If one battery isn't enough a signal called "Enable"
is sent from the battery controller to the second battery to tum on. This model should help to
correct frequency drops in the system during peak load times.
7.2 Battery Model Introduction
The main purpose in creating a power system model of the battery is so the battery
controller can be tested to see if it operates correctly with the other controls in the microgrid.
The battery model used in the project works as an ideal current source with three different
settings. A generic ideal current source was used and is shown in Figure 7.7. These settings
correspond to the values described later in this chapter. There are three levels of supplying
or charging for the batteries, which are low, medium and high. A value of I means supply
or absorb the least amount of current, a value of 3 means supply or absorb the highest
amount of current possible, and a value of 2 is medium level.
The battery controller, which is described in more detail later in this chapter will
measure and compare the frequency, and then determine if the batteries should be supplying
or absorbing power and at what rate. Once this decision has been made, the battery
)Z
controller will send commands to the batteries themselves. The controller will update the
commands to the batteries in real time. For example, if the batteries are in supply mode and
the controller determines it needs to start absorbing power a new command will be sent to
the batteries to change modes.
7.3 Battery Controller in RSCAD
The design of the controller with the present microgrid model was done in RSCAD
using the CBuilder functionality described in Chapter 6. By using CBuilder a user can
create control blocks, like the one shown in Figure 7.2.The control block consists of inputs
and outputs that are transmitted to and from code programmed in a C-based coding
environment.
Figure 7 .2: Battery Controller
The inputs of the system are shown in Figure 1.3.The "Freq" refers to the frequency
input used for comparison. "CLK" is a pulse function with a period set to the user's
preference. The period of the pulse function will designate how often the controller can
Enable in
Wait out
State
D
Freq
CLK
Enable out
Wait in
aaJJ
make adjustments. 'ol-_in", which is "Enable in" in Figure 7.3,is the signal from the
previous controller in a predetermined sequence. If "L_in" is a zero the controller will not
change state because the previous controller is working to resolve the problem. This input
will not allow multiple batteries to be set to charge or discharge at the same time. If more
than one battery is required the batteries are set to go into a charge or discharge state based
off of a predetermined sequence. The last input is "'W_in", which is "Wait in" in Figure 7.3,
is used to communicate with the next controller. This input allows the batteries to be turned
off sequentially and not simultaneously.
v IITFOIS
Freq
CIJ(
T'.in
Il_iD
REAI v IO Point: Freq (REAU v
REAL v IO Poinr: CUt (REAL)V
IIII v IO Poinr: I in (INI)v
ilt-r v IO Point: I{ in (It{I)V
U
ffi
E[
ffi
EEEE
Figure 7.3: Controller Inputs
Each of these inputs are points on the controller symbol and are declared by RSCAD
to be used in the coding environment. Like the inputs of the system, the outputs are set, as
shown in Figure 7.4,to be used in the C-code of the program. 'ol_out", which is "Enable
Out" in Figure 7.3, is the output to the next controller and will output zero until the battery is
operating at full capacity for charge or discharge. When releasing energy into the system
'ol_out" will be '1'. If the battery is absorbing energy from the system the output will be '-
l'. The output 'State' of the battery will be an integer value of 1,2, or 3. This value
communicates to the battery how much power the battery can either supply or absorb. A
value of I means the state is at the lowest power setting, this correlates to the baffery
charging at the slowest rate possible, or being able to supply a smaller amount of power for
the longest amount of time possible. A value of 3 is opposite of this in that the battery will
34
charge at the fastest rate possible, or supply the largest amount of power for the shortest
period of time. Having value of 2 means the batteries supply power or charge at a medium
rate, which isn't the highest or lowest amount possible. "P_out" and "C_out" are binary
values where "P_out" is a value of '1' when the battery is supplying power to the system.
"C_out" is a value of '1' when the batteries are charging, or absorbing power, from the
system. If "P_out" is a value of 1 then "C_out" has the value of 0. This means that the
batteries can't be supplying and absorbing power at the same time. "'W'_out" is the output to
the previous controller and is '1' when the previous controller needs to wait. The wait
command will be sent whenever the battery is supplying or absorbing the power allowing
only one battery to change conditions at a time.
v tltt?tfs
f,_out
Saate
P_out
C ,out
Il_out
Figure 7.4: Controller Outputs as Assigned in RSCAD
The last of the items used in the coding of the controller, shown in Figure 7 .5, are the
parameters. These parameters are set when the controller is introduced in the draft portion of
the model and can be changed to fit the user's needs. "High_Freq" is the value at which the
controller considers the frequency to be high enough to start charging the batteries.
"Low_Freq" parameter determines the value for the frequency at which the batteries will
supply power to the microgrid. The values in Figure 7.6 are the frequency input values of
Figure 7.3.
ffi
ET
4
l0I
Eil
ET
vn[I v IO Poinu: t ouE (INT)
II{I v IO Poinr: State (IIirI)
vIHTvIO Poinc: P out (II[I)
I}II V IO PoiEt: C out (Iml v
I}rJ IO PoinE: fl out (IIII]vV
35
V PARAilE'IERS/trilHITATIMIS
High_Ereq
Lou_Freq
Figure 7.5: Controller Parameters
7 .4 Battery Controller Implementation in RSCAD
The completed controller will be connected to the battery component and the other
battery controllers in the system. The stacking of the controller will allow there to be no
limitation in the number or rating of batteries that can be added to the system. A two-battery
system controller is shown in Figure 7.6.Here "CLK" has a frequency of I Hertz so the
controller can change states every second as needed. Enable in, or "L_in", is set on the first
controller to '1' to tell the controller there is no previous controller. "Enable out" and "Wait
in" is connected to the second controller. The second controller has "Wait in" set to '0'
which indicates, to the first controller, that it is the last controller in the stack. C, P, and State
are connected to their corresponding battery in the system.
5tat6
Fteq
Fraq
ffi
ffi
StalEl
REAI V Parameter: Hlgh Ereq v
REAL V Parer*eEer: Ior Freq V
Enable in
wa[ out o
513tr
Freg
cLh
Enable out
wa.lt tn
EnabE m
WaBott
c
F
State
Freq
CLI{
Enable out
Walt ln
Figure 7.6:2 Controller System
LI
36
The last input is'oFreq" which is the frequency measurement input from the system.
The controller will use the value for "Freq" to make decisions for the batteries. Those
decisions are if the battery should be supplying or absorbing power, and at what rate of
power transfer.
7.5 Battery Power System Model Configuration in RSCAD
The power system model was developed in RSCAD CBuilder using a C-based
coding environment. The inputs to the model are shown in Figure 7.7 and Figure 7.8 for the
power system model. The input values described in this paragraph are the same values
described earlier and in Appendix D. "C_in" is a binary value that indicates to the battery to
absorb energy, or charge the battery. 'oP_in" is a binary value which indicates to the battery
to supply power. "State" is an integer value from the controller which indicates how much
power to absorb or supply to the system. *CLK" is an input of a pulse fi.urction which has a
frequency of 1 Hz for real time calculations. Figure 7.7 depicted below shows this model.
P_in
,: LltState
Figure 7.1:Battery Model in RSCAD
6
State CLK
P
'7 INPIITS
C_in
P_in
State
NV
JI
IIflI IO Point: C in (Iffil
rIfi IO Point: P in (Il{t)
IlIT v IO Point: SEate {Im}
REAL IO Poinr: CU{ {REAI}V
g I(]JJ
ffi
ffi
ffi
-&
-&
-&
Figure 7.8: Battery Inputs as Assigned in RSCAD
The nodes for the battery are shown in Figure 7.9.The nodes connect to the rest of
the system. The nodes are also used to measure the voltage values from which to calculate
the response of the system. "A_1", 'oB_1", and "C_1" are the positive terminals of the
battery. ((Y 2)' is the negative terminal of all three phases and will be ungrounded in the
system.
v EI}ES
REAL vl[uooe: v z v
REAL vllroae: n r v
REAI -llroae: n r
REAL -lluoae: c r v
Figure 7 .9 : Battery Nodes
The parameters shown in Figure 7.10 are set by the user to be employed in the
calculations in the code. "Powerl", "Power2", and "Power3" are power settings of the
battery. Depending on the situation the system may require a higher or lower level of power.
The controller will determine which power setting the battery will use. "Powerl" is the low
power setting and "Power3" is the high power setting of the system. "Initial_Energy''is set
by the user and is the starting stored energy of the battery in Joules. "MonI" is a binary value
controlled by a switch used to trigger whether internal values will be monitored in runtime
or not. When activated, "Current_Batt","Energy_Stored", and "Power" can be monitored in
Runtime. "Current_Batt" is the current through one of the phases of the battery.
"Energy_Stored" is the amount of energy in joules remaining in the battery. "Power" is the
output power of the battery at any given time in watts. "Max_Charge" is in units ofjoules
-&ffi
-effi
-effi
-effi
v_2
A_1
B_1
c_I
38
and refers to the limit of energy that the battery can absorb. "Max_Discharge" is in units of
joules and refers to the limi of energy discharge for the battery. "Max_Charge" and
"Max Discharge" is defined by the user for each of the battery units.
V PIBIDETES/(II|Pi!'AfIIf,S
trare
Pomrl
Porn12
Pouer3
Inital_EEetqy
HonI
Cutlent_Batt
Energy_Stoled
lGr_CharqE
llar_Dischalge
Pofler
Bage Poner
.7 I}IiTEtrIM{S
RE.f,L vlluoae: I 1 v
REAI -llltoae: a r v
REAL vllroae: c 1 v
RE.AI -llrooe: v e v
Figure 7.1 1: Battery Current Labels
The variables shown in Figure 7.12 are used in the code and are connected to the
monitored values mentioned in Figure 7.10. Setting one of these variables equal to a value
will output that value and be monitored when simulating the model. "IMON" outputs the
curent from phase A to the "Current_Batt" variable. "En" outputs the energy stored in the
ffi
ffi
ffi
ffi6
ffi
ffi
ffi
ffi
ffi
ffi
ffi
IA
IB
IC
IG
ffi
ffi
ffiu
E
-&
B
-M
CH.AR v Fareneter: Narre v
REAL v ParanreEer: PoHerl v
REAL v ParaEeEer: Forerz v
REAL v parareeEer: polrer3
REAL v Paraneter: IniEal Energy v
I}II V Parameter: Uoni,Eor v
CHAR Para:neEer: CurrenE BatE v
CIIAR v Faramter: Enerqy SEored v
REAL v Parirpecer: Nax Ctrarqe
RE.LL v PareFter: Na:r Discharge v
CHAR v Para.neter: Forr'er v
REAL V Farafieter: Base Poner v
Figure 7.10: Battery Parameters Entered by User
When read, the nodes for the power system device will give the voltage atthat point.
ln order to control and measure the current through the nodes, the system uses injections for
each of the nodes. IA, IB, and IC are the current injections on the positive terminal of the
battery system. IG is the negative terminal connection curent for the power system. The
sum of IA, IB, IC, and IG should equal to 0.
39
battery at that time and outputs that value to "Energy_Stored". "P_out" outputs the power
that the battery is generating to the variable'oPower".
v create: GETIERIC 0IITPtffS
r!.foll
En
P_out
RETL v
REAL v
REAL v
{Current_Batt, "Branch Currents", -10,110, "A", . . .
(Enerqy_5tored, -Enerqy Storage-, -10, 110, "J", . . .
( Porrc r, "Enerlrl Storage', -t 0, 1 1 0,'H", l{onI:1 ),'
fffiiffiiffi
Figure 7 .12: Monitored Battery Values
7.6 Software Development in Simulink
The energy storage in the microgrid system will help to offset load peaks in the
system. To achieve this a controller will evaluate the current state of the system and react
appropriately. Under conditions where generation exceeds load in the microgrid the system
will store energy and at peak times that system will release energy from the batteries to
balance the system and avoid load shedding. The energy storage devices, or batteries, will
activate one at a time to correct the system. The software used to develop the controller is
Matlab Simulink. A finite state machine was chosen to develop the battery controller as
shown in Figure 7.13. FH indicates a high frequency condition input, while FL indicates a
low frequency condition input. S0 is the initial idle state the batteries are in, which means
the batteries are not charging or discharging. This system will constantly be monitoring
frequency. If FH, a high frequency condition, is measured then the system will move to
state one, which is charging battery 1. Once in S1 the system will compare the frequency
again, if it stays high it will move to 52 and start chargingbattery 2. This process is
followed throughout Figure 7.13.
40
Tr- In
fH f*fH
f;frh fL fL f fL
fg f1 Tl TH rLh fL Itr
fL
Ir-h
f*fn f*fH TH
Iih fr- Tn fi- In Ir- fir Tlh f*
Figure 7.13: State Transition Diagram
The State Transition Diagram shown in Figure 7.13 shows the transition logic of the
controller for the energy storage devices. The controller in this example uses five batteries
and each battery has three states; on, off, and charging. S0 indicates all batteries are off, S1-
55 indicates the respective batteries are charging, states 56-5l0 indicate the batteries are
supplying power, and Sl I is when the batteries have reached their power limit so load needs
to be shed. The Boolean output for each of the five batteries in the system are shown in
Table 7 .l.In the table L is the command to shed load and P is the command for each of the
batteries to supply power to the system. The last variable is C and that is to charge the
battery when everything is set to a zero value, the batteries will be disconnected. Table 7 .1
shows the exact same information as Figure l.l3,but in a tabular format.
4t
$l;rt,r,t J", l'r Jir
('lrtl.lrulr
J'J Pr 1-':,r,i cr a rlrilftilurlilrl{}{l{}{}
5r
5j
5x
"!r\'.
il
{t
{t
il
{l
t,
t,
{l
ll
{}
{l
r;
r)
t,
li
tl
il
(t
t)
{t
{t
{l
{i
il
{t
il
{l
il
il
(l
t,
l'}
{}
t
I
{l
{}
tI
ll
I
I
I
I
I
I
t,
I
r
I
r
{,
{,
,
I
l
5r;
.t;
Tt*
5i,
Strr
5u
{t
{t
{f
{t
{l
I
rl
{,
t,
I
I
,
{}
ll
{}
tl
tl
il
{}
I
I
I
1
I
rl
I
I
t
l
!I
{l
{t
{}
{}
t
t
(
{
I
I
I
I
I
I
{f
{t
II
il
1t
il
{}
{,
{}
{r
tl
n
{t
II
{l
{t
{t
il
Table 7.1: Output Transition Table
7.7 Implementation in Simulink
Figure 7.14 shows a block diagram of the battery controller built in Matlab Simulink.
This figure was built from the data in Figure I .13 and Table 7.1. The inputs fL and fII are
constants and simulate frequency in the system being low or high. Display C1-C5 and P1-P5
will show a value of 0 or 1. For C1-C5 a value of 1 means the battery is in a charging state
while a 0 means it is not charging. For Pl-P5 a value of 1 indicates the batteries are
discharging (supplying power) and 0 indicates no power is being supplied. The battery
controller was implemented to be used in conjugation with a load shedding scheme, but in
the case of looking strictly atbattery control it is not needed.
42
Generator
Pulse
Display C'l Display P1
Constant
Constantl
Battery Logic (Fraquency Regulalion)
Display C5 Display P5
Conslant2
To Load Shedding Scheme
Figure 7.14:Battery Controller in Simulink
7.8 Simulation of Battery Controller in Simulink
Table 7.2 and Table 7.3 describe the battery controller response if there is a
sustained low frequency and high frequency input. Charging or discharging a battery will
reduce or increase the frequency level of the system respectively. For the cases shown in
Tables 7.2 and7.3 the batteries were not programmed to affect system frequency, but rather
to show that the battery converters can be stopped or started sequentially. In the Tables
below we see that at each time step of .2 ms the next battery will either start charging or
discharging.
time
ms ut
0
0.2
0.4
0.6
0.8
Ouput:C
C1
c2
C3
c4
C5
0
0
0
0
0
ut: Po
P1
P2
P3
P4
P5
1.
1.
1.
7
1.
0
0
c3
c4
P,1
CLK
P4
Reset
fl-
fll
0
Table 7 .2: Battery Controller Simulation Low Frequency Input
43
time
m ut:COu
C1
c2
C3
c4
c5
0
0.2
0.4
0.6
0.8
1.
7
1,
t
1
Output:P
P1
P2
P3
P4
P5
0
0
0
0
0
Table 7 .3: Battery Controller Simulation High Frequency Input
7.9 Chapter 7 Summary
The main point of this chapter was to design a battery controller that could sense a
low or high frequency condition and then start charging or discharging batteries in a set
sequence over time. This task was accomplished and can be seen in Figures 7.2 and7.3.
As discussed in the results section charging or discharging the batteries didn't affect the
frequency since it wasn't part of the full system.
Another key point discusses the design of the controller within the microgrid model.
This was done in RSCAD using the CBuilder functionality and in Matlab Simulink. Some
of this issues addressed in this chapter include, "Should the baffery be charging or supplying
power?" Then once in this charging or supplying state; "What should be the rate of
charge?" The design of the controller was based around those questions.
This chapter also gives a very simple overview of anenergy storage model. The
objective of this model is to add the batteries and their energy storage control in the present
microgrid model. This model will use an ideal current source as the subject of application
for this model. These batteries have three power settings listed which are used to develop the
characteristics of the supplying and absorbing power abilities of the batteries.
44
The results shown in Figures 7.2 and7.3 are programmed with a constant low or
high frequency input changed by the operator. The simulations show the converter can be
stopped or started. The next step would be to program the batteries so a more realistic
simulation could be performed to show how they affect system frequency, and actually
include in the microgrid simulation. This is discussed more in the future work section of this
thesis.
45
Chapter 8: Diesel to Natural Gas Conversion Kits
8.0 Introduction to Diesel to Natural Gas Conversion
There are currently 18 backup generators distributed in the microgrid area, mostly at
the hospitals. Together all the generators can provide approximately 14.75 MW. This
generation would add a significant amount of power to the current generation available [1].
By using the power from the generators, it would be possible to keep critical loads from
shedding. Currently, all of the hospital backup generators are setup to feed only their
respective facilities. There is no existing connection or method, for them to synchronize with
the microgrid and transfer their output power to the local distribution system.
The hospital generators are currently diesel-fueled. Because of environmental
restrictions, the use of diesel generators is limited to short durations during emergencies. To
allow more use of the generators, environmental standards require a more eco-friendly fuel
source, such as natural gas. After the conversion, the generators can be re-commissioned and
approved for longer run times. It is anticipated that the conversion will allow the generators
to offset the daily peak load and, therefore, reduce peak load rise ofenergy prices.
Being able to use the excess power from the hospital generators would affect the
microgrid in two ways. The first is the power supplied has the potential to keep a load from
having to be shed, assuming the natural gas pumping stations have power in a regional
blackout. The second is the power could be used to charge the batteries.
8.1 Diesel to Natural Gas Conversion Kit
A company called GFS Corp is experienced in converting commercial grade diesel
generators to a hybrid mix of diesel and natural gas. Most of its projects provide the
46
customer's existing generators with peak shaving capabilities. GFS Corp is able to perform
this conversion for any type ofdiesel generator.
8.2 Conversion Kit Specifications
According to a phone interview performed with a representative of the company, the
conversion process will provide the following benefits to the newly converted engines:
1) Non-invasive retrofit. The existing engines will not need to be disassembled for
any part ofthe conversion process. [3]
2) The engines will start up on diesel, then convert to a hybrid operation of diesel
and natural gas. [3]
3) Maximum operation of 7UYo natural gas to 30% diesel. [3]
4) Conversion for one unit usually takes two days (one day to install and another
day to re-commission). [3]
5) No added power loss after the conversion process. [4]
6) Extends generator run time using pipelined natural gas [4]
The information described in above would help the microgrid in several ways. A few
of them include providing more power which in turn could keep load from being shed,
charging the batteries, and a cleaner burning fuel source. Another perk of these kits is they
are installed in a minimal amount of time which lessens impact on the area. A key point for
these kits is that there can be benefits beyond the microgrid.
8.3 Chapter 8 Summary
This chapter has discussed some basic research done on diesel to natural gas
conversion kits. In detail it goes over the conversion kit specifications and some of the
benefits it could have for the microgrid. Not only is it cleaner buming, which result in longer
47
run times during normal operations, but there is also no power loss after the conversion. The
next step would be the implementation of a model of these converted generators in
PowerWorld and RSCAD. The future work section goes over this in more detail.
48
Chapter 9: Automation Controller
9.0 Automation Controller Introduction
Automation control is the use of various control systems for operating equipment
such as machinery, processes in factories, and other applications and vehicles with minimal
or reduced human intervention [1]. To introduce more control the microgrid, an automation
controller was constructed by combining hardware shown in Figure 9.1, automation logic,
such as the load-shedding and battery control logic used in this project, and a Human
Machine Interface (HMI). The automation logic in the protective relays decides when action
is necessary to improve the state of the system. The sensors are placed throughout the
microgrid to collect and transmit data to the real-time automation controller (RTAC)
communications processor for control action and for the information to be displayed through
an HMI. The operator uses the HMI to analyze and interact with the system.
9.1 Hardware Development
Figure 9.1: Hardware Implementation Scheme
As shown in Figure 9.1, the data flow within the hardware is bidirectional. The
currents, voltages, and frequencies from the microgrid are measured by the protective relays
and sent to the RTAC to make informed commands, if action is necessary. The operator's
computer, far right on Figure 9.1, monitors the process through the HMI and has the ability
to intervene and reconfigure device settings.
Hardware lmplementation
SEL 451
Protective Relay
+
<-SEL 3530 RTAC
-
a-Operator's
Computer
49
9.2 Software Configuration
Figure 9.2: Software Implementation Scheme
In Figure 9.2, vendor settings software programs were used to configure and
commission the RTAC and implement load-shedding in relays that are already present for
system protection. From the settings configured in the RTAC, the HMI can be customized
for the system. Appendix F shows the software and hardware used in automation controller
development. Once the software is configured and the HMI is developed it can be used to
monitor values from with the hardware described in Figure 9.1, and control things such as
breakers in the system.
9.3 Protection Relay
When setting the automation controller, reference tags are used to transmit and
receive desired outcomes in the relay. Tags act as pointers to program commands within the
relay. Based on assigned tag values and automation logic, the automation controller either
performs actions on the microgrid as shown in Table 9.1 or turns on corresponding LED
alarm lights as shown in Figure 9.3.
Software Development
AcSELerator
QuickSet AcSElerator RTAC #AcSELerator
Diagrarn Builder
50
Setting Description Range Value
BKlMTR Breaker I Manual
Trip (SELogic)
Valid range: The
legal operators: AND
OR NOT R TRIG
F TRIG
OCl OR
PB8 PUL
Table 9.1 : Example of SEL Relay Breaker Trip Tag
In Figure 9.4, value is the tag monitored by the relay to trigger the assigned tag in
settings.In this case, the setting is the breaker manual trip action. OC1 OR PB8_PUL is a
pushbutton that triggers the manual trip when pressed.
#ALARM ELEMENTS
PSV34 '= IL{LAI{M OR SALARM
Figure 9.3: Example of SEL Relay LED Alarm Light Protection
Figure 9.3 shows an example of protection logic in the relay written in SELogic.
PSV34 is a protection variable tag that the relay monitors for HALARM OR SALARM
which are alarms for hardware failure or settings error. PSV34 points to an LED alarm light
tag so the relay will turn on an LED light in response to hardware or settings failure.
The relays also have alias tags to transmit information to the RTAC that will modifu
the state of the visuals in the HMI. All of this is being done so that the automation
controller can be used to interact with the microgrid in terms of battery control and load
shedding.
5l
Setting Description Range Value
SITMl SER Points and
Aliases, Point 1
Device Word
Element, Alias Name,
Asserted Text,
DeAsserted Text,
HMI Alarm
50P1,50P1,
ASSERTED,
DEASSERTED, Y
Table 9.2:Example of Establishing Alias Relay Tag
Table 9.3 shows a case where an alias tag is used as a value for an LED alarm light,
and if asserted, the HMI will display a triggered LED light.
Table 9. 3: Example of HMI Relay Trigger Tag
In Table 9.3, the defined alias overcurrent element tag 50P1 will trigger the
PB6_LED assigned LED light on the HMI.
9.4 GOOSE Protocol Communications
The communications protocol used in this project is called General Object-Oriented
Substation Events (GOOSE) protocol which was chosen because it was the best option
available at the time to work with the RTDS. This protocol is described in the Intemational
Setting Description Range Value
PB6 LED Pushbutton LED 6
(SELogic)
Valid range: The
legal operators: AND
OR NOT R TRIG
F TRIG
50Pl
52
Electrotechnical Commission (IEC) 61850 standard for substations which enables the
integration of all protection, control, measurement and monitoring functions [6]. GOOSE
communication is a publisher-subscriber protocol that contains publisher and subscriber
Intelligent Electronic Devices (IEDs). Publisher IEDs send data packets to the system while
subscriber IEDs read data packets published by a given IED.
To configure the publications and subscriptions, every IED must have a
corresponding IED Capability Description (ICD) file. The ICD file provides a description of
the items supported by an IED. ICD files for the devices that are to be interconnected on the
same network are merged into the Substation Configuration Description (SCD) file. The
SCD file is an XML based text file that describes the IEC-61850 compliant devices
comprising a substation. An SCD file is often generated by an editor program which takes
component capability f,rles (.icd files) and merges them into the .scd file. IEC standard
61850-6 defines the configuration description language for communication in electrical
substations related to IEDs [6]. This is all done so that the automation controller can
successfully communicate with the microgrid model developed in RSCAD.
9.5 The Human Machine Interface
The Human Machine Interface (HMI) is the platform for cognition and
communication between human and machine, and is the approach for information
transmission [8]. The HMI facilitates this communication through the use of visuals
designed to effectively convey the state of the running system.
The HMI also allows the operator to control a system through the use of interactive
visual controls, such as buffons, switches, and dials. [n order for the operator to make
efficient use of these controls, it is necessary for the operator to quickly and accurately
53
understand the state of the system. The status of the substation can be conveyed to the
operator in different ways. Colors, shapes, values and labels can all be used to transfer data
to the operator. Figure 9.4 shows an example of how an HMI could be designed for a single
breaker. When the breaker is open, the box turns red, and after an alarm is triggered, it
flashes. The buttons below the box allow the operator to trip or close the breaker.
Figure 9.4: HMI for One Breaker
Bus voltages and line currents may also be added in the HMI to allow the user to
monitor the transmission lines individually. Figure 9.5 is an example of recently labeled
values giving information to the operator, for the operator to interpret.
A Cunent0.0A
B Orneril0.0A
C Current 0.0 A
A Voltage 0.0 kV
B Voltage 0.0 kV
C VottaEe0.0 kV
Figure 9.5: Bus Voltages and Line Currents for One Relay
Breaker 1
o CLOSE BREAXER
TRIP BREAKER
54
9.6 Chapter 9 Summary
Chapter t has discussed the purpose of an automation controller, which is to
automate various control systems for operating equipment such as machinery, processes in
factories, and other applications and vehicles with minimal or reduced human intervention
[1]. Chapter discusses the steps needed in setting up an automation controller. Those steps
include: hardware development, software development, HMI, and communication protocols.
Chapter 10 goes over in detail future work for the automation controller.
55
Chapter 10 Summary of Accomplished Work and Future Work
10.0 Summary of Accomplished Work
As this research project ended several valuable milestones were achieved in the
pursuit of improving the microgrid. A major problem that often occurs in a microgrid is not
being able to meet the demand of the system. This research project successfully designed
and tested a load shedding scheme in RSCAD to deal with this issue. The load shedding
scheme works by following a few simple steps. The first is to measure the voltage at all the
busses throughout the microgrid and check those values to see if any of them fall below the
tripping value. If any of these values fall below the tripping value of 0.9 per unit the load
shedding scheme will select the lowest priority load and shed it. Currently load is only being
shed. When more power is available an operator will manually have to bring the loads back
online. Batteries as well as a battery controller have been added to the system to help keep
loads from being shed when possible, but no combined simulations of load shedding and
battery control were performed. This controller included commands such as when to charge,
when to discharge, and how much to discharge. By doing those things a large portion of the
microgrid area will continue to have power in the event of a blackout. This would be able to
power critical loads the user selects which could include: hospitals, police stations, and
government buildings. In conclusion the benefit gained from implementing a load shedding
scheme and battery controller into the microgrid is the ability to keep the power on for
critical loads during outages.
56
10.1 Future Work: Transfer of Automation Logic to RTAC
The load shedding controller is designed to fi.rnction independently and would be
fully realized if the control logic was taken from the RSCAD software and ported into an
automation controller development environment.
10.2 Future Work Time Based Load Shedding
The load-shedding controller will trigger the opening of breakers in a sequential
manner. As implemented in simulation the only way to change the sequence is to alter the
draft file's outputs from the controller. This would allow dynamic bus monitoring thought-
out the year. With the change of seasons and temperature the hydro generation plus varies
greatly as seen in Figure 10.1 []. There are various values of needed power during the
course of the year and each day. The ability to change which loads are shed and what is
critical to the stability of the microgrid. It should be noted that the system rarely be in island
mode. In the event that happens the duration will be unknown.
i,'
ix,
25
20
l0
5
0
c$$$t!*ss$$sss$'F'b-b-b'b'F-}}$.slod
Horrr d th. d.y
-Spdn!
elg6jn6l
-Fall -Ulinte.
Figure 10.1: Combined Solar and Hydro Average Generation Output Profile Over a24Hr
Period in 4 Seasons [1]
5l
10.3 Future Work Fully Implementing Load Shedding in RTDS Hardware in Loop
Simulation
Moving the loads in RSCAD can be done by using the GTNET-SKT communication
protocol functions to pass a date and time into the draft file. The GTNET-SKT protocol uses
a LAN/WAN connection with the TCP or UDP sockets on the processor cards. With the
proper configuration of the GTNET card(s) it should be possible to pass a date and time to
the load-shedding controller. A user would then be able to write additional code to take the
information from the GTNET card(s) and assign different load priorities during specific
times of the day andlor or year.
10.4 Battery Control Future Work
A preliminary battery controller was designed and presented in Chapter 7. It was
attempted to go beyond the scope of the project and create abattery controller in RSCAD,
but the project was over before it could be finished.
The work that was completed is shown in Appendices D-F. In the RSCAD model
when the battery controller and battery model were tested the battery controller measured
the frequency performed a comparison, determined how much power should be supplied and
absorbed, and then sent the command to the battery. The battery in tum supplied the correct
amount of power, but introduced a transient into the system that never dampened out. The
next step for this would be to determine if the battery or the controller is introducing the
transient into the system and fix it. Once this is completed the battery control could be
implemented in the microgrid with the load shedding scheme.
58
10.5 Future Work on Diesel to Natural Gas Conversion
The next step has two parts. The first would be to perform a cost-benefit analysis of
converting the diesel generators to the hybrid diesel/natural gas generator. This would be
done to see if it the benefits are great enough to justiff the expense to install them. Step 2
would be performed if the kits were installed, which is to model the converted generators in
RSCAD. Once modeled in RSCAD, a stability and transient study could be performed to see
how the generators interact with the rest of the system and if they improve the quality of the
microgrid.
10.6 Future Work Communication Between an RTDS and Automation Controller
An RTDS can simulate complex networks that have been developed in the
simulation software RSCAD. A GTNET card was used to interface the RTDS to the relay
over a Local Area Network (LAN) connection using GOOSE protocol [9]. The work in
Chapter 9 discussed creating an automation controller, but not the communication between
an RTDS model and the automation controller.
The SCD editor program within the GTNET-GSE function block facilitates the
creation of the SCD file. To configure communication from the RTDS to the protective
relay, outputs were added to the SCD editor. In Figure 10.2, both integer values and binary
statuses were outputted to monitor and control the breakers, bus voltages, and breaker
currents.
59
Edit D.tasd GOOSE_odpds_ l/G.bol
DataSet Entries Float Deadb.nd lntoFlffit32-x d min ru
1.O -'t.0
- 1.0
1.0
1.0
1.0
'l 0
't.0
1.0
1.0
1.0
1.0
1.0
1.O
'1.O
.1.O
1.O
10
10
Floal32-2 1.O
Float32-3 '1 O -1 O
Floal32-4 1.0 -1.0
Float32-5 1 O -1.O
1.0
1.O
1.O
1.O
"t.o
1.O
1.0
10
10
-1.0
-1.O
-1.0
- 1.0
-1.0
- t.o
-1.O
-1 0
-'i.o
Float32-'15 1.O -1.O
Float32-16 1-O -1.O
DataSet Preview
ToK I Cancsl
;
<FCDA ldlnst=-CTRL- prefx="ouT_" InClass=-GGIO- lnlnst='43' doName=-lntln' daName=-stval- tF Sr/><FCDA ldlnst='CTRL- preila:-OuT_- lnClass='GGIO- lnlnst='44- doName=-lntln- daName=-stval- fF Sr/>
<FCDA ldlnst='CTRL' prel5x='OuT_' InClass='GGIO- lolnst='45- doName=-lntln' daName='stval'fc=-Sfh<FCDA ldlnst=-CTRL' prefix='OuT_' lnClass=-GGIO' lnlnst='46' doName=-lntln- daName='stval- fF-Sfr><FCDA ldlnst='CTRL- prefix='OuT_'InClaas='GGIO'lnlnst='4f doName=-lntln- daName='sval- fc=-Sff-<FCOA ldlnst=-CTRL- prefix=-OuT_^ InClass=-GGIO'Inlnst=-49- 60Name=Tnlln- daName=-swal-fc='Sf/><FCDA ldlnst=-CTRf prefix="OuT_- lnClass=-GGIO'Inlnst=-49' doName=-lntln- daName='sval- fc=-Sr/><FCDA ldlnst=-CTRf prefix="OuT_' InClass='GGIO- lnlnst=-50' doName=-lntln- daName='stval- fc='ST./>
<FCDA ldlnst=-CTRL- prefir='OuT_'InClass='eGlO- lolnst=-51' doName='lntn- daName='slval- fF-Srf.
<FCDA ldlnst=-CTRf prefix='OuT_- InClass=-GGIO- Inlnst=-52' doName=-lntln- daName='stval- fF ST-F<FCDA ldlnst=-CTRL- preix='OuT_' InGlass='GGIO- Inlnsl='53- doName=-lntln- daName='stval- fc=-Sfi"-
Up lnsl O€lType
Y
^A
l
A
A v
o
11
E*
d
V
Vd
E*
E+
Y
Y
V
E*
t
t
Down
Y
Y
Figure 10.2: Editing Outputs in the SCD file
It is necessary that the parameters within the GTNET-GSE function block are edited
to link information within the simulation to those outputs, as shown in Figure 10.3.
x
Float32-6
F loal32-7
Float32-E
FIoat32-g
Flost32-1 0
FIoat32-1 J
Float32-1 2
FIoat32- 1 3
t
Y
Y
60
_rtds_GTllET_G SE_Y5.def
CONFIGURATION
Output Deadband Parameters
1 Signal RIUTX 1 lnput Slgnal Name#Types
R)UTX 1 Output Retransmit Curve
urationGOOSE
Descripton Value
bitnum (32.,11
Min
.fI
0 0
1
I0
1
Name
IED1DlT
101
IEDlOlB
IED 11127
tED102
ED1O2B
IEL 1O3T
lED103
rtlf,-riiTt*
1 SignalName
1 Boolean bitmap bit num [32..1]
2 Signal Name
2 Boolean bitmap bit num (32..1)
3 SignalName
32
0
12
Update Cancel CancelAll
TI
rI
IT
Figure 10.3: GTNET-GSE Output Parameters
The work described in this section is to determine how the automation controller
communicates with the microgrid model built in RSCAD.
10.7 Future Work Establishing GOOSE Protocol Communications for the RTAC
Designing and configuring SEL devices in IEC 61850 installations are possible by
using AcSElerator Architect Software. This software provides a means of configuring and
documenting the IEC 61850 communications settings between vendor-agnostic devices [0]
Upon importing the SCD file extracted from RSCAD, GOOSE communication can
be made accessible to the RTAC by packaging the data as directed by the IEC-61850
standard as shown in Figure 10.4.
6l
E S€L45r.Gcb0l
B CTRL
r:i.OLT-GG|OI
Llr lnd
Commmt Control 1n..,
5P50001.3t...
950$1'q
sPsmot.t
sP5{Xn2.n...
$rbrcribrd Data hem
StL45l.GcH)l.CTRI.OUT_6GlOl.lnd-stValo
"B
.tcEI stVal
Figure 10.4: Establishing GOOSE Protocol for the Relay
The AcSElerator RTAC software configures the automation controller by uploading
project files through a connected ethernet cable. GOOSE protocol is established in the
RTAC once an architect file is imported into the project file. A virtual data map must then
be created using tags to point to command settings to transmit and receive data using
GOOSE protocol.
62
References
tll Penkey, P. K. (2016). Critical Load Serving Capability by Microgrid Operation
(Master's thesis, University of Idaho) (p. 32 to 49). ProQuest Dissertations Publishing.
12) UET.(n.d). Uni.System Grid-Scale Energy Storage Solution. Retrieved June 20th,
2017 from
http://www.uetechnologies.com/imaees/product/UET-UniSystem_Product_SheetJeduced.p
df
t3l P. (2012, May 4). Bi-Fuel Enters the Digital Age.. . Introducing the EVO-SP@
System. Retrieved October 02,2017, from http://rvrvrv.sft-corp.com/industry.php/bifuel/
14) GFS Corp. (".d.).Natural Gas Conversions for High Horsepower Diesel Engines
Offering Substantial Fuel Cost Savings and Cleaner Emissions. Retrieved February 09,
2017, from http:i/www. gfs-corp.corn
[5] Bennett, S. (1993). A History of Control Engineering 1930-1955. London: Peter
Peregrinus Ltd.
[6] Fernandes, Chilton, et al. "Testing of Goose Protocol of IEC6l850 Standard in
Protection IED." International Journal of Computer Applications,vol.93, no. 16,2014,pp.
30-3 5., doi: I 0.5 l20l 16301-6112.
63
[7] Xuesong,Z'Liyang, Y., & Youjie, M. (2014). An Overview of Micro-Grid. Applied
Mechanics and Materials, 552.l-6.
[8] Gong, Chao. "Human-Machine Interface: Design Principles of Visual Information in
Human-Machine Interface Design." Intelligent Human-Machine Systems and Cybernetics,
2 009. IH MSC' 0 9. International Conference On, v ol. 2, 2009, pp. 262-265.
[9] RTDS Technologies. Real Time Digital Simulation for the Power Industry: Manual Set.
Winnipeg, MB
[10] AcSElerator Architect Software. (n.d.). Retrieved May 18, 2017,from
https :i/selinc.com/produ cts I 5032 I
[11] Mahmoud, M., Hussain, S. A., & Abido, M. (2014). Modeling and control of
microgrid: An overview. Journal of the Fronklin Institute, -151(5), 2822-2859.
doi: I 0. 1 0 1 6/j jfranklin.2O 1 4.0 I .0 1 6
[12] Boungwook, C., Heechul, K., Mussab, A.M., & Seeley, N. C. (2008). The Application
of a Redundant Load-Shedding System for Islanded Power Plants. 35th Annual Western
Protective Relay Conference, l-10.
64
[13] Manson, S., Zweigle, G., & Yedidi, Y. (2014). Case Study: An Adaptive
Underfrequency Load-Shedding System. Indus try Applic ations, I E E E Trans actions on,
50(3), t659-1667
[14] Ifuoposki, Lasseter, Ise, Morozumi, Papatlianassiou, & Hatziargyriou. (2008). Making
microgrids work. Power and Energy Magazine, IEEE,6(3), IEEE Power and Energy
Magazine, May-June 2008, Vol. 6(3 ).
[5]N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, 'Microgrids,' IEEE Power
Energt Mog., vol. 5, no.4,pp.78-94,2007
[16] S. Parhizi, H. Lotfi, A. Khodaei and S. Bahramirad, 'State of the Art in Research on
Microgrids: A Review,' in IEEE Access, vol. 3, no. , pp. 890-925,2015. doi:
1 0. 1 I O9/ACCESS.201 5 .2443119
[17] B. Kroposki, R. Lasseter, T. Ise, S. Morozumi, S. Papathanassiou, and N.
Hatziargyriou, 'Making microgrids work,' IEEE Power Energy Mog., vol. 6, no. 3, pp. 40-
53,2008.
ll8) IEEE Guide for the Selection and Sizing of Batteries for Uninteruuptible Power
Systems. Place of Publication Not ldentified, IEEE, 1995.
[19] Nazaripouya, H., Wang, Y., Chu, P., Pota, H.R., & Gadh, R. (2015). Optimal sizing
and placement of battery energy storage in distribution system based on solar size for
voltage regulation. 2015 IEEE Power & Energl,, Society General Meeting
doi: 1 0. 1 109/pesgm .2015 .7286059
[20] Thipse, S., Kavathekar, K., Rairikar, S., Tyagi, A. et al., "Development of Environment
Friendly Diesel-CNG Dual Fuel Enginefor Heaty Duty Vehicle Application in India," SAE
Technical Paper 2013-26-00L5,2013,https:lldoi.orgl10.4271l20l3-26-0015.
[21] Studies of performance and emission characteristics of compressed natural gas fuelled
S.I. engine and developing CNG conversion kit. (2013). IOSR Journal of Mechanical and
C iv il E ngineering, 9 (4), 23 -29. doi : 1 0. 979 0 I | 684-09 42329
[22] Kolluri, S. V., Ramamurthy, J.R., Wong, S.M., Peterson, M., Yu, P., & Chander, M
R. (2015). Relay-based undervoltage load shedding scheme for Entergys Westem Region.
20 1 5 IEEE Power & Energy Society General Meeting. doi: 1 0. 1 109/pesgm .2015 .7285651
[23] Jensen, S. (2006, December 1). Converting Diesel Engines to Dual Fuel The Pros and
Cons of Common Engine Types. Retrieved from
https ;/iwww. energyconversi ons. com/whitepaperdual fuelengines
[24]leee Pes Task Force on Real-Time Simulation of Power Energy Systems. "Real-Time
Simulation Technologies for Power Systems Design, Testing, and Analysis." Power and
Energy Technologt Systems Journal, IEEE, vol. 2, 201 5, pp. 631 3.
65
66
Appendix A: Truth Tables for Finite State Machine
The truth table is apart of the proper design of a Finite State Machine. The table is
used to map the State, Next State and Output logic. The input U_V signifies a signal from
the RSCAD simulation model that will initiate the load shedding process. The input B_C is
a signal from an external source that will send a signal to close arelay of the latest open
load.
Table A. l: State Transition Table With Encoding
s UV BC s*
SO 0 0 SO
SO 0 1 s0
SO 1 0 s1
SO L 7 S1
S1 0 0 S1
S1 0 L SO
S1 1 0 S2
S1 t L S2
S2 0 0 52
S2 0 1 s1
S2 t 0 s3
S2 1 1 S3
S3 0 0 S3
S3 0 L s2
S3 t 0 S4
S3 L 1.S4
S4 0 0 S4
S4 0 L S3
S4 1.0 S5
S4 1.L S5
ss 0 0 S5
0 t s4
S5 1.0 S5
S5 L 7 S5
Current State lnputs Next State
S5
67
Table A. 2: Output Table With Encoding
The outputs are integer that values will be passed to the breaker control relays in the
RSCAD system.
L4 L2 L1SL5L3
SO 0 0 0 0 0
LS10000
S2 0 0 0 7 L
S3 0 0 1.1,t
S4 0 L 1.1 L
L 1 1.1.1
Current State Outputs
S5
68
Appendix B: Next State and Output Logic Diagrams
Appendix B is a collection of the FSM logic diagrams of the Boolean logic used in
implementing the load shedding scheme.
Figure B.1: 52 Logic
Es,sht/_v'+s'rfi S E+s'r3[ ff u _v +siS[s.TlI +s'2Eso{/_v
,tj
I
*,t,
I
si
S?'LogE
l i iIlI[l
I L-&t
I
I
t
-1
r
I
-t
S 1 ' Logic
u-{o1
I
A\D
AND
,i'
A.\- IJ
.{:\iD
69
Figure B.2: Sl Logic
si -EEsnu_v +3Es,3rTF +3EsrS(J_v +Esrsh?,T
.:T :}-T ;J
70
S0' Logic
Figure B.3: S0 Logic
"? -?0,,-,0
IA*E::l
rt
l
I
I
, l:r ^,1,
I
@ I _.ti:l -"l'*l-'U *til
L_)
f*'
F)-----JL-.J aI
r
----J--lrj/-D-I
I
-f--lD_
t''
lI
t_-,
F\N
7l
+ szSsoTIT + &3fsnt.r-r
L1 -E fis'" + .qrs,.5n + Es',sn + s'rE S + s'rf,sh
Output Logic Load_l
{\1!oH
,L\IJ
rL\ir
Figure 8"4: Output Logic Load I
I*6
I
l5*
I
72
L2 -Es,S + E.5rsh + s'rE s- + szfso
Figure B.5: Output Logic Load2
Oulput Logic Load 2
Lry9
hrxM
oo.ftlaE
AND
r50i
AND
ANt)on
AI\iD
t3
Figure 8.6: Output Logic Load 3
L3 - E.i,so + S'rEt S + s'?Esh
Output Logic Load_3
Gord turo2
Lqid
OFar&A
_{Nt)
AND OR
A-I\iIJ
.'Q
74
SI SO
togic!,
Oomlor4
llgicd
Orr.rorS
Loticd
Oprr*.16
TEmin!br1 TdnNlor
Golo E lD2
Frmt
Fm2
Fm6 Logtal
Op!rurot
L6rl
Fm3
Fm,l
Frm togGt
Opcntol8Logrcl
OpcEldl
ANI)
OR
AND
Figure B.7: Output Logic Load 4
La-sr["5o+SrEsn
Output Logic Load_4
75
Output Logic Load_S
S2 S1 SO
Logical
Operato14
Logical
Operator5
Logical
Operator6
Terminatorl Terminator Termlnator2
Golo Goto2 Goro5
Froml
L0aLi5
From2
From
Logical
Operator
Logical
OperatorS
[slNoTl
IANDOR
Figure B.8: Output Logic Load 5
L5 - S'rES',
16
,il
!
r3
Figure B.9: Overall Diagram of Finite State Machine in Simulink
I
I
€
E
I
fl
I
!u
I
ri{
EA
I
77
Appendix C: RSCAD Files
The figure below is the RSCAD CBuilder .def file of the load-shedding controller
Figure C.1: Cbuilder.def File
.h and .c files
Header file:
MODEL TYPE: CTL
#define PI 3.1415926535897932384626433832795 // definition of PI
#define TWOPI 6.283185307179586476925286766559 // definition of 2.0*PI
#define E 2.71828182845904523536028747135266 // definition of E
#define EINV 036787944117144232159552377016147 // definition of E Inverse (l/E)
#define RT2 1.4142135623130950488016887242097 // definition of square root 2.0
#define RT3 1.7320508075688772935274463415059 // definition of square root 3.0
#define INV ROOT2 0.7 07 10678 I 1 86547 5244008443 6210485
NPUTS:
int reset;
double clock;
double U*Y2;
double U_V3;
double U V4;
!-
-_.---r---_
ln, .,,,1 l: - ,r,ft ,il
9& d :!*r [& & aE lHk
I
f'
rfff'Y'?rf T f ? ? T ?'+ f 'r?TT El|dMt6m
tsd etry aEdr
0,ffi
M
S..ufs
e*8
&d@ E& dEB>
vud@r&ro&ih&
vk{*Far&h*
&d!o@aEd6h&t
str{@!&8ffihfr
vdErtq! rfts@.e
hEd @ fta ffit*
lwffficeoBl*!
!lG_8_! Lbi 6l htulrl .106
M_U_IO lS d 614 r cb- @a
ffi
,@.
o
,1,
78
double U_V5;
double U_V6;
double U_V1;
double B_C;int LOADSHED;
OUTPUTS:
int load1;
intload2;
int load3;
int load4;
int load5;
PARAMETERS:
double U_Vrange; //Range of undervoltage detection
int prtyp; //Solve Model on card type:
int FaultDelay; llDelay to rule out faults
int Close BRK_t; llDelay until breaker(s) close
double Close_BRK_Rg; //Range of voltage to close breakers
int CloseLOAD3; llYalue of output int to close Breaker
int CloseLOAD4; llYalue of output int to close Breaker
int CloseLOADS; llYalue of output int to close Breaker
int OpenLOADl; lNalue of output int to OPEN Breaker
int OpenLOAD2; lNalue of output int to OPEN Breaker
int OpenLOAD3; lNalue of output int to OPEN Breaker
int OpenLOAD4; lNalue of output int to OPEN Breaker
int OpenLOAD5; llYalue of output int to OPEN Breaker
int CloseLOADl; llYalue of output int to close Breaker
int CloseLOAD2; llYalue of output int to close Breaker
C file:
VERSION:
3.001
#include "loadshed.h"
STATIC:
ll double dt;
// i through n are counters for load shed delay
int i:0;
79
int j :0;
int k: 0;
int l:0;
int m:0;
int n:0;
i/ v through z are counters for closing breakers
int v: 0;
int w:0;
int x:0;
int y:0;
int z: 0;
// These are the ints for initializing each state's counters
int SO_init: 1;
int Sl_init: 1;
int S2_init: l;
int S3_init: 1;
int S4_init: 1;
int S5_init: 1;
int state: 0;
int prev_clock : 1;
// STATES DEFINED
#define S0 0
#define S1 1
#defrne 32 2
#define 53 3
#define 54 4
#define 55 5
ll -End of STATIC: Section-
RAM FUNCTIONS
RAM PASSO:
l/ ---------- End of RAM PASS0: Section
RAM PASS2
80
ll ---------- End of RAM PASS2: Section
//FTNITE STATE MACHINE LOGIC
if(clock){
run
if (!prev_clock) {
prev_clock:l;
if(LOADSHED:: 1){// Controller Will only shed load if this value is true
switch(state){
case S0:
if(So_init:: 1){
i:0;j:0; k:0; l:0; m:0; n:0;
v:0; w:0; x:0; y:0; z:0;
)
if((B_c &&((u_v 1 +u_v2+u_v3+u_v4+u_vs+u_v6y6))
> Close_BRK_Rg){ // This statement checks the bus voltages to reconnect a load
state:S0;)
if(U_Vl < U_Vrange){
if(i> FaultDelay){
state: Sl;
)
else {
i++;
)
)J
else {i:0;
)
if(U_V2 < U_Vrange){
if(j> FaultDelay){
lllf statement that will use the clock to dictate when the code will be
CODE:
81
state: Sl;
j++;
{j :0;
if(U_V3 < U_Vrange){
if(k > FaultDelay){
state: Sl;
)
else {
k++;
)
k:0;
i(U_V4 < U_Vrange){
if(l> FaultDelay){
state: Sl;
It
else{
l++;
)
J
I :0;
if(U_Vs < U_Vrange){
i(m> FaultDelay){
state : S l;
)
else{
m*-l;
)
else {I
\tIJ
else
)I
t
\t
else
)
)
J
else
)
{
82
\t
else {m:0;
l
J
if(U_V6 < U_Vrange){
if(n> FaultDelay){
state : S 1;
)
else{
n**;
)
)
J
else {n:0;
\t
S0_init: 0;
break;
case Sl:
if(Sl init :: l)Ullnitialize the state counters
i:0; j:0; k:0; l:0; m:0; n:0;
v:0; w:0; x:0;50; z:0;
\t
if(B_c &&((u_v 1 +u_v2+u_v3 +u_v4+u_v5 +u_v6)/6))>
Close_BRK_Rg) {
if(v > Close_BRK_t){
SO_init: 1;
state:S0;
)
else {v**l
II
)
else {
tJ
V: O;
83
if(U_Vl < U_Vrange){
if(i> FaultDelay){
state: 52;
)
else{
i++;
)
)
else {i:0;
)
if(U_V2 < U_Vrange){
if(> FaultDelay){
state: 52;
)
else {j++;
)
)
J
else
j :0;
if(U_V3 < U_Vrange){
if(k> FaultDelar{
state: 52;
)
else {
k++;
)\t
else {k:0;
)
if(U_V4 < U_Vrange){
)
84
tI
else {
i(l> FaultDelay){
state: 52;
\I
else {
l++;
)
l:0;
if(U_V5 < U_Vrange){
if(m> FaultDelay){
state: 52;
)
else {
m**l
)
)
J
else {m:0;
IJ
if(U_V6 < U_Vrange){
if(n> FaultDelay){
state: 52;
)
else {
n-l*;
)It
else {n:0;
)Sl_init:0;
break;
if(S2_init:: 1){
i:0; j:0; k=0; l:0; m:0; n:0;
II
case 52:
85
)
v:0; w:0; x:0; y:0; z:0;
if(B_c &&((u_v 1 +u_v2+u_v3 +u_v4+u_v5 +u_v6)/6)) >
Close_BRK_Rg){
if(w > Close_BRK_t){
51 init: l;
state:S 1;
)
else {w**l
)
)
else {w:0;
if(U_Vl < U_Vrange){
if(i> FaultDelay){
state: 53;
)
else {
i++;
)
)
else {i:0;
i(U_V2 < U_Vrange){
if(j> FaultDelay){
state: 53;
'I
J
else{
j++;
)
\t
If
)
else {j:o;
86
if(U_V3 < U_Vrange){
if(k> FaultDelay){
state: 53;
)
else {
k++;
)
k:0;
if(U_V4 < U_Vrange){
if(l> FaultDelay){
state: 53;
)
else {
l++;
)
I :0;
if(U_V5 < U_Vrange){
if(m> FaultDelay){
state: 53;
)
else {
m**;
)
m:0;
if(U_V6 < U_Vrange){
if(n> FaultDelay){
(t
)
else
t{
t
\t
else
\t
(\
)
else
'II
87
state: 53;
)
else {
n*-l;
)
)J
else {n:0;
)
S2_init: 0;
break;
case 53:
if(S3_init:: 1){
i:0; i:9' k:0; l:0; m:0; n:0;
v:0; w:0; x:0;50; z:0;
if(B_c && (u_v 1 +u_v2+u_v3 +u_v4+u_vs +u_v6)/6))>
Close_BRK_Rg) {
if(x > Close_BRK_t){
S2_init: l;
state:S2;
)
else {xt*;
)
)
else{
x 0;
\t
if(U_Vl < U_Vrange){
if(i> FaultDelay){
state: 54;
)
else{
i++;
)
)
else {
)
88
i :0;
)I
if(U_V2 < U_Vrange){
iffi> FaultDelay){
state: 54;
)
else {j++;
)
)
else (
1 j 0
)
if(U_V3 < U_Vrange){
if(k> FaultDelay){
state: 54;
)
else{
k++;
)
)
else {k:0;
\t
if(U_V4 < U_Vrange){
if(l> FaultDelay){
state: 54;
)
else {
l++;
)
)
else {
)
l:0;
89
if(U_Vs < U_Vrange){
if(m> FaultDelay){
state: 54;
I
J
else {m**l
)
)
else{
m:0;
tt
if(U_V6 < U_Vrange){
if(n> FaultDelay){
state: 54;
)
else {
n**;
)
)
else {n:0;
)
S3_init:0;
break;
case 54:
if(S4_init:: 1){
i:0; j:0; k:0; l:0; m:0; n:0;
v:0; w:0; x:0;50; z:0;
II
i((B_c && ((u_v I +u_v2+u_v3 +u_v4+u_vs +u_v6)/6))>
Close_BRK_Rg){
i(y > Close BRK_t){
S3_init: 1;
state:S3;
)
else {
y++;
)
90
)
else {y:0;
if(U_V1 < U_Vrange){
IJ
)
)
else{
if(U_V2 < U_Vrange){
if(i> FaultDelay){
state: 55;
)
else {
i++;
)
i:0;
if(> FaultDelay){
state: 55;
)
else {j++;
\t
if(k> FaultDelay){
state: 55;
)
else {
k++;
)
)
else fI
J
if(U_V3 < U_Vrange){
0;
)
)
else{
k:0;
9t
)
i(U_V4 < U_Vrange){
)
else
\t
else
l:0;
)
if(U_Vs < U_Vrange){
if(l> FaultDelay){
state: 55;
)
else {
l++;
)
if(m> FaultDelay){
state: 55;
)
else {m**;
)
if(n> FaultDelay){
state: 55;
)
else {
n**;
m:0;
)
if(IJ_V6 < U_Vrange){
)\t
else{
n:0;
)
S4_init: 0;
break;
case 55:
92
if(S5_init:: l){
i:0; j:0; k:0; l:0; m:0; n:0;
v:0; w:0; x:0;50; z:0;
)
i f((B_c && ((u_v r +u_v2+u_v3 +u_v4+u_vs +u_v6)/6)) >
Close_BRK_Rg){
if(z> Close_BRK_t){
S4_init: l;
state:S4;
)
else{
z*1;
)
)
else{
Z:0;
S5_init: 0;
break;
if(!clock){
prev_clock:0;)
i(reset){
state: S0;)
// OUTPUT LOGIC CloseLOAD# AND OpenLOAD# ARE PARAMETERS
)
)\t
)
if(state::S0){
loadl : CloseLOADl;
load2: CloseLOAD2;
load3 : CloseLOAD3;
load4: CloseLOAD4;
load5 : CloseLOAD5;
)
if(state::S 1) {
loadl : OpenLOADl;
//SO ALL OF THESE WOULD BE I'S
// THIS OUTPUT WOULD BE 8. AND SO ON.
)J
93
load2: CloseLOAD2;
load3 : CloseLOAD3;
load4: CloseLOAD4;
load5 : CloseLOAD5;
\t
i(state::S2){
loadl : OpenLOADl;
load2: OpenLOAD2;
load3 : CloseLOAD3;
load4: CloseLOAD4;
load5 : CloseLOAD5;
\t
if(statr:S3){
loadl : OpenLOADl;
load2: OpenLOAD2;
load3 : OpenLOAD3;
load4: CloseLOAD4;
load5 : CloseLOAD5;
)
if(state::S4) {
loadl : OpenLOADl;
load2: OpenLOAD2;
load3 : OpenLOAD3;
load4: OpenLOAD4;
load5 : CloseLOAD5;
)
J
if(state::S5) {
loadl : OpenLOADl;
load2: OpenLOAD2;
load3 : OpenLOAD3;
load4: OpenLOAD4;
load5 : OpenLOAD5;
)
ll ------------ End of CODE: Section
94
Appendix D: Battery Controller Coding
#include "Ctr_Batt.h"
STATIC:
int prev_CLK,state;
ll -End of STATIC: Section-
RAM-FLINCTIONS:
RAM-PASSO:
setStackingload( this_, 10, ""); llThis may need to be removed depending on the version
RSCAD
ll ---------- End of RAM PASSO: Section
RAM-PAS52:
prev_CLK:O;
state:O;
ll ---------- End of RAM PASS2: Section
CODE:
if(clK) {
if(lprev_ClK){ /iRising Edge of Clock
prev_ClK:l;
if(!L_in){
state:0;
)
else if(!W_in){
ext State Logic------
switch(state){
case (0):
if(High_Freq<:Freq){
state:l;
)
else i(Low_Freq>:Freq) {
state:5;
)
else {
state:0;
)
break;
case (1):
if(High_Freq<:Freq){
state:2;
)
else if(Low_Freq>:Freq) {
95
state:O;
)
else {
state:l ;
)
break;
case (2):
if(High_Freq<:Freq) {
state:3;
)
else if(Low_Freq>:Freq) {
state:l;
)
else{
state:2;
)
break;
case (3):
if(High_Freq<:Freq){
state:4;
)
else i(Low_Freq>:Freq) {
state:2;
)
else{
state:3;
)
break;
case (4):
if(High_Freq<:Freq){
state:4;
)J
else if(Low_Freq>:Freq) {
state:Z;
)
else {
state:3;
)
break;
case (5):
i(High_Freq<:Freq){
state:0;
)
else if(Low_Freq>:Freq) {
state:6;
lJ
96
else{
state:5;
)
break;
case (6):
if(High_Freq<:Freq){
state:5;
)
else if(Low_Freq>:Freq) {
state:7;
)
else{
state:6;
)
break;
case (7):
if(High_Freq<:Freq){
state:6;
)
else if(Low_Freq>:Freq) {
state:S;
)
else{
state:7;
)
break;
case (8):
if(High_Freq<:Freq){
state:6;
)
else if(Low_Freq>:Freq) {
state:8;
)
else {
state:1;
)
break;
\t
)
)
else {
prev_CLK:0;
)
I I -----------Output Logic------
switch(state){
)
97
0:case
case
2
J
case 4:
case 5:
C_out:0;
P_out:0;
State:1;
L_out:0;
W_out:0;
break;
C_out:l;
P_out:0;
State:l;
L_out:0;
W_out:l;
break;
C_out:l;
P_out:0;
State:2;
L_out:0;
W_out:l;
break;
C_out-l;
P_out:O;
State:3;
L_out:0;
W_out:l;
break;
C_out:l;
P_out:0;
State:3;
L_out:-l;
W_out:l;
break;
C_out:0;
P_out:l;
State:1;
L_out:0;
W_out:l;
break;
C_out:0;
P_out:l;
State:2;
I
case
case
case 6:
98
case 7:
case 8:
L_out:0;
W_out:l;
break;
C_ouF0;
P_out:l;
State:3;
L_out:0;
W_out:l;
break;
C_out:0;
P_out:l;
State:3;
L_out:l;
W_out:l;
break;
I
J
Appendix E: Battery Power System Coding
#include "Battery3.h"
STATIC:
// Static Variables to be used throughout the function
int prev_ClK,start;
double Energy,VA,prev_IA,power;
double Loss2:1.09091;
double Loss3:l .45455;
ll -End of STATIC: Section-
RAM_FLINCTIONS:
RAM-PASSO:
setStackingload( this_, 10, "");
l/ ---------- End of RAM PASSO: Section
RAM PASS2:
ll Initial values in the system this section of
// of code is only ran once at the beginning of
// the simulation
Energy : Inital_Energy;
prev_IA:O;
prev_CLK:0;
start: I ;
power:0;
CODE:
BEGIN TO:
// RSCAD uses the current injections to calculate
// the voltage of the system in BEGIN_TO currents for
ll thebattery is considered to be a balanced system
// using the previous value determined at the end
ll of the code initializedatzerothe current is set
99
ll ---------- Endof RAM PASS2: Section
100
TO T2:
IA:prev_IA;
IB:IA;
IC:IA;
IG:-I *(IA+IB+IC);
VA:A-I-V-2;
if(CLK){ //Clock High
if(lprev_Cl-K){ // Rising Edge of Clock
prev_ClK:l;
switch(State){ //State defined by controller
case (l):
if(!vA){
power:O;
)
else if(P_in){
if(Max_Discharge<(Energy-Power I )) {
power:-Powerl;
Energ5(Energy-Power 1 ) ;
)
else {
power:O;
)
)
else if(C_in){
if(Max_Charge>(Energy+Power I )) {
power:Powerl;
Energ5Energy+Powerl;
)
else{
power:0;
)
)
else {
power:O;
)
break;
case (2):
if(!vA){
power:0;
)
else if(P_in){
if(Max_Discharge<(Energy-Power2 * Loss2)) {
power:-Power2;
l0l
Energ5(Energy-Power2 * Loss2);
)
else {
power:0;
)It
else if(C_in){
if(Max_Charge>(Energy+Power2 * Loss2)) {
power:Power2;
Energ5Energy*Power2 * Loss2 ;
)
else{
power:0;
)
)
else{
power:0;
)
J
break;
case (3):
if(!vA){
power:O;
)
else i(P_in){
i(Max_Discharge<(Energy-Power3 * Loss3 )) {
power:-Power3;
Energ5(Energy-Power3 * Loss3 );
)
else {
power:O;
)
)
else i(C_in){
if(Max_Charge>(Energy+Power3 * Loss3 )) {
power-Power3;
Energ5Energy*Power3 * Loss3 ;
)
else{
power:0;
)
)
else
)
break;
power:O;
)
J
t02
)
)
else {
prev_CLK:0;
)
if(vA){
prev_IAloweri (VA * B ase_Power) ;
)
else {
prev_IA:0;
)
Monitored Variables--
En:Energy;//Stored energy in Battery
P_out:power; //Power out of Battery
IMON:prev_IA; l/Ctment of a single phase out of Battery
ll ------------ End of CODE: Section
103
Appendix F: Software and Hardware Used in Automation controller Development
. SEL-5030 AcSElerator Quickset software: used to configure and commission the
SEL-451 Protection, Automation, and Bay Control System.. SEL-5032 AcSBlerator Architect software: used to configure Generic Object-
Oriented Substation Events (GOOSE) Communications.. SEL-5033 AcSElerator RTAC software: used to configure the SEL-3530 Real-Time
Automation Controller (RTAC).. SEL-5035 AcSElerator Diagram Builder software: enabled the creation of a
Human-Machine Interface (HMD.. SEL-3530 Real-Time Automation Controller (RTAC): contains automation logic and
sends commands to the SEL-45 1 to act upon it. It also supplies data to the HMI.o SEL-451 Protection, Automation, and Bay Control System: measures values off of
the transmission lines and controls the connected breaker according to commands
sent from the RTAC.
APPENDIX G
Final Report: GAES Water Energy Conseruation
Aig//,sra
Introduction:
The growing demand on energy and water supplies jeopardizes the balance between food
production and energy and water use. Food processing is one of the largest consumers of water
and energy in the Pacific Northwest. Because of this, the Northwest Food Processors Association
(NWFPA) has a goal for its 450 members to reduce energy consumption 50o/oby 2030. Savings
to date has been accomplished by making simple and obvious changes. The goal of this study is
to assist in reducing energy consumptionby analyzing process systems at Litehouse Foods inc, a
member of the NWFPA, and create process models that can be used to discover new changes
that can be made to increase efficiency that may not seem as simple or obvious.
Litehouse is a refrigerated salad dressing company headquartered in Sandpoint, ID where
its oldest production facility is also located. Litehouse is located within the Avista Utilities
coverage area in the state of Idaho and meets the scope of both Avista Utilities and the
University of Idaho. The process models created use the system specifications present at the
Litehouse facility but include capabilities to be implemented and used at in other Avista
customer facilities. The models serve to show current energy and water usage, and can be
manipulated to model potential process improvements that can help reduce total consumption.
Litehouse:
Influence for models:
The refrigeration systems at Litehouse were chosen for modeling and efficiency purposes
after it was determined that there would be significantly more energy saved in changing
refrigeration practices compared to process equipment. The production process itself is very
dynamic and flexible. Process equipment can be brought on and off line several times throughout
the day with inconsistent run schedules day to day and week to week. The processes themselves
involve mixing ingredients in large mixing tanks to produce the desired product and then
packaging these products and storing them before sending them to the customer. The nature of
batch production coupled with the inconsistent production schedule makes it difficult to
determine any kind of potential energy efficiencies issues. tn addition, the typical production
motor size is 5 hp with arare case reaching as high as 20 hp, the lack of an automated system,
and a largely human controlled production process all contribute to the lack of a need for
modeling to determine better efficiency in the processes themselves.
Due to the perishable nature of Litehouse products, refrigeration is the major energy
consumption at their production facility. From ingredient storage to the shelf at the local grocery
store, Litehouse's goal is to maintain a constant temperature of 38oF. This is achieved using
refrigerated storage tanks, refrigerated mixing tanks, a refrigerated warehouse, and refrigerated
truck trailers, all with a set point of 38oF.
As the customer base has grown so has the Litehouse facility in Sandpoint. Higher
production demands and aoosciated deadlines has led to a larger production facility through add-
ons and repurposing of building space. New refrigeration and air conditioning equipment has
been brought online to meet the production demands with little to no delay; however, no overall
capacity or efficiency studies have been performed on these systems. The result is the capability
to meet production quotas and temperature set points for all storage tanks and areas on site but
liule information on how oversizedthe whole facility might be.
Observations on efficiency practices:
There are some refrigeration practices that could improve the overall energy efficiency of
the Sandpoint facility. While examining the facility there were many current practices that
should be considered for change to improve the energy usage of the facility. The current system
design houses the large refrigeration compressors in the mechanical room on the southeast side
of the building. The expansion valves and evaporators on these systems are housed within the
walk-in refrigerator on the southwest side of the building. The building schematics shown in
Figure I show the layout of the ground floor of the Sandpoint facility. Based on the dimensions
of the walk-in refrigerator provided by Litehouse (105' x 125'x 30') the approximate scale of
the layout indicates that the mechanical room has a 150'-200' of pipe to and from the walk-in
refrigerator. This design, although simple from a maintenance standpoint, provides long lengths
of pipe for increased pressure drop and temperature change of the refrigerant in these pipe
segments.
These sections of pipe are
also not insulated, which
increases the heat gain in the
refrigerant flow in the pipes. This
is not the only area where the
piping is uninsulated. Generally,
all refrigerated pipes remain
uninsulated resulting in overall
loss of capacity in the
refrigeration systems. The glycol
shell-and-tube heat exchanger
attached to a new 100-ton chiller
is also uninsulated. This chiller is
used to create a glycol-water
mixture at l8oF to provide
refrigeration to 8 processes and
three ingredient storage tanks.
The uninsulated heat exchanger
has instead been insulated by a
thick layer of ice making the
outer diameter of the pipe an
estimated 8 in. Assuming this is
2-inch pipe, there is an extra 3-
-e?4(5
I
1)
l.1
ts-t
[ ,^: fu(e
I ra = S{'oZtoSi (,.o* g*'tc)
inches of pipe around the piping of the heat exchanger. Although ice is colder than the ambient
air during the warmer months of the year, there is still a l4"F difference between the outside pipe
surface temperature and the temperature of the glycol-water loop. This temperature difference is
even higher for the refrigerant side of the heat exchanger. This temperature difference could be
1of ft
!o$t {-t\
P-cot .o.'
!a'.+I c
I
n
.)-+s 6lol
E
f EJ.:
r-::la:3
l-tr:a <-==l4
&5
il+
@
largely decreased by having insulation on the pipes resulting in an outside pipe surface
temperature much closer to that of the refrigerant or glycol inside the pipe.
There is also a 150-ton cooling tower above the mechanical room which is currently
connected to one of the refrigeration loops that uses a water-cooled condenser. This is the only
system that is connected to the tower. Previously there were a total of three system connected to
the cooling tower utilizing almost the entire capacity. With only one system connected to the
tower currently, the tower is oversized which results in a freezing problem during the winter
months. A heater must be used on the water inlet pipe to the cooling tower to keep the water in
the tower from freezing. Not only is the capacity of the cooling tower unused, but there is an
increased use of energy while heating the water during winter months of operation.
The walk-in refrigerator is not the only refrigerated room at the facility. An additional
refrigerated storage space was created by adding refrigeration at 38oF to a room which became a
spice cooler. This cooler is also labeled in Figure 1. The walls in the room are a typical dry-wall
construction, providing little insulation value. tn addition, there is no mechanism to remove the
moisture from the air as the water condenses out of the air. This has led to an increased use of
energy to maintain the room at the set point and water damage to the walls and ceiling which has
resulted in collapsed walls and ceiling, leading to increased maintenance. This work could easily
have been avoided by taking the proper steps to properly convert the storage space into a
refrigerator.
In addition to refrigeration,
air conditioning is also being
considered. After new corporate
headquarters were finished in
Sandpoint, the majority of the
office space at the plant was
emptied. Some of the office space
has been converted to a laboratory.
This entire space had been air
conditioned by a large compressor
connected to a glycol-water heat
exchanger. Because this office
space in now empty, only one of
the five air handlers on this system
is in use to air condition the new
laboratory. This air-conditioned
area is outlined in yellow in Figure
2 which shows the layout of the
upper floor at the Litehouse
facility. The compressor runs
constantly and'hot water" is used
to evaporate the refrigerant that is
not evaporated from the glycol
loop. This increases the amount of
water used and adds to the overall
oversizing of the refrigeration and
.*.r
1
f,
&*t*Jtf.'-1
lr,. *1T, Z( trc
lr* =]6,?1?f ft(n^a"c f**d
J
I +-{
I
J
:
H+tH
Figure 2 Loyout of the upper floor of the Litehouse focility in Sandpoint, lD
;:
t_t^--r-l--
r"*lJ ,..
air conditioning systems at the plant while increases the amount of wasted energy from the
system which runs constantly.
Modelling:
The research team based at the Idaho Falls campus of the University of Idaho set out to
assist in determining refrigeration capacities and requirements, and to provide recommendations
to Litehouse. These recommendations will help to improve overall energy efficiency and save in
operating costs. Additionally, the team will provide Avista Utilities with models that can be used
to simulate similar systems to assist in further energy efficiency projects. This was done by
creating 5 models: 4 using Aspen-HYSYS and the 5th model using Python code.
Software:
Aspen-HYSYS is industry standard software with basic components such as pumps,
compressors, turbines, piping, chemical columns, chemical reactors, valves, heat exchangers, and
other unit operations used to simulate thermal and./or chemical processes. The software also has
the capability to design specific modules using a combination of built-in components,
spreadsheet calculations, and other information. The software uses a large database of
thermodynamic, fluid, and thermal properties for a wide variety of gases, liquids, and solids as
well as accurate mixture calculations for multiple components in the same stream. This is
beneficial when using a working fluid that is a combination of several built-in fluids such as R-
407C, the refrigerant used at Litehouse for their refrigeration temperatures. HYSYS operates in
both steady state and transient modes allowing for real time energy and water usage calculations
based on real time demands.
Python is a widely used high-level programming language for general-purpose
programming. As an interpreted language, Python has a design philosophy that emphasizes code
readability, and a syntax that allows programmers to express concepts in fewer lines of code than
might be used in other languages. The language provides constructs intended to enable writing
clear programs on both a small and large scale. The model developed for this project primarily
uses Python's mathematical coding syntax within the open-source Anaconda software with few
logical inputs and statements.
HYSYS Models Introduction and Assumptions:
The Aspen-HYSYS models represent four different refrigeration system designs
presently in operation at Litehouse foods.
1. Refrigeration cycle with air-cooled condenser
2. Refrigeration cycle with water-cooled condenser and cooling tower
3. Residential Air Conditioning Cycle
4. Air Conditioning using a refrigerant-glycol heat exchanger
The models created are based on designs, layouts, and set points at the Litehouse
Sandpoint production facility and contain the basic components of a refrigeration cycle:
compressor, condenser, expansion valve, and evaporator, with pressure drop and ambient
temperature interaction capabilities using pipe segments. The models were created from an
operational standpoint and not a design standpoint. This means that equipment specifications for
duties, temperatures, and pressures were used in the models to compare simulated results to
measured results. Using process parameters eliminates the need to design equipment modules
within the program and allows for easier usability and implementation of current or suggested
cycle conditions. Process parameters were obtained only on the cycles represented in model 1
and model 2 and they are tabulated in Table 1.
Each model will contain two different circuits: one which models the condensers and
evaporators as heaters and coolers only, the other uses shell-and-tube heat exchangers to evaluate
the air temperatures from the evaporators and the air or water temperatures in the condensers. By
using heaters and coolers only, the duties of the heat transfer equipment may be specified from
literature data or determined by the model to assist in the design of the refrigeration system. Heat
exchangers must also be used in order to determine the required heat exchanger design aspects
for the tube side of each heat exchanger. It is important to ensure that proper engineering
judgement be used in the design work and that there will be no temperature cross from the design
of the refrigeration system. The measured values from Table I were used as inputs into models I
and2 along with several assumptions. The following assumptions were used in order to complete
models 1and2.
l. Refrigerant flow rate is solved for by HYSYS assuming 7 5oh adiabatic efficiency in the
compressor
2. Pipe outer surface temperature is the assumed refrigerant temperature (all cycles)
3. Pipe pressure drop is only considered for long lengths of pipe
4. Temperature inlet to evaporators is set by good engineering judgement
5. Expansion valve is controlled to produce evaporator inlet temperature
6. Relative humidities inside are assumed
Parameter Cycle I Cycle 2
Compressor Suction Pressure 35 psi 25 psi
Compressor Suction Temp 40'F 57.5'F
Compressor Discharge Press 210 psi 1 90 psi
Compressor Discharge Temp 164.2"F 200"F
Compressor Motor HP 40 hp 50 hp
Condenser Tube-Side Inlet T 163.5'F 145.7'F
Condenser Tube-Side Outlet T 88.3"F 740F
Condenser Shell-Side Inlet T 73.4"F 69.4"F
Condenser Shell-Side Outlet T 85.4"F 13.4"F
Evaporator Tube-Side Delta T 80F 80F
Coolins Tower Air Inlet T N/A 73.4"F
Cooling Tower Air Outlet T N/A 70.4"F
Cooling Tower Water Inlet T N/A 73.4"F
Cooling Tower Water Outlet T N/A 69.4"F
7. "Hot water" is 76oF at 25 psi and a flow rate of 150 gpm
8. Compressor suction conditions are set points controlled by evaporator pressure regulator
and hot water
These assumptions were made to complete the model since not all operating parameters were
available for measurement. [n order to make several of these assumptions some justification
and/or explanation is needed.
Assumption 1: The circuit itself is controlled by the inlet pressure set point and the volume
displacement capacity of the compressor. By using a reciprocating compressor with the specified
temperatures, pressures, power, and efficiency, the built-in HYSYS solver produces the resulting
mass flow rate.
Assumption 2: The temperatures on all unit operations were measured using and infrared
temperature gun, measuring the outer surface temperature of the pipe. The estimated outside air
temperature was 73.4"F (from weather underground at Sandpoint, ID on June 7 ,2017 at 1 1:30
AM). When comparing the thermal resistances of the three modes of heat transfer: outside
convection, conduction through pipe, and inside convection, the dominant resistance is the
outside convection resistance.
R1res x 0'7
Rcond =( 0'01
Ryorced =( 0.001
In addition, the comparison between the forced convection and the conduction leads to the
condition that the fluid temperature will be almost exactly equal to the inside pipe surface
temperature.
Assumption 3: An ideal refrigeration cycle uses two pressures with the pressures changing at the
compressor and the expansion valve. The pressure drop through the condenser and the pipe up
until the expansion valve can theoretically be disregarded entirely since the valve is controlled to
produce the pressure and temperature on the evaporator inlet. In addition, the pressure drop
returning to the compressor is in the end controlled by the evaporator pressure regulator which
will return the pressure to the design suction pressure for the system. However, pipe segments
have been included in the models when judged to be necessary to account for pressure drop and
temperature change due to ambient conditions, as well as serve for further instruction on HYSYS
use for implementation by Avista or other customers.
Assumption 4: The set point for the refrigerated systems is 38'F and the assumed set point for air
conditioned systems is 76'F. When designing a heat exchanger, a temperature difference of 1OoF
is required on either side of the exchanger,
whether it be counter-flow, co-current, or T
cross flow. In this case, the evaporators and r r''
air-cooled condensers are cross flow, and
the water-cooled condenser is assumed to be
Tt.*
Tr,t
stream I
strearn 2counter-flow. The evaporators are the Trol
exchangers of concern for this assumption, because the condensers have large temperature
differences. The duty of the evaporators is set by the temperature change of the refrigerant (8"F),
so the inlet and outlet temperatures must produce around 1OoF temperature difference between
the two fluids on both sides of the heat exchanger.
Assumption 5: This has been addressed in other justifications. The evaporator inlet temperature
on the refrigerant side is determined using good engineering judgement, and the expansion valve
creates the pressure drop necessary to produce the designed temperafure.
Assumption 6: The relative humidity of the refrigerators is assumed to be close to 100% due to
the low temperature set point. We have assumed 95%.In other locations of the production
facility, steam is used to clean equipment and surrounding to prevent contamination during food
processing. This produces a higher mass fraction of water in the air. The relative humidity of the
air conditioned areas is assumed tobe l7o/o.
Assumption 7: Model 4 was created based on a system currently at Litehouse which uses a 40
ton compressor for glycol air conditioning. After new corporate headquarters were finished in
Sandpoint, the office space was almost entirely emptied resulting in the areas being mothballed.
Of the 5 air handlers on the glycol cycle, only one is in operation. However, in order to evaporate
the remaining refrigerant that is not being heated due to fewer air handlers, 'hot water" is used.
This "hot water" only needs to warmer than the refrigerant to perform its task. The HYSYS
models show that to make-up the required heat, only a small amount of industrial water at this
temperature is required (Delta T:?). This "hot water" heat exchanger has been included in
models I and 2 in addition to model 4 in order to account for the temperature discrepancy
between the evaporator outlet temperature and the compressor inlet temperature.
Assumption 8: In order to create a steady state model, there needs to be a static set point in the
system. This was determined to be the inlet conditions of the compressor because these
conditions describe the rest of the process.
-ru,j.,a, ]1v r.nl
ll q sr"*o
sllo
Models L andZ
*i-,J
D*r7-4,Fsad"
iFr-tE
s'ilL
i
t Osor4,
P' (.
.at
Figure 3 Evaporotor loyout of the wolk-in refrigerotor ot
the Litehouse focility in Sandpoint, lD
Models I and2 represent the two refrigeration
cycles responsible for maintaining the walk-in
refrigerator at 38"F. The facility products are
moved into the walk-in refrigerator from the
production floor to be stored and prepared for
transportation to the customer retail stores.
This walk-in refrigerator is 105' x 125' and is
30' tall. Figure 3 shows the room with the
layout of the 12 evaporators from models I and
2 as well as what each door into the refrigerator
connects to. Model 1 is connectedtothe2
evaporators along the east wall at the south end
ofthe room and the 3 evaporators across the
middle of the room. Model 2 is connected to the 7 evaporators along the north end of the room.
Each cycle only has only one expansion valve for all the evaporators and the refrigerant tees off
of the main manifold and forms a return manifold. Each evaporator is 7.5 tons and there is an
auto defrost control system that removes the ice build-up on 2 compressors at a time for a 30-
minute cycle. This means that at any given time only 10 evaporators have refrigerant flow, 4
from one cycle, and 6 from the other.
Figure 4 and Figure 5 are shown below. These are the two circuits that make up model l. The
circuits use R-407C which is a mixture of three refrigerants: Difluoromethane (R-22,23%by
mass), Pentafluoroethane (R-125, 25%by mass), and 1, I, 1,2-tetrafluoroethane (R-134a, 52Yo
by mass). The refrigerant is specified by using the mass fractions of the three different
components in as the composition for the working fluid. The equipment used in the models is
marked and the specifications are included in the description. The weather data used for the
outside conditions was taken from weather underground at the time and date that the temperature
and pressure measurements were taken. g
o-'t11 3
1.
HX-'100
Figure 4 Model 1 without heat exchongers
265
7
E-102
E-l04
HX-102
MX-101 19
E-103
TEE.I03
TtE-102
8
65
2
Figure 5 Model l with heat exchangers
7
3
M!( 1&2
T= 1.1.
Carrier 5H40
compressor with a 40
HP motor
Efficiency, HP,
temperafure and pressure
of suction and discharge
Flow Rate:
4045.09lb|hr
I
Bohn air-cooled
condenser with four
condensing fans on a
single circuit
Refrigerant inlet and outlet
temperatures, Air inlet
temperature and flowrate,
pressure drops in both shell
side and tube side
Outlet Air Temperature:
86.03'F
Duty:
401079 Btulhr
2
Outlet Pressure:
38.17 psi
Pressure Drop:
143.8 psi
Outlet Temperature:
130F
Vapor Fraction:
0.2896
3 Standard expansion
valve which serves all 5
evaporators
Outlet pressure to achieve
desired evaporator inlet
temperature
Outlet Air Temperature
22"F
Duty:
71517 Btulhr
SoF temperature change
across refrigerant line,
pressure drops in both shell
side and tube side, air inlet
temperature and flow rate
5-7.5 ton evaporators4
"Hot Water" shell-and- Outlet temperature equal to Outlet Water Temperature5
Specification ResultUnit(s) Description
t'rs{
! l'la 3
TaE 1'l? 2
$D{ i(a 2
i l,It 2 4
! liXl
tube heat exchanger compressor suction
temperature, water inlet
pressure, temperature, and
flow rate
75.87'F
Duty:
14285 Btu/hr
6 Evaporator Pressure
Regulator
Outlet pressure equal to
compressor suction
pressure
Outlet Pressure: 35 psi
Pressure Drop: 0.67 psi
7 Vertical pipe segment to
roof
Outer and Inner diameters,
length, elevation change,
ambient temperature,
average heat transfer
coefficient
Pressure Drop:
0.61 psi
Temperature Change:
0.922"F
8 Horizontal pipe segment Outer and Inner diameters,
to expansion valve length, elevation change,
ambient temperafure,
average heat transfer
coefficient
Pressure Drop:
17.45 psi
Temperature Change:
2.14"F
The tees follow the same pattern as the diagram in Figure 3, with an equal division of
mass flow. In the current set up, only one of the two evaporators along the east wall is operating
and the three across the middle of the room are operating. The refrigerant flow rate is divided
equally among these four evaporators. In reality, this is not exactly the case; however, it is close
enough using this assumption. The average heat transfer coefficient is the same natural
convection heat transfer coefficient used in assumption 2 for natural convection around the pipe.
Model 2 will be presented and follow a similar results analysis as model 1
Model 3
Figure 6 shows an aerial view of the Litehouse facility. Several locations have been
marked above the building to show relative locations. The circled and numbered boxes outline
several residential air conditioners in place. The sizes of the air conditioners are included as well
as the areas that they serve and their approximate square footage, where known. On June 7,2017
none of these air conditioning units were operating so no measured data was available for the
model. The model is created assuming the validity of models I and2 and is designed based on
the duty designations of actually residential air conditioning units.
An additional concern with the air handlers in the building is the condensation build-up
on the coils. There are four air handlers in the production floor which are constantly dripping
condensate into the area. Steam is used for cleaning the equipment and floors in the production
floor resulting in a relative humidity close to 100% at all times. This condensate is not healthy
for a food production facility and is a constant concern for maintenance. Additionally, the
chemical used to clean the filters needs to be compliant with food processing facilities adding
another load on the maintenance crew. The suggestion to Litehouse is to improve the residential
units to an industrial air conditioning system with the refrigeration cycle housed on the roof and
extensive duct work providing conditioned air to the facility.
-L.'dl
The purpose of model 3 is to help determine the current cooling load on the floorplan and
the energy load on the individual systems. This will assist in finding the proper industrial air
conditioner to apply to the facility.r-r -
";f
tt: ?'zf'"
lrr.:r tij h tt
4-. Z -5+o c$&.&,.{*irs E. en.,u.t%
= tae ?.e *a J
Z. Z.f r.. *jlr t. F^+*- a'ir{t
:z ro1+6i, ldi
l. -tr r'^ 6n'['J brtr'rro- (* ''&l
H r, L/r lor otd yi'rt o^\f t -3 ia' t: ',' ^x- no!'
,--1s
5.5 t. $r 9rr{'u't'-o&&'s 'z G:5 Cf
i t * ttr. ilt* \ana'u ror* < r*o 3e1
Figure 6 Aeriol view of the Litehouse focility in Sandpoint, lD
Model 3 will be presented and follow a similar results analysis as model 1
Model4:
Model4 will be presented and follow a similar results analysis as model I
Python Model:
The model created with python code is a mathematical representation of the
environmental, human, and mechanical load on a rectangular room. The purpose of this model is
to help determine the approximate size of refrigeration equipment needed to maintain a room at a
specified temperature. The initial goal of the project was to model the temperature changes of the
walk-in refrigerator from the human and mechanical load as well as the environmental load, and
determine the amount of refrigeration to maintain the set point temperature during one month of
winter and one month of summer. The results from this transient model would be used to
compare a modulating compressor system with a stagnant operating system. The current system
in use for the walk-in refrigerator is steady system. The compressors operate at full load during
all times of the year. The amount of energy used in this system could be compared to the amount
of energy required from a modulating power compressor set only to maintain the set point in the
walk-in refrigerator. In an effort to create this transient model, the mathematical model for the
room was created. Since the research team was not able to pursue the transient model due to lack
of accurate information on the Litehouse system, the model has been modified to perform
calculations over a specified period of time to determine the total temperature change in the
room in the absence of cooling, or sufficient cooling, the total heat input into the room over the
specif,red period of time, and the maximum instantaneous duty on the room, for use in sizing
refrigeration equipment.
:t
?
(oot<,{^
-il;.--{-. t.
tlI -r
This is accomplished by addressing each of the walls of the room: north, south, east,
west, and roof, as a one dimensional thermal resistance circuit made up of outside convective
and radiative heat transfer, conduction heat transfer through the building, and inside convective
heat transfer. The total heat transferred through the walls is calculated and summed up between
the 5 different surfaces. Additionally, the model takes into account the heat transfer into potential
materials stored within the rectangular room, salad dressing in the case of Litehouse.
Several assumptions and building specifications are required to construct the model. Most
of the building specifications required have to do with materials, insulation, and thickness. The
inputs to the model include specific material and size specifications including building materials
and potential materials stored within the building. The environmental load is considered by
inputting different outside temperature conditions including: air temperature, dew point
temperature, relative humidity, air pressure, wind speed, wind direction, and solar flux. The
inputs are tied in based on the time that they were measured. Sample information can be obtained
using weather underground for a specific measure location. The solar flux can be obtained from
the National Renewable Energy Laboratory's Solar Position and Intensity Calculator. The
horizontal solar irradiance can be determined by using the latitude, longitude, time zone, and
time interval.
Additionally, the average humans present in the room per hour can be applied as an input
as well as the total potential heat from any machinery in the room. As an example the average
humans present can be determined by summing each human present multiplied by the time
present in minutes and then dividing by sixty. If there are 6 people in the room for 20 minutes
each than there is an average of 2 people per hour in the room. This is used to determine the heat
load per person which is assumed to be 0(reference). The heat released by machinery is
estimated based on the size of motor and efficiency of the motor. Multiplying the motor size by I
minus the efficiency will estimate the average heat released by the motor. These two factors are
included with the environmental factors in order to determine the total heat load on the room
over time.
Each wall can be specified as either an outside wall, inside wall, or neither. An outside
wall will experience the influences of the environment outside. An inside wall shares a wall with
another room within the facility with a different temperature set point. Neither represents a wall
that is shared with another room at the same set point temperature. These specifications are used
to determine the outside convection heat transfer. In the case of an outside wall, the solar flux is
used in conjunction with the outside convection to determine the outer surface temperature of the
building. Since this temperature is used for each calculation and is first order with respect to
convection and fourth order with respect to radiation, an explicit solution of the quartic equation
is used to determine the converged surface temperature.
As a validation for the model, two periods of time are used from the walk-in refrigerator.
Both periods of time are considered to be times of no activity. The refrigeration equipment to the
room was shut off for maintenance on June 3,2017 and June 4, 2017 . The set point of the room
was brought to 35"F instead of 38oF. On June 3 the maintenance took place from 6:00 AM until
2:00 PM and the final room temperature was read as 4loF. On June 4 the maintenance took place
form 6:00 AM until l2:00 PM and the final room temperature was read as 38'F. The
environmental conditions are known from the use of weather underground, and the human and
machine load on the room is zero. Using the known size of the room and the approximate
amount of salad dressing stored at that time, the insulation can be solved iteratively to match the
temperature difference on both days and serve as validation for the relative accuracy of the
model.
The figures below show the input sections for the model. These are included at the
beginning and are meant to be the user interface for simplicity. There are divided between a
static input section and a transient input section. The current set up of the code provides the
outputs in the terminal to the bottom left of the software interface as shown in Figure 7.
#%% Inputs (constants)
# Room Surroudings (does the wall border the outside or inside of the building)
rooF'outside'; #'outside' will be airtemperature
north:'inside'; #'inside' means inside room at differnt temperature
south:'outside'; #'no'means inside room at same temperature
east:'inside';
west:'outside';
T:6; #Number of hours under consideration in whole number
g:9.80468312; #Acceleration due to gravity at elevation of room
thuman:350000/3600; #Joules/hour released by the human body
Qmachine:O; #Sum of total motors and equipment multiplied by (1-efficiecy) in hp
#Radiation heat transfer material properties
alpharooFO.26; #Radiative absorbance of outside roof material
epsilonrooF0.9; #Radiative emmittance of outside roof material
alphanorth:0.26; #Radiative absorbance of outside north wall material
epsilonnorth:O.9; #Radiative emmittance of outside north wall material
alphasouth:0.9; #Radiative absorbance of outside south wall material
epsilonsouth:0.26; #Radiative emmittance of outside south wall material
alphaeast:0.9; #Radiative absorbance of outside east wall material
epsiloneast:0.26; #Radiative emmittance of outside east wall material
alphawesFO.9; #Radiative absorbance of outside west wall material
epsilonwest:O.26; #Radiative emmittance of outside west wall material
#[nsulation
Insulation:'Rvalue';#Options are 'Rvalue' or 'Conductivity'
#Conduction heat transfer properties (thickness/thermal conductivity for 'conductivity')
krooF0.02; #Thermal conductivity of roof insulation material (Wm*K)
lroof=1; #Thickness of roof insulation (in)
knorth:0.O2; #Thermal conductivity of narth wall insulation materiallnorth:l; #Thickness of north wall insulation
ksouth:O.02; #Thermal conductivity of south wall insulation material
lsouth:l; #Thickness of south wall insulation
keast:0.02; #Thermal conductivity of east wall insulation material
least:l; #Thickness of east wall insulation
kwest:0.02; #Thermal conductivity of west wall insulation material
lwest:1;#Thickness of west wall insulation
#Conduction heat transfer properties (for 'Rvalue')
RrooF8.0; #Rvalue per inch
trooF6.0; #Thickness in inches
Rnorth:8.0;
tnorth:6.0;
Rsouth:S.0;
tsouth:6.0;
Reast:8.0;
teast:6.0;
Rwest:8.0;
twest:6.0;
# Room Dimension
L:l05; #Length of room north and south walls in ft
W:I25; #Length of room east and west walls in ft
H:30; #Height of room in ft
Percentair:60; #Approximate volume percentage of air in the room (includes humidity in air)
Percentmateriall:40; #Percentage of volume occupied by lst material
Percentmaterial2:O; #Percentage of volume occupied by 2nd material
Percentmaterial3:O; #Percentage of volume occupied by 3rd material
Percentmaterial4:O; #Percentage of volume occupied by 4th material
rho_1:8.29;
rho_2:0;
rho_3:0;
rho_4:0;
#Density in oz per US cup
cp-l:3 #kJ/kg*K
k_l
k2'
I ,4 #Material I container thermal conductivity (W/m*K)):0;
k_3:0;
k_4:0;
l_1:0.05; #Material 1 container thickness in m
U:o;
l_3:0;
I 4:0;
#%% Inputs(transients)
# Ambient Conditions
Tair:numpy.array([55.4,59,57.2,5].2,59,59,59,59,60.8, 60.8, 60.8, 60.8, 60.8, 60.8, 60.8,
62.6,60.8,60.8, 60.81); #Outside Air Temperature in Fahrenheit
Pressure:numpy.array(129 .93, 29 .93, 29 .94, 29 .93 , 29 .93, 29 .94, 29 .94, 29 .93, 29 .94, 29 .94,
29.94,29.94,29.96,29.96,29.95,29.96,29.96,29.96,29.961); #Pressur in inHG
Tdew:numpy.array([53.6,51.8,53.6, 53.6,55.4,55.4,55.4,53.6,53.6,51.8,53.6, 53.6,53.6,
55.4, 55.4, 55.4, 53.6,53.6, 53.61); #Dewpoint Temperature in Fahrenheit
RH:numpy.anay(1.94, .77, .88,.88, .88, .88, .88, .82, .77 , .72, .77, .77, .77 , .82, .82, .77 , .77, .77 ,.77)); #Relative Humidity
WindSpeed=rumpy.array([3.5,4.6,3.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.8,4.6,0.0,3.5,6.9,6.9,
9.2,9.2,8.11); #Wind Speed in mph
WindDirection:numpy.array(['sw', 'se', 'south', 'calm', 'calm', 'calm', 'calm', 'calm', 'calm', 'calm',
'north', 'ne','calm', 'south','se', 'south', 'south', 'sw', 'south']); #Direction of wind origin
# Options for wind direction are 'north', 'south', 'east', 'west', 'ne', 'nw', 'se', 'sw', or'calm'
Tw:numpy.anay(16,6.25,6.58333,6.91667,7.25,7.58333,7.91667,8.25,8.58333,8.91667,
9.25,9.58333,9.91667,10.25,10.58333, 10.91667,11.25,11.58333, 11.94667D; #Time
of day in whole hours after midnight for weather data
Humans:numpy.aray([0, 0, 0, 0, 0, 0]); #Average humans present per hour (sum of
individuals * fraction of hour present)
Th:numpy.array([6, 7 ,8,9, 10, I l]); #Time in whole hours after midnight for average
humans present
# Solar Fluxes into the room from varying directions (all are in W/m^2)
#Data can be found using NREL website:
#https ://www.nrel. gov/midc/solpos/solpos.html
Solar:numpy.anay(1223.1022,328.2921, 434.6714, 540.2419,643.144,741.5801,833.8438,
918.3596, 993.6703, 1058.481, 1 1 I 1.689, 1 1 52.381); #Solar Flux onto roof
#SolarNorth:0; #Solar Flux onto north wall
#SolarSouth:0; #Solar Flux onto south wall
#SolarEast:0; #Solar Flux onto east wall
#SolarWest:0; #Solar Flux onto west wall
Ts:numpy.array(16,6.5,7,7.5,8,8.5,9,9.5,10,10.5,11,11.5]) #Time in whole hours after midnight
for solar fluxes
# Room Conditions
Troom:numpy.zeros(T* 3 600+ I );Troom[0]:35; #Initial Room Temperature in Fahrenheit
RHRoom:RH; #Relative Humidity of room (if different from outside)
PressureRoom:Pressure; #Pressure of room (if different from outside)
RoomSetpoint:Troom[0]; #Setpoint Temperature for room (if not the same as initial
temperature)
Tinside:76; #Neighboringinsiderooms'temperatureinFahrenheit
Pressureinside:Pressure; #Pressure ofinside room (ifdifferent from outside)
RHinside:RH; #Relative humidity of inside room (if different from outside)
T1:numpy.zeros(T*3600+1); #Material 1 temperatures
Tl f0l:Troom[0]; #Material 1 initial temperature is same as room
In [1]: runflle('C:/Users/Stephen/Desktop/Folders/Lltehouse/Room Calculatlon Sunday,
lune 4. py' , wdir='C: /UserslStephen/Desktop/Folders/Litehouse' )Flna1 Temperature ln degrees F
38.3563720318
Total heat lnput to room ln btuh
7L793057.0771
Maximum Duty in btuh
L4824.639fi741
fr
Figure 7 Sample Python console output including groph of room temperature
As part of the insulation input, only the main insulation material is required. This is
because the insulation creates the largest temperature difference in conduction to the point that
the other materials in the wall can be ignored. The information can be input either as material
thermal conductivity and thickness or as "Rvalue" and thickness as determined based on
manufacturer specifications. The materials stored inside of the room can be specified and
included as part of the heat loss to the room based on the approximated percentage of the room
occupied by the materials, and their approximate densities and heat capacities. For the modeling
of the Litehouse walk-in refrigerator estimated densities and heat capacities were assumed for all
of the salad dressing stored in the refrigerator. Additionally, conduction can be considered within
the stored materials. For the case of Litehouse, the conduction material was assumed to be glass
bottles with an estimated thickness to determine the heat input into the product materials.
The results from the python model comparison of the two days will be included here to
help show the estimated accuracy of the model.
Discussion?:
The models that were developed as part of the study have been validated by comparing
certain parameters determined by the model to the measured data point. In the case of models 1
and 2 in HYSYS, the calculated condenser outlet temperature for air or water was compared to
the measured temperature point. This evaluation shows the accuracy of HYSYS which is then
used to validate models 3 and 4 based on the results of I and 2 since there is no measured data to
accompany these models. The python model was shown to be accurate from the observed
temperature change over the time periods on two different days.
The purpose in providing the mathematical model in addition to the process model is to
provide the basis of the process models. The estimated load requirement on the room calculated
by the Python model can be used as an input into the process model to help determined the size
and number of evaporators required as well as the size of the compressor and condenser on the
refrigeration cycle. These models can be used in conjunction for the preliminary design of a new
refrigeration of air conditioning system.
Even though these models have shown accuracy, it is still recommended that they be used
as preliminary design tools to begin the design process for a new refrigeration or air conditioning
system. Several assumptions and approximations were used to develop and implement the
models to make them easier to use. The results obtained from the models will save designers
significant amounts of time in narrowing the requirements for the system. At that time, in depth
design can take place that will account for all equipment required in the system and the
accompanying flow rates, temperatures, and pressure drops of the refrigeration system.
Future Work:
Future work on this project includes work done by Litehouse foods, potential work for
University of Idaho, and the work for Avista Utilities. As part of the project deliverables, Avista
has been given detailed instructions on how the models were created including all inputs and
design aspects. This will allow the engineers at Avista to recreate the models and provide their
other customers with the estimates from the models in order to move towards greater energy
efficiency.
Litehouse is willing to improve efficiency practices and are open to recommendation
made by both the University of Idaho and Avista utilities. Before any recommendations were
made they began construction on a new walk-in refrigerator which will be refrigerated by 4
parallel systems that provide power modulation and redundancy. This new design is a large step
towards better energy efficiency at the Sandpoint facility. By using 4 parallel modulating
systems, the compressors will run only when needed and this will avoid the constant full load of
the large compressors during all times of the year. Additionally, the University of ldaho commits
to presenting Litehouse with estimated savings from increased energy efficiency of systems
present at Litehouse. Avista commits to assist with the capital costs of new equipment with the
condition that energy savings be large enough to be profitable on both ends. These
recommendations will be presented directly to management at Litehouse.
The initial purpose of the project was to compare the loads of the refrigeration equipment
at Litehouse. A static system and a modulating system can be compared using the process
modeling software and the estimated energy savings from a modulating system are an output of
that model. The University of Idaho has shown its capabilities to perform this type of comparison
and evaluation when given sufficient, accurate information and express its willingness to
participate in any further energy efficiency study through Avista Utilities which might be within
the scope of the Universities capabilities.
Aiyrlsra
APPENDIX H
Final Report: IDL Energy Management Phase 2
I NTEG RATI D
DESIGN LAB
Universityof ldaho
Rrouceo Onorn Wxou Burlorrrc ErurRcv
SrrvrumrroN FoR VrRruRl Conn u rsstoNrNG
PRorecr RepoRr PRrpRRro ron Avlsra Ulltttrs
August 37,20L7
Prepored for:
Avista Utilities
Authors:
Sean Rosin
Damon Woods
Elizabeth Cooper
Jinchao Yuan
AlEwsrA
Report Number: 1608_01
Prepored by:
University of ldaho lntegrated Design Lab I Boise
306 S 6th St. Boise, lD 83702 USA
www.uidaho.edu/idl
IDL Director:
Elizabeth Cooper
Authors:
Sean Rosin
Damon Woods
Elizabeth Cooper
Jinchao Yuan
Prepared for:
Avista Utilities
Please cite this report os follows: Rosin S., Wood, D., Cooper, E.,
and Yuan, J. (2017). Using reduced-order models for simulotion
bosed commissioning of buildings. {1"608_01). University of ldaho
lntegrated Design Lab, Boise, lD.
Contract Number: R-39872
DISCLAIMER
While the recommendations in this report have been reviewed for
technical accuracy and are believed to be reasonably accurate,
the findings are estimates and actual results may vary. All energy
savings and cost estimates included in the report are for
informational purposes only and are not to be construed as
design documents or as guarantees of energy or cost savings. The
user of this report, or any information contained in this report,
should independently evaluate any information, advice, or
direction provided in this report.
THE UNIVERSITY OF IDAHO MAKES NO REPRESENTATIONS,
EXTENDS NO WARRANTIES OF ANY KIND, EITHER EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF
MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE
WITH RESPECT TO THE INFORMATION, INCLUDING BUT NOT
LIMITED TO ANY RECOMMENDATIONS OR FINDINGS, CONTAINED
IN THIS REPORT. THE UNIVERSITY ADDITIONALLY DISCLAIMS ALL
OBLIGATIONS AND LIABILITIES ON THE PART OF UNIVERSITY FOR
DAMAGES, INCLUDING, BUT NOT LIMITED TO, DIRECT, INDIRECT,
SPECIAL AND CONSEQUENTIAL DAMAGES, ATTORNEYS' AND
EXPERTS' FEES AND COURT COSTS (EVEN IF THE UNIVERSITY HAS
BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES, FEES OR
cosTs), ARtstNG ouT oF oR lN coNNEcTloN wlTH THE
MANUFACTURE, USE OR SALE OF THE INFORMATION, RESULT(S),
PRoDUCT(S), SERVICE(S) AND PROCESSES PROVTDED BY THE
UNIVERSITY. THE USER ASSUMES ALL RESPONSIBILITY AND
LIABILITY FOR LOSS OR DAMAGE CAUSED BY THE USE, SALE, OR
oTHER DTSPOS|T|ON BY THE USER OF PRODUCT(S), SERVICE(S), OR
(pRocESSES) TNCORPORATTNG OR MADE BY USE OF THrS REPORT,
INCLUDING BUT NOT LIMITED TO DAMAGES OF ANY KIND IN
CONNECTION WITH THIS REPORT OR THE INSTALLATION OF
RECOMMENDED MEASURES CONTAINED HEREIN.
Taslr or Corurerurs
2. Executive Summary
3. Research Motivation
L. Acknowledgements.
4. Project Background
4.1 Whole Building Energy Simulation
4.2 Reduced Order Thermal Models (Grey Box Models)..............
4.3 Prior Research Using Reduced Order Models to Describe the
Buildings........
4.4 Simplified HVAC Model
4.4.L Economizer Controls Model
4.4.2 AHU Preheat Coil and Cooling Coil Model
4.4.3 Terminal Reheat Model
4.4.4 Supply Air Flow Rate....
5. Results
6. Discussion and Future Work
7. Budget Summary....
8. References .............
3
4
5
7
8
9
Dynamic Behavior of
','.',.20
.,',..,22
.,.,...23
26
72
27
................ 35
................ 37
................ 38
lntegrated Design Lab I Boise 2
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
AHU
BACnet
BCVTB
COBE
EMS
HVAC
IDL
ROM
SASP
TOT
UI
VAV
VFD
Air Handling Unit
Building Automation Controls Networking Protocol
Building Controls VirtualTest Bed
College of Business and Economics
Energy Management Control System
Heating, Ventilation and Air Conditioning
lntegrated Design Lab
Reduced Order Model
Supply Air Set Point
Termina I Outlet Temperature
University of ldaho
Variable Air Volume
Variable Frequency Drive
AcnoruYrus AND ABBREVIATIoNS
lntegrated Design Lab I Boise 3
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
This research was made possible through funding support from Avista Utilities via ldaho PUC Case
Number AVU-E-13-08. The research team expresses gratitude to Avista staff and project managers for
their support of this project. This project could not have happened without the coordination and help
received from the University of ldaho's facilities team for the access they provided. Particular thanks is
due to Keven Hattenburg who went out of his way to help with our research.
1. AcxrrrowrEDGEMENTs
lntegrated Design Lab I Boise 4
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
The University of ldaho - lntegrated Design Lab (Ul-lDL) modeled an existing building using reduced
order modeling techniques as a tool for virtual commissioning. The research focused on the
Albertson's College of Business and Economics building at the University of ldaho Moscow Campus.
The reduced order model was composed of sets of differential equations with system parameters
which describe the dynamic nature of heat transfer though a building. These parameters were
determined through software optimization in order for the model to best predict the zone
temperature of the building when compared to the zone temperature as predicted by EnergyPlus.
The reduced order model was coupled with an HVAC model to predict the total annual energy
consumption of the building which was then used to determined potential energy savings measures.
It was found that the COBE building lacked thermostat setbacks during periods of unoccupancy, and
the ROM model was used to predict the energy savings associated with updating the controller. lt
was found that approximately 104,000 kWh of potential energy savings could be realized if the
thermostat had properly programed temperature setbacks during times the building is unoccupied.
2. ExecuruE SUMMARY
lntegrated Design Lab I Boise 5
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
While new buildings increasingly rely on automated controls for their Energy Management
Systems (EMS), commissioning of these controls in new buildings and verification of existing sequences
in existing buildings are a time intensive process. They could run the risk of suboptimal occupant
comfort, and expose the building owners to unnecessary liability and higher energy costs during the
time period before commissioning is accomplished. The building commissioning process encompasses a
wide range of stages, starting with design development and ending at least one year after the building is
occupied [1]. New building control commissioning typically takes one full year of building operation, so
that all weather conditions and all modes of operation are experienced, and often takes two full years
before the system is operating nearest to its potential. ln addition, it is often difficult to set up, or is time
consuming to wait for, specific space or outdoor conditions to occur for all aspects of an EMS control
logic to be tested and addressed, leaving large gaps in the verification process for some modes of
operation. Older buildings can also suffer from poor controls that are out of tune with the current
building occupancy patterns or not up to date with current best practices control techniques. ln current
practice, suboptimum and incorrect control programming can take months or years to detect, if they are
ever detected [2]. When controls issues arise, they can also be difficult to reproduce and take weeks or
months to rectify [3]. Operational issues can often go undetected, especially if they do not directly affect
human comfort. Simulation-based commissioning holds promise as a way to reduce or avoid these
hazards.
The previous research the lntegrated Design Lab (lDL), Boise, conducted energy simulations as a tool
to virtually commission buildings. This research focused on using an EnergyPlus model and connecting it
to a duplicate of the Alerton Building controller that is used at the University of ldaho Albertson's
College of Business and Economics (COBE) building in Moscow, ldaho. This was accomplished by
enabling communications from the EnergyPlus simulation to the building controller, which was done
3. ResrancH MonvATtoN
lntegrated Design Lab I Boise 5
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
using the Building Controls Virtual Test Bed (BCTVB). BCWB is "middle-ware" which translates the
outputs from the EnergyPlus simulation to either a voltage or digital input that the building controller
can understand [4]. The variables that were chosen for the study included outdoor air temperature,
outdoor air damper position, mixed air temperature, and return air temperature. The Alterton controller
requires inputs from other equipment and feedbacks from each of the thermal zones, which was not
practical to model in EnergyPlus due to computational limitations. These inputs were bypassed by
adjusting the logic to allow the controller to continue to function without each individual feedback loop.
This method of simulation-based commissioning is less time intensive than traditional commissioning
approaches, but developing an accurate energy model takes time and knowledge that most
practitioners do not possess. Although the cost of commissioning a building is prohibitive for many
owners, the research demonstrated that virtually commissioning a building is a viable alternative. The
current phase of the research explored ways to reduce the time and monetary expenditures of virtual
commissioning still further.
The current research aims to simplify the modeling process to allow practitioners a means of
virtually commissioning a building without the steep learning curve associated with modeling in
EnergyPlus. This approach reduces the modeling time, allows for innovative control strategies to be
investigated quickly, and can be used by practitioners to quickly diagnose an operational or control
issues. There are still limitations with reduced order energy modeling that need to be addressed before
the methods of virtual commissioning can be fully utilized.
4. PnorecrBncrcRouruo
lntegrated Design Lab I Boise 7
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
The team selected the COBE building, on the University of ldaho campus in Moscow, lD for
study. The 50,000-square-foot facility was constructed in 200L and is a mix of classroom spaces and
faculty offices. lt also has a unique simulation room with over twenty computer stations and a terminal
for real-time market analysis and trading, requiring a high electric load. The building is equipped with a
Variable Air Volume (VAV) system and relies on district heating and cooling from campus water lines
that serve two air-handling units (AHUs) in the building. The smaller of these two AHUs conditions the
basement, while the larger AHU provides air to the upper three floors. Non-fan powered VAV boxes are
located in each zone. Some of the building's geometry can be seen in Figure 1.
Figure 1. COBE Building photo (left) model geometry (right)
The COBE Building was chosen for the previous research because the building's controllers communicate
through a standard building automation and control network protocol: BACnet. This communication
protocol was essential for the research so that the energy model could interact with the controllers in a
standard way. The team continued using this building for the reduced order model (ROM) virtual
commissioning reseach to so that the new method could be compared against the calibrated baseline
EnergyPlus modelfrom the previous research. Reduced Order Modeling Background
b--L.*I
--T1- r T::
lntegrated Design Lab I Boise 8
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
4.1 Whole Building Energy Simulation
There are several different types of modeling approaches used in whole building simulation.
Black box modeling is a data driven modeling approach which uses actual building time series data to
statistically fit a model to determine building parameters. Black box modeling does not provide
insightful information about the mechanism or the behavior of the building [5]. Rather, it is solely a
statistical representation of a building's data correlations. Black box models only focus on finding
relationships between the model's inputs and outputs [6], which is not very useful for virtual
commissioning
Another practice of whole building energy simulation is through white box modeling, or models
that are developed through physics and first principles [6]. One of the best known white box modeling
techniques for whole building energy simulation is EnergyPlus. EnergyPlus was developed by the
Department of Energy for engineers and architects to model energy and water usage of buildings. This
method is an exhaustive process which can take several months to build accurately. All the building's
geometry, construction, zone characteristics, heating ventilation and air-condition (HVAC) controls and
Iayouts must be defined properly for EnergyPlus to accurately predict the energy consumption of the
building. When the building ages, there is no way to account for the degradation of equipment and
insulation in the modeling process. Therefore, after the building has been virtually constructed, the
model must undergo calibration using actual building energy data. Due to this lengthy process, whole
building energy modeling with EnergyPlus is often neglected, and as a result many energy savings
opportunities go u ndetected.
A third modeling technique that has been underutilized is grey box modeling. Grey box modeling
is still built on the foundation of first principles, but it uses real run-time data to optimize model
parameters [6]. For thermal systems, grey box modeling uses a set of differential equations to model the
dynamic nature of heat transfer throughout the structure. There is no limit to the order of the system,
lntegrated Design Lab I Boise 9
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
but as the complexity of the model increases so does the computational expense. Each model order is
an additional differential equation, and each additional order equates to an additional set of model
dynamics that must be accounted for. The time constant of a building is related to time of thermal
energy decay and it is composed of thermal resistance and thermal capacitance of the building. These
parameters are then optimized using actual data from the physicalstructure to obtain the best fit of the
model. This modeling technique is less computationally expensive than white box modeling, and takes
much less time to develop a predictive model for the thermal performance of buildings
4.2 Reduced Order Thermal Models (Grey Box Models)
There are two parameters that are used to describe the dynamics of thermal systems using
ROMs: the thermal capacitance and thermal resistance. The thermal capacitance of a substance is a
function of known material properties and is defined in Equation 1 as:
6 = pCrV (1)
Where p is the density of the material [kg/m'], Co is the specific heat U/kgK] , and V is the volume [m'].
When a building has a large cumulative thermal capacitance, otherwise known as massive building
construction, the rate at which the building's temperature can change due to environmental and
internal effects is low. The thermal capacitance is an important parameter to estimate the transient
behavior of a building [7], but is oftentimes hard to calculate even when the material properties of a
building are known. Another parameter used to describe thermal systems is the thermal resistance of a
material. The thermal resistance describes the material's natural tendency to resist the flow of heat.
There are several different forms of thermal resistance, but they all have the units of [WK] and all
describe resistance to heat transfer. The thermal capacitance and the thermal resistance are the basis of
ROMs.
lntegrated Design Lab I Boise 10
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
Reduced order thermal models are commonly referred to as a lumped RC model and are
represented using thermal circuits which are similar to electrical circuits, an example is shown below in
Figure 2.
r*R1 T1 R:Qroaas
Figure 2. 2nd Order Lumped RC Thermal Network
To model a building using reduced order techniques there are several assumptions that have to be
made:
1.. The zone air is well mixed and at a consistent temperature
Heat transfer is one directional
There is no temperature gradient in the wall making the heat flux a constant value for
the entire surface
4. There is a homogenous temperature throughout each 'lump'
Theoretically, a material can be broken into an infinite number of lumps. However, each lump increases
the order of the system which Increases the computational complexity and slows down the runtime of
the simulation. Reduced order modeling is one of the most powerful methods of modeling dynamic
systems due to the simplicity when compared to other approaches. For these types of models there
exists a minimum number of variables, i.e. states, that when known can completely describe the system
[8]. These states are well known and measurable, and for thermal systems they are the temperatures of
nodes distributed throughout the system. These state variables can be described using a vector and the
Ic1Ic1
2.
3.
linearized state space representation of the thermalcircuit illustrated above is shown in Equation 3
lntegrated Design Lab I Boise ll
Using reduced-order models for simulation based commissioning of buildings (Report 1608-01)
f=AT+BA (3)
Where 7 is a vector of all nodal temperatures, and D is a vector of all system inputs. A and B are
coefficient matrices containing the thermal parameters that describe the relationship between the
inputs and the desired outputs. Equation 3 can be expanded and expressed in matrix form to represent
the thermal circuit shown above in Figure 2.
E]
I rl 1r-aE * *,)
t
T
RzCr
L
CzRz CzRz].1
T*
&
0
(4)
Load.s
Where:
Vorioble
Table 1.2R2G Variable Definitions
Description Units
T-
T7
T2
R1
R2
CL
c2
Qmoa,
The outside ambient temperature
The walltemperature
The zone temperature
The effective thermal resistance of the wall
The effective thermal resistance between the wall and the zone
The effective thermal capacitance of the wall
The effective thermal capacitance of the zone
The heat load of the system (solar, internal, infiltration, HVAC)
['c]
['c]
['c]
lw/'cI
lw/'cI
lJl"cl
U/'C]
twI
The inputs for this system are the ambient temperature and all heat gains (i.e. plug loads, solar gains,
and fenestration gains), which are all applied at zonal node. A simplified diagram illustrating the
locations of the Rs and Cs is shown below in Figure 3.
lntegrated Design Lab I Boise 12
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
Qg.ins Wall Construction
T-
cr T cr
Figure 3. 2R2C Thermal Parameter Diagram
Cz is the thermalcapacitance of the zone, which includes the air and allthe interior mass, i.e. furniture,
carpet, etc. Cr is the effective thermal capacitance of the building constructions. The location of Cr is
arbitrary and the only known information about its location is that it falls somewhere in between the
building's wall construction. The wall is not partitioned in any symmetrical manner; it is divided such
that the temperature at each node is uniform throughout the entire lump of material. Rr is the effective
thermal resistance in between the ambient temperature and T1, dnd Rz is the effective thermal
resistance in between Tr and the center of the zone. This model structure was utilized in a simplified
case study to investigate the effects of the thermal parameters before modeling the COBE Building. The
model of the COBE building is more intricate than the 2R2C model shown above due to the complexity
of the building's dynamics.
4.3 Prior Research Using Reduced Order Models to Describe the Dynamic Behavior of
Buildings
The simplest way to represent the construction of a building is by a l-R1C model, which only has
a single thermal resistance and a single thermal capacitance. This representation of a building is
unrealistic because it lumps the mass of the wall and the mass of the interior together [9]. This forces
the temperatures of these two masses to be equal at all times by not identifying them as two separate
Rr R1
thermal capacitances. Additionally, most of the thermal capacitance of a building is contained in the wall
lntegrated Design Lab I Boise 13
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
of the structure, and there is a thermal barrier between the wall construction and the interior zone,
which this model ignores. lt has been concluded that if you add an additional resistance between the
zone temperature and the external temperature it greatly reduces the peak instantaneous loads during
warmups [9] which helps the model's overall fidelity.
Bacher et al. [10] investigated which models offer the best performance for the least complexity
to fully describe the dynamic of the building. The process starts with determining the simplest model
that describes all the information embedded in the data, being the l-R1C thermal network. The model's
order was increased to see how the higher order model statistically compares the previous model. This
was done using a likelihood tests which compares the predicted results with the previous model to
determine the likelihood there exists a higher order model that statistically predicts the zone
temperature more accurately. This method was applied to an experiment facility in Denmark where all
the construction materials were known. lt was determined that a 3R3C model describes the building's
dynamics adequately enough and a higher order model is not worth the extra computational expense.
The three thermal capacitances were associated with the heater, the interior space, and the
construction of the building.
Based on the findings of Bacher et al. [10], the model structure that was chosen to describe the
dynamics of the building was a 3R3C model, which was coupled with a 2RLC model to describe the
dynamics of the ground and foundation. A diagram showing the model's thermal circuit superimposed
on a simple bullding diagram illustrating the relative location of the model parameters can be seen in
Figure 4.
Where
lntegrated Design Lab I Boise 14
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
R*
T-
Rs
Ts
Figure 4. Diagram of Thermal Network used in Modeling COBE Building
Variable Description Units
R1
T*
Ts
R1
R2
R3
Rf
['c]
["c]
["c]
["c]
["c]
The outside ambient temperature
The temperature of the ground
The effective thermal resistance of the wall
The effective thermal resistance of the wall
The effective thermal resistance between the
interior of the wall and the zone
The effective thermal resistance of the windows
The effective thermal resistance between the
center of the foundation and the central interior
zone
The effective thermal resistance of the ground and
Rw
Rs
[W"C]
IW"C]
lJ/'cl
R1
lc{
lntegrated Design Lab I Boise 15
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
C
C
1
2
the midsection of the building's foundation
The effective thermal capacitance of the wall
The effective thermal capacitance of the zone
The effective thermal capacitance of the
foundation
The heating and cooling loads of the system (solar,
internal, infiltration, HVAC)
u/"cl
u/'cl
U/'C]
twl
cf
Qmads
This thermal circuit can be expressed using a set of linear differential equations to describe the states of
the structure. The selected states used to fully describe the system are the temperature of the
foundation (Tr), the internal zone temperature (T.), and the wall temperatures in between the interior
and the fagade (Tr), and (Tz). lt should be noted that states are the effective temperatures of the
modeled capacitances and they represent the overall average temperature for each lump. An expanded
matrix showing the full set of Equations can be seen below.
7R, * R21_t-t\nrRrcr l
1
RzCt
zR" * R"r_ I __:_______:l\RrRrcr)
1
1
RzCz
R3R/+RpR/*RryR3
R3Ru.,R1C,
1.
Rrcr
Rrc,
/Rf + Rs\- \rt\,rt )
T*
R1
0
(fi* o^*'1
Ts
Rs
0 0
IA
RrCz
0 lil.(s)
I
0
I
0
RzC,
0
The loads included in the model were the internal loads, and the solar gains which were
outputted from EnergyPlus and used as an input into the ROM. These values can be determined
computationally using ASHRAE standards in the EnergyPlus mode. ln this study, equivalent values used
in EnergyPlus was again used to avoid any additional errors. All of these loads were applied at the center
of the zone which is an oversimplification of the system. lt is known that the solar loads will be
distributed throughout the interior of the structure, and the distribution pattern is determined by the
geometry, reflectance, and many other parameters of the building. As well, the conditioned air will be
distributed throughout the entire space and not just supplied to the center of the zone, without
lntegrated Design Lab I Boise 16
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
knowing the exact distribution pattern, the loads have to be applied at the center. Additionally, the solar
gains do not only come from the radiation directly admitted into the space via the window, the also
come from the thermal storage properties of the exterior wall construction. As the day progresses the
building materials store energy and their internal temperature increases, thus causing conductive heat
transfer to increase throughout the entire surface of the exterior wall. These gains are happening
simultaneously over the entire exterior wall, not just at the center of the zone.
The model parameters were estimated using a Simulink' Optimization package that iterates
through different parameter values until the model best predicts values when compared to a user
inputted time series, the estimated parameter values are shown in Table 2.
Table 2. Optimized Model Parameter Values
Thermal Resistance ThermalCopacitance
Rt
Rz
Ra
R*
Ry
Rq
6.617 E2
1.272 E-1,
3.027E-4
3.768 E-4
2.909 E-5
2.968 E-4
7.029 E8
2.s83 E72
4.520 E7
1.296 E9
Ct
Cz
C,
C1
The model parameters shown in Table 2 best predicted the ROM's zone temperature when compared to
the results of EnergyPlus. The zone temperatures from EnergyPlus have been plotted against those from
the ROM in Figure 5. The left-top and left-bottom figures show the hourly zone temperature for the first
week of February and the first week of August respectively
lntegrated Design Lab I Boise 17
Using reduced-order models for simulation based commissioning of buildings (Report 1508_01)
Zona Tamparrturo Comp.rlson Aver.ga Dally Tempo6turG Rosldualr
21
i20
: 19
g 18
Ee 17
16
15 60 60 t@
Ims (hr.l
120 140 160 1ao
40
E3goo
20
26
c
E
20
16
-t.5-2-2.5 1.550-1 -0.5 0 0.5
T6mpemture Residual [CJrm€ thrl
Figure 5. (Left - Top) EnergyPlus vs Simulink Zone Temperature for first week of February. (Left - Bottom)
EnergyPlus vs Simulink Zone Temperature for first week of August. (Right) Daily Average Temperature
Residual Distribution
During February the EnergyPlus zone temperature settles to the set point whereas the lumped RC model
does not accurately predict this behavior. This is believed to be from the nature of ROMs, which are
typically utilized to model dynamic systems. The ROM has better fidelity when there are instabilities in
the zone temperature and it does not settle to a set point, which is illustrated in the August zone
temperature figure. The daily average temperature difference between EnergyPlus and the ROM have
been plotted in a histogram which shows the frequency and the magnitude of the temperature. Figure 5
(Right) shows a right skewed histogram centered around an average of 0.039'C. This is an indication that
the ROM over-predicts the zone temperature by an average of 0.039oC. This should translate into a
higher magnitude of cooling needed to compensate for the over-prediction of the zone temperature
when compared to EnergyPlus. While the reduced order thermal model predicts the indoor zone
temperature, indoor zone temperature is not a direct indication of energy consumption. ln order to
"convert" these temperature predications to energy, an HVAC model and controller are needed. To use
this model as a tool of virtual commissioning, the HVAC model needs to describe the mechanical
systems as accurately and simply as possible, a diagram illustrating the flow of the fully integrated
model can be seen below in Figure 5.
60
50
120 140 160 1a0
Qrvrc
lntegrated Design Lab I Boise 18
Using reduced-order models for simulation based commissloning of buildings (Report 1608_01)
Tzon.
Figure 6. lntegrated Reduced Order Thermal Model Flow Diagram
The thermal model will predict the zone temperature of the buildings, which will be passed through to a
controller to inform the simplified HVAC model how to condition the space. The HVAC model will predict
the magnitude and duration of zone conditioning which will be relayed back to the thermal model and
applied at the center of the zone like the other zone loads, which completes the feedback loop
4.4 Simplified HVAC Model
As discussed earlier, the COBE Building is a mixed use educational facility. lt has over fifty zones
that have varying occupancy, internal loads and thermostat set points. The HVAC equipment that the
COBE building uses is a district heating/cooling variable VAV system with non-fan powered terminal
reheat. There is a district chiller and boiler that provide each building with chilled water and hot water,
which is utilized as the working fluid in the main AHUs. The COBE building has two AHUs, one that
services only the basement, and the other that services the three above ground floors. For simplicity,
the HVAC model was altered to only have one AHU to service the entire building. Figure 7 shows a
diagram of a typical air handler unit.
r-
AFh fhrmrl Mod.l
+
+
lntegrated Design Lab I Boise 19
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
netsm f.n
Erhrurt
Supply frn ?nhot C.cll Coollrr3 Coll
Ourdoo.
Fllt.t
Figure 7. AHU Diagram [11]
There are four main parts of a typical HVAC system used at the COBE building: the supply and
return air fans, air dampers, heating and cooling coils, and the terminal reheat box (not illustrated
above). The supply air fan provides the necessary air flow to meet the minimum outdoor air standard
and to condition the zone to the required thermostat set point. The supply air fan is connected to a
variable frequency drive (VFD) which controls the speed of the fan, which is the most efficient way of
controlling the air flow. The next elements of the HVAC system are the AHU dampers, which are used to
vary the amount of outside air and return air vented into the mixing chamber. The dampers optimize
their position to meet the mixed air temperature set point, which is done through modulating the flow
of the two streams entering the mixing chamber. After the air passes through the mixing chamber it is
conditioned to the supply air temperature set point, which oftentimes is the same as the mixed air set
point. The heating and cooling coils in the AHU are only used when the dampers cannot meet the mixed
air set point. The last components of these systems are the terminal units which are located at each
individual zone and are used to reheat the air before it enters the space. These boxes have their own
fr
*
lr'lay ldm ftfgft idutW.t- W.l( W.lo W.tn
Alr
&
Rrcirculrtion
Mh.d Ait
*
3opply Alr+E
hot water heating coils, which are also supplied from the district boiler. Each component of the VAV
lntegrated Design Lab I Boise 20
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
district heating/cooling HVAC system needs to be modeled individually to have an accurate
representation of the entire system.
4.4.1 Economizer Controls Model
The HVAC system used in the COBE building relies on an economizer to capture free cooling
during times when the ambient conditions permit. Economizers have four different operational modes:
heating, modulating, integrating, and mechanical cooling mode [12]. When the outdoor temperature is
less than 1oC (heating mode) the economizer only allows in the minimum required outdoor air for
ventilation. The outdoor air is mixed in with the return air in the mixing chamber and is then heated to
the necessary temperature to meet the heating demand. During mild outdoor temperatures (1oC to
13'C)the full cooling demand of the building can be met by modulatingthe fraction of outdoor airthat
is mixed with the return air (i.e., modulated economizer mode). This operational mode allows the
economizer to provide the most amount of free cooling to the building. The next operational mode is
integrated economizer mode which occurs when the outdoor temperature is too high for to meet the
full load by solely using outdoor air (13oC to 24"C), during this temperature band some mechanical
cooling must take place to meet the cooling demand of the building. The last operational mode occurs
when the outdoor air temperature is above the economizer's high limit shut off. During this mode the
economizer only allows the minimum required outdoor air to meet ventilation requirements and the
space conditioning is accomplished through mechanical cooling. Figure 8 illustrates of all economizer
operational modes throughout the year.
lntegrated Design Lab I Boise 2l
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
modulated
economizer cooling
integrated
economizer mechanicalcooling cooling
100
heating
mechanical
Epacity
mechanical
cooling €pacity
Etum airllowae 80
!_
oq360o
.E
o:40o
Bot'@ 20
or6drod
0
highlimit shutoff Jmaximum
heating load
minimum
cooling load
maximum
cooling load
Figure 8. Typical Economizer Operation [12]
The above operational modes are the basis for the economizer controls utilized in the HVAC model. They
have been transformed into block diagram logic, which can be seen below in Figure 9.
Hlgh
Figure 9. Economizer Simulink Model
The economizer model has several subsystems which all carry out essential functions that allow the
model to accurately calculate the percent outside air. The percent outside air is a function of the air's
physical properties, which have to be determined before the economizer positioned can be calculated
outdoor airflow
lntegrated Design Lab I Boise 22
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
4.4.2 AHU Preheot Coil ond Cooling Coil Model
Once the air has been mixed in the mixing chamber, it passes through the supply air fan and is
blown over the AHU heating/cooling coils, where the air is conditioned to the supply air set point (SASP)
temperature. When the AHU is operating in modulated economizer mode, the temperature differential
between the mixed air set point and the supply air set point is minimal, reducing the energy required to
condition the supply air. During the other modes of operation, the main AHU heating/cooling coils will
have to condition the supply air to the correct set point.
The driving force in moving air around the building is the pressure differential between the
supply air and the zone air, which is supplied by the supply air fan. This process is non-adiabatic and the
air stream collects a small amount of residual energy as it passes though the blower, which increases the
overall temperature of the air stream. This rise in temperature can be determined through
computational methods, but for simplicity and accuracy the temperature rise was outputted from the
EnergyPlus model and included in the Simulink model.
The AHU heating/cooling model compares the supply air set point temperature to the current
mixed air temperature to determine the amount of heating or cooling that needs to be added. To avoid
issues of simultaneous heating and cooling, a dead-band was modeled which represents the realistic
characteristics of actual systems, and the dead band temperature range was set at +1oC. lf the
differential between the mixed air and the supply air set point is more than one degrees Celsius, the
conditioning is turned on until the temperature falls within the dead band of the controller. The amount
of heating/cooling required is determined through Equation 6, which was developed using first
principles of heat transfer.
Qeruu = Procrvro(Tres, - Trun)(6)
lntegrated Design Lab I Boise 23
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
tu
Figure 10. AHU Coil Heating/Cooling Block Diagram
The energy calculated in the AHU model is not a direct feedback loop for the thermal model but it does
contribute to the overall energy consumption of the building.
4.4.3 Terminal Reheot Model
Once the air stream has been conditioned to the supply air set point temperature, it is
distributed throughout the building. The supply air set point for the COBE Building is 72.78"C, meaning
during the winter and shoulder seasons the air is going to need reheat before entering the zone. This is
accomplished through the terminal reheat boxes located at each zone. The terminal boxes at the COBE
Building are known as single-duct VAV pressure-independent terminal boxes with reheat. The terminal
reheat unit only has a hot coil which is supplied from the same central plant as the main AHU heating
coil in. Along with the hot water coil there is a terminal box damper, and a flow sensor. As the zone
temperature changes the controller modulates the damper position to vary the amount of air delivered
r 005SS-tsE.e
sMy
lul
Where psa is the density of the supply air [kg/m3], C, is the specific heat of air [J/kgK], 75a is the
volumetric supply air flow [m'/s], Tsasp is the supply air set point temperature [oC], and Tr,aa is the mixed
air temperature ["C]. The relay in Figure 10 corresponds to the dead band discussed above. When the
absolute value of the difference between the mixed air temperature and the supply air set point is more
than unity, the relay outputs a zero which forces the heating/cooling to go to zero.
lntegrated Design Lab I Boise 24
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
into the zone. The flow sensor serves as a failsafe to insure the flow does not fall below the minimum
requirements for ventilation. lf the heating load of the building is not being met by modulating the
damper, the controller opens a valve allowing more water to flow through the coils which increases the
temperature of the air supplied to the zone [13]. A typical terminal box control loop diagram can be
seen below in Figure 1-1-
HeatingDampercoil
/r\\
Thermostat
Figure 11. Typical Terminal Box Feedback Diagram [13]
These dampers are an essential component to multi-zone VAV HVAC systems. Our model was simplified
to a single zone building so the terminal damper feedback loop was not modeled directly, but its main
function was captured and utilized elsewhere in the model. ln multi-zone system, the terminal box
dampers modulate the flow supplied to the zone. When the dampers are at their minimum allowed
flowrate, and all the zone are being adequately conditioned, the main air handler reduces the supply
flow rate using the VFD. ln the simplified COBE model there is only a single zone, so when the zone is
meeting the temperature set point there is a feedback loop, acting similarly to the VFD signal, to reduce
the supply air flow rate. The modulation happens at the main air handler instead of the terminal box
damper so there is no need for an additional damper in the reduced order system.
lntegrated Design Lab I Boise 25
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
When modeling the terminal reheat there were two separate perspectives that had to be
accounted for: the amount of energy supplied to the zone, and the amount of energy consumed by
conditioning the air. The air entering the terminal reheat box is at the supply air set point temperature,
which is below cooling thermostat set point, so even if the terminal reheat unit is not being utilized, it is
still cooling the zone which is a feedback loop for the thermal model. Even though the terminal reheat
unit is only capable of supplying heat to the air stream, all the energy contained in the air stream needs
to be accounted for when coupling the HVAC model with the reduced order thermal model. The amount
of energy being supplied to the zone from the terminal unit can be seen below in Equations 7 and 8.
Qrermtnat Reheat zone Heat = {prorrCpVro(Tro - Tron"), tf Tro ) Tronr} Ol
Qrerminat Reheat zone coot = {PsespCpilse(Tro - Trorr), tf Tro l Tron"} (8)
Where psa is the density of the supply air [kg/m3], C, is the specific heat of air U/kgKl, 7ro is the
volumetric supply air flow [m'/s], Tro is the terminal outlet temperature [oC], and T.onu is the zone
temperature ["C]. The amount of energy supplied to the zone is proportional to the temperature
differential between the terminal outlet temperature and the zone temperature. lt should be noted that
the cooling energy supplied to the zone is negative which keeps the HVAC model compatible with the
thermal model. When the terminal outlet temperature is less than the current zone temperature, the
zone is currently being cooled, but due to the operational modes of the terminal reheat boxes, the air
may still need be reheated even though the room is being cooled. During cooling mode, the terminal
outlet temperature will be modulated between the range of the supply air set point (12.78'C) and
approximately 18"C, and during heating mode, the terminal outlet temperature will be modulated from
18oC up to 33oC.
lntegrated Design Lab I Boise 26
Using reduced-order models for simulation based commissioning of buildings (Report 1608_0L)
4.4.4 Supply Air Flow Rate
The supply air flow rate is modeled the same way as the terminal outlet temperature; for both
heating and cooling, the flow rate is controlled by applying a linear gain to the temperature differential
between the zone temperature and the current set point, which can be seen in Equation 7. The supply
air flow rate modulation is turned on and off through a 'VFD' signalthat originates in the terminal outlet
temperature model. When the cooling or heating demand cannot be met by controlling the terminal
outlet temperature, the VFD signal is turned on which allows the flow rate to be modulated, if the signal
is ofl the flow rate vented into the space is set at the minimum required for ventilation.
SA FIow = l(Tron" - Trtot). 1.1] + Mtn Flow Rate (9)
The linear gain used in the controller was determined using an iterative process. The minimum and
maximum flow rate of the system was determined by surveying at the EnergyPlus' flow rate. Once again,
the actual COBE Building may achieve a higher or lower flow rate, but since the ROM's effectiveness is
based off a comparison with EnergyPlus, the values predicted by the EnergyPlus are more critical. Once
the flow rate of the system is accurately calculated, the amount of heat or cooling entering the space
can be determined. The energy flow entering the space is a feedback for the thermal model and is
applied at the center of the zone. The HVAC system was integrated with the thermal model and energy
consumption could be compared between the reduced order and EnergyPlus model.
5. Resulrs
lntegrated Design Lab I Boise 27
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
The ROM was compared to both the single zone and the fully zoned EnergyPlus model of the COBE
building. The single zone EnergyPlus model was used to compare individual output parameters such as
zone heating and cooling loads, supply air flow rate, etc., whereas the fully zoned EnergyPlus model was
compared to the ROM for the virtual commission recommendations. This comparison method was
chosen due to the simplifications that were made earlier in the modeling process. The ROM was
optimized to thermally perform close to the single zone EnergyPlus model. The parameters were
optimized to match the response of the single zone EnergyPlus model, and as such the individualoutput
parameters should represent the single zone model more accurately. The commissioning
recommendations are going to be compared against the fully zone model of the COBE building. The fully
zoned COBE building has been calibrated according to ASHRAE's standards and is a more accurate
representation of the actual building than the single zone model.
Common energy consumption metrics between the two models were chosen for comparison
with the parameters being the zone heating and cooling loads and the AHU coil energy consumptions. lt
should be noted that the heating a cooling loads do not account for system efficiency, it is the ideal load
that will keep the space conditioned at the given set point, given all ambient and internal effects. The
zone heating and cooling load was chosen to improve errors caused by the VAV box system efficiency.
The ROM did not include any measure of efficiency, makingthe modeled value more representative of a
load, and not a consumption. lf this parameter was not chosen for comparison, it would have to be
assumed that VAV box system efficiency is independent of the heating or cooling load, which is typically
not the case. The losses in the terminal box are from the heat exchanger, where the efficiency of the
unit being dependent of both fluid flows (supply air cross flow and hot water flow). The inlet and outlet
temperature of the heat exchanger water was not modeled directly, so determining the VAV system
lntegrated Design Lab I Boise 28
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
efficiency of the ROM was not feasible. This made it necessary to compare the ROM predicted terminal
reheat energy usage to the EnergyPlus model's predicted heating and cooling load of the zone.
ASHRAE has designated a comparison method for energy modeling and according to ASHRAE
Guideline 14, the two recommend modes of comparison are the coefficient of variation of the root
mean square error (CVRMSE) and the normalized mean bias error (NMBE). ASHRAE Guideline 14
considers a building model calibrated with hourly data to have a CVRMSE within the range of *30%, and
NMBE in the range of +tO% [1a]. The CVRMSE and the NMBE error are shown below in Equations 8 and
9 respectively
xI n-(8)
CVRMSE =t
z!:r(y, - g,)(e)
NMBE:(n - p)v
Where li and li are the ROM and EnergyPlus predicted value respectively, n is the number of sample
data points, p is the number of parameters in the baseline model used in comparison (in this case one),
and y is the arithmetic mean of the EnergyPlus observations. The CVRMSE value is representative of
how well the two parameter values trend together throughout the year. Whereas the NMBE is an
indication of how accurate the overall magnitudes compare to one another. Both values must fall within
the range set by ASHRAE to be considered 'calibrated'. Typically, this standard is used to compare the
energy consumption predicted by an energy model and the actual building energy consumption, as
reported on the energy bills, but this method should still serve valid when comparing one energy model
to another.
The amount of heating or cooling supplied to the zone is, in part, a function of the supply air
flow rate. This parameter was compared by looking at the difference between the daily average values
lntegrated Design Lab I Boise 29
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
between the ROM and the single zone EnergyPlus model, otherwise known as the residuals. A
histogram of the daily average residualvalues is shown below in Figure 12
35
30
2!at
!8o&
i
Ez$
t0
.2.1 .2 -1,5 a5 r5
DatyAtflF tueud Imr6l
Figure 12. Daily Average Supply Air Flow Rate Residual
The daily average difference between EnergyPlus and the ROM is -0.095 m3/s.The CVRMSE and the
NMBE for the supply air flow rate were 6.057% and -0.561% respectively. These results are an indication
that the flow rate was modeled correctly and the linear gain factor, as discussed earlier, are similar
between the two models.
The next parameter compared was the energy supplied to the zone. As discussed earlier, EnergyPlus
does not decouple this parameter from the VAV energy consumption, making the comparison only
possible if the HVAC heat and cooling zone loads are used from EnergyPlus. These heating and cooling
Ioads are going to be compared to the ROM predicted value of energy supplied to the zone, as seen in
Equations 7 and 8. These two parameters are comparable due to the fact that the heating and cooling
supplied to the zone, disregarding efficiency, should be the amount of energy required to maintain the
space at the thermostat set point, otherwise known as a 'load'. lf the thermal parameters have been
estimated correctly, the zone load and the supplied energy should be equivalent, which indicates both
lntegrated Design Lab I Boise 30
Using reduced-order models for simulatlon based commissioning of buildings (Report 1608_01)
models have the same effective overall heat transfer coefficient. A histogram of the residuals can be
seen below in Figure 13
Figure 13. Daily Average Zone Demand Residual
The CVRMSE and the NMBE for the Zone Deman d was 63.7% and 2.38% respectively. The zone demand
showed poor performance for the CVRMSE, but that should be expected. ROMs lump masses together
and assume each mass has an equivalent temperature, varying the magnitude of heat transfer at any
given time when compared to EnergyPlus. The NMBE shows the two models use similar overall
magnitudes of energy throughout the year which indicates the model is performing as expected. The
average daily residual was approximately -1,336 watts, meaning the ROM is predicting a zone energy
demand of 1,336 watts less than EnergyPlus. This is what should be expected after the ROM zone
temperature was over predicted, as seen in the previous chapter. This over prediction of zone
temperature equates to the HVAC system having to add less thermal energy to condition the zone and
match the thermostat set points.
The last parameter compared was the total energy consumption of conditioning the building,
which includes the energy supplied to preconditioning the air stream after the mixing chamber and at
lntegrated Design Lab I Boise 31
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
the terminal boxes. The total energy consumption does not include any mechanical energy consumed by
the supply or return fans, this parameter is just the thermal energy supplied to the air stream. The first
method of comparison was with an energy signature of the COBE building as predicted by both models.
An energy signature is a plot of the energy consumption vs. the average ambient temperature, typically
tabulated daily [15]. Characterizing building using an energy signature offers a quick comparison to
determine how the building is performing and is a way to graphically illustrate the amount of heating or
cooling required for any given outdoor temperature conditions. The energy signature of the COBE
building, as predicted by the ROM, was compared against the EnergyPlus energy signature, which is
shown in Figure 14.
t
!8,,#
E
6 r---:
c
, 1 'arl
=o ,-
U
? !.,4
a7 .,-'
3$
< !r.1:
,
?
3
!E
EIo
ts
t
!
Es.}
co]
c
\_11
tTIa/.
nn i/
\\.
rA {f- t'
.1 ,/,r t, ,,,1-r) t -'fa'.
Avcr.lc Wrllly Ambl.ntlcfig.ratue Iq Av6ttc WGGHV AmbilntTlrylpe6ture Iq
, lmrirllur@l " tOutdr6r-nm.. lttrrlrfi6fdlEUilI{llDMMh{l ' t'!'r0'lxrhErra ' itilk€ilry-q''wll@}h!tbiqr-lins'l{OM*81n4)
Figure 14. EnergyPlus and Reduced Order Model Energy Signature of COBE Building. (Left - Cooling Energy
Signature, Right - Heating Energy Signature)
The balance temperature of the building is the ambient temperature at which the building does not
require heating or cooling after adjusting for internal loads was determined to be approximately 2.5oC
for both the EnergyPlus and the reduced order model. Both heating and cooling energy signatures from
the reduced order model are similar to the predicted signatures from EnergyPlus. This is an indication
that both model have similar thermal properties. However, the cooling energy signature for the reduced
order model mirrors that of the EnergyPlus' model better than the heating energy signature. This is
lntegrated Design Lab I Boise 32
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
thought to originate from the terminal reheat VAV box sub model. The daily average residuals were also
calculated and a histogram of the results can be seen below in Figure 15.
Figure 15. Daily Average Total Energy Demand Residual Histogram
The total energy demand residual had an average of approximately -1,401 watts. The overall CVRMSE
and the NMBE for the total energy demand when compared to EnergyPlus was 42.4% and 1.7o/o
respectively. This is indicating that the ROM is using, on average, 1,40L watts less energy per hour than
the EnergyPlus model. This error originates from the same over estimation of the ROMs zone
temperature, as discussed above. The ROM accurately predicted the magnitude of total energy
consumption when comparing the results to the previous EnergyPlus model. The CVRMSE value was
greater than allowed by the ASHRAE standard, but due to how the model lumps various thermal masses
together this variance is to be expected. The ROM is verified to be an accurate representation of the
COBE building and the next step is using this model as a tool for virtual commissioning by looking for
recommendations that can yield realized energy saving at the COBE building in Moscow.
X
lntegrated Design Lab I Boise 33
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
5.1 Virtual Commissioning
The team was allowed remote access to the EMS for the COBE building at the University of ldaho,
and while logging into the system we noticed that the building was operating in "occupied" mode at a
time when the building should have been unoccupied. While running in occupied mode the HVAC
conditions the building to different temperature set points and uses more electricity than when
operating in unoccupied mode. We logged in several times and the problem persisted which points to
the discrepancy not being by chance, but on overall operational and control issue. This was confirmed in
a follow-up meeting with the facilities team at the end of the project. The reduced order model was
used to determine the energy savings associated with having thermostat setbacks. Table 3 shows the
current and recommend thermostat settings, which were the values used in the study.
Table 3. Current and Recommended Thermostat Set Points
Occupancy Stotus
Current Thermostot Set Points
22.78
Recom mended Thermostot Set
Points
24.0
26.7
The values for the current set points were determined by examining the trend logs of zone
temperatures from the COBE's EMS system. The values represent an average of the zones. The
recommended thermostat set points were determined using ASHRAE's Standard 90.1 [15]. We found by
having thermostat setbacks during unoccupied times, it would save approximately 9.6% of HVAC energy
consumption. This study was also conducted using the fully calibrated COBE EnergyPlus model that was
developed during the previous Avista Research Grant. With the full EnergyPlus model, it is predicted to
save approximately 9.97% of heating and cooling energy by adjusting the thermostat set points. This
Heating Set Point
trl
Cooling Set Point
trl
Occu pied/U noccupied 20.0
Occupied
Unoccupied
21.0
15.6
I
lntegrated Design Lab I Boise 34
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
energy savings does not include pump of fan power savings; the reported value is only the amount of
energy consumed while conditioning the space. The 9.6% energy savings equates to approximately
104,000 kWh annually.
The access to live data provided two other insights that could lead to significant energy savings
at the site. The economizer was unable to run in integrated mode and would lock out the cooling coil
any time it was engaged. The outdoor air sensor was also located in a concrete well and was not
providing accurate readings of the outdoor air intake. ln the words of one of the facilities managers: "it's
whacked." Our team was able to coordinate a meeting with several facilities and HVAC managers at the
University of ldaho as well as several representatives from Avista to discuss the findings and follow up
on these issues. The discussion served to make several connections and spur action on correcting these
issues. The controls contractor is sending out a service engineer on Tuesday September 5th to specifically
address the outdoor air sensor, setback scheduling, and correcting of the air handler/economizer
operation at the COBE building.
6. DrscussroN AND Furunu Wonr
lntegrated Design Lab I Boise 35
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
This research has shown that using ROM for virtual building commission is a viable option for whole
building commissioning. This approach lessens the time and money constraints that are prohibitive for
many building owners. The difference between the results of the fully-zoned EnergyPlus model and the
reduced order model was insignificant when predicting the amount of energy savings from thermostat
setbacks. Approximately 104,000 kWh annually can be saved with temperature setbacks during
unoccupied periods.
Without access to accurate data from the COBE building, outputs from the previously developed
EnergyPlus model had to be used to find the thermal parameters of the ROM. ldeally, this method of
building modeling would be a standalone process and would not rely on an EnergyPlus model; all the
ROM inputs would be calculated, or measured, or estimated using ASHRAE standard 90.1 [16]. For ROM
virtually commissioning to progress, there needs to be research done in calculating the thermal
parameters of the building without using software optimization. Promising results were achieved at
determining the parameters using the buildings response for the BESTEST case study. lt was shown that
the thermal parameters could be determined both ways, through optimization and through numerically
fitting the parameters to best fit the building's thermal decay of energy. The differences between the
time constants of the two models were insignificant and would have minimal effects on the overall
building energy consumption. The method of using the buildings temperature decay needs to be further
investigated, and eventually needs to be used with an actual building to see if the parameters can be
found from a large temperature setback similar to the process used in the BESTEST case study.
The facilities team were open to learning about the results and have blocked out a day to address
each of the commissioning issues found during this research. Data will continue to be collected at the
site so that the savings might be verified and used as an example for future commissioning projects and
applications for incentives. A group meeting between University of ldaho facilities, Avista, and IDL on
lntegrated Design Lab I Boise 35
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
August 29th served to enhance understanding between parties and build stronger partnerships for future
efficiency projects.
7. Buoe rr SururueRv
lntegrated Design Lab I Boise 37
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
These hours reflect only Avista's contribution to this project and are not reflective of total project
investment by the research team, industry sponsors, or other university staff.
FY76'-Y77
Personnel lAll Hourly Rotes are overages over FY76 and FY77)
Salary: Support Requested for Elizabeth Cooper at 57 hours (551.28/hr), Dr. Jinchao Yuan at 75 hours
(S40.15/hr), Damon Woods at 504 hours (S21.00/hr)and Sean Rosin at 820 hours (S16.00/hr). Total
Salary amount requested for project period is 529,596.
Fringe Benefits: Total Fringe requested for project period $2,422.
Other Direct Costs
Trovel: Estimated travel cost for project period $2,250
Operoting Expenses: Estimated research supply cost for project period is S1,500
Tuition: Estimated tuition costs for one graduate student for Fall of 2Ot6 and Spring of 2017 53,080
Indirect Costs
For this contract, Ul-lDL was considered an on-campus unit of the University of ldaho with a federally
negotiated rate of 50.3%.
Personnel Hours estimate Description
Elizabeth Cooper 56 Provide overall management of the project
Jinchao Yuan, Ph.D., P.E.75 Provide technical support in supervising the
student intern(s)
Damon Woods, P.E.504 Provide technical support and execute daily
tasks of this project
Sean Rosin 820 Execute daily tasks of this project
Integrated Design Lab I Boise 38
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
8. RerrReucrs
t1I
12)
t3l
L7l
t8I
IeI
ASHRAE. (2005). ASHRAE Guideline 0-2005: "The Commissioning Process". Atlanta: American
Society of Heating, Refrigerating, and Air-Conditioning Engineers.
Nouidui, T., Wetter, M., Li, 2., Pang, X., Bhattacharya, P., & Haves, P. (2011). BacNet and
Analog/Digital lnterfaces of the Building Controls Virtual Testbed. Conference of lnternationol
Building Performonce Simulation Associotion, (pp. 294-301). Sydney.
Haves, P., & Xu, P. (2007). The Building Controls VirtualTest Bed - A Simulation Environment for
Developing and Testing Control Algorithms Strategies and Systems. lnternotionol Building
Performance Simulotion Association (pp. 1,440-1,446). Beijing: Lawrence Berkeley National
Laboratory.
l4l Nouidui, T., Wetter, M., Li, 2., Pang, X., Bhattachatya, P., & Haves, P. (2011). BacNet and
Analog/Digital lnterfaces of the Building Controls Virtual Testbed. Conference of lnternotionol
Building Performonce Simulotion Associotion, (pp. 294-301). Sydney.
Harish, V. S. K. V., & Kumar, A. (2015). A Review on Modeling and Simulation of Building Energy
Systems. Renewable and Sustoinoble Energy Reviews, 56, 1272-1292.
IsI
l6I Amara, F., Agbossou, K., Cardenas, A., Dube, Y., & Kelouwani, S. (2015). Comparison and
Simulation of Building Thermal Models for Effective Energy Management. Smart Grid and
Re newo bl e Ene rgy, 6, 95-112.
Antonopoulos, K. A., & Koronaki , E. (1997). Apparent and Effective Thermal Capacitance of
Buildings. Notional Technical University of Athens, 42, 183-192
Kulakowski, B. T., Gardner, J. F., & Shearer, L. L. (2007). Dynomics Modeling ond Control of
Engineering Systems (3rd ed.). New York, NY: Cambridge University Press.
Rabl, A. (1988). Parameter Estimation in Buildings: Methods for Dynamic Analysis of Measured
Energy Use. Journal of Solar Energy Engineering, 1-10,52-66.
t10l Bacher, P., & Madsen, H. (2011). ldentifying Suitable Models for the Heat Dynamics of Buildings.
Energy and Buildings, 43, 1511-1522.
[11] Li, J. (2015, August). Modeling and Analysis of an Air Handling Unit to lmprove Energy Efficiency.
Purdue University.
U2l Trane. (2005). Keeping Cool with Outside Air - Airside Economizers. Trone Engineers Newsletter
lntegrated Design Lab I Boise 39
Using reduced-order models for simulation based commissioning of buildings (Report 1608_01)
[13] Liu, G., Zhang, J., & Dasu, A. (2012, March). Review of Literature on Terminal Box Control,
Occupancy Sensing Technology and Multi-zone Demand Control Ventilation (DCV). U.S.
Department of Energy.
l14l ASHRAE. (2002). ASHRAE Guideline 14 - Meosurement of Energy ond Demond ond Water
Savings. Atlanta: American Society of Heating and Refrigerating Engineers.
[1s]Hitchin, R., & Knight, l. (2015). Daily Energy Consumption Signatures and Control Charts for Air-
Conditioned Buildings. Energy and Buildings, 772, 101-109.
[16] ASHRAE. (2016). ANSI/ASHRAE Stondord 90.1-20L6: "Energy Stondord for Buildings Except Low-
Rise Residential Building". Atlanta: American Society of Heating, Refrigerating, and Air-
Conditioning Engineers.
APPENDIX I
lnterim Report: RSVC Year 4
frYlsta
B AE--stsrrrBOISE SYATE UNlVERSITY
A DEMAND-SIDE APPROACH TO CONSERVATION BY
VOLTAGE REGULATION ENABLED BY RESIDENTIAL
STATIC vAR COMPENSATORS (RSvCs)
Project Duration: September 2017 - August 2018 Project Cost: Total Funding $90,574
OBJEGTIVE
The overall goal of the RSVC project during
Year IV is to simulate, design, and implement
a laboratory prototype of a Residential Static
VAR Compensator (RSVC) equipped with two
automatic control loops for customer load
voltage regulation and minimum load power
consumption. The faster voltage regulation
control loop regulates the customer load
voltage to any desired reference voltage within
Range A of ANSI C84.1 (120 V nominal plus or
minus 5olo) and a secondary slower control
loop adjusts the reference voltage to track the
point of minimum power consumption by the
customer load. This is a new approach to
conservation by voltage regulation (CVR)
which is tailored to the nature of the customer
load, which may decrease or increase its
energy consumption under a reduced voltage.
This demand-side approach to CVR is a radical
depafture from current strategies which have
been in existence for over 30 years but have
not been widely adopted by electric utilities
due to high costs and technical challenges.
BUSINESS VALUE
The deployment of multiple RSVCs offers a
cost-effective method for energy savings by
applying conservation by voltage regulation
during peak demand hours. It can become a
valuable tool in a utility energy efficiency and
demand-side management programs.
INDUSTRY NEED AND BACKGROUND
The new single-phase RSVC device has a
distinct advantage over a conventional shunt
capacitor of being able to operate in a
capacitive or inductive mode without
generating substantial harmonics. This RSVC
employs a pulse-width-modulation (PWM)
technique applied to two specially-designed
bidirectional switches controlling the variable
reactive power output. This flexible device can
be used in multiple applications such as
continuous voltage control at a load point,
power factor control, mitigation of power
quality issues, etc. The new RSVC device is a
competitive device on several fronts including
cost, footprint, and smart-grid applicability or
compatibility. Figure 1 shows the prototype of
the open-loop RSVC developed during previous
phases of the project. Several relevant studies
were carried out to study of the operation of
an RSVC for regulating a residential voltage
and to study the potential benefits of deploying
multiple RSVCs on the residential side of
distribution networks.
Transformer Output
Voltage
Figure l: Simulation Model for RSVC
PROJEGT TASKS - Year !V
Project Management
Internal weekly project coordination meetings
take place every Wednesday morning. Bi-
weekly status meetings with Avista are
scheduled on Thursdays.
Task 1: Voltage Regulation Loop Design
The objective of this task is to design and
implement the voltage regulation loop for an
RSVC using MATLAB/S|mulink models. This
control loop regulates the residential voltage
by changing the reactive power provided by a
PWM-based switched inductor. Figure 2 showsa top-level block diagram for both control
loops.
,lFeeder
Voltage
Lslc
sw1
Based
Bidirectional
Switch
Switched
InductorSwitch
Fixed
Csvc
Figure 2: RSVC Voltage and Power Regulation Loops
Task 2: Power Regulation Loop Design
The RSVC power regulation loop is designed as
a slower control loop than the inner voltage
control loop to build an inherent time
decoupling between the two control loops and
to avoid a possible loop interaction. This task
consists of designing a minimum power point
algorithm (derived from maximum power point
tracker available in the literature) that will
track the power consumed by the residential
customers with the objective of reducing the
customer power. Simulation results for the two
control loops show that the output power for a
2 kW resistive load can be reduced 1.8 kW by
reducing the residential voltage from 120 toLl4 V. Figure 3 below shows the power
consumption reduced as a result of decreasing
the voltage.
Voltags Regulation Control Loop
prototype consisted of a 190-pF capacitor anda 30-mH inductor choke. Preliminary test
results were compiled at 60 VAC with a 20-ohm
resistive load. The switching frequency for the
switches was varied between 3 kHz and 12
kHz. Table 1 shows the RSVC output voltage
(Vo) and RSVC power losses (PL) as a result of
varying the duty cycle at discrete frequencies.
Table l: Hardware Prototype Results
In the next phase of hardware development,the voltage regulation loop will be
implemented using a voltage transducer
measuring a replica of the RSVC voltage which
is also the customer load voltage. The power
loop will require the same voltage transducer
as well as a current transducer monitoring the
customer load current. The two transducer
outputs will allow the computation of the
average power consumed by the load.
Task 4: Testing of an RSVC prototype in a
Iaboratory or home environment
These tasks are ongoing.
PROJEGT TEAM
SGHEDULE
2125
o
< 120
e 115
o
g 21oo
izooo
' rsoooc; 18004! 1700
0.5
0.5 1 1.5 2
1.5 2 2.5 3 3.5
time (sec)
Powo. Rogulation Control Loop
4.5 5
3.5 4 4.5 52.5 3
tlme (sec)
Figure 3: Minimum power point tracking with 2 kW
residential load (work in progress)
Task 3: Design of a Functional Laboratory
Prototype of an RSVC
An RSVC prototype is being developed in a
power laboratory at Boise State University.
This prototype is based on the simulation
model shown in Figure 1. The device can
regulate a residential load voltage with a fixed
capacitor in shunt with a reactor controlled by
two bidirectional switches. The two switches
are controlled in a complementary mannerusing a pulse-width-modulation (PWM)
technique that allows the reactor to function asa continuously-variable inductor. An early
1 PI
controller RSVC
Voltag€ Retulation
Control Loop
Fille.
RMS Vofrar€
I
Block
Control Loop
Volta8e Reference
lncrement (Av)
114,125 )
PolntAlsorlthm
Fsw
kHz
DUTY CYCLE (D) OF SWTTCH SWI
D=O D = O.5O D = O,71 D='l
Vo
(v)
PL
(w)
Vo
(v)
PL
(w)
Vo
(v)
PL
(IY)
Vo
(v)
PL
(lY)
3 75.4 9 72.4 L7 70.7 22 5B 19
6 75.4 9 72.5 1B 70.8 20 5B 19
9 75.4 9 72.5 19 70.6 27 5B 19
L2 75.4 9 72.4 22 70.8 29 5B 19
PR!NCIPAL !NVESTIGATORS
Name Dr. Said Ahmed-zaid
Orqanization Boise State University
Email sahmedza idfOboisestate.edu
Name Dr. John Stubban
Oroanization Boise State Universitv
Email iohnstubban @boisestate.edu
RESEARGH ASSISTANTS
Name Muhammad Kamran Latif
Orqanization Boise State University
Email muhammadlatif(ou. boisestate.edu
Name Ziyanq Lianq
Oraanization Boise State Universitv
Email zivano liano@u- boisestate.edu
TASK TIME
ALLOCATED
START
DATE
FINISH
DATE
Task 1 3 month SeD'17 Oct'17
Task 2 4 months Nov'1 7 Dec'17
Task 3 1 month SeD'17 Mav'18
Task 4 2 months Jun'18 Auq'18
Task 5 2 months Julv'18 Auq'18
The information contained in this document is proprietary and confidential.
I
VoltaSe
frusta
APPENDIX J
lnterim Report: Energy Storage
Universityotldaho AFwsrn
College of Engineering
Framework for Siting and Sizing Energy Storage for
Enhanced Peformance of the Avista System
Project Duration: 12 months Project Cost: $83,712.89
OBJECTIVE
To develop and trial-test a Distribution
Resources Plan (DRP) appropriate for the
nofthern region of Idaho. The DRP will include
methods for integration capacity analysis (ICA)
and locational net benefits analysis (LNBA).
These analyses, respectively, determine how
many distributed energy resources (DER) the
system can handle and how the value of those
DERs will be calculated. To test these methods,
a section of interest of the Avista utility grid
will be modeled in GridLAB-D. After this model
is verified, the ICA and LNBA methods will be
applied. Using the results the methods will be
refi ned.
BUSINESS VALUE
In recent years, DERs such as solar panels
have been seen as a means to reduce electrical
costs to the facility to which it is attached. DER
also reduce power lost during transmission
from a distant power plant, Another class of
DER, energy storage, may be needed to
compensate for non-dispatchable renewable
generation. By solving local power issues, DERcan defer or eliminate costly system
improvement projects.
DRPs help a utility plan and utilize DER
efficiently.
INDUSTRY NEED
Renewable energy has been shaking the utility
industry in the last decade. Solar panels andother renewable resources break the
traditional utility model and are becoming
increasingly popular as a source of "green"
energy as the price drops to within par of
traditional electricity rates. Unlike nuclearplants and other sources of traditional
generation, most sources of renewable
generation are unable to be dispatched.
Unplanned and haphazard integration of
renewable generation and other DER can put
local system stability at risk. Careful planning
and control will manage the risk that DER bring
so all parties can fully enjoy the benefits. Thus
utilities are motivated to develop policies
regarding DER in a DRP such as an ICA.
BACKGROUND
As the cost of renewable energy has become
increasingly competitive with traditional
sources of energy, utilities are pressured to
include more DER. In order to safely and
efficiently use DER, utilities need to update
their traditional distribution planning processes
to incorporate DER. California's investor owned
utilities (IOUs) have integrated DERs into theplanning process by using DRPs. This
document outlines the companies plan to
measure the system capacity for DER and
possible new economic models to cover this
new service. Such a plan can forestall the needto upgrade transmission infrastructure by
using a local non-wired solution.
SGOPE
Task 1: Research Existing DRPs
The research into existing DRPs focused on the
DRPs submitted by California IOUs. These
documents were found on the California Public
Utilities Commission (CPUC) webpage. Notes
were taken on the DRPs starting with the one
submitted by San Diego Gas & Electric
(SDG&E). These notes were then compiled and
processed into the Final Research Report. This
research provides a baseline knowledge of the
methods of other utilities who are also solving
the problem of distributed resource planning.
Task 2: Familiarize with Avista Policies
Research Avista's needs and policies to begin
development of a locational net benefits
model. The teams observations were
developed into a Midterm Research Report.
Task 3: Learn GridLAB-D
GridLAB-D is a new power distribution system
simulation and analysis tool. One of its defining
features is its ability to model emerging smart
grid energy technologies. This feature coupled
with its ability to be integrated with a variety
of third-party data management and analysis
tools makes GridLAB-D a leading resource for
testing integrated capacity and locational net
benefits analyses.
The team will learn the GridLAB-D program to
prepare for modeling a representative sectionof the Avista system where a non-wires
solution could be applied to solve near-term
challenges. This reduces modeling time after
the target section data is received.
Task 4: GridLAB-D Modeling
The target section data will be received in the
Common Information Model (CIM) format. The
team will convert this data into a GridLAB-D
model.
Task 5: lntegrated Capacity Analysis
Once the GridLAB-D model is complete, the
team will implement SDG&E's ICA method.
This method divides the target section's
feeders into zones and then inserts
increasingly greater generation until a
thermal, voltage, or protection limit is violated.
The results of the analysis will be added into
the Final Research Report.
Task 6: LocationalValue Pricing
SDG&E's locational net benefits method will be
applied to 3 different locations in the GridLAB-
D model. The pricing for each location can then
be calculated and the results will be compared
and added into the Final Research Report.
DELIVERABLES
o Midterm and Final Research Reports. GridLAB-D model of a representative
part of the Avista power system where
a non-wires solution could be applied to
solve near-term challenges. Results of ICA of the GridLAB-D model. Describe options for performing
Integration Capacity Analysis. Describe options for performing
Locational Net Benefits Analysis
PROJECT TEAM
SCHEDULE
Appendices
A: Midterm Research Report
Schnitker, Maximilian; Flyn, Nicholas;
Alruwaili, Barjas; Chen, Tianyi. Mid-Term
Research Report. First-Term Research
Summary. University of Idaho. tL/27/20t7
PRTNGTPAL !NVESTTGATOR(S)
Name Dr. Herbert Hess
Orqanization University of Idaho
Contact #(208) 885 - 4341
Email hhess@uida ho.edu
Name Dr. Brian lohnson
Oroanization University of Idaho
Contact #(208) 885 - 6902
Email bioh nson @u idaho. edu
Name Dr. Yacine Chakhchoukh
Orqanization Universitv of idaho
Contact #(208) 885 - 1550
Email vacinec@ uidaho. ed u
RESEARCH ASSISTANTS
Name Jacob Dolan
Orqanization Universitv of Idaho
Email dola9260@vandals. uidaho.edu
Name Maximilian Schnitker
Orqanization University of Idaho - Senior Desiqn
Email [e-mail]
Name Nicholas Flynn
Orqanization University of Idaho - Senior Desiqn
Email [e-mail]
Name Barjas Alruwaili
Orqanization Universitv of Idaho - Senior Desiqn
Email [e-mail]
Name Tianvi Chen
Oroanization University of Idaho - Senior Desiqn
Email chen [e-mail]
TASK
TIME
ALLOGATED
{# Months}
START
DATE
FINISH
DATE
Research Existinq DRPS 4 09/77 01/ 18
Familiarize with Avista
Policies 2 09/t7 7t/77
Learn GridLAB-D 3 tt/17 02/74
GridLAB-D Modelinq 2 02/78 04/78
Inteorated CaDacitv Analvsis L 04/78 05/18
Locationa Value Pricinq 2 03/18 05/18
The information contained in this document is proprietary and confidential,
First Term Rese arch
^
Authors: Maximilian Schnitker, Nick Flynn, Barjas Alruwaili,
Tianyi Chen
FIRST TERM REPORT
UNIVERSITY OF IDAHO SERIOR DESIGN TEAM:
A REACTIVE FUTURE
2017
-r-
Table of Contents
Section L: Economics between energy vs capacity.,,.,2
....3Section 2: The relationship between renewable energy resources and peaking facilities.
Section 3: Research Topics ,4
Generation Strategy..
Transmission Load Studies and Seasonal Operational Constraints
Operational Guidelines for System Flexibility.... .........10
Section 4: Detailed Design.........L3
Works Cited 17
,,.,,,4
......5
Section 1: Economics between energy vs capacity.
Energy is the amount of electricity customers consume over time measured in Megawatt-hours.
Capacity is related to the peak demand and is the total amount of generation the company has
available. Capacity is measured in megawatts and represents a power plant's potential to generate
electricity. Energy and capacity have different markets. ln an energy market electricity is treated like any
other commodity while capacity is the amount of available energy to the public. Energy is bought at
wholesale value then resold at retail value and capacity can be created by enlarging the infrastructure or
upgrading existing hardware. Capacity is funded by the consumers of electricity. The cost of operations
and maintenance is spread across the energized grid to keep the retail cost of energy low for the
consumer.
Section 2: The relationship between renewable energy resources and
peaking facilities.
Peaking facilities are resources that provide power during the peak demand hours of the day
They are the last power plants to be turned on and the last to provide power during peak demand. Some
common peaking plants are combustion turbine generation, natural gas fired generation, and
hydroelectric generation. An emerging alternative to peaking facilities is electrical storage. Currently
storage has a higher upfront cost than peaking facilities but may add more flexibility than peaking
facilities. Storage hasthe abilityto start up much fasterthan peakingfacilities, and its output can be
varied faster than peaking facilities. There are two types of storage that could be used to replace
peaking facilities. Bulk facilities that would be comprised of one plant that may be far away from the
load, and distributed storage located nearthe load. Bulkfacilitieswould likely be pumped hydroelectric
storage while distributed storage could be PV or batteries. While up-front costs of energy storage are
still high the benefits from it may make it a better option over traditional peaking facilities.
Section 3: Research Topics
Generation Strategy
To meet current customer loads Avista's assets include ownership of eight hydroelectric
developments, partial ownership in two coal fired units, five natural gas fired projects, and a biomass
plant. Avista also purchases energy from several independent producers, including Palouse Wind,
Rathdrum Power, and the city of Spokane. Avista's largest supply in the peak winter months comes from
hydroelectric al51% the second largest is natural gas. A small portion of customers have installed their
own generation systems. Over the past two years the number of customers installing their own
generation has been increasing. lf this continues to increase Avista may need to adjust their rate
structures for customers who rely on the utilities infrastructure, but do not contribute financially for
infrastructure costs.
ln Avista's lntegrated resource plan they clearly layout what their preferred generation resource
strategy for the future is. The IRP uses a decision support system called PRISM to develop their
preferred resource strategy. PRISM evaluates resource values by combining operating margins with
capital and fixed operating costs. Then uses a number of inputs to develop an optimal resource mix over
time at varying levels of risk. The preferred plan for the future includes the existing resources along with
thermal resource upgrades, energy efficiency, demand response, storage, and natural gas fired peaking
plants. Also included is a 15MW solar facility for Avista's voluntary solar select program. The selected
storage unit has a 5MW capacity rating, and 15MWh of storage. These resources combined with the
existing resources satisfy the generation needs until 2037.
Transmission Load Studies and Seasonal Operational Constraints
Native Load
Avista's peak hour is in winter, and that's between November and early February. However, the
use of air conditioners in the summer months creates a peak load higher than those of winter. Avista
stated that the summer peak load is sometimes higher than the winter peak load. Avista highest peak
load was at'J,821MW in winter 2009. ln the last 20 years Avista's native peak load growth rate was 0.74
percent in the winter and 0.85 percent in summer, as shown in the figure 4.A.
Figure 4-A (Groph of notive load Jorecost)
Winter Balancing Authority Area (BAA) Load
Figure 4.8 shows the balancing area variations of the load between 2000 to 2030 and that's
during the winter season. The power consumption reached a maximum at 2350 MW in 2008. Also Avista
has predicted a forecast of the peak load based on the current data. According to the prediction from
2018 to 2030 the average peak load is roughly 2500 MW. The power reaches the maximum peak load of
approximately 2500 MW in 2030.
2,200
2,ffi
t.800
.r,600
1,4{]{}
I,2()0
r,ooo cDmSFAmnF@?oaE@noEtsa a8 B aa E 6E 5 5 BBa g I BB A INNNNNCTNNNNNNilNNNNfrN
- ,lAllnt:r. Hlgh - - - Wlntcr- Low
.- Summr' Hlgh * - Sumrnor- Low
I
a
a;a!EIG
=
7
3Ufl
tmfi
rfipr(
[,**
l r-*
lHto
tril00
u$.!0
Wlnttr Brhnclnf Arce Forscr:t
.t,,
p
J
,'|
\./
c4.nn
. . , trl10krsrt
- ' IgUFc*rt
* .l$Utffit
"* U1lrffit
* lgla Fq.Brr
I0llFffir
-l0lfFmd
Figure 4-B (Graph of winter bolancing area lood for orea lood forecast)
Summer Balancing Authority Area (BAA) Load
During the summer season, in 2074 the peak load reached a maximum of 2109 MW. According
to the forecast from 2018 to 2030 the average peak load is roughly 2300 MW. The maximum forecasted
peak load is approximately at 2350 MW in 2030. The summer experiences linear growth during the next
decade, as shown in figure 4-C.
Sufitm.r Olhnclng Atar Forrca.t
It
"lt t0 iL.i"!
. ttotM
- - flllxff5
- ,ulfrrt
--. ttltl tfl*E
' tr4tru
-*rl.ltt5
-:glla*inrt
Figure 4-C (Groph of the summer boloncing area load forecost)
**l
,:-l
"** I".- I
IEdtoo I
I*l
.t** l
I,** im
ln fact, the unusual growth of the peak load is basically related to the increasing number of the
population over the few years.
Distribution Planning
The primary goal of the distribution resource plan is to supply safe and reliable service to all
customers, efficiently at the lowest life cycle cost. The integrated resource plan and distribution
resource plan (DER) includes Solar, Storage, Conservation, Commercialand lndustrial (C&l) Backup
Generation, C&l Demand Response. The problem is that the benefits are much larger than the costs. An
example of this is the 5 MW Solar array in Othello, WA (summer peaking feeder) (S/MWh Levelized).
The valued benefits are SgZ.Zg per MWh, but the costs are expected to be S55 to 565 per MWh for a
long-term power purchase agreement (PPA)
Figure 4-D (Toble of PRiSM economics)
For optimizing DER Value, there are foundational enabling systems smart grid demonstration &
amp, investment projects including American recovery and reinvestment act grants, smart line devices
and distribution management system, fault detection, isolation and restoration -reliability, integrated
volt/var control -energy efficiency, advanced metering infrastructure pilot project. For future enabling
systems, there is a Washington advanced metering infrastructure project concerning communication
Energy
Peqk rEduetion
Defened T&D capital
Reduoed line losses
Reduced portfElio risk
Ancitlary services {storage I
lrnproved re liabil ity (storage1
Senefrts
Costs
Capttalrecdvery
Taxes
o&M
TRC {consarvation}
Energy consurnpiion {slomge I
mis;/!
Ecanomics
network and a supervisory control and data acquisition expansion project. This project utilizes three-
I
frvlg,ta
phase measurement at substation feeders. There are two Pilot projects, One is Turner Energy Storage
Project - Washington Department of Commerce. lt is a 1MW - 3.5 MWhr Vanadium Flow Battery and it
is located Adjacent to SEL Manufacturing. Another is called Shared Energy Economy - Washington
Department of Commerce. lt has Solar, Storage and Building Management Systems. lt is located in the
University District - Spokane, Washington.
Figure 4-E (Shored energy Diogrom)
Currently, Avista has 346 circuits but only 195 Circuits have 3-Phase Supervisory Control and
data Acquisition (SCADA). Avista determined that to upgrade all the circuits with SCADA which would
cost S115M
l,:j
Operational Guidelines for System Flexlbility
The ability to tolerate and adjust a system to variations in operating conditions is a possible
definition of flexibility in a system. However, the concept of flexibility can be multidimensional and relies
on the process or goal behind the analysis to explore the idea properly. Therefore, flexibility will be first
defined under power grid conditions and mapped out for the purposes of operational guidelines.
Within the idea of flexibility there are two distinctions that this paper will go into: Short-term
flexibility and Long-term flexibility. Each of these terms will be defined to outline possible guidelines for
future flexibility. Short-term flexibility is the ability to tolerate variations in the grid without changing
large amounts of hardware. An example of short term flexibility would be the tap-changing transformers
that alter voltage to offset the increased load. Long-term flexibility is the ease with which a system can
be changed in a major way or one that alters the system's goal. An example of long-term flexibility is the
ease of changing a small town or city's grid to include distributed energy resources as a main generation
strategy. There are also three sections that this senior design group will incorporate into the design of
products and services that are produced. These sections are: Smart Management, Smart Protection and
Smart lnfrastructure. These concepts are often referred to by some researchers as key components to
creating and maintaining system flexibility. Figure 4.F. Shows how Smart Protection, Management and
lnfrastructure are related and additional ideas regarding each component.
Smart Management is the maximum utilization of products, system objectives and potential
cost to provide the greatest number of services available to the consumers. Smart management is a
subsystem of a grid that will provide advanced control of various facets within the grid through two-way
communication and other communication methods. An example of utilizing this component would be
convincing customers to lower their power demand during peak demand times using real-time pricing.
Smart Protection is the subsystem that would provide advanced grid reliability analysis, fault
protection, security and privacy protection. This protection focuses on providing effective and efficient
mechanisms for detecting faults and minimizing the damages
Smart lnfrastructure provides the hardware that connects the other components of the smart
grid. With the proper infrastructure distributed energy resources can be utilized effectively with smart
communication via wireless, digital network. Flexibility can be linked to the available hardware in a
power grid and can be created by addition of equipment and therefore capacity. These hardware
additions can provide needed redundancy throughout the system and thus supply additional flexibility
With each of these components there is the opportunity to analysis a system for improvement.
Flexibility, in many ways, is the resiliency of a system undergoing various input changes and reacting to
these changes without resorting to operations failure. ln this senior design project, we will be analyzing
a section of the power grid for possible advances of flexibility toward Avista and the consumer. There
are numerous methods for testing systems and figure 4-F can act as a rubric for potential solutions into
system analysis and design for future projects.
. Smart Protection System
. System reliablity and failure protection
. System reliablityr FnilEG protection lKhrnign
. failure prediction and prevention
. failure identification, diagnosis, and recoyery. tuilwe idcatiMiq azd lw.fratim
. @idsef-heofinC. Mr@v€ry. Microgrid prtection
. Seorlty and privacy
. lnfsrption fiEtEring and reazuruntntr lnfqmation trmmisdon
Smart Grid
$
r Smart Management
System
. ManaEemmt obiectlYes
o Errtr8, eficrry ard danand pmfik
. Oemand profileshaping. Energy loss minimizEtion
' tltilitg rost, ond prkc
. Emiriq. Mana8sm€flt metlods and tools
. optittiz*bn. Cofir€x prqra'Iunhg
. Dfnamic programming
. Stodrdtic prcgramming
. Robust pmgnmming
' hrtiderwarm optimiz*ionr l*achinc bsning
. 6aqE tfietry
. Atdlrn
. Smsrt lnfrastructurc Systcm
o smaat energy subsyrtem. ?orcr r:c,sltim.Immmisiangll
. Distribolionerid. iltw grfrt pradkm: mirogid aod grl*to-
vd$clel*hkle-to-grid
. trnart info nnation subrytem
r hfom*im metgiqad nagrcrncnt. Smrt rneter. Scnsor
. Phawmeilremmtmil. lr*omstim msaaanpnt
r Dabrnodeliq. lnhrmation anatF4 intEgration, 6nd optimidon
r Srnart cammur&atinn srbrytem. wbdcsE. Witdesrme*fi networl<s. Ca$rar mmrmrnlction it*ems. Cognfive radio. Wi.elcss comrnuni(atonsbas€d on 80115.4. SaHlite comnu.rnicatiorr. Mioorrarit or frEcrpacc odicd @mmuniEtions. llrhcd. Eb€r-oplic omExrniations. PdicrliErmictiru
. En*tmdcmkation
Figure 4-F (Diogram of smort grid relotionships) [1]
lnfrastructure
lBi:;
,l*rl
-4
I
rL.
Section 4: Detailed Design
The first step in our design will be determining the integration capacity of the circuit. lntegration
Capacity" is the amount of DER capacity that can be installed on a distribution circuit without requiring
significant distribution upgrades. From our locational value analysis research we know that the most
successful locations are where the installation of DER's will not require significant upgrades.
lnitialSoftware Setup:
t. Become familiar with Gridlab-D and the Common lnformation Model data provided by the
client
2
3
lmport the CIM data into Gridlab-D and begin capacity testing.
Create a test system or use a sample system from Avista.
lntegration Capacity Design:
L. Divide each feeder into three segments by identifying the start and end of each impedance zone on
the main feeder
a. Determine the maximum impedance of each feeder. The size and number of segments may
vary if the feeder is in an urban or rural area
2. Synthesize the circuit demand profile from AMl, SCADA, or other data, and input into Gridlab-D. This
process may need more information on the Avista feeder or use a public data set from NREL.
3. Conduct power flow analysis to determine thermal and voltage limits on each line section
a. Place a single 1MW generator, at unity power factor, at different points along each segment,
and perform a power flow analysis to determine if the generator violates thermal or voltage
limits anywhere along the feeder. lf a violation occurs then a smaller generator will be used and
the power flow analysis rerun. lf no violations are identified, capacity of the generator will be
increased and the power flow analysis rerun. This process will continue until a violation is
identified or reverse power flow occurs.
4. Conduct short circuit analysis to determine protection limits on each line section.
a. Model a single 1 MW generator, at unity power factor, at different points along each segment
and perform a short circuit analysis to determine if the generator violates protection limits
anywhere along the feeder. lf a violation occurs then a smaller generator will be used and a
short circuit analysis will be rerun. lf no violations are identified, the generator's capacity will be
increased and the short circuit analysis rerun. This process will continue until a violation is
identified. Some protection limits that may be looked at are breakers interrupting duty and
conductor limits. Also, will look into whether new relay setting would be needed or if
relays/breakers are needed in new locations.
5. Once a violation is identified, the integration capacity will be the largest generation capacity that
passes both analyze without any violations.
For initial analysis the DER generation will remain below the minimum load on each circuit so
that there is no reverse power flow back to the substation. We may also consider using generators that
are not at unity power factor to increase the amount that may be added
After the integration capacity is determined for the circuit. We will then use the results of our
locational value analysis research to determine what type of projects have the potential for DER
deferral. We will focus on mainly capacity and voltage regulation projects. The categories of avoided
cost that we will look at:
7. New distribution substations, new distribution transformers, new distribution circuits, and
reconductoring circuits, and new breakers
2. Capacitors, regulators, and substation transformer load tap changers to regulate voltage
3. New transmission substations, new transmission transformers, new transmission lines and
reconductoring transmission lines.
Some of the cost associated with these projects and DER projects that will be used in evaluating whether
a DER project will be used over a traditional project are:
1,. Capital Expenditure- This consists of the net present value of the traditional project
2. Capital Expenditure of DER- This consists of the net present value of the DER
3. Capital Expenditure of interconnection- This consists of the cost of interconnecting the DER into
the distribution grid.
4. Operation and maintenance- This represents the uninsured costs of not having to operate and
maintain the traditional project
5. Operation and maintenance of DER- added cost of operation and maintaining the DER
We will use all this data to determine where and what kind of traditional projects may be deferred with
DER projects. For this part of the project we may need information from Avista for some rough
DER's under consideration:
1. Energy Storage- Batteries used to lower the peak demand and eliminate circuit overload. Can
also be used in conjunction with smart inverters to provide voltage regulation and power factor
benefits
estimates of these costs.
3
4
2. Dynamic voltage controllers- An inverter based device placed on the secondary side of a
transformer that has a set voltage and can source or sink VER's to meet that voltage.
PV with smart inverters- Used for voltage regulation
Solid State Transformer- lntegrated transformer and power regulator, that regulates voltage,
capabilities
provides dynamic power compensation, and harmonic cancellation. Also, remote control
Works Cited
[1] Fang, X., Misra, S., Xue, G. and Yang, D. (2Dt7l. Smort Grid - The New ond lmproved Power Grid: A
Survey - IEEE lournals & Magozlne. [online] leeexplore.ieee.org. Available at:
http://ieeexpIore.ieee.orgldocument/60995191?part=L [Accessed ].0 Nov. 2017).
[2] J. Newlander, Son Diego Gos ond Electric Distribution Resource Plon,1.st ed. San Diego: San Diego Gas
and Electric ,20L5, pp.22-84. Available at: www.cpuc.ca.gov/WorkAreo/DownloodAsset.aspx?id=5L60
[Accessed 10 Nov. 20171.
[3] C. Kalich, J. Gall, J. Lyons, G. Forsyth and R. Maguire, Avisto lntegroted Resource P/on. Spokane
Avista, 2OL7, pp. 4:!-1-1.,11:1-13. Available at: https://www.mvavista.com/about-us/our-
comoanv/intesrated-resource-planning [Accessed 10 Nov. 2017]
[4] S. Waples, (2Ot7l. Avisto System Plonning Assessment [ebook] Spokane: Avista Transmission System
Planning (Urbannova). Available at: http://www.oasis.oati.com/avat/index.html [Accessed 9 Nov.
20171.
[5] James Gall, Avista Distribution and Transmission Planning,. Spokane: Avista, 20L7, pp.4-73. Available
at:
https://www.utc.wa.gov/ layoutsi 15/CasesPublicWebsite/GetDocument.ashx?doclD=137&year=201
6&docketNumber=1 61 024.
APPENDIX K
lnterim Report: IDL Temperature Efficiency
frYtsta
Universityolldaho
Cottege of Art and Architecture
Efficiency Based on Operative Temperatures
AHwsrx
Project Duration: 12 months Project Cost: Total Funding $24,011
OBJECTIVE
The outline of the work is to profile typical
thermostat setpoints found in operation, test
alternative control methods, and estimate
potential savings. This study, based on
extensive data collection paired with energy
modeling, would provide key insights on how
to best balance thermal comfort and energy
savings for small commercial office buildings in
the Pacific Northwest. This research provides
critical data to determine how effective an
operative temperature strategy might impact
both energy use and occupant comfort. The
research leverages data analytics, energy
modeling, and comfort standards to inform
efficient control schemes that both save
energy and ensure comfoft.
BUSINESS VALUE
Operative temperature control would allow for
wider air temperature setpoints, thus saving
energy each year. Increasing the cooling
setpoint by 5oF has been shown to reduce total
HVAC energy by 27o/o. The adoption of
operative thermostats could result in
widespread energy savings and dramatically
enhance occupant comfoft, thus making it an
attractive service to offer to clients.
INDUSTRY NEED
Most buildings manage their heating or cooling
based solely on zone air temperature. Even
when the thermostats are kept within a narrow
band of 4oF, occupant complaints persist
(Arens, et al., 2010). A large factor of human
comfort is being missed in conventional
thermostats: the inclusion of the mean radiant
temperature. While operative temperature is
widely used for predicting human comfort,
rarely is it directly used in building controls. A
control system that incorporates the operative
temperature of a zone would allow for a wider
range of supply air temperatures and better
meet the needs of occupants. The wider rangeof air temperatures would reduce energy
consumption and help to capitalize on
operational features such as natural
ventilation, night-flush and optimized set-
points.
BACKGROUND
Many previous studies have established that
the temperatures of the surfaces surrounding
us have a far greater impact on our comfort
than the air temperature (Olesen 2007). A
better comfoft metric is the operative
temperature which is a mix of air and surface
temperatures. While operative temperature is
widely used for predicting human comfoft,
rarely is it directly used in building controls,
SGOPE
The new strategy will be modeled in annual
EnergyPlus simulations. The EnergyPlus
models will be informed by the temperature
and thermostat data gathered at the site
instead of ideal assumptions. The simulation
results will be used to estimate energy savings
and comfort impacts of incorporating surface
temperatures into thermostat controls. The
goal of the research is to identify the savings
possible by shifting controls from their current
baseline settings to a strategy that is more in
line with the demands of occupant comfort.
Task 1: Project Planning and Reporting
Conduct team meetings and ongoing project
updates, reports and deliverables as requiredby Avista staff, project management
contractor and the PUC. Ongoing throughout
the project.
Task 2: Establish Baseline Setpoints
The team will conduct a short literature reviewon typical operational setpoints of small
commercial office spaces in practice and will
reach out to controls engineers, consultants,
and building operators in Avista territory to
establish typical thermostat setpoints in
operation.
Task 3: Select Sites
IDL will work with Avista to select several small
commercial office buildings as case studies.
One site in the Avista territory includes
Lewiston Idaho's old city hall, now the public
works office at 215 D street. The team will
identify spaces within these selected buildings
for testing.
Task 4: Collect Operational Data
The research team will deploy an array of data
loggers to collect surface and air temperatures
along with humidity levels. The team will work
with the building operators to collect data on
energy consumption and thermostat setpoints
either through EMS recordings or by linking the
building controls to a program like SkySpark
that can catalog this information.
Task 5: Develop Energy Model
IDL will develop a basic energy model for the
spaces selected in EnergyPlus for analysis. This
will follow Task 3 and be in parallel with Task
4.
Task 6: Test Alternative Controls
Using the energy model, IDL will test
thermostat setpoints based on the operative
temperature instead of the air temperature.
This work may begin at the start of the project
for default building models and will be refined
after the completion of Task 5.
Task 7: Estimate Savings
The team will study energy simulation results
comparing the typical settings found in Task 1
against the alternative controls developed in
Task 6.
Task 8: Develop Workflow for
Practitioners
The final task will be a study of the process that
will add to the current literature and promotethis technology service to furthercommercialization techniques. This
documentation will be part of a master's
student's academic work and publication in a
conference or journal will be sought.
DELIVERABLES
. Project team has coordinated with
several building managers and control
personnel to gather current operational
data and setpoints.. Project team has logged surface and air
temperatures to create thermal comfoft
profiles of selected spaces based on
ASHRAE 55 criteria.. Project team has created simple
EnergyPlus models to reflect the
geometry and thermal properties of the
selected spaces.. Project team has tested operative
temperature control schemes on the
energy model and compared the energy
consumption and comfoft predictions to
the original thermostat settings.. Project team is able to estimate typical
energy savings for small commercial
office buildings in the Pacific Northwest
and present a replicable work flow witha control scheme that could be
implemented into a case study.
PROJECT TEAM
SGHEDULE
The information contained in this document is proprietary and confidential.
PRTNCTPAL TNVESTTGATOR(S)
Name Elizabeth CooDer
Oroanization University of Idaho Inteqrated Desiqn Lab
Contact #[phone]
Email ecoooer@u idaho. edu
RESEARCH ASSISTANTS
Name Damon Woods
Oroanization Universitv of Idaho Inteorated Desion Lab
Email dwoods@ u ida h o. ed u
Name Neha Pokhrel
Orqanization University of Idaho Inteqrated Desiqn Lab
Email nookhrel@u idaho.edu
Name Ryker Belnap
Orqanization University of Idaho Inteqrated Desiqn Lab
Email [e-mail]
TASK TIME
ALLOGATEO
START
DATE
FINISH
DATE
Proiect Planninq t2 08/t7 08/18
Establish Baseline Stots 2 05/t7 07 ttg
Select Sites 2 08/L7 10/17
Collect ODerational Data 7 09/t7 07 /t7
DeveloD Enerov Models 5 toltT :/18
Test Alternative Controls 4 03/18 07/L8
Estlmate Savinos 3 05/18 07tt8
Develop Workflow TiEITI
The information contained in this document is proprielary and confidential
APPENDIX L
lnterim Report: Aerogel
Aivtsra
Universityotldaho AHwsrfr
College of Engineering
Aerogel Insulation System: An Innovative Energy
Efficient Thermal Wall
Project Duration: 12 months Project Cost: Total Funding$88,777
OBJEGTIVE
In 2015, about 4Oo/o of the total U.S. energy
consumption was consumed in residential and
commercial buildings, and the government
promotes renovation of existing buildings tomeet minimum energy performance
requirements. The main objective of this
project is to investigate the efficiency of
Aerogel insulation blankets as a new
insulation material for residential buildings.
The research tasks are conducted using field
measurements and computer simulations.
The field measurements are being collected
from a real apaftment where walls were
insulated with the Aerogel blankets. In
parallel, a computational fluid dynamic (CFD)
code is developed to validate the field data
and perform additional key parameters.
BUSINESS VALUE
It is anticipated that the use of Aerogel
reduces the heat transfer across building
envelopes and therefore will reduce the
annual heat loss by 50o/o compared to the
currently used insulation material(s). In
addition, a life cycle cost analysis with a full
consideration of service life-cost will be
performed and compared to the performance
of conventional insulation materials.
INDUSTRY NEED
Energy sustainability is a crucial issue for the
humanity's development in modern era,
which can significantly impact future quality
of life and the environment. The industry is
moving towards the construction of
sustainable-energy efficient buildings. The
outcomes of the current research will be
applied to help Avista customers save energy/
reducing need for natural gas and electricity.
The Aerogel blankets will be efficiently used in
retrofitting existi ng structures.
BAGKGROUND
Aerogel, also called solid smoke, is a
synthetic porous material with remarkable
properties. Aerogels are dried gels with a very
high porosity. It was discovered in the early
1930s. Aerogel molecules do not decomposeat high temperatures and do not release
harmful gases. Even at 800oC, the thermal
conductivity of Aerogel is only 43 mw/(m. K).More information could be found in
https://www.aerogel.com/ resources/commo
n / u se rf i I es/f i I e/ D a ta o/o 2 0 S h e ets/ S p a ce I oft -
Eu rooea n-Datasheet- EN. odf.
SGOPE
Acceptable insulation materials need to
achieve as low thermal conductivity as
possible, enabling a high thermal resistance
as well as a low thermal transmittance. The
scope of this project is mainly focused on
evaluating the ability of the Aerogel blanketsto save energy through a comprehensive
study which is broken down as follows:
Task 1: Literature Survey
Various studies have repofted on the
characterization of Aerogel, along withpreliminary investigation of its
implementation as a super insulator,
(Somana 2012, Huang 20t2, Neugebauer
2004, and New York State of Energy 2013).
Task 2: Aerogel Acquisition
The team has purchased 800 ft2 of the
Aerogel blankets. The commercial product
name is Spaceloft. It has a 10mm thickness,
and it was delivered as a roll. The team also
purchased a Campbell Scientific data logger,
thermocouples and two heat flux sensors to
collect data from walls insulated by the
Aerogel with other needed supplies.
Task 3: Field Data Collection
Dr. Ibrahim and his team has started field
data collection. Thirty thermocouple sensors
have been placed in one room of the
apartment. Sensors have been mounted on
all walls in the room to record data with
existing insulation and with Aerogel. Figure 1
shows the thermocouples attached to two
walls of the room, while Figure 2 shows
Temperature vs. time for the south wall
(without Aerogel). The Team built a cubical
frame with all walls made from a pure
&}|'U6,
Aerogel. The temperatures at multiple
locations across the box were measured andwill be used to verify the computer
simulations.
Task 4: Modeling and Simulation
Dr. Tao Xing, his Postdoctoral Fellow Dr.
Rabijit Dutta, and his graduate student are
using computational fluid dynamics (CFD) to
simulate a square box built using the Aerogel
and are using EnergyPlus to simulate the
apartment under test. The purpose of using
CFD is to determine the heat conductivity of
the Aerogel by comparing the predicted inner
wall temperature with the measurements.
Figure 3 shows the preliminary comparison.
Ongoing work includes trying different mesh
resolutions, boundary conditions, and wall
models. Once heat conductivity of the Aerogelis determined, the value will be used for
apartment simulations using EnergyPlus.
Figure l: Interior view of apartment with thermocouples
This task will be performed after task 4.
[enr[)erattire \ri 1 rnre
I
f,E
ilmm ,o(It((r
'lml{
80
70
50
Figure 3: Comparison between the predicted inner wall
temperature using CFD and experimental measurements
for a square Aerogel box.
DELIVERABLES
The main deliverable of this project will be a
final report includes:
1. All the needed information regarding the
field data collection and the computer
simulations, and the exact thermal
conductivity of the Aerogel blankets.2. Cost analysis to compare the existing cost
of the current insulation material(s) with
the anticipated cost of the walls insulated
with the Aerogel.
PROJECT TEAM
SGHEDULE
-* so
c'tE40acE30
m
10
-s-I+s-5
-s-2*s.7
-s.:,.-Ccffrllmp
+Orrtidrl@p
o o 5oo lq)o ,.u.*, zqx, 2soo 3.*x,
Tire {*conds)
Figure 2: Temperature versus time for the south wall
The team is currently working on the Analysisof the collected data and how that will be
used in the generation of results using the
computer simulation.
Task 5: Cost Analysis
This task will be performed after task 4.
Task 6: Environmental lmpact
The informalion contained in lhis document is proprietary and confidential
PRTNCTPAL TNVESTTGATOR(S)
Name Ahmed Ibrahim
Orqanization University of Idaho-Civil Enqineerinq
Contact #208 885 1328
Email aibra h im@u idaho. edu
Name Tao Xino
Oroanization University of Idaho-Mechanical Enqineerinq
Contact #208 885 9032
Email Xino(ouidaho.edu
Name Brian Johnson
Orqanization Universitv of Idaho-Electrical Enqineerinq
Contact #208 885 5902
Email biohnson @uidaho.edu
RESEARCH ASSISTANTS
Name Moammed Mudaoio
Oroanization University of Idaho-Civil Enqineerinq
Email muda6801 @vandals.uidaho.edu
Name Shimul Hazra
Orqanization University of Idaho-Mechanical Enqineerinq
Email [e-mail]
TASK TIME
ALLOCATED
START
DATE
FINISH
DATE
{Task 1: Literature
review){5 Months}{8/2077}{t/20L8}
{Task 2: Material
purchasinq){2 Months}{7o/2oL7){L2/2077}
{Task 3: Field Data
Collection){3 Months}{1/2018}{3/20 18}
{Task 4: Simulation {5 Months}{9t2017){4t20ta)
{Task 5: Cost
Analysis){2 Months}{4/2077}t6120 18)
{Task 6: Enviro
ImDact){2 Months}{6/2017}{8/2018}
at
ll
I
I
1t
-
^yar rtsrfi. ! tfrr iJ-r
!r
ti
p Ia
The infr confidenti;