20201125Exhibit 4 - Avista 2019 R&D Report.pdf

Exhibit No. 4: Avista 2019 Idaho Research and Development Report
Exhibit No. 4:
Avista 2019 Idaho Research and Development Report

AVISTA UTILITIES
SELECTED RESEARCH AND DEVELOPMENT
EFFICENCY PROJECTS - IDAHO

Annual Report

March 31, 2020

Avista Research and Development Projects Annual Report
March 31, 2020

THE FOLLOWING REPORT WAS

PREPARED IN CONFORMANCE WITH

IDAHO PUBLIC UTILITIES COMMISSION (IPUC)

CASE NO. AVU-E-13-08

ORDER NO. 32918

March 31, 2020

Avista Research and Development Projects Annual Report
March 31, 2020

ANNUAL REPORT
SELECTED RESEARCH AND DEVELOPMENT EFFICENCY PROJECTS
IPUC CASE NO. 32918

TABLE OF CONTENTS

I. SCOPE OF WORK ................................................................................................................. 1
A. Introduction ........................................................................................................................... 1
B. Background ........................................................................................................................... 2
II. KEY EVENTS ......................................................................................................................... 2
A. Request for Proposal ............................................................................................................ 2
B. Selection of Projects ............................................................................................................. 3
C. Description of Selected Projects ........................................................................................... 4
D. Project Manager and Related Communications ................................................................... 5
E. Agreements .......................................................................................................................... 5
F. Project Milestones ................................................................................................................ 6
III. ACCOUNTING ....................................................................................................................... 8
A. Schedule 91 Available Funds ............................................................................................... 8
B. Funds Authorized for R&D Projects in 2018/2019 ................................................................ 8
C. Funds Expended and Remaining Balance ........................................................................... 9
D. Cost-Recovery ...................................................................................................................... 9
IV. PROJECT BENEFITS .......................................................................................................... 10
A. Aerogel Phase II ................................................................................................................. 10
B. All-Iron Battery .................................................................................................................... 10
C. Energy Trading Phase I ...................................................................................................... 10
D. IR Camera Phase I ............................................................................................................. 11
V. RESEARCH IN-PROGRESS ............................................................................................... 11
A. Summary of Research In-Progress .................................................................................... 11
B. Other Relevant Activity ....................................................................................................... 13

LIST OF APPENDICES

APPENDIX A Two-Page Reports
APPENDIX B Request for Proposal
APPENDIX C University of Idaho Agreements
APPENDIX D Final Report: Aerogel Phase II
APPENDIX E Final Report: All-Iron Battery
APPENDIX F Final Report: Energy Trading System Phase I
APPENDIX G Final Report: IR Cameras Phase I
APPENDIX H Two-Page Report: Energy Trading Phase II
APPENDIX I Two-Page Report: Gamification of Energy Use
APPENDIX J Two-Page Report: IR Cameras Phase II
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I. SCOPE OF WORK

This report is prepared in conformance with Idaho 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. At the end of 2019, Avista Utilities supplied retail
electric service to approximately 393,000 customers and retail natural gas service to
approximately 361,000 customers across its service territory. Avista Utilities' service
territory covers 30,000 square miles with a population of 1.7 million. Avista also
provides retail electric service in Juneau, AK through a subsidiary called Alaska
Electric Light and Power Company.

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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 Idaho’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 for
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. It is also consistent with the former Idaho Governor’s Global
Entrepreneurial Mission “iGem” initiative in which industry would provide R&D funding
to supplement funding provided by the State of Idaho.

In 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.

In 2010, the former Governor announced Idaho 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.

II. KEY EVENTS

The Request for Proposal (RFP) for projects funded in the 2018/2019 academic year
was prepared and distributed to all three Idaho Universities in March 2018. A full copy
of the RFP is included in Appendix B.

On April 21, 2018, Avista received 10 proposals from the University of Idaho, 2
proposals from Boise State University, and 1 proposal from Idaho State University.
Following is a list of the proposals received:

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University of Idaho
1. Asset Health Management for Avista System
2. Designing and Evaluating an Energy Trading System for Prosumers
3. Develop New Sustainable Planning Process to Provide Non-wire Solutions to
Capacity Planning
4. Energy Interdependency Risk Analysis
5. Energy Use at Clearwater Paper: Evaluating the Potential for Innovations
with an Integrated Approach to Energy
6. Fluid Flow Electric Load Scheduling and Demand Side Management in
Smart Districts
7. High Energy Efficient Aerogel-Glazing Coupled with Aerogel-Insulated Walls
in Residential
8. Proof of Concept All-Iron Battery with Carbon Electrodes
9. Optimizing Energy Modeling of Dedicated Outdoor Air Systems in Cold and
Dry Climate Zones
10. Using IR Cameras in Building Controls
Boise State University
1. Demand-Side Management of Non-Critical Residential Loads Using Power
Electronic Modulators and/or Electric Springs
2. Analytics Toolkit for Interval Data from Aggregate Loads
Idaho State University
1. LED Street Light Technology

Avista prepared an evaluation matrix for the 13 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 criteria, in no particular order, were considered in the ranking process.

 Research Areas Already Being Done (EPRI, WSU, AVA)
Complement/Redundant/New
 Potential Value to Customers kwh/KW/$ (1-10)
 CO2 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)
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Following is a brief description of each of the four selected projects from the
2018/2019 academic year. Project teams compiled “Two-Page Reports” which
summarized and highlighted project details. These Two-Page Reports are included
in Appendix A. Additional details are included in the final project reports in Appendix
D, Appendix E, Appendix F, and Appendix G.

High Energy Efficient Aerogel-Glazing Coupled with Aerogel-Insulated Walls in
Residential Buildings: Phase II (referred to as Aerogel Phase II)

Summary of Phase I
The main objective of this project was to investigate the thermal efficiency of Aerogel
insulation blankets as a new insulation material for future implementation in the
exterior walls of residential buildings. The research tasks were conducted using
laboratory experiments, field measurements and computer simulations. The
computer simulation tools included the EnergyPlus software, which is available to
users through the U.S. Department of Energy, and the computational fluid dynamics
(CFD) tool; FLUENT in ANSYS Multiphysics. The field measurements were collected
from a room in a real apartment before and after the walls were insulated with the
Aerogel blankets.
Project Description for Phase II
The goal of Phase II was to develop the means of implementing new energy-efficient
aerogel glazing systems in residential buildings for Avista customers to combine with
the aerogel insulated walls evaluated in Phase I.

The objectives of the project’s second phase were to: 1) determine the thermal
properties of aerogel-based glazing systems, 2) develop and validate a numerical
model using computational fluid dynamics to predict heat transfer performance of
these systems, and 3) apply this model to a simulated, scaled 3D single-family house
with walls and windows insulated with aerogel.

Ultimately, the feasibility of using aerogel blankets for walls and windows as a super
insulator was verified using small scale laboratory testing, and high-fidelity computer
simulations.

Proof of Concept All-Iron Battery with Carbon Electrodes (referred to as All-Iron
Battery)

The objective of the project was to scale-up and refine the all-iron battery. The all-
iron battery uses inexpensive and safe chemistry to store electrical energy. Through
the scaling of the battery, the research demonstrated that the energy storage capacity
is close to the theoretical limit. The research also showed that the power delivered by
the cell can be increased to a practical level using carbon additives.
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Designing and Evaluating an Energy Trading System for Prosumers (referred to as
Energy Trading Phase I)

The objective of this project was to analyze and develop a prototype system that
would enable Avista prosumers and consumers to trade power on-demand with utility
oversight or with the utility while also ensuring the utility’s network resilient operation
with respect to the enablement of such prosumer-consumer transactions. A prosumer
for this project is any retail customer that who may both consume and produce
energy. A prototype software system was designed and developed that enabled the
request of prosumer-consumer energy transactions through an online web and/or
application interface. In addition, a system for evaluating the feasibility of the
proposed transactions within the Avista distribution network was developed.

Integrated Design Lab (IDL): Using IR Cameras in Building Controls (referred to as
IR Camera Phase I)

This project demonstrated the feasibility of using a low-cost infrared camera to
estimate the mean radiant temperature in a room. This radiant temperature
information was used to more effectively heat and cool buildings. The team set up a
camera in an experimental chamber and collected data from various scenarios. Once
collected, the team processed the data into a comfort prediction and sent that
information as a standard control signal. Energy models were used to estimate the
potential energy savings of incorporating an infrared camera into building’s
thermostats.

Avista set out to find an independent third-party project manager based in Idaho. On
September 26, 2014 Avista entered into an agreement with T-O Engineers as this
independent third-party project manager. T-O Engineers is a company based in
Idaho, with offices in Boise, Coeur d’Alene, Meridian and Nampa, Idaho, as well as
Cody, Wyoming, Heber City, Utah, and Spokane, Washington.

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 and the deputy project manager is
Natasha Jostad, PE. JR and Natasha are based out of the Coeur d’Alene and
Spokane offices, respectively.

By July 16, 2018 Avista executed individual task orders for each of the University of
Idaho research projects selected. The agreements are included in Appendix C.
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The following graphics identify the overall research and development milestones, as
well as the milestones for each project. Final reports from each Principle Investigator
were submitted in the fall of 2019. In addition to the written report, each research
team presented their findings in person to Avista. The Aerogel Phase II and IR
Camera Phase I teams both presented their findings to Avista on August 19, 2019,
and the All-Iron Battery and Energy Trading Phase I teams presented their findings
on August 28, 2019.

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III. ACCOUNTING

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.

Academic
Year
New
Funding
Balance
from
Previous
Year
Total
Funds
Available
Contracted
Amount
Actual
Expenditures Balance
2014/2015 $300,000.00 $0.00 $300,000.00 $287,941.00 $243,467.32 $56,532.68
2015/2016 $300,000.00 $56,532.68 $356,532.68 $252,493.00 $235,809.03 $120,723.65
2016/2017 $300,000.00 $120,723.65 $420,723.65 $372,665.16 $358,641.82 $62,081.83
2017/2018 $300,000.00 $62,081.83 $362,081.83 $317,074.89 $313,757.29 $48,324.54
2018/2019 $300,000.00 $48,324.54 $348,324.54 $299,463.00 $265,826.86 $82,497.68
2019/2020 $300,000.00 $82,497.68 $382,497.68 $287,400.00

Contracts for 2018/2019 are as follows:

Agency Project Contract
Amount Point of Contact
University of Idaho Aerogel Phase II $ 82,873.00 Dr. Ahmed Ibrahim
University of Idaho All-Iron Battery $ 48,141.00 Dr. Peter Allen
University of Idaho Energy Trading Phase I $ 89,771.00 Dr. Yacine Chakhchoukh
University of Idaho IR Camera Phase I $ 48,678.00 Dr. Damon Woods
T-O Engineers Project Manager $ 30,000.00 James R. Norvell
Total $ 299,463.00

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Following is the final budget summary for 2018/2019 FY R&D Projects.

Agency Project Contract
Amount
Total
Expended
Budget
Remaining
University of Idaho Aerogel Phase II $ 82,873.00 $ 83,916.10 See below
University of Idaho All-Iron Battery $ 48,141.00 $ 30,529.55 $ 17,611.45
University of Idaho Energy Trading Phase I $ 89,771.00 $ 83,037.16 $ 6,733.84
University of Idaho IR Camera Phase I $ 48,678.00 $ 48,678.00 $ 0.00
T-O Engineers Project Manager $ 30,000.00 $ 19,666.05 $ 10,333.95
Totals $ 299,463.00 $ 265,826.86 $ 33,636.14

Aerogel Phase II was a continuation of Phase I, conducted in FY 2017/2018. Due to the
extension of the project, the research team finalized their Phase I work concurrent with
the kick-off of their Phase II work and therefore, requested the budget total be evaluated
as the contract amounts for Phases I and II. The following table outlines the contract
amounts and funds expended for Phases I and II of the Aerogel project.

Project Contract
Amount
Total
Expended
Budget
Remaining
Aerogel Phase I $ 88,777.00 $ 87,710.49 $ 1,066.51
Aerogel Phase II $ 82,873.00 $ 83,916.10 -$1,043.10
Totals $ 171,650.00 $ 171,626.59 $ 23.41

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 III 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 Avista’s annual tariff filing in June if the rider
balance requires a true-up.

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IV. PROJECT BENEFITS

Many buildings in the United States were constructed before energy efficiency was a
concern. Therefore, a strong incentive exists to renovate existing buildings to meet
minimum energy requirements as mandated by the federal and state governments.
Aerogel is a transparent insulating material that meets this need to improve energy
efficiency in residential buildings because of its extremely low thermal conductivity,
high solar factor, and high daylight transmittance.

Experimental data and computer simulations were used to show that the overall yearly
savings using 1 layer of window aerogel insulation is 39%. The overall yearly savings
using 1 layer and 4 layers of wall aerogel insulation coupled with window aerogel
insulation is 13.3% and 38.3%, respectively for a single-family house, compared to
traditional insulation. The use of aerogel insulation in retrofitting existing structures
and in new construction would result in energy savings for Avista customers.

Expansion of renewable energy capability has resulted in periods of oversupply; when
more energy is generated than can be used generators are forced to shut down. In
sunny locations like Mexico, solar energy can cost as little as $14 per megawatt hour.
Prices in the Pacific Northwest are approximately $30 per megawatt hour. This
represents an economic opportunity in energy arbitrage: if oversupply of renewable
energy could be stored and sold during peak demand, it would provide a new revenue
source. In the long term, this will also increase overall demand for the expansion of
wind and solar energy. However, there is no stationary battery technology that is
sufficiently safe and inexpensive to make a practical grid storage solution. Cost is the
critical factor for a stationary battery. Weight and volume are less important (unlike in
mobile applications). The extremely low-cost, non-toxic, non-flammable all-iron
battery can help meet this need. The team estimates that a shipping container sized
all-iron battery could store approximately one megawatt-hour and deliver 1 MW of
peak power for a total cost of $0.09 per watt-hour stored.

The adoption of consumer-level electric energy production and storage and of smart
consumer appliances is accelerating. Examples of technologies leading this rapid
adoption are photovoltaics, small wind, electric vehicles, and smart water heaters and
freezers, among other technologies. Within such a new prosumer-enabled Smart Grid,
enabling the managed but timely and selective trading and transmission of power
between prosumers (producer-consumer) and with the utility, with utility oversight may
enable a more efficient use of the Power Grid as well as provide utility savings on
generation and transmission.

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Such type of integrated system would enable Avista to develop a new market for
prosumer-consumer power trading and also to plan, manage, and control such
market and the flow of power through its network.

The research proved that it is possible to use an inexpensive thermal camera to
predict comfort and occupancy in an office and run a thermostat based on those
comfort predictions. The energy models showed net energy savings for Avista
customers. The goal is to commercialize this device so that Avista customers may
benefit from its development. During Phase II (in progress), the project is
incorporating glare detection into the algorithm so that the device may serve multiple
functions and eliminate superfluous sensors and wiring in buildings.

V. RESEARCH IN-PROGRESS

There are currently three projects in progress for the 2019/2020 academic year.
Project kick-off meetings were held on-site at each University of Idaho location or via
web conference in early fall 2019. Two stage gate meetings will be held throughout
the academic year where the teams have an opportunity to showcase their research
and plan upcoming work with input from Avista. Two-Page Reports were prepared in
the spring of 2020 and describe the project objectives, business value and industry
need. Additionally, the individual project tasks are summarized. Two-Page Reports
are included in Appendices H through J. Each team will present their research and
findings to Avista in the fall of 2020, as well as prepare a final research report. Final
reports will be filed with the 2021 Annual Report.

The individual project tasks for the current IPUC funding projects are presented
below.
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A progress meeting is held twice monthly for each project. These meetings typically
take 0.5 hours and include a review of schedule, bi-monthly progress reporting,
invoicing, Avista comments, and action items for the next meeting. The meetings are
organized and led by the Independent Program Manager, T-O Engineers. Attendees
for each meeting include the Principal Investigator, Co-Investigators, Student
Researchers, Avista personnel, and the Independent Program Manager.

Contracts for these projects total $287,400.00 and are summarized below.

Agency Description Contract Amount Point of Contact
University of Idaho Energy Trading Phase II $ 96,164.00 Dr. Yacine Chakhchoukh
University of Idaho Gamification of Energy
Use $ 108,736.00 Richard Reardon
University of Idaho IR Camera Phase II $ 52,500.00 Dr. Damon Woods
T-O Engineers Project Manager $ 30,000.00 James R. Norvell
Total $ 287,400.00

Funds expended, and additional budget details will be summarized in the 2021
Annual Report.
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APPENDIX A
Two-Page Reports

High Energy Efficient Aerogel-Glazing Coupled with
Aerogel-Insulated Walls in Residential Buildings: Phase
II
Project Duration: 12 months Project Cost: Total Funding $82,873
OBJECTIVE
The PIs validated the thermal conductivity of
the aerogel blankets in Phase I and found it to
be very low as close to 0.014 Wm-1K−1, which
confirms that aerogel blankets could be
considered a super insulator. The proposed
work will be based on experimental laboratory
testing (Dr. Ahmed Ibrahim), ANSYS
Multiphysics simulation including
computational fluid dynamics (CFD) (Dr. Xing
and Dr. Sean Quallen), and EnergyPlus (Drs.
Ibrahim, Xing and Johnson). The team is
proposing to develop full detailed 3D single
family house simulation to capture the
thermal performance of walls insulated with
aerogel blankets and aerogel-based window
panes. A practical tool for energy
consumptions’ alternatives will be developed.
BUSINESS VALUE
It is anticipated that the use of Aerogel-based
window panes reduces the heat transfer
across building envelopes and therefore will
significantly reduce the annual heat loss
compared to the currently used window
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.

Benefits to Avista: 1) Huge projected energy saving: the team performed a preliminary
analysis on the proposed aerogel-based
glazing and found that heating saving is
estimated to be 15% when using one layer
(0.4-inch) of aerogel insulation in a window
and a wall of a typical building, and 2) Tool to
assess energy consumption and potential
saving and that will be based on parametric
studies using the validated numerical
simulations to quantify the various energy
leakage sources and optimize the design of
the windows and walls.
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 have a
direct impact on Avista customers saving
energy, reducing need for natural gas and
electricity. The Aerogel-based windows will
be efficiently used in retrofitting existing
structures and in new constructions as well.
BACKGROUND
The investigators have conducted a
comprehensive literature survey for the up-
to-date studies related to the performance
and implementation of aerogel-based glazing
insulation in residential buildings.
Representative publications are listed below:
1.Ghoshala, S., Neogi, S. (2014). Advance
Glazing System – Energy Efficiency Approach
for Buildings a Review, Energy Procedia 54
(2014) 352 – 358
2.Cinzia Buratti, Elisa Moretti Transparent
insulating materials for buildings energy
saving: experimental results and performance
evaluation, Third International Conference on
Applied Energy - 16-18 May 2011 - Perugia, Italy
3. High Energy-Efficient Windows with Silica
Aerogel for Building Refurbishment:
Experimental Characterization and
Preliminary Simulations in Different Climate
Conditions
SCOPE
The scope of this project is mainly focused on
evaluating the ability of the Aerogel-based
windows to save energy through:
Task 1: Literature Survey
The information contained in this document is proprietary and confidential.

Various studies have reported on the characterization of Aerogel, along with
preliminary investigation of its
implementation as a super insulator,
(Ghoshala, S., Neogi, S. (2014)., Cinzia et al.
(2011).
Task 2: Aerogel Acquisition
The team has purchased 54 ft2 of the Aerogel
based glazing. The panes have a 16mm
thickness. The team also purchased a
computer workstation for the CFD
simulations.
Task 3: Experimental Study
Dr. Ibrahim has started field data collection. Twenty thermocouple sensors have been
attached to the cubical scaled room as shown
in Figure 1. The scaled room was completely
sealed and the internal temperature was
raised up to 950F and then the decay in
temperatures was measured. Figure 2 shows Temperature vs. time for the vertical walls.
The results will be used to verify the
computer simulations.
Task 4: Modeling and Simulation
Dr. Tao Xing, and Dr. Sean Quallen are using
computational fluid dynamics (CFD) to
simulate a cubical scaled room built using the
Aerogel panes.

Figure 1: View of cubical scaled room

Figure 2: Temperature versus time for the south
wall

The results of the CFD simulations have
helped to determine an expected convection
transfer coefficient inside our experimental
domain as well as helping to determine ideas
for temporal and spatial discretization for future simulations. These results also
demonstrate that the transition to a natural
top-to-bottom temperature gradient happens
rapidly, especially relative to the 1-2-hour
experimental timeframe (see Figure 3).

Figure 3: 2D simulation showing temperature
distribution at t = 0s (left), t = 30s (middle), and t
= 90s (right)
Task 5: Energy Simulation: will start on 5/2019
Task 5: Cost Analysis: will start on 6/2019
DELIVERABLES
The main deliverable of this project will be a
final report includes all the experimental data
the data and the computer simulations, cost
analysis to compare the cost saving of the
insulation panes.
PROJECT TEAM
PRINCIPAL INVESTIGATOR(S)
Name Ahmed Ibrahim
Organization University of Idaho-Civil Engineering
Contact # 208 885 1328
Email [e-mail]
Name Tao Xing
Organization University of Idaho-Mechanical Engineering
Contact # 208 885 9032
Email [e-mail]
Name Brian Johnson
Organization University of Idaho-Electrical Engineering
Contact # 208 885 6902
Email [e-mail]
RESEARCH ASSOCIATES/ ASSISTANTS
Name Sean Quallen
Organization University of Idaho- Mechanical Engineering
Email [e-mail]
Name Santu Golder
Organization University of Idaho-Mechanical Engineering
Email [e-mail]
SCHEDULE
TASK TIME
ALLOCATED
START
DATE
FINISH
DATE
{Task 1: Literature review} {5 Months} {9/2018} {12/2018}
{Task 2: Material
purchasing} {2 Months} {9/2018} {11/2018}
{Task 3: Computer Validation} {3 Months} {12/2019} {2/2019}
{Task 4: Simulation} {6 Months} {2/2019} {5/2019}
{Task 5: Energy simulation {1 Months} {5/2019} {6/2019}
{Task 6: Cost
Analysis} {2 Months} {6/2019} {8/2019}

Proof of Concept All-Iron Battery with Carbon Electrodes
Project Duration: 10 months Project Cost: Total Funding $48,141

OBJECTIVE
This project objective is to refine an extremely
low-cost, non-toxic, non-flammable battery
chemistry. This battery stores energy using an
iron anode and an iron salt cathode. A new
carbon electrode material developed in the
Cheng lab at the University of Idaho is being
used to increase performance. The primary
advantage of the iron/carbon design is a low
price per Watt-hour of storage. Our current results suggest that this chemistry will be
economical for stationary applications. We are
building a demonstration battery and are
planning to spin out a company to
commercialize its production.
BUSINESS VALUE
A safe and inexpensive battery could help
expand the use of low-cost renewable energy.
Energy storage also enables energy arbitrage.

INDUSTRY NEED
Expansion of renewable energy capability has
resulted in periods of oversupply; when more
energy is generated than can be used
generators are forced to shut down. In sunny
locations like Mexico, solar energy can cost as
little as $14 per megawatt hour. Prices in the
Pacific Northwest are approximately $30 per
megawatt hour. This represents an economic
opportunity in energy arbitrage: if oversupply
of renewable energy could be stored and sold
during peak demand, it would provide a new
revenue source. In the long term, this will also
increase overall demand for the expansion of
wind and solar energy. However, there is no
stationary battery technology that is
sufficiently safe and inexpensive to make a
practical grid storage solution. Cost is the
critical factor for a stationary battery. Weight
and volume are less important (unlike in
mobile applications). The extremely low-cost,
non-toxic, non-flammable all-iron battery can
help meet this need.

BACKGROUND
Prior to Avista funding, the Iron battery was a
crowdfunded effort to make an “Open Source”
energy storage solution. The Allen lab
screened iron salts and separator materials for
low cost and high reliability. Avista funds have
allowed for optimization and scale-up of the
preliminary cell.
The overall cell is shown above. An iron anode
is oxidized at the left. This produces electrons
that flow to the right and react with iron (III) sulfate. We started with a 20 ml cell made
from iron, iron (III) sulfate, and a polymer
separator.
SCOPE

Task 1: Collect data on 20ml cell
The project started with a prototype 20 ml cell which was
not fully characterized. We replaced the inexpensive
polymer (sodium polyacrylate) with Nafion (a commercial
separator). We also refined assembly methods to make a
sealed cell that lasted far longer than our original design.
We characterized the resulting cell and found that it
delivered ~10 mW of peak power. It also could be
charged and discharged dozens of times with little loss in
capacity.

The information contained in this document is proprietary and confidential.

Task 2: Construct 200 ml cell

The 200 ml cell is a scaled version of the 20 ml cell. A
plastic bag holds the “pouch” separator membrane. The
pouch separates the interior (anode) from the exterior
(cathode) electrolyte. The diagram above shows an
assembled prototype. The first assembly procedure
produced pinhole leaks in the plastic. The current
assembly procedure produces a cell without leaks.

Task 3: Characterize 200 ml cell

The initial characterization of the 200 ml cell produced
close to the desired total energy capacity but a low total
power. The discharge of the cell takes 24 hours (as
above) or longer. Cell improvements to meet the desired
power requirements (1W) include alternative
membranes and conductive additives (e.g. GUITAR
carbon) to the electrolyte solutions.

Task 4: Build 1L battery

The 1 L battery will consist of five of the 200 ml cells
connected in series. We have constructed a housing to
contain the cell stack. Images of the prototype (left) and
final acrylic (right) are shown above.

Task 5: Characterize 1L battery
The performance of the 200 ml stack will yield
performance benchmarks for the 1L cell. The
performance objectives are a 15 Wh capacity and 15 watts of peak power.

Task 6: Prepare follow-up proposal
Options in the table include SBIR funding and
private capital. We need to reach our goals
for power and energy capacity.
DELIVERABLES
• Construct a 0.2 L cell (complete)
• 0.2 L cell stores 0.4 Wh (goal 1.5 Wh)
• 0.2 L cell delivers 40 mW (goal 1W)
• Construct a 1L battery (in progress) • 1 L cell should store 15 Wh of energy
• 1 L cell should deliver >5 W peak power
• Apply for SBIR and/or propose venture
funding for commercial production
• Report on appropriate path to market
PROJECT TEAM
PRINCIPAL INVESTIGATOR(S)
Name Peter Allen
Organization University of Idaho
Contact # 208-885-5807
Email [e-mail]
Name (Co-I) Francis Cheng
Organization University of Idaho
Contact #
Email
Name (Co-I) Dean Edwards
Organization University of Idaho
Contact #
Email
RESEARCH ASSISTANTS
Name Nicolas Yensen
Organization University of Idaho
Email
Name Depak Koirla
Organization University of Idaho
Email
Name
Organization University of Idaho
Email

SCHEDULE
TASK TIME
ALLOCATED
START
DATE
FINISH
DATE
Collect data on 20ml cell 1 9/18 10/18 Construct 200 ml cell 3 10/4 1/19
Characterize 200 ml cell 2 1/19 3/19 Build 1L battery 2 3/19 5/19
Characterize 1L battery 2 5/19 7/19 Prepare follow-up proposal 1 7/19 8/19

i

Designing and Evaluating an Energy Trading System for
Prosumers
Project Duration: 12 months Project Cost: Total funding $89,771
OBJECTIVE
The objective of this project is to analyze and
develop a prototype system that would enable
Avista prosumers and consumers to trade
power on-demand with utility oversight or with
the utility while also ensuring the utility’s
network resilient operation with respect to the
enablement of such prosumer-consumer
transactions. A prototype software system will
be designed and developed that enables the
request of prosumer-consumer energy
transactions through an online web and/or
application interface. In addition, a system for
evaluating the feasibility of the proposed
transactions within the Avista distribution
network will be developed and integrated.
Such type of integrated system would enable
Avista to develop a new market for prosumer-
consumer power trading and also to plan,
manage, and control such market and the flow
of power through its network.

BUSINESS VALUE
Avista will benefit from such prototype in the
following ways:
• Provide a platform for testing new
technologies and algorithms that would
enable larger scale implementations of
such customer-driven trading markets
• Enable the collection of data on sharing
behaviors and trading patterns,
• Enable data analytics that may lead to
increased efficiency in the distribution
system,
• Enable data analytics that may lead to
adequate and profitable electric power
pricing strategies for prosumers and
smart-grid enabled consumers and
Avista power transaction fee structure.

INDUSTRY NEED
The adoption of consumer-level electric energy
production and storage and of smart consumer
appliances is accelerating. Examples of
technologies leading this rapid adoption are
photovoltaics, small wind, electric vehicles,
and smart water heaters and freezers, among
other technologies. Within such a new
prosumer-enabled Smart Grid, enabling the
managed but timely and selective trading and
transmission of power between prosumers
(producer-consumer) and with the utility, with
utility oversight may enable a more efficient
use of the Power Grid as well as provide utility
savings on generation and transmission. For
example, a temporary increase in demand
from a smart-grid-enabled customer could be
served from a grid-near prosumer with stored
capacity or current wind or solar generation.

BACKGROUND
The following is an example of how the
proposed prosumer-consumer power trading
may work. One of Avista’s customers installs
solar panels or has a storage system such as
an electric vehicle and generates or stores
electricity. To sell some of this prosumer’s
excess electricity, this prosumer goes to an
Avista-managed website and enters the
amount of energy that they want to sell, the
target price, its nature, and other constraints
such as timing. An Avista customer needs
some additional electricity at a given or
scheduled time. Such customer goes to the
same website and indicates an interest in
buying energy under certain conditions,
pricing, and time period. The proposed system
then creates a match between the prosumer
energy offers, requests, restrictions, timing,
and pricing, informs the customers, enables
and records the business transaction.

TASKS and STATUS:
Task 1: System Requirements
Research has been completed in determining
the desired system architecture and modeling
packages. OpenDSS will be used for modeling the power system.
Task 2: Database Design
Design and model the system using modern
system modeling tools such as ERD.
Completed design an implementation to
support transactions. Refinements are needed to fully support the Power System Model
interaction.
Task 3: Database Architecture
Develop the database architecture and schema
and select a DBMS. Completed as needed for
initial prototype. Refinements are ongoing.
Task 4: Security Risks
Analyze security risks and design and
implement security mechanisms. Ongoing as
implementation proceeds.
Task 5: Simulation Model
The IEEE 13 bus distribution model will be used
for testing functionality. A model has been modified to include Photovoltaics, battery
storage, electric vehicles, wind, and switch
gear. This model will be used along side test
cases for PV to test the application software.
Task 6: Web Interface
Design and sketch Web and/or App interfaces:
User and Administration. Prototype system is
complete. Enhancements are ongoing as
needed.
Task 7: Prototype Software
Build the prototype software. Prototype
system is complete. Enhancements are
ongoing as needed.
Task 8: Transaction Control
The communication between the web
application and OpenDSS has started and is
being interfaced through a SQL query and
Python. The team has the individual parts: SQL
query, OpenDSS, and Python in the testing
phases.
Task 9: Functionality Testing
The team has started testing individual pieces
of the software. Communication between
python and OpenDSS has been established,
and the next steps are to incorporate the SQL
query and database transactions.
Task 10: Usage Guides
This task will be performed after Task 9.
Task 11: OpenDSS Test Case
Three test cases are being explored for
implementation in OpenDSS. These cases are primarily for different time scales such as: one
day, one week, one month data sets to provide
enough Solar and customer data for testing.
DELIVERABLES
The deliverables on successful completion in
this project including the software prototypes
will be:
• Written final report of the results of
these studies in the format approved by
Avista.
• Interim reports and online conferences
with Avista. Mid-term report.
• Proof-of-concept software and user and
administrative documentation.
• Proof-of-concept evaluation within a
small-scale simulated transmission and
distribution system.
• Outlines of proposals for follow-on
funding to further develop and refine
the prototype.
PROJECT TEAM
PRINCIPAL INVESTIGATOR
Name Dr. Yacine Chakhchoukh Contact # 208-885-1550
Email [e-mail]
CO-PRINCIPAL INVESTIGATOR
Name Dr. Brian Johnson
Contact # [phone]
Email [e-mail]
Name Dr. Herbert Hess
Contact # [phone]
Email [e-mail]
Name Dr. Daniel Conte De Leon
Contact # 208-885-6520
Email [e-mail]
Name Dr. Hangtian Lei
Contact # 208-885-0952
Email [e-mail]
SCHEDULE

Task Item

Start Date

Finish Date
%
Completion
Task 1 09/ 01/18 11/01/18 100%
Task 2 09/ 01/18 11/01/18 100%
Task 3 09/ 01/18 01/01/19 100%
Task 4 09/ 01/18 01/01/19 30%
Task 5 09/ 01/18 01/01/19 100%
Task 6 09/ 01/18 01/01/19 60%
Task 7 09/ 01/18 03/01/19 40%
Task 8 01/01/19 06/01/19 10%
Task 9 01/01/19 08/01/19 5%
Task 10 08/01/19 08/31/19 0%
Task 11 01/14/19 02/14/19 70%

Using IR Cameras in Building Controls
Project Duration: 12 months Project Cost: Total Funding $46,678

OBJECTIVE
This project will demonstrate the feasibility of
using a low-cost infrared camera to estimate
the mean radiant temperature in a room. This
radiant temperature information is used to
more effectively heat and cool buildings. Over
the course of this study, the team will set up a
camera in an experimental chamber and
collect data from various scenarios. Once
collected, the team aims to process the data
into a comfort prediction and send that
information as a standard control signal.
Energy models will be used to estimate the
potential energy savings of incorporating an
infrared camera into building thermostats.
BUSINESS VALUE
Managing office temperatures by factoring in
the surface temperatures 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 27%. If the IR camera can
successfully detect both surface temperatures
and occupancy, then the need for additional
occupancy sensors could be eliminated and
this device can serve both purposes. If it is
successful in detecting glare in a room, it could
potentially serve in the role of a photosensor
as well. The adoption of IR-enhanced
thermostats provides a holistic sense of
occupant comfort that enables offices to
maintain efficient environments that improve
worker productivity.

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 surface temperatures are
widely used for predicting human comfort,
rarely are they directly used in building
controls. A control system that incorporates
the surface temperatures of a zone would allow
for a wider range of supply air temperatures
and better meet the needs of occupants. The
wider range of 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 surrounding surface temperatures are
more important to human comfort than the air
temperature (Olesen 2007). Standard comfort
predictions require both the air and surface
temperatures, yet surfaces are rarely used in
building controls. Surface temperatures can be
measured with globe sensors, IR beams, or
thermocouples, each of these methods has
drawbacks of cost and complexity. With the
recent reduction in cost of IR technology, low-
cost cameras could provide an alternative.
SCOPE
The research team has set up an experimental
chamber with an IR camera that has been able
to accurately estimate surface temperatures to
within 1oF. These readings have been used to
calculate the impact that each surface’s
temperature might have on a hypothetical
occupant. Energy models of the test chambers
have been created to estimate energy savings
of enhanced thermostat controls. At the end of
the project, the research team will have
developed a method to take a photo of a room
using a low-cost IR camera and from that data,
predict occupant comfort. That information will
be sent out as a standard control signal which
could be read by a typical HVAC system.
The information contained in this document is proprietary and confidential.

Task 1: Project Planning and Reporting
Conduct team meetings and ongoing project
updates, reports and deliverables as required
by Avista staff, project management contractors, and the PUC. This task continues
throughout the project.

Task 2: Set up Experimental Chamber
The team will deploy instruments in a test
chamber at the U of I IDL site to monitor all
surface temperatures, air temperatures, light
levels, and CO2 levels.

Task 3: Deploy IR Camera in Chamber
IDL will set up the camera to take pictures at different angles and send the digital files to a
computer for analysis to determine the ideal
settings and method for MRT measurement.

Task 4: Collect Operational Data
The research team will use different setpoints for the HVAC system and occupy the room at
different times over a period of several weeks
to establish baseline data.

Task 5: Develop Algorithm for Processing
Measurements
In parallel with Task 4, IDL will use the ongoing
operational data collection to correlate the IR
camera files to readings of temperatures,
lighting, and occupancy.

Task 6: Translate IR Camera Files to BACnet Signals
The IDL team will develop a process to
translate IR measurements into a BACnet
signal that could be used by any controller.

Task 7: Estimate Savings
The team will use energy simulation results to
compare the energy impacts of using an IR
camera to manage controls versus traditional
control signals (air temperature and CO2) over
a sample period.

Task 8: Develop Workflow for Practitioners
The final task will be to detail the methods of
how to set up an IR camera for monitoring
occupancy and surface temperatures and how
those signals can be translated into building
control networks. This will ideally inspire
further commercialization of this concept and
result in energy savings once implemented.
This documentation will be submitted to an
academic journal.
DELIVERABLES
• The team has completed a current
literature review of the relevant
technology in this sector.
• The team has prepared a test chamber
for data collection with a mounted IR
camera that is able to read the surface
temperatures to within 1oF of accuracy.
• The camera and chamber have been
used to collect a set of data that
includes variations in the following
categories: air temperatures, surface
temperatures, occupancy, and lighting.
• The team has determined a set of
equations that will allow the IR camera
to estimate the mean radiant
temperature at a location in the room.
• The next deliverable (not yet complete)
is the ability to send a measurement of
predicted occupant comfort as a
standard control signal.
• The team will estimate energy savings
for typical small commercial office
buildings in the Pacific Northwest
• Documentation will be written on how
to use an IR camera to record surface
temperatures and provide an overall
verification of the proof of concept for
this technology.

PROJECT TEAM
PRINCIPAL INVESTIGATOR(S)
Name Damon Woods
Organization University of Idaho Integrated Design Lab
Contact # [phone]
Email [e-mail]
RESEARCH ASSISTANTS
Name Jubin Mathai
Organization University of Idaho Integrated Design Lab
Email [e-mail]
SCHEDULE
TASK
TIME
ALLOCATED
(Months)
START
DATE
FINISH
DATE
Project Planning 12 09/01/18 08/30/19
Set up Exp Chamber 2 09/01/18 10/26/18
Deploy IR Camera 2 10/01/18 11/16/18
Collect Data 2 12/01/18 01/25/19
Develop Algorithm 4 01/25/19 05/24/19
Translate Ctrl Signal 3 03/01/19 06/21/19
Estimate Savings 3 04/29/19 06/26/19
Develop Workflow 2 07/01/19 08/16/19

APPENDIX B
Request for Proposal
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
Request for Proposal (RFP)
Contract No. R-41895
for
Avista Energy Research (AER) Initiative
INSTRUCTIONS AND REQUIREMENTS
Proposals are due by 4:00 p.m. Pacific Prevailing Time (PPT), April 21, 2018 (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 our operating division that provides
electric service to 378,000 customers and natural gas to 342,000 customers. Its service territory covers
30,000 square miles in eastern Washington, northern Idaho and parts of southern and eastern Oregon,
with a population of 1.6 million. Alaska Energy and Resources Company is an Avista subsidiary that
provides retail electric service in the city and borough of Juneau, Alaska, through its subsidiary Alaska
Electric Light and Power Company. Avista stock is traded under the ticker symbol "AVA." For more
information about Avista, please visit www.myavista.com.
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-41895 Page 2 of 9
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 certifying 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.
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-41895 Page 3 of 9
Instructions 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 DOCUMENTS
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, MSC-33
Spokane, WA 99220-3727
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 April 23,
2018 (“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
PO Box 3727
Spokane, WA 99220-3727
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-41895 Page 4 of 9
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
March 20, 2018 Avista issues RFP
April 9, 2018 Bidder’s Questions/Requests for Clarification Due
April 13, 2018 Avista’s Responses to Clarifications Due Date
April 23, 2018 Proposals Due
May 7, 2018 Successful Bidder selection and announcement
June 1, 2018 Contract and Statement of Work Executed
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 alternate 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 By Bidder: Bidder may withdraw its Proposal at any time. Bidder may modify a submitted
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 modify 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 Proposal Processing
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: All 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.
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-41895 Page 5 of 9
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 mutually 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 11 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 (if different individuals for each project
submitted)
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-41895 Page 6 of 9
6.3 Proposal Cover Sheet
Bidder must fill 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
Institution Qualifications
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).
Project Personnel Qualifications
Provide a proposed organization chart or staffing list for a project of this size and scope and
identify the personnel who will fill these positions. If applicable, identify project managers who
will be overseeing the Services and submit their resume identifying their work history, (please
see Section 6.2, question #4).
Approach to Subcontracting
If Bidder’s approach to performing the Services will require the use of subcontractors, include
for each subcontractor: (a) a description of their areas of responsibility, (b) identification of the
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 of references for such work.
6.5 Evaluation Criteria
Avista will evaluate each proposal based upon the following criteria:
6.5.1 Project Requirements
Strength of Proposal
Responsiveness to the RFP
Creativity in Leveraging Resources
Samples of Work Products
Overall Proposal (Complete, Clear, Professional)
6.5.2 Strength & Cohesiveness of the 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
6.5.3 Qualifications and Experience
Experience working with electric utilities
Project management and multi-disciplined approaches
Experience working with organizations in a team atmosphere
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-41895 Page 7 of 9
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:
Modify, 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;
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;
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;
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;
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;
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;
Rescind, at any time prior to the execution of a final contract, any notice of intent to contract issued
to Bidder.
[END OF REQUEST FOR PROPOSAL INSTRUCTIONS AND REQUIREMENTS]
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-41895 Page 8 of 9
APPENDIX A - Proposal Cover Sheet
Bidder Information
Organization Name:
Organization Form:
(sole proprietorship, partnership, Limited Liability Company, Corporation, etc.)
Primary Contact Person: ____________________________ Title: __________________________________
Address:
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 verify numbers) that Avista may contact to
verify the quality of Bidder’s previous work in the proposed area of Work.
Avista Corporation
East 1411 Mission Ave.
Spokane, WA 99202
RFP No. R-41895 Page 9 of 9
Project Title:
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, if required.
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 “INSTRUCTIONS
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 ***
APPENDIX C
University of Idaho Agreements
APPENDIX D
Final Report: Aerogel Phase II
High Energy-Efficient Aerogel-Glazing Coupled with
Aerogel-Insulated Walls in Residential Buildings: Phase
II
Project Duration: 12 months Project Cost: Total Funding $82,873
OBJECTIVE
This project proposes a second phase for the
aerogel insulation for buildings. The goal of
Phase II is to develop the means of
implementing new energy-efficient aerogel
glazing systems in residential buildings for
Avista customers to combine with the aerogel
insulated walls evaluated in Phase I.
The objectives of the project second phase are
to: 1) determine the thermal properties of
aerogel-based glazing systems, 2) develop and
validate a numerical model using
computational fluid dynamics to predict heat
transfer performance of these systems, and 3)
apply this model to a simulated, scaled 3D
single-family house with walls and windows
insulated with aerogel.
BUSINESS VALUE
Using aerogel is expected to significantly
decrease the annual heat loss in residential
buildings compared to standard insulation
materials. Recommendations for further
development of aerogel systems will be made
so it can be applied to various types of
residential buildings and may also suggest
ways to implement the use of aerogel in
commercial buildings.
BACKGROUND
Many buildings in the United States were
constructed before energy efficiency was a
concern. Therefore, a strong incentive exists to
renovate existing buildings to meet minimum
energy requirements as mandated by the US
and state governments. Aerogel is a
transparent insulating material that meets this
need to improve energy efficiency in
residential buildings because of its extremely
low thermal conductivity, high solar factor, and
high daylight transmittance. The material is
also lightweight.
In Phase I of this project, the investigators
validated the thermal conductivity of aerogel
blankets and found them to be very low, close
to 0.014 Wm-1K−1. Phase II of this project has
included laboratory experiments and
computer analysis using the ANSYS
Multiphysics simulation tool; computational
fluid dynamics (CFD) [1] to investigate the
predicted energy savings when aerogel-glazed
window panes are used with walls insulated
with aerogel wall blankets.
The main deliverable of this project is a final
report that includes the description of the
methods used in the laboratory data collection
and the computer simulations, and an analysis
of the results.
SCOPE
The scope of Phase II is limited to residential
buildings, but the findings may show promise
for use in commercial buildings.
The project tasks are broken down as follows:
Task 1: Literature Survey
The investigators conducted a comprehensive
literature survey related to the performance
and implementation of aerogel-based glazing
insulation in residential buildings.
Task 2: Experimental Testing
A prototype window wood frame has been built
in the Civil Engineering Laboratory at the
University of Idaho. The prototype consists of
a cubical box with six aerogel window panes
installed on all faces. The temperature gradient
between the inside and outside of the box were
measured using a heater/burner to increase
the inside temperature relative to the outside.
The information contained in this document is proprietary and confidential.
More details about the experimental results
will be discussed later in this report. The
results were used to verify the accuracy of the
CFD simulations, as will be explained later.
Task 3: Computer Validation of Aerogel-Based
Glazing
The main goal of this task was to use the
experimental results obtained from Task 2 to
validate computer simulation results obtained
using ANSYS Multiphysics including CFD.
ANSYS was used to model air flows, heat
convection between the air and walls, heat
conduction through the wall layers, and heat
radiation. This allowed the team to accurately
model the 3D heat transfer phenomena of the
aerogel windows assemblies. The results were
used for visualizing and validating the
effectiveness of the proposed window types in
a simulated, 3D single-family house under
typical winter and summer conditions.
Task 4: Simulation of a Single-Family House as
a case study
In this task, a scaled 3D single-family house
was simulated. It had walls insulated with
aerogel and included aerogel-based windows
in the house envelope. The simulation results
were validated using measurements for the
same geometry and environmental conditions
for typical summer and winter weather
conditions. The airflow patterns, temperature
gradient, and heat flux across the wall and
windows were simulated and the impact of the
aerogel on the overall thermal environment
was evaluated.
Task 5: Parametric Study
The proposed parametric study factors were
the number of aerogel layers, aerogel
thicknesses, aerogel wall insulation thickness,
etc. The amount of energy savings for each
case is reported in Section V.
PROJECT TEAM
PRINCIPAL INVESTIGATOR(S)
Name Ahmed Ibrahim, Ph.D.
Organization University of Idaho-Civil Engineering
Contact #208 885 1328
Email [e-mail]
Name Tao Xing, Ph.D.
Organization University of Idaho-Mechanical
Engineering
Contact #208 885 9032
Email [e-mail]
Name Brian Johnson, Ph.D.
Organization University of Idaho-Electrical
Engineering
Contact #208 885 6902
Email [e-mail]
RESEARCH Team
Name Sean Quallen, Ph.D.
Organization University of Idaho-Mechanical Engineering
Name Santu Golder
Organization University of Idaho-Mechanical Engineering
Name Conal Thie
Organization University of Idaho-Mechanical
Engineering
SCHEDULE
TASK TIME
ALLOCATED
START
DATE
FINISH
DATE
{Task 1: Literature
review}{4 Months}{9/2018}{12/2018}
{Task 2: Experimental Testing}{1 Month}{9/2018}{11/2018}
{Task 3: Computer
Validation}{6 Months}{12/2018}{4/2019}
{Task 4: Simulation}{10 Months}{4/2019}{7/2019}
{Task 5: Parametric Study}{4 Months}{7/2019}{8/2019}
I. RESEARCH MOTIVATION
The chief objective of this research is to
develop means of using aerogel to save energy
for the largest number of Avista customers.
II. LITERATURE REVIEW
A limited set of studies have been done to
report the behavior and efficiency of aerogel-
based window glazing.
Buratti et al. (2017) [2] investigated the
efficiency of high energy-efficient windows
with silica aerogel used in buildings. Two types
of aerogels were incorporated with two pes of
glass layers. The investigation focused on
measuring thermal transmittance and optical
properties. The aerogel has decreased the U-
value by 63% compared to conventional
windows. In addition, a 30% reduction of light
transmittance.
Ihara et al. (2015) and Cotanaet al. (2014) [3
and 4] reported the aerogel granulate glazing
facades and their application potential from an
energy saving perspective. The study
concluded that a combination of aerogel and
triple glazing systems may offer an energy
efficient facade for cold climates. These
findings may contribute to new architecture
The information contained in this document is proprietary and confidential.
techniques. Various other studies have also
reported results, [5-11].
III. TECHNOLOGY UTILIZED
ANSYS Multiphysics CFD is the software tool
used in this project. Thermocouple sensors and
a data acquisition system were also used in the
laboratory for data collection.
IV. ANALYSIS APPROACH
Thermal efficiency has always been a problem.
Traditionally the higher the light transmittance
levels, the lower the energy efficiency. Cellular
Polycarbonate was the first breakthrough in
window glazing systems because they offered
good thermal efficiency and the light levels
were better than almost all other materials
[12]. Aerogel has proven to be the next big
innovation. The six aerogel windows used in
this project were acquired from Duo-Gard
Industries Inc. under the commercial name of
“Lumira aerogel” as shown in Figure 1.
The total thickness of each pane is 16 mm (10
mm aerogel enclosed by 6 mm polycarbonate
panes). We investigated the ability of aerogel-
based glazing to save energy as follows:
a) Data Collection
The data was collected through laboratory
testing by recording the data of a small-scale,
aerogel-based glazing prototype, as shown in
Figure 2a. The testing prototype measurement
setup developed in Phase I was attached to
panels and the thermocouple sensors were
used to measure the temperature history
inside and outside the window-glazed
prototype at multiple locations.
One of the main goals of this project was to
validate the aerogel glazing thermal
conductivity and use the results in the
computer simulations. A small-scale prototype
was built first for verification under a controlled
setting and to derive the heat conductivity of
the aerogel glazing.
The prototype was a 3.0 x 3.0 x 3.0 ft. wood
frame as shown in Figure 2a. The six walls of
the cubical box were covered with glazing
panes for the purpose of collecting a
temperature versus time history. A
conventional space heater/burner was
positioned at the center of the cubical volume
and 19 thermocouples were mounted in
different places along the different faces of the
cube as shown in Figure 2a.
Figure 2b is a schematic diagram showing the
locations of sensors on one face (A). The
sensors were mounted internally and
externally on the six faces as shown in Figure
2c. The faces were given letters A to F with
numbers referring to the location of the
thermocouples relative to each face. As an
example, A1 is the sensor located between the
wall center and the cube center, A2 is mounted
on the central point of the internal face of wall
and A3 is the sensor mounted on the central
point of the external face of the same wall.

In the experiments, the heater/burner was
turned on until the temperature reached
around 1200 F and then the heater was turned
off and data was recorded as the temperature
decayed until it reached the outside room
temperature. This process was repeated
multiple times until the results became
consistent and stable.
b) Modeling and Simulation
Computational fluid dynamics or CFD is a
powerful tool that is used to capture fluid
structure interactions, heat convection, heat
flow and heat transfer. The team has continued
using CFD as they moved forward from Phase
I. The CFD tool has proven to be reliable in
capturing the temperature time history
responses, which is used to determine the
amount of energy savings.
The Principal Investigator’s (PIs) have used the
data collected from the experiments to validate
the computer model.
The information contained in this document is proprietary and confidential.
Computational and time costs have increased
as we’ve moved into 3-dimensional
simulations. Several grids representing our
experimental setup were created—all of which
are similar to Figure 3 with smaller grid
spacings at corner edges to properly capture
velocity gradients. A mesh sensitivity study
was performed with this grid topology to
determine the proper trade-off between grid
resolution and required computational
resources; the results of this study are
presented in Figure 3. Decimating the grid
from 2.89M points to 1.07M points (a reduction
of 63%) provides very similar results beyond
~150 s into the simulation—we expect to run
this simulation for over 2000 s. Decimation
also decreased the required solution time by
77% allowing for longer simulations and
allowed available time to be used to run a
larger set of experiments.
Figure 2a: Cubical box with Aerogel glazing
Figure 2b: Sensors locations across one of the
box walls.
Figure 2c: Cubical box with sensors attached
Figure 3: 3D Grid of Experimental Box
V. RESULTS
a) Laboratory Results
Figure 4 shows the average temperature decay
through all of the scaled aerogel box walls. It
can be seen that internal sensor has the
highest temperature compared to the outside
room temperature. This temperature data
was used to validate the computer simulation
and verify the thermal conductivity of the
aerogel glazing. The temperatures recorded by
all of the sensors have decayed with various
rates over time depending on the sensor
location as shown in Figure 4.
The information contained in this document is proprietary and confidential.
0 10 20 30 40 50 60 70 80
0
20
40
60
80
100
120
140
Wall Inside
Wall Outside
Room Temp.
Te
m
p
e
r
a
t
u
r
e

(
F
0
)
Time (Min.)
Figure 4: Average temperature-time history
across all the four vertical walls of the
prototype
CFD simulations were conducted on the small-
scale aerogel box using a constant outer wall
temperature, To = 2950 K and a specified outer
wall temperature profile based on the
experimental data as (shown in Figure 4).
Further, the thermal conductivity coefficient of
the interspace aerogel was used as (k=0.014)
(cited from Phase I results). Figure 4 shows the
comparison of inner and outer wall
temperatures.

Initial simulations have shown that natural
convection inside the experimental box is
significant and should be predicted and
modeled in the simulations of the simulated
house later in the project. Especially when
considering taller vertical walls, which will see
larger top-to-bottom temperature gradients.
Figure 5 shows a slice though the center of the
box at 600 s. After a full 10 minutes, the air
inside the box still shows considerable activity.
Note the natural convection layers forming on
the vertical sides and the swirling at top.
Capturing this motion is critical to proper
prediction of a convective heat transfer and will
be a major feature of our future residential
simulations.
Figure 5: Temperature contour during heating
period (top), and temperature as burner cools
down (down)
A 3-layer panel grid (polycarbonate-aerogel-
polycarbonate) was developed in the
simulation model, which more accurately
models the experimental box. After
successfully logging quality experimental data,
the simulations showed excellent agreement
with the experimental data using a nominal
convection room rate (see Figure 6). In
addition, the team has included two additional
gases in between the glazing panes (Air and
Argon gases) to show the differences
compared to the aerogel. It can be seen from
Figure 6 that a very good agreement between
the aerogel experimental and CFD results exist
with a slower temperature variation versus
time.
The information contained in this document is proprietary and confidential.
Figure 6: Comparison between experimental
results and the CFD for temperature variation
versus time
b) CFD Single-Room Results
The team developed a computer model for a
single-room to test the mesh sensitivity and
time needed for run the simulations. A 12’x10’
single-room test grid has been developed as
shown. This grid features a fully resolved
multi-layer wall as well as multi-paned
windows encased in 1-1/2 frames. A cross-
section of the multi-layered grid is shown in
Figure 7.
Figure 7: 3D mesh if the single-room (top)
and wall crosss section showing all wall
elements (bottom).
Proper temperature or convection boundary
conditions were assigned to this grid to solve
for energy loss. These results include both net
heat loss as well as local heat flux to compare
losses through windows vs. walls or ceiling.
The grid, as designed, allows for different
configurations and types of insulation for
comparisons. Figure 8 shows the results of the
single room through conventional wall
insulations, heat flux through standard
insulation and windows, and heat flux through
aerogel Windows.
Case 1: Heat Flux through Aerogel Wall
Insulation
The heat flux through a slice of the room
shows a high rate of flux through the door
(left of picture), window (right of picture),
and the floor.
Case 2: Heat Flux through Standard
Insulation and Windows
Here we still have high rates of flux through
the door, window, and floor. However, the
flux through the wall has increased about
12%.
Case 1
Case 2
The information contained in this document is proprietary and confidential.
Figure 8: Heat flux in three different cases.
Case 3: Heat Flux through Aerogel
Windows
Inserting aerogel between the two (2) window
panes decreased the heat flux through the
window by 36%.
In contrast to the air-gap window, the aerogel-
insert window allows very little heat flux
directly through the window. The heat flow
from inside to out is forced more strongly
through the window frame around the window
as shown in Figure 9.
In conclusion, when a window with aerogel was
used in the single room, the heat transfer was
reduced by 10%, and that was for only one
layer of aerogel used in between the
polycarbonate layers.
c) Single-Family House Simulations
Next, a simulation of single-family house was
developed for CFD simulation to evaluate
savings for a typical house with the addition of
aerogel windows. Figure 10 shows the 3D view
of the CFD mesh of the single-family house.
The house is a ranch style and has a floor plan
area of 2000 ft2.
The 3D CFD mesh of the house grid is shown
in Figure 10. A multi-layer floor and external
walls were modeled. The team decided to
change the vaulted ceiling option to a flat one
due to simulation complexities. The complete
plans provide ample dimensions and details to
ensure a high-fidelity simulation.
Figure 9: Heat flux through window with
aerogel
Figure 10: Single family house CFD mesh
The CFD model of the single-family house
(11.4M grid points) has the following
characteristics:
Variable insulation layers in vertical
walls (2 inner/2 outer) and in ceiling (2
inner).
Variable ‘gap’ layer between window
panes that can be specified as either
gases (air or Argon) or solid (aerogel).
Ability to isolate and swap different
window/door configurations into the
main grid.
The weather data from the city of Moscow,
Idaho has been used to model the outside
temperature over the course of a year while
the inside house temperature was kept at 700
F.
Case 3
The information contained in this document is proprietary and confidential.

Figure 11: Total energy cost
Figure 11 shows the total monthly energy cost
of walls, windows, and both windows and walls
for various insulation combinations. Compared
to no aerogel insulation, using aerogel saves
energy for both walls and windows but much
more significant for the latter. As a result, the
total cost shows that the energy cost savings
from the largest to the smallest are aerogel for
both walls and windows, window insulated but
not walls, wall insulated but not windows, and
neither wall or window is insulated. It is noted
that the savings for all insulation combinations
reach the maximum during the winter and
minimum during the summer, i.e., the larger
temperature difference between inside and
outside temperatures, the larger the energy
savings are.
VI. CONCLUSIONS
The feasibility of using aerogel blankets for
walls and windows as a super insulator has
been verified using small scale laboratory
testing, and high-fidelity computer
simulations. The following conclusions have
been drawn from the project tasks conducted:
CFD model was validated to be a
promising tool to predict transient room
temperature decay.
CFD can be used to accurately predict
the temperature, heat flux, and energy
(cost) through windows, walls, floors,
doors, ceilings, and their combination
under various insulation conditions
difficult to create using experiments.
CFD simulations were used to show that
the overall yearly savings using 1 layer
of window aerogel insulation is 39%
(Figure 10).
The overall yearly savings using 1 layer
and 4 layers of wall aerogel insulation
coupled with window aerogel insulation
is 13.3% and 38.3%, respectively for
the single-family house, compared to
traditional insulation.
Overall, the savings with 1 and 4 layers
of aerogel are 21% and 30% when
taking heat flux through the insulated
floor and ceiling into account.
Savings percentages are typically
affected (and limited) by original house
insulation; and also, the window’s
quality and configurations.
VII. References
1.ANSYS® Academic Research Mechanical,
Release 18.1, Help System, Coupled Field
Analysis Guide, ANSYS, Inc.
2. Cinzia Buratti, Elisa Moreti, Michele Zinzi.
High energy-efficient windows with silica
aerogel for building refurbishment:
Experiment characterization and
preliminary simulations in different climate
conditions. Buildings 2017, 7(1),
The information contained in this document is proprietary and confidential.
8; https://doi.org/10.3390/buildings7010
008
3. Cotana, F.; Pisello, A.L.; Moretti, E.;
Buratti, C., Multipurpose characterization
of glazing systems with silica aerogel: In-
field experimental analysis of thermal-
energy, lighting and acoustic
performance. Build. Environ. 2014, 81,
92–102

4. Ihara, T.; Gao, T.; Grynning, S.; Jelle, B.P.;
Gustavsen, A., Aerogel granulate glazing
facades and their application potential from
an energy saving perspective. Appl. Energy
2015, 142, 179–191.
5. Gao, T.; Ihara, T.; Grynning, S.; Jelle, B.P.;
Gunnarshaug Lien, A., Perspective of
aerogel glazings in energy efficient
buildings. Build. Environ. 2016, 95, 405–
413.
6. Gao, T.; Jelle, B.P.; Ihara, T.; Gustavsen,
A., Insulating glazing units with silica
aerogel granules: The impact of particle
size. Appl. Energy 2014, 128, 27–34.
7. Buratti, C.; Moretti, E., Experimental
performance evaluation of aerogel glazing
systems. Appl. Energy 2012, 97, 430–437

8. Huang, Y.; Niu, J., Energy and visual
performance of the silica aerogel glazing
system in commercial buildings of Hong
Kong. Constr. Build. Mater. 2015, 94, 57–
72.
9. Huang, Y.; Niu, J., Application of super-
insulating translucent silica aerogel glazing
system on commercial building envelope of
humid subtropical climates: Impact on
space cooling load. Energy 2015, 83, 316–
325.

10.Ghoshala, S., Neogi, S. Advance Glazing
System – Energy Efficiency Approach for
Buildings a Review, Energy Procedia 54
2014, 352 – 358

11.Cinzia Buratti, Elisa Moretti, Transparent
insulating materials for buildings energy
saving: experimental results and
performance evaluation, Third
International Conference on Applied
Energy - 16-18 May 2011 - Perugia, Italy
12.Baetens R, Jelle BP, Gustavsen A., Aerogel
insulation for building applications: A
state–of–the–art review, Energy and
Buildings. 2011, 43, 761– 769.
APPENDIX E
Final Report: All-Iron Battery
Avista Final Report: All-Iron Battery for Renewable Energy Storage
Allen Lab at the University of Idaho Department of Chemistry
Introduction:
The Allen Lab set out to scale-up
and refine the all-iron battery. The all-iron
battery uses inexpensive and safe
chemistry to store electrical energy. The
all-iron battery uses an iron electrode and
an iron salt cathode. An outline of the
iron chemistry is shown in Figure 1. We
have scaled the battery to a 1 L size. We
have demonstrated that our energy
storage capacity is close to the theoretical
limit. We have also shown that the power
delivered by the cell can be increased to a
practical level using carbon additives.
Part 1 of this report describes the
scale-up of the iron battery. This includes
a description of the best features of the
various iterations. We extrapolate the
best features of our cells to a size suitable
for grid energy storage. We estimate the
cost of a shipping container size battery
as well as its possible energy and power
capacity. We conclude that such a cell
could store approximately one megawatt-
hour and deliver 1 MW of peak power for
a total cost of $0.09 per watt-hour stored.
Part 2 of this report describes our
efforts to develop a low-cost battery
tester. The cost of battery test equipment
was an impediment to our progress. We
had access to one high-performance
potentiostat, which could be used for
battery cycling. Commercial battery test instruments that can test multiple batteries simultaneously are
very expensive. We endeavored to create an open-source battery tester that could cycle a battery
repeatedly while measuring voltage, and current applied to (or delivered by) the battery.
Part 1: Scale-up and performance of the Iron Battery
Scale-up of the cell:
All-Iron Battery. (A) Images show the three
stable oxidation states of iron. (B) A schematic shows
the overall construction of the all-iron battery.
Ferrous iron
Fe
Ferric iron
Fe
Iron Metal
Fe
Gr
a
p
h
i
t
e

F
o
i
l
Fe


Fe
2+
Se
p
a
r
a
t
o
r
Fe
3+

Fe
2+
Ca
r
b
o
n

F
e
l
t
Gr
a
p
h
i
t
e

F
o
i
l
e-e-Load
A
B
We increased the size of our battery from 0.2 mL in 2018 to 1 L in 2019. The progress is shown in
Figure 2. Our smallest cells were built using acrylic plastic. We used these cells to test changes to the
chemistry and the membrane quickly. Larger cells (20 mL) were used to test the total capacity per unit
volume and the total current per unit volume. These cells were assembled by folding a membrane into
an envelope and filling the envelope with iron. The space outside of the envelope was filled with iron
salt. A stack of these cells wired in series was made to 120 mL. When we established that these cells
performed as expected, we moved to a 200 mL cell and a 1 L battery.
As our chemistry was refined, we had a general trend toward higher capacity per unit volume.
The maximal capacity per unit volume is defined by the number of iron atoms in the battery. If every
atom of iron is used, we get a total capacity directly proportional to the concentration of iron in the cell.
Experimentally, we were only able to prepare the cathode material at a concentration of 11% iron by
mass. This translates to a theoretical maximum of 27 mAh per milliliter. Figure 2 shows that we were
able to approach this theoretical maximum with our experimental cells.
Figure 2B shows that our initial current was very low. Because the cell provides approximately
Figure 2: Progress in the size and performance of the cell over time. (A) Graph shows the capacity
per unit volume (mAh/mL) as a function of time. Each point on the graph is a battery prototype. (B)
Graph shows the current per unit volume (mA/mL) as a function of time. (C) Images show the
progress from the smallest cell (0.2 mL) to the full 1 L battery.
Theoretical Max
-3.0
7.0
17.0
27.0
4/10/2018 7/19/2018 10/27/2018 2/4/2019 5/15/2019 8/23/2019Capacity/Volume
(mAh/mL)
Date
Capacity/Volume over Time
0
0.5
1
1.5
4/10/2018 7/19/2018 10/27/2018 2/4/2019 5/15/2019 8/23/2019Current/Volume
(mA/mL)
Date
Current/Volume over Time
A
B
200 mL
1000 mL
120 mL
20 mL
0.2 mL
C
0.8 V, a 1.25 mA current translates to 1 mW. In practice, the power would be lower than this upper limit.
Our early prototypes gave impractically low current per unit volume. Over the course of the project, we
were able to increase the current (and thus the power) to a usable value. Figure 2B shows that the
sustained current delivered by the cells at the end of the project period was approximately 1.5 mA per
milliliter or approximately 1 W per liter.
Overcoming Internal Resistance:
Over the course of our scale-up, we
discovered that the power output from our
battery chemistry was very limited. Our first 1 L
battery produced only 40 mA continuous
current. In order to address this, we
endeavored to find an additive that could
transfer electrons more easily between the
load and the chemical compounds. We
hypothesized that the long distance between
the conductive carbon and the insulating
crystals of iron sulfate was causing high
resistance in the battery (Figure 1A). We
introduced a range of concentrations of
conductive carbon to reduce the diffusion
distance between the carbon electrical
connection and the iron phosphate (Figure 3B).
At concentrations above 4% carbon by mass,
the internal resistance was greatly reduced.
Figure 3C shows the results of these
experiments. The current produced by the
battery was increased by more than a factor of
ten.
We also tested several alternative
membranes and sodium sulfide as an additive.
Sodium sulfide is electrochemically active (it
can be oxidized to polysulfide or reduced to
sulfur). We hypothesized that it could be added
to the anode to act as a “shuttle” for electrons
from the iron to the iron sulfate. The results
were not encouraging. Added sodium sulfide
decreased the current. It did reduce the
viscosity of the solution, however, and could be
an interesting additive in combination with
conductive carbon to make battery assembly
easier. We also tested the possibility that the separator membrane was introducing additional
resistance, but this was not found to be the case. Rather, the physical construction of the battery and
thickness of the anode and cathode materials were found to produce a larger resistance than the
difference between low-cost and high-performance battery membranes.
We tested GUITAR-coated carbon felt as well as GUITAR coated halloysite as additives. GUITAR,
graphite from the University of Idaho thermolyzed asphalt reaction, is a proprietary conductive carbon
developed in the Cheng lab. The material does not wet easily with water without surface treatments.
We oxidized the surface with hydrogen peroxide so that water was not excluded from the material. With
Figure 3: Performance benefits result from adding
conductive carbon black (CB) or sodium sulfide
(Na2S) to the battery electrode suspensions.
Fe
3+

Fe
2+
Iron
sulfate
Gr
a
p
h
i
t
e
Short Diffusion
Distance
Fe
3+

Fe
2+e-
Fe3+
Fe2+Iron
sulfate
e-
Electron transfer
through solution
A
B
C
Ca
r
b
o
n

Fe
l
t
0
2
4
6
8
10
12
0%

C
B
0.
7
8
%

C
B
1.
5
%

C
B
4%

C
B
7.
8
%

C
B
0%

N
a
2
S
1%

N
a
2
S
10
%

N
a
2
S
Su
s
t
a
i
n
e
d

C
u
r
r
e
n
t

d
e
n
s
i
t
y

(m
A
/
m
L
)
peroxide-treated GUITAR on carbon felt, we observed very impressive storage capacity. The GUITAR felt
cathode stored 28.8 mAh/ml, slightly higher than the theoretical maximum of our iron (III) sulfate
cathode. We took this to indicate that the surface-treated GUITAR itself was an active cathode
independent of the iron (III) sulfate. Unfortunately, this proved to be impossible to obtain reliably and
the cost for production of kilogram scale GUITAR is still too high for practical purposes.
Stability:
The iron battery is very stable.
Many battery chemistries slowly degrade
over time. The more cycles of charging and
discharging to which they are subjected,
the lower their total capacity. The iron
battery chemistry survives thousands of
repeated cycles without significant loss of
performance. We used a microcell (0.2 ml)
to test the number of cycles the battery
could survive. We charged and discharged
four thousand times without significant
losses.
Best Performance:
The performance of our improved
chemistry in the 1 L cell was not as high as
the performance within the 0.2 mL cell.
We hypothesized that this was due to the
physical construction of the cell. The
pouch design (Figure 2C, 200 mL cell) has
relatively thick pockets of anode and
cathode material. The 0.2 mL cell has a
very thin anode and cathode
compartments (1 mm thick). Thinner
geometry may decrease resistance. We
scaled the 0.2 mL cell to 3 mL to test the
hypothesis that this would increase the
current. Indeed, our new 3 mL thin cell
produced extremely high performance in
terms of current and capacity. Figure 4
shows the results. The 3 mL cell produced
40 times as high a maximal current per
unit volume as the previous best.
Therefore, we conclude that the thinner
cell with 4-5% conductive carbon is
optimal.
Cost:
We found wholesale sources for
the components of the battery to estimate
the cost per unit energy. We took the sustained current and electrical storage capacity per unit volume
Figure 4: Best performance cell. (A) A photograph of
the laser-cut acrylic cell. (B) Potential (V) during a
complete discharge. (C) Current during a complete
discharge.
0
0.2
0.4
0.6
0.8
0 0.2 0.4 0.6 0.8
Po
t
e
n
t
i
a
l

(
V
o
l
t
s
)
Time (Hours)
A
B
C
0
10
20
30
40
50
60
0 0.2 0.4 0.6 0.8 1
Cu
r
r
e
n
t

(
m
A
/
m
L
)
Time (Hours)
40x previous best
from our best cell and used this to extrapolate the capacity of a shipping container-size battery (66 m3).
This yielded a projection for price and capacity. The most expensive components are the graphite sheet
and Nafion-coated paper. Even without further development, the price for the components is lower
than current storage solutions on the market (which are usually between $0.3 - $1.0 per watt-hour).
Cost Estimate: 1.7 MW-h Container
4.2 Metric Tons Iron
7 Metric Tons Carbon Black
38,000 Liters of Iron (III) Chloride
38,000 Liters of Iron (II) Chloride
38,000 m2 Nafion Paper
38,000 m2 Graphite Sheet
Totals:
Cost per Energy:
Part 1 Conclusions and Future Work:
The thin cell with carbon additives has
commercially useful storage and power
capacity. The next step is to scale this more
effective design to a larger size (1 L). This
should then be tested to determine if the
same performance and reliability are
obtained. We will also test less expensive
alternatives to our current graphite foil and a
“bipolar” design in which the cells are
constructed in layers separated by a graphite
foil. This would reduce the demand for
graphite by a factor of two at the possible cost
of more difficult manufacturing. Finally, we
will continue to test less expensive
alternatives to Nafion-coated paper
separators. We have tested polyacrylic acid as
one alternative, and we have investigated
Amberlite and Purolite ion exchange resins as
possible alternatives. We can test these more
quantitatively by developing Electrochemical
Impedance Spectroscopy methods.
Part 2: Open-Source Battery Tester
As we tested many cells over the
course of our study, we found that a major
bottleneck was the availability of an
instrument to charge and discharge our
battery. We endeavored to build an open-
source instrument to perform these tasks (see
Figure 5). We used a Pine Instruments
Figure 5: Battery tester performance.
0
100
200
300
400
500
600
700
800
900
0:00:00 0:07:12 0:14:24 0:21:36 0:28:48 0:36:00 0:43:12
Vo
l
t
a
g
e

(
m
V
)
Time Elapsed (HH:MM:SS)
Sensed vs Real Voltage Sensed VoltageReal Voltage
0
2
4
6
8
10
12
0:00:00 0:07:12 0:14:24 0:21:36 0:28:48 0:36:00 0:43:12
Cu
r
r
e
n
t

(
m
A
)
Time Elapsed (HH:MM:SS)
Sensed vs Real Current Sensed Current
Real Current
A
B
C
Potentiostat to do most charge-discharge experiments. This is a $3,000 instrument that is more capable
than necessary for simple charge-discharge experiments. We needed the ability to automatically
connect a power supply to the battery for charging, then connect a load to the battery for discharging.
In its simplest form, this can be connected to a data logger (e.g., Vernier voltmeter and ammeter) for
data collection. In further iterations, we propose to integrate data logging. This solves our internal
problem (availability of the instrument) and may contribute to the open-source hardware community;
the most comparable open-source battery tester requires components that cost $5,000, which is still
more affordable than an Arbin battery test instrument ($30k or more).
Performance:
Our open-source battery tester currently is only accurate to ~10% of the real value for electrical
current and voltage (see the discrepancy between real and sensed current and voltage in Figure 5B-C).
The relay modules and control software are functional with a precision of less than one second. It will be
important to reduce the error in the sensed voltage and current with better analog-to-digital converters
and a calibration protocol (still in development).
Cost:
The components for the system can be purchased for just over $200 (see table below).
The system marks an order of magnitude improvement compared to even the low-cost open-source
alternatives. This means that we can build several battery testers and run multiple experiments in
parallel.
Cost Estimate: Open Source Battery Tester
Raspberry Pi Model 3 B+$39.95
Wall Adapter Power Supply $7.95
SanDisk Ultra 32GB microSDHC $6.69
RELAYplate (8 relays for connecting power/load)$41.95
Alligator Clip Test Lead Multicolor $11.90
Wire Jumper Kit 350pc $12.95
E12 Resistor Assortment $9.95
MCP3304-BI/P $4.44
830 Point Solderless Breadboard $8.79
KORAD KD3005P - Precision Power Supply $106.99
Total:$211.61
Part 2 Conclusions and Future Work:
Our battery testing system is still quite preliminary. We hope to integrate newer, cheaper parts
into our next design. Our current analog-to-digital converters are only 12-bit systems. By increasing to a
16-bit integrated circuit, we can sense much smaller currents with equivalent high-quality shunt
resistors. Additionally, a higher quality ADC will allow us to make more precise voltage measurements.
We plan to use a common D-cell battery and calibrated resistor to calibrate the measurements from the
ADCs. We will then confirm the quality of our calibration using our Pine Instruments potentiostat. This
will allow future users to perform a similar calibration. We will use the calibrated instrument for cycling
our iron battery, and we hope to publish the designs separately as well.
Overall Conclusions:
While our best 1 L battery did not produce sufficient current to be of commercial value, our
further results indicate that this can be overcome with the addition of conductive carbon black. Our next
steps are to construct a full 1 L battery in the high-performance, thin cell design. We hope then to
publish an updated paper in HardwareX describing the higher performance battery and its construction.
We will test this larger, higher performance battery using our automated tester. This automated tester
will charge and discharge the battery hundreds or thousands of times while collecting current and
voltage data. Based on previous results, we anticipate that the battery will be robust to thousands of
charge-discharge cycles. The construction and operation of this automatic battery tester are sufficiently
novel to warrant submission for publication as well.
APPENDIX F
Final Report: Energy Trading Phase I

Avista Transactive Power: Phase I: Final Project Report
Center for Secure and Dependable Systems, University of Idaho1

Abstract
We analyzed, designed, and developed a prototype software system with the
objectives of supporting the creation and management of a market that enables
prosumers and consumers to trade power between themselves or with the utility,
with utility oversight. This prototype software system supports the creation and
management of power transaction agreements between prosumers and
consumers and also supports the determination of transaction execution
feasibility by fully integrating Power Flow analysis within the prototype.
Keywords: Transactive energy trading platform

∗Corresponding author
Email address: (Center for Secure and Dependable Systems, University of Idaho) 1Ctr. Secure &
Dependable Sys., University of Idaho.

Figure 1: High-level architecture of the Avista Transactive Power Application
2
Preprint submitted to Avista Utilities August 31, 2019
1. System Architecture
ATPA’s system architecture consists of four modules. They are: 1) Distribution
System Model & OpenDSS Simulation, 2) Web-based management interface; 3)
MySQL Database; and 4) Communication Manager.
The Management interface is the module that is intended to be used by
managers as an interface to the ATPA. The management interface module, in turn,
interfaces with the MySQL database to populate transaction and entity profiles
data. MySQL database obtains the entity profiles data from OpenDSS simulation
by interfacing with the communications manager. The system was evaluated
using an example distribution model that is a modification of the IEEE 13-bus
system. The communication manager is invoked by the management interface and
interacts with the Database and OpenDSS modules. Figure 1 presents the ATPA’s
system architecture.
In the following sections we describe each of the four system modules. The
distribution system model and its OpenDSS simulation are laid out in Section 2.
Section 3 discusses the management interface. MySQL database information is
presented in Section 4. Section 5 lays out the communication manager’s workflow.
2. Distribution System Model
Modeling small generation facilities, home and buildings management
systems, storage technologies and the local network is the first step in an electrical
framework. We propose to model the electrical behavior of small generation, such
as home solar panels or wind turbines, and the issues of electrical compatibility
to the distribution network. Simulation of this model will provide an appropriate
technical electrical understanding of the seller’s electrical side of the energy
trading system. Issues of safety for people and equipment, capacity to supply what
has been agreed upon, availability of energy from the means at hand (solar energy
deals for nighttime delivery may not work well), and interface to the distribution
network must be investigated and appropriate methods to ensure technical
capability established.
Modeling of the grid provides understanding of how to deliver the purchased
energy through the distribution system. When the energy arrives, the same
instrumentation and metering issues apply. The customer must have capacity and
appropriate protection. From the customer’s point of view, this process is similar
to the service that the public utility already provides. For example, reactive power
is necessary to stabilize the voltage. If the public utility produces this, then a fee
may be in order. The same is true for grid management services. In this research
we propose to develop a model of a typical electric grid in coordination with the
utility’s engineers. Appropriate supervision based upon grid conditions must be
communicated to the producer.
In developing a transaction verification system, an accurate model to test and
experiment on was needed. It was decided that the IEEE 13 bus system would be
implemented as the base model for testing and verifying sets of transactions.
3

Figure 2: Modified IEEE 13 Bus System

Figure 3: Definition of Prosumer Connections
4

Figure 4: OpenDSS Diagram
The IEEE 13 bus system is based on a 4.16kV voltage level with relatively short
line distances. The base system is loaded quite high to provide for analytics for a
smaller distribution system. The lines are modeled as overhead (OH) and
underground (UG) lines with the availability of a shunt capacitor and regulating
transformer. The IEEE 13 bus system also has unbalanced loads on single phase
feeders to provide additional complexity’s of having unbalanced lines. System
aggregated load systems such that each load is modeled as a group of houses or
subdivision. Additionally, there is an industrial load connected to bus 671. In
Tables 1, 2, and 3 the system description can be found with the ratings of each
load (customer), PV, and storage.
The IEEE 13 bus system was modified to provide photovoltaics and battery
storage at every customer location as seem in Figure 2. Figure 3 is an example of
how each customer is comprised of a load, PV panel, and storage items. Within this
prosumer location, it is possible to consume and produce power back onto the
grid. The PV located on each load is to represent the prosumer portion of the
research where a residential or commercial load also has rooftop PV. Due to the
IEEE 13 bus system representing an aggregated system, the rating of the PV was
determined to match the load values. Additionally, storage is located on each bus
as well to simulate the buildings or areas that which may have a storage device
located at their facility.
2.1. OpenDSS Model
To verify the transactions, a method for calculating the voltages and currents
at the busses and lines was required for system stability requirements. The
powerflow calculations was performed by the distribution software OpenDSS.
OpenDSS was selected for this research because of the ease at which it can be
implemented in line with the web application and allows analysis for each phase
of the system.
5
Load
#
P
(kW)
Q
(kVA)
Bus # Phase
1 170 80 611 1
2 160 110 634a 1
3 120 90 634b 1
4 120 90 634c 1
5 170 125 645 1
6 230 132 646 2
7 128 86 652 1
8 17 10 670a 1
9 66 38 670b 1
10 117 68 670c 1
11 1155 660 671 3
12 485 190 675a 1
13 68 60 675b 1
14 290 212 675c 1
15 170 151 692 2
Table 1: Load Description Modified IEEE 13
PV # P
(kW)
Bus # Phase
1 170 611 1
2 160 634a 1
3 120 634b 1
4 120 634c 1
5 170 645 1
6 230 646 1
7 128 652 1
8 17 670a 1
9 66 670b 1
10 117 670c 1
11 500 671 3
12 485 675a 1
13 68 675b 1
14 290 675c 1
15 170 692 1
Table 2: PV Site Parameters
6
Battery
#
P
(kW)
kWh Bus # Phase
1 100 100 611 1
2 100 100 634a 1
3 100 100 634b 1
4 100 100 634c 2
5 100 100 645 1
6 100 100 646 1
7 100 100 652 3
8 100 100 670a 1
9 100 100 670b 1
10 100 100 670c 1
11 100 100 671 1
12 100 100 675a 1
13 100 100 675b 1
14 100 100 675c 1
15 100 100 692 1
Table 3: Battery Site Parameters
The modified IEEE 13 bus system was created as specified in the previous
section, with the additional PV and storage capabilities. Furthermore, to simplify
the system and allow for analytics on purely the prosumer information, the
voltage regulator and capacitor were removed from the test case. Figure 4 shows
the system as the dedicated test case exported from OpenDSS.
At each customer location there is a meter recording voltage, current, angle,
and power. When a case is run, it calculates the power flow and exports the
metered data for analytics. When a transaction is sent to the web application a
load profile is modified for the respective PV panel or battery (Sell). Additionally,
when a purchase order is sent to the web application the prosumers load profile
is modified to reflect it within the simulation.
With metered data collected at each customers location, the application may
monitor the outputs for bounds of operation. The main focus is over voltage of
1.05 Per Unit. If the voltage reaches 1.05 Per Unit the transaction will be cancelled
and the application will re-initiate a simulation to verify this.
2.2. Photovoltaic Load Profiles
Load profiles used for industrial and residential loads have been implemented
as recurring dynamic and static loads throughout a 24-hr load profile. To
accurately model a distribution system with photovoltaics, data was gathered
from the National Solar Radiation Database and used as the load profiles’ baseline
for the photovoltaics [1]. We used data collected at the Spokane Airport during
June 2010. A weeks’ worth of data was used for the test model to simulate real
7
PV profiles that the customers may experience, such as clouds, and rain. Figure

Figure 5: One Week of PV Data
5 depicts a graph of our PV profile. OpenDSS uses Per Unit value instead of
iridescence as an input for PV. As such, we normalized with the maximum
iridescence value (of the year) to create a Per Unit based input vector for OpenDSS.
The residential load profile consists of 6 loads. Figure 6 presents a graph of our
residential load profile.
We use this test case to verify that our distribution system will not be
overloaded due to a specific transaction. We collect data points such as voltages,
currents, and angles as metered on the buses and lines. These data points will be
used by the ATPA’s communication manager to accept or deny a transaction.
Once the electrical energy is produced, it must be metered and documented as
it enters the electrical distribution network. Appropriate metering such as smart
meters must be installed, if not already in place, to document the quantity and
timing of energy flow. Such things as demand and power factor, as well as energy
delivery, must be measured and recorded. Interface to the distribution grid in
accordance with rules established by appropriate authority must be enforced. In
the long term, designing and programming the means to allow or inhibit energy
transfer based on compliance must be assessed and enforced at the metering
interface.
3. Management Interface
Figure 7 shows the login screen for the management interface. Using this
8

Figure 6: Residential Load Profile One Week
interface, managers can potentially create accounts, reset passwords, and log into
the management interface. ATPA’s management interface’s main menu can be
accessed by clicking on the three horizontal bars in the top left corner of the
homepage. Figure 8 depicts the management interface’s main menu, which is
divided into three sections: Customer, Management, and Administration. The
main menu also has a search functionality, which searches through the menu
items for a keyword and displays relevant matches. Customer Portal can be
accessed by clicking on the circle in the top right corner of the homepage. Figures
10 and 9 present the customer portal and the customer menu, respectively.
Managers can change their account password and log out of the application using
the customer menu/portal.
3.1. Management Menu: Transaction Management
Figure 11 presents the management menu. Using the menu option “Main
9

Figure 7: Log in interface for the ATPA management interface

Figure 8: Main menu of the ATPA management interface
10

Figure 9: Customer menu of the ATPA management interface

Figure 10: Customer portal of the ATPA management interface
11

Figure 11: Management menu of the ATPA management interface
Page and Dashboard”, an administrator can potentially configure how the
management interface’s main page and dashboard look like. Within the
“Transaction Management” menu option, there is one sub-menu option named
“Transaction
Agreement Manager”. Figure 27 depicts a snippet of the “Transaction Agreement
Manager” screen. All the power transaction agreements available in the database are
displayed in the “Transaction Agreement Manager” screen. Transaction agreements
can be filtered based on date and time ranges by clicking on the blue pocket button
in the upper left side of the “Transaction Agreement Manager” screen. Transaction
agreements can also be filtered by transaction column fields,
12

Figure 12: Admin menu of the ATPA management interface
using the search bar that’s available beside the blue filter button. Figure 13 de-
13

Figure 13: Transactions search and filtering menu in the transaction agreement manager screen

Figure 14: Transaction columns field filtering menu in the transaction agreement manager screen
picts the filter and search options.
Each transaction has multiple column fields, which are: transaction agreement
ID, agreement datetime1, start datetime, end datetime, producer site

1 Datetime is also known as Timestamp.
14

Figure 15: Transactions export menu in the transaction agreement manager screen

Figure 16: Report generation in the transactions export menu
name, storage site name, customer site name, additional power consumption
check, additional power production check, and support check. Additional power
consumption will be checked if a consumer is requesting more power than the
maximum specified for their profile. Similarly, additional power production will
be checked if a producer is willing to provide power on the grid for more than one
transaction. Support box will be checked for a transaction if transaction is
accepted by the agreement algorithm.
15

Figure 17: Add new transactions button in the transaction agreement manager screen
On the upper right side of the “Transaction Agreement Manager” screen, there
is a blue gear icon. This is the transaction view configuration menu. Using the view
configuration menu, the transaction’s column fields can be hidden or shown as
desired. Figure 14 represents the view configuration menu. Beside the blue gear
icon, there is a download icon. This is the export menu. Using the export menu, the
management interface’s managers can download a transaction report or export
the transactions. Figures 15 and 16 respectively present the export menu and the
report generated by the “Report” menu option. There is also an “ADD NEW” menu,
which can be used to add new power transactions into the database. Figure 17
depicts the “ADD NEW” button.
3.2. Management Menu: Customer Management
Under “Management Menu”, there is the “Customer Management” sub-menu, which
contains the “Customer Manager” screen. Figure 29 represents the “Customer Manager”
screen. Each customer has multiple column fields. Which are: an ID, a name, a
description, an address and US State, and a geo-location. All of the customer column
field values can be edited by the ATPA management interface’s admin user. The
filtering, searching, view configuration, export menu, and menu to add new customers
are all designed similar to the ones found in the “Transaction Agreement Manager”
screen.
3.3. Management Menu: Site Management
Under “Management Menu”, there is the “Site Management” sub-menu, which
contains the “Producer Site Manager” screen. Figure 30 represents the “Producer Site
Manager” screen. Each producer site has multiple column fields. Which are: an ID, a
name, a site image, a site geolocation, site creation timestamp, and Cap kW. Cap kW is
a cap on maximum power that the producer site can sell. All the producer site column
field values can be edited by the ATPA management interface’s admin user. A
producer site has several field values that are not yet displayed on the “Producer Site
Manager” screen. Some of which are: Phase, Bus Label, and relationship with a
particular customer. The filtering, searching, view configuration, export menu, and
menu to add new producer sites are all designed similar to the ones found in the
“Transaction Agreement Manager” screen.
Under “Management Menu”, there is the “Site Management” sub-menu, which
contains the “Consumer Site Manager” screen. Figure 31 represents the “Consumer
16

Figure 18: An overview of the Communication Manger’s workflow
17

Figure 19: Workflow of the Communication Manger’s ‘Connect to Database’ module
Site Manager” screen. Each consumer site has multiple column fields. Which are:
an ID, a name, and a site image. All the consumer site column field values can be
edited by the ATPA management interface’s admin user. A consumer site has
several field values that are not yet displayed on the “Consumer Site Manager”
screen. Some of which are: Phase, Bus Label, power rating in kW, approval info,
and relationship with a particular producer. Figure 32 depicts the dialogue box
with additional configuration options for a consumer site. The filtering, searching,
view configuration, export menu, and menu to add new producer sites are all
designed similar to the ones found in the “Transaction Agreement Manager” screen.
3.4. Management Menu: Profile Management
Under “Management Menu”, there is the “Profile Management” sub-menu,
which contains the “Producer Site Profile Manager” screen. Figure 33 represents
the “Producer Site Profile Manager” screen. Each producer profile has two column
fields, which designate ID and name of the producer profiles. The “Customer Site
Profile Manager” screen can also be found within the
18

Figure 20: Workflow of the Communication Manger’s ‘Acquire data’ module
19

Figure 21: Workflow of the Communication Manger’s ‘Generate Files’ module
“Profile Management” sub-menu of the “Management Menu”. Figure 34 represents
the “Customer Site Profile Manager” screen. Each customer profile has two column
fields, which designate ID and name of the customer profiles. All of the column
field values can be edited by the ATPA management interface’s admin user, for
both producer and consumer profiles. The filtering, searching, view configuration,
export menu, and menu to add new producer/consumer profiles are all designed
similar to the ones found in the “Transaction Agreement Manager” screen.
3.5. Management Menu: Bus Management
Under “Management Menu”, there is the “Power System Model Management”
20

Figure 22: Workflow of the Communication Manger’s ‘Run OpenDSS’ module
sub-menu, which contains the “PSM Bus Manager” screen. Figure 35 represents
the “PSM Bus Manager” screen. PSM stands for Power System Model. In this screen,
all the buses available in ATPA’s database are listed. Each bus has multiple column
fields. Which are: label, geolocation, number of phases, and list of phases. The bus
labels correspond to the bus object codes used by OpenDSS. OpenDSS bus object
codes can be found in their reference guide [2]. The buses already available in the
database can be edited and new buses can be created.
3.6. Management Menu: Line Management
Under “Management Menu”, there is the “Power System Model Management”
sub-menu, which contains the “PSM Line Manager” screen. Figure 36 represents the
“PSM Line Manager” screen. PSM stands for Power System Model. In this screen, all
the lines available in ATPA’s database are listed. Each line has multiple column
fields. Which are: label, name, number of phases, list of phases, and length in
meters. Line label and name correspond to the buses the line is attached to. The
lines already available in the database can be edited and new lines can be created.
21

Figure 23: Workflow of the Communication Manger’s ‘Update Database’ module
3.7. Management Menu: Line Type Management
Under “Management Menu”, there is the “Power System Model Management”
sub-menu, which contains the “PSM Line Type Manager” screen. Figure 37
represents the “PSM Line Type Manager” screen. PSM stands for Power System
Model. In this screen, all the lines available in ATPA’s database are listed. Each line
has multiple column fields. Which are: ID, name, and number of phases. The line
type IDs correspond to the line type ID object codes used by OpenDSS. OpenDSS
lint type ID object codes can be found in their reference guide [2]. The line types
already available in the database can be edited and new line types can be created.
3.8. Administration Menu
The management interface allows managers to perform administrative
actions. These actions can be found under the “Administration Menu”. Figure 12
depicts a snapshot of the “Administration Menu”. Managers can administer
22

Figure 24: Customer 1 Voltage Waveform
users, their roles, interface’s menus, authentication types, application windows,
and security policies for the interface. Managers can also control database’s
connections and deployment configuration.
4. MySQL Database
The relationships between the database’s entities are presented in Figure 26.
The database consists of profiles for consumer, producer, and storage sites for a
duration of one year and at a frequency of one hour. The data within these profiles
is obtained from their respective site entities. The site entities, in turn, obtain data
from actor entities. Actors can be either a producer or a consumer or both. The
site entities also obtain bus and line information from the bus entity. The bus
entity interacts with the line entity to obtain line information. The transaction
agreement algorithm maintains a data entity that obtains data from all three
entity sites.
23

Figure 25: Customer 1 Voltage Waveform

Figure 26: An entity-relationship diagram for the ATPA’s database
5. Communication Manager’s Workflow
ATPA’s Communication Manager is a set of scripts written in Python language.
An overview of the communication manager’s workflow can be found in Figure
18. The communication manager processes data acquired from the database and
generates files that are fed into OpenDSS and files resulting from OpenDSS process
are used to update the database. The communication manager consists of the five
modules: 1) Connect to Database; 2) Acquire Data;
24

Figure 27: Transaction Agreement Manager screen of the ATPA management interface

Figure 28: Transaction Agreement Manager screen of the ATPA management interface

Figure 29: Customer manager screen
3) Generate Files; 4) Run OpenDSS; and 5) Update Database. Following is a brief
description of each of these modules’ workflows. The workflows of the five
modules are depicted in Figures 19, 20, 21, 22, and 23.
The database connection module imports any given database’s connector,
which is mysql.connector in our case. The connector is used to create database
connection and to initialize the database controller. To acquire data from the
database, the data acquisition module first looks for a given time window. With
the time window, the module queries data from transactions, producer sites, and
25

Figure 30: Producer site manager screen

Figure 31: Consumer site manager screen

Figure 32: Additional configuration options in the consumer site manager screen
consumer sites. If both the producer site and consumer site data fall within the
time window, their respective vectors are generated. Otherwise, the data is
26

Figure 33: Producer Site Profile Manager

Figure 34: Consumer Site Profile Manager

Figure 35: Bus Manager

Figure 36: Line Manager
discarded.
Meanwhile, the file generator module generates producer site and consumer
site data at an hourly rate. Loadshape and the master data for the simulation’s
power system model is also generated. All the relevant OpenDSS files are copied
to the communication manager’s input directory. The communication
27

Figure 37: Line Manager
manager invokes OpenDSS, which then generates monitor files. The monitor files
are placed in the output directory. A transaction decision algorithm takes the
monitor files, checks for over-voltage and produces updated transaction status.
Transaction status will be ‘denied’ if there occurs an over-voltage in the
transaction hour. Otherwise, the transaction status will be ‘accept’. The updated
transaction status is written into the database.
6. Simulations
Once the Communications Manager has called OpenDSS, the modified IEEE 13
bus system model containing all the transaction agreement information will run
and output .csv files for every customer containing voltages, currents, and power.
An example of such a .csv file is depicted in Figure 25. As before the main criteria
is based upon over voltages with a limiting factor of 1.05. In simulation, it was
found while using realistic load profiles for residential, sun, and storage the IEEE
13 bus system model would only reach 1.03 per unit voltage, and this was used as
a threshold to test the application of approving or denying transactions.
Each simulation will be run on at a time step of 1 hour with the full data set of
24 hours. As shown in Figure 24 of customer 1 located at bus 611 we may see the
customers voltage does not reach the voltage threshold and will be accepted for
each transaction. In the figure it can be seen that their voltage reaches 1.018 per
unit and doesn’t drop below 0.95 per unit.
This process of running OpenDSS has been implemented to run every 5
minutes to simulate running the application as transactions are enabled. Once
OpenDSS has fully run, the .csv files are read into the web application and the
transaction agreement algorithm decides to keep or reject the transaction. Figure
28 depicts the “Transaction Agreement Manager” screen where some transactions
are rejected by the transaction agreement algorithm.
7. Related Work
Transactive Energy (TE) systems is in research and development phase since
2006 [3]. TE systems with Distribution Locational Marginal Price (DLMP) model
is being researched and developed since 2016 [4]. In our extensive literature
searches of the current state-of-the-art, we were unable to discover reports
28
prototype systems such as the one described in this report. We report here on
related research work.
Ghamkhari presents a theoretical power market to demonstrate DLMP model
for real time TE systems [5]. Ghamkari’s model also uses a supply and demand
formula to derive a real time value of the DLMP model [5]. Ghamkari’s work is
based on mathematical analysis using real world test case derivations [5]. Sajjadi
et al. present a stockholder-based scheme for implementing DLMP model in TE
systems [4]. The scheme includes all participants in the TE system as potential
stockholders and net benefits for all stockholders is analyzed [4]. Sajjadi et al.
validate their scheme via a mathematical analysis applied on the IEEE 9-bus test
system [4]. In contrast to both Ghamkari’s and Sajjadi et al.’s works, our work
presents a prototype software platform for implementing DLMP model for real
time TE systems. Our work also presents a decision algorithm for validating
transactions to be traded on the platform.
The Pacific Northwest National Lab (PNNL) has developed the Pacific
Northwest Smart Grid Demonstration (PNWSGD) project [6]. The PNWSGD
project proved the feasibility of DLMP model for TE systems using real world test
cases [6]. These test cases consisted of 55 instantiations across 11 utilities. Our
work builds on the proof of concept provided by the PNWSGD project and in
addition, presents a decision algorithm for validating transactions. Under project
named gridSMART, the American Electric Power Ohio campus (AEP Ohio)
implemented a real time and real world DLMP model-based TE system for
110,000 producer-consumer users in the state of Ohio [7]. gridSMART enabled
users to monitor price fluctuations in their neighborhood [7]. However, the
gridSMART project did not present a platform for selling and buying power. In
contrast to gridSMART, our work presents a platform that enables
producerconsumer users to sell and buy power.
In Europe, the Flexible Power Alliance Network has developed the EF-Pi
software platform, which enables users to choose their source of electricity
production [8]. Although EF-Pi provides options to qualify as a transactive energy
system, it is not a trading platform.
8. Conclusion and Future Work
We described the architecture, design, and evaluation of a prototype system
for enabling the creation and management of a transactive power market while
ensuring the integrity of the electric power distribution grid.
Acknowledgments
We would like to thank the the University of Idaho, College of Engineering,
Center for Secure and Dependable Systems, and Computer Science and Electrical
and Computer Engineering Department’s technical and administrative staff for
their help designing, implementing, and maintaining our research infrastructure
and services. We would also like to thank Natasha Jostad of TO Engineers and
Randy Gnaedinger and John Gibson of Avista Utilities Corp. for their help,
29
guidance, and expertise. This work was funded by the Avista Utilities Corp. The
opinions expressed in this report are those of the authors and not necessarily
those of Avista Utilities Corp. or the University of Idaho.
References
[1] Redwood Region Economic Development, National Solar Radiation Database,
Online, accessed: 10th June 2019.
URL https://rredc.nrel.gov/solar/old_data/nsrdb/
[2] R. C. Dugan, D. Montenegro, Reference Guide for the Open Distribution System
Simulator (OpenDSS), Electric Power Research Institute, Inc., 7th Edition.
[3] D. A. Cohen, Gridagents: Intelligent agent applications for integration of
distributed energy resources within distribution systems, in: 2008 IEEE
Power and Energy Society General Meeting - Conversion and Delivery of
Electrical Energy in the 21st Century, 2008, pp. 1–5. doi:10.1109/PES.
2008.4596818.
[4] S. M. Sajjadi, P. Mandal, T.-L. B. Tseng, M. Velez-Reyes, Transactive energy
market in distribution systems: A case study of energy trading between
transactive nodes, in: Proceedings of the Third Annual North American Power
Symposium (NAPS16), IEEE Xplore, 2016. doi:10.1109/NAPS.
2016.7747895.
[5] M. Ghamkari, Transactive energy pricing in power distribution systems, in:
Proceedings of the Eleventh Annual IEEE Green Technologies Conference
(GREEN17), IEEE Xplore, 2019.
URL https://userweb.ucs.louisiana.edu/~C00438828/Papers/ GreenTech.pdf
[6] D. Hammerstrom, Pacific Northwest Smart Grid Demonstration Project
Technology Performance Report, Tech. rep., Paceific Northwest National Lab
(PNNL), Richland, WA (US) (2015).
URL https://www.smartgrid.gov/document/Pacific_Northwest_
Smart_Grid_Technology_Performance.html
[7] gridSMART: A Community-based Approach to Leading the Nation in Smart
Energy Use, Tech. rep., American Electric Power (AEP), Columbus, OH (US)
(2014).
URL https://www.smartgrid.gov/files/AEP_Ohio_DE-OE-0000193_
Final_Technical_Report_06-23-2014.pdf
[8] Flexible Power Alliance Network, Energy Flexibility Platform and Interface
(EF-Pi), Online.
URL https://flexible-energy.eu/ef-pi/
APPENDIX G
Final Report: IR Camera Phase I
Report Number: 1808_01

USING IR CAMERAS IN BUILDING CONTROLS
PROJECT REPORT PREPARED FOR AVISTA UTILITIES
September 30, 2019
Prepared for:
Avista Utilities
Authors:
Damon Woods
Jubin Mathai
Ken Baker
This page left intentionally blank.
Prepared by:
University of Idaho Integrated Design Lab | Boise
306 S 6th St. Boise, ID 83702 USA
www.uidaho.edu/idl
IDL Director:
Ken Baker
Authors:
Damon Woods
Jubin Mathai
Ken Baker
Prepared for:
Avista Utilities
Contract Number: R-39872
Please cite this report as follows: Woods, D., Mathai, J., Baker, K.
(2019). Using IR Cameras in Building Controls. (1808_01).
University of Idaho Integrated Design Lab, Boise, ID.
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), ARISING OUT OF OR IN CONNECTION WITH THE
MANUFACTURE, USE OR SALE OF THE INFORMATION, RESULT(S),
PRODUCT(S), SERVICE(S) AND PROCESSES PROVIDED BY THE
UNIVERSITY. THE USER ASSUMES ALL RESPONSIBILITY AND
LIABILITY FOR LOSS OR DAMAGE CAUSED BY THE USE, SALE, OR
OTHER DISPOSITION BY THE USER OF PRODUCT(S), SERVICE(S), OR
(PROCESSES) INCORPORATING OR MADE BY USE OF THIS REPORT,
INCLUDING BUT NOT LIMITED TO DAMAGES OF ANY KIND IN
CONNECTION WITH THIS REPORT OR THE INSTALLATION OF
RECOMMENDED MEASURES CONTAINED HEREIN.
This page left intentionally blank.
TABLE OF CONTENTS
1. Acknowledgements ....................................................................................................................3
2. Executive Summary ....................................................................................................................3
3. Research Motivation...................................................................................................................4
4. Project Methods.........................................................................................................................6
4.1 Importing Data from the IR Camera......................................................................................6
4.2 Occupancy Detection Methods.............................................................................................8
4.3 Generating a Control Signal ..................................................................................................9
5. Results ......................................................................................................................................10
5.1 Energy Modeling Methods..................................................................................................10
6. Discussion and Future Work.....................................................................................................12
7. Budget Summary ......................................................................................................................13
9. Appendix...................................................................................................................................17
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ACRONYMS AND ABBREVIATIONS
AHU Air Handling Unit
ASHRAE American Society of Heating Refrigeration and Air conditioning Engineers
EMS Energy Management Control System
EPW Energy Plus Weather file
HVAC Heating, Ventilation and Air Conditioning
IDL Integrated Design Lab
MRT Mean Radiant Temperature
PMV Predicted Mean Vote
PNNL Pacific Northwest National Laboratory
PPD Percentage of Population Dissatisfied
TMY Typical Meteorological Year
UI University of Idaho
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1. ACKNOWLEDGEMENTS
This research was made possible through funding support from Avista Utilities via Idaho PUC Case Number
AVU-E-13-08. The research team is very grateful for the project management from Natasha Jostad at T.O.
Engineers. Thank you to Randy Gnaedinger and Carlos Limon at Avista for their supervision, guidance, and
support of this project.
2. EXECUTIVE SUMMARY
The University of Idaho – Integrated Design Lab (UI-IDL) combined an infrared camera with a
thermostat to deliver more efficient heating and cooling signals. The team set up a camera in an
experimental chamber and verified the accuracy of the camera’s temperature readings. The team
then used an algorithm to process the camera’s measurements into a comfort index. In addition to
assessing comfort, the team processed the camera inputs through a separate machine learning
process to detect occupancy. The comfort and occupancy data is translated into a standard control
signal for a thermostat. The team used Energy models to estimate the potential savings of such a
controller and documented the results.
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3. RESEARCH MOTIVATION
Last year, the Integrated Design Lab collected data at several office buildings in the northwest. Data
collected from these sites was used to quantify the occupant comfort levels. The research team found
that all offices surveyed dipped below the ASHRAE comfort guidelines and were calculated to be
uncomfortably cold. Typical thermostat settings were found to be 70oF for heating and 74oF for cooling
with setbacks that varied by location. In every case that was monitored, instruments installed next to the
thermostats recorded higher air temperatures than instruments installed closer to the working areas of
the office employees.
Controlling a building based on both surface and air temperatures could reduce energy
consumption and promote healthier buildings. 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 [1]. A large factor of human comfort is being missed in conventional
thermostats: the inclusion of the mean radiant temperature. 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 [2]. While operative temperature is widely used for predicting human comfort, rarely is it
directly used in building controls.
Building operations could benefit greatly from operative temperature control. Operative
temperature control would allow for wider air temperature setpoints, thus saving energy each year. A
study by the Pacific Northwest National Lab estimated the following general energy savings: a 2oF
adjustment on both heating and cooling setpoints led to a fairly uniform HVAC energy savings of 12-20%
[3]. Nationwide, such savings would be equivalent to 370-615 trillion Btu saved annually [4]. This research
focus is decidedly narrower in scope: focusing purely on small commercial office buildings in the Pacific
Northwest, where HVAC energy is typically 35% of the annual load [4]. Research has shown that a
thermostat reset could result in total annual energy savings of 4-7% of annual energy consumption per
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building [5]. The adoption of wider setpoints based on operative temperatures by controls engineers and
consultants could quickly scale up and result in widespread savings. In addition to energy savings, the
operative temperature control could dramatically enhance occupant comfort, thus making it an attractive
service to offer to clients.
The simple shift in control from air temperature to operative temperature may have a dramatic
impact on energy consumption. Controlling for operative temperatures can be done with relatively simple
and inexpensive instruments: thermocouples. Expanding just the air cooling setpoint by 5oF can save up
to 27% of the total HVAC energy [5]. However, expanded setpoints are only viable if occupant comfort is
maintained. While establishing high cooling setpoints can save a lot of energy, if the occupant is
uncomfortable, they will either adjust the thermostat on their own or place considerable pressure on the
building operator to do so (thus nullifying any potential savings). Occupant satisfaction (or the lack
thereof) is the driving force behind thermostat adjustments. Up to $330 per employee per year in lost
productivity at work can be attributed to an office environment outside the bounds of ASHRAE 55’s
comfort setpoints [6]. Therefore, any thermostat control scheme, operative or otherwise, should focus on
comfort so that the setpoints are not immediately changed after they are implemented. An operative
thermostat control scheme focused on comfort has a far greater likelihood of remaining in place. Most
energy modeling misses this crucial interaction and instead assumes standard setpoints and setbacks.
That is why the first phase of this research was identifying typical setpoints at real buildings instead of
relying on ideal assumptions.
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 range of air
temperatures would reduce energy consumption and help to capitalize on operational features such as
natural ventilation, night-flush and optimized set-points. The outline of the work is to develop a proof of
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concept for a thermostat that incorporates surface temperature measurements to deliver a better
comfort profile. This could save energy by reducing overcooling during the summer.
4. PROJECT METHODS
4.1 Importing Data from the IR Camera
The first step of the project was to verify that an inexpensive camera could pick up accurate
surface temperatures. The research team purchased a Flir C2 camera, which retails for $499 (the camera
specifications are available in the Appendix). The team set up the camera on a tripod in the experimental
chambers at IDL. The chamber is an empty office room with two windows and a ductless mini-split heat
pump. The room was heated and cooled at various points to test the camera at a range of temperatures.
The team measured surface temperatures in three ways: by using thermocouples attached to the wall
with insulation tape, specialized thermocouples (TMCx-HE) with thermal paste, an IR beam gun, an
expensive thermal camera, and the inexpensive Flir C2 camera.
Figure 1: Thermocouple sensor as viewed through the Flir C2 IR camera
After conducting experiments at a range of conditions, the team was able to verify that the Flir C2
could predict the surface temperature within 1oF of accuracy compared to the other methods. This
provided enough accuracy for the team to use the camera to estimate the mean radiant temperature of
the space. The camera requires inputs of reflected (air temperature), emissivity values (0.9 is the default
that worked best) and the camera’s distance from the object (set between 0 and 3 meters).
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Once the camera settings were verified, the team used that data to calculate the average of the
surrounding surface temperatures in the room or the Mean Radiant Temperature (MRT). The mean
radiant temperature is based on view factors between a point in space and the surfaces that surround it.
P.O. Fanger [7] developed a set of formulas that can be used to predict the view factors of a seated
occupant and their surrounding surface temperatures. The team began with this approach. Fanger uses a
set of multiple integrals to calculate the radiation impact of each surface. The research team wished to
avoid using multivariable calculus, and so curve approximations were developed in Excel. The best method
was found to be recommended by the RHEVA guidebook [8]. These equations were programmed into an
Excel worksheet and are listed below:
Fp-N = Fmax (1-e-(a/c)/Ʈ ) . (1-e-(h/c)/ƴ )
Ʈ = A+B(a/c)
Ƴ = C+ D(b/c) + E(a/c)
Table 1 : Equations for calculations of the angle factors
[8]
The mean radiant temperature is calculated using the below equation in excel,
𝑇𝑚𝑟𝑡=4 ∑(𝐹𝑝‒𝑁∗𝑇𝑁4)
Where Tmrt is mean radiant temperature
Fmax A B C D E
Seated Person, Figure
2.2a Vertical Surfaces:
Wall, Window
Seated Person, Figure
2.2a Horizontal Surfaces:
Wall, Window
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Fp-N is the view factor from person to surface
TN is the temperature of the surface being measured by the infrared camera
The team now had the ability to take a photo of the wall, export that temperature data into Excel,
and estimate the mean radiant temperature for an occupant. The mean radiant temperature was
imported into a separate spreadsheet that calculates thermal comfort predictions. Thermal comfort
metrics include the following: humidity, dry-bulb temperature, mean radiant temperature, air velocity,
clothing level, and activity level. The team measured humidity and dry-bulb temperature with HOBO data
loggers. Assumptions were made for air velocity (20 fpm) and occupant clothing and activity levels (seated,
typing, wearing jeans and a long-sleeve shirt).
The comfort metrics can be calculated in two ways: either as a Predictive Mean Vote (PMV) or as
a Percentage of Population Dissatisfied (PPD). The PMV ranges in its values from -3 to +3, where negative
values are associated with being too cold, and positive values corresponding to being too warm. Thermal
comfort is defined as having a PMV between -0.5 and +0.5. This value is directly related to the percent
that are predicted to be uncomfortable in that situation. For example, a PMV of 1 equals a PPD of 25%.
The minimum PPD is 5%.
4.2 Occupancy Detection Methods
One of the advantages of using an IR camera to measure surface temperatures is that it can also
be used the count the occupants in a room. This provides crucial information to the HVAC system on the
ventilation requirements in a space, which are now required in many systems under Washington’s new
energy code (WEC C403.1.6.1). The team used machine learning to detect occupants with the low
resolution camera. There are numerous open source codes available to detect humans in RGB images.
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The IDL researchers used the same codes by changing the infrared image inputs to an RGB format. The
application program interface used is the TensorFlow object detection.
TensorFlow is an open source platform from machine learning. The TensorFlow project has many
useful framework extensions, one such is Object detection API. As the name suggests, the extension
enables TensorFlow users to create powerful object detection models using TensorFlow’s directed
compute graph infrastructure. The TensorFlow Object Detection API is an open source framework built
on top of TensorFlow that makes it easy to construct, train and deploy object detection models. This API
can be used to detect, with bounding boxes, objects in images or video using either some of the pre-
trained models made available. The object detection code was downloaded and used from Github, a host
to open source projects owned by Microsoft. TensorFlow was developed by Google’s AI team and codes
are available on Github to download. It can be implemented and modified according to user’s application
needs. The repository is maintained by individual researchers on Github.
4.3 Generating a Control Signal
The purpose of using the camera is to send better heating and cooling signals to the HVAC system for
an office. The established protocol for sending control signals is the Building Automated Control Network
Protocol, or BACnet. The research team bought a wireless thermostat as well as a digital router that can
process BACnet signals. With this, the team was able to establish both wired and wireless communication
with a thermostat. Typically a thermostat tries to maintain a certain air temperature keeping the room
within a certain deadband between heating and cooling setpoints. For this project, the setpoints were
manipulated in order to maintain certain comfort levels. For example, if the PMV comfort calculated for
the room drops below -0.5, then the setpoints are increased above the current air temperature to force
the heating system on. Similarly, if the calculated PMV rises above +0.5, the thermostat is to call for
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cooling. During unoccupied times, the setpoints are expanded dramatically and the PMV is allowed to
float anywhere between +3.
5. RESULTS
5.1 Energy Modeling Methods
The research team used a small office prototype model to estimate energy savings. The energy
model uses the DOE software program, EnergyPlus. The geometry and loads are derived from the Pacific
Northwest National Lab’s database of standard building types. These prototype models are used by
organizations to test the impacts of new energy codes.
Figure 2: PNNL Small office prototype energy model
The model can be paired with different weather files to estimate utility costs in different locations.
The American Society of Heating Refrigeration and Air conditioning Engineers (ASHRAE) partitions the US
into eight climate zones that account for minimum/maximum temperatures and humidity. All of Avista’s
service territory falls into either climate zone 5B, or 6. A sketch overlaying climate information onto the
general Avista territory is shown in Figure 3
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Figure 3 A general outline of Avista’s service territory and the climate zones present in that area.
The weather files used in this project included locations in Lewiston, Spokane, and Kalispell.
Lewiston and Spokane, which are both within ASHRAE climate zone 5. The Kalispell weather file is in
climate zone 6 and was used to approximate the weather Avista’s rural customers experience in northern
Idaho and Washington.
Initially, the prototype model was simulated with a standard thermostat that uses air temperature
setpoints and setbacks. The team then adjusted the code within the model to have the thermostat trigger
heating and cooling based on PMV instead of air temperatures. During business hours, the PMV was
maintained between +0.5, while at night, the PMV was allowed to float between +3. Savings were
identified for each climate. For each of the weather files tested, the heating load increased, while the
cooling load decreased. In each situation, the decrease in cooling load was greater than the increase in
heating, leading to a net energy savings in every location. The savings were smallest for the colder climate
zone 6, with the Kalispell model predicting 3% HVAC savings annually in a typical year. In Spokane, the
small office model predicted 5% annual HVAC energy savings from using a thermostat based on PMV,
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while in Lewiston, the building saved 7% of its annual HVAC energy. The small office in Spokane reduced
its cooling energy by over 1,500 kWh, while keeping the occupants more comfortable.
6. DISCUSSION AND FUTURE WORK
The research work this year proved that it is possible to use an inexpensive thermal camera to
predict comfort and occupancy in an office and run a thermostat based on those comfort predictions. The
energy models showed net energy savings for Avista customers in both ASHRAE climate zones 5 and 6.
The camera was able to accurately record surface temperatures, those surface temperatures were
converted into a comfort signal. The team also trained the camera to recognize occupants in low-
resolution IR images using machine learning. By processing that information through an Excel
spreadsheet, that information can be sent as a BACnet signal to a standard thermostat.
Over the next year, the research team will work to streamline and automate this process. The goal
is to commercialize this device so that Avista customers may benefit from its development. The IDL also
hopes to incorporate glare detection into the algorithm so that this device may serve multiple functions
and eliminate superfluous sensors and wiring and in buildings. So far the team has only sent and received
thermostat signals from a computer. In the next year, the team aims to connect a heating and cooling
device to the thermostat to complete the control feedback loop. This will allow for further testing and
refinement as well as providing physical data on energy savings.
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7. BUDGET SUMMARY
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.
Personnel Hours estimate Description
FY18/FY19
Expense
PI/Faculty Salaries (Cooper and TBD ME) $ 13,243
PI/Faculty Benefits (Cooper and TBC Woods) 3,509
Graduate Student Salaries 8,960
Student Benefits 340
Graduate Student Tuition Remission (partial)5,387
Travel 2,250
Equipment 500
F&A / Overhead (Excludes Tuition)13,200
Total $ 47,935

Indirect Costs
For this contract, UI-IDL was considered an on-campus unit of the University of Idaho with a federally
negotiated rate of 50.3%.
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8. REFERENCES
[1] E. Arens, M. Humphreys and H. Zhang, "Are 'Class A' temperature requirements realistic or
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Integrated Design Lab | Boise 15
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Integrated Design Lab | Boise 16
Using Infrared Cameras in Building Controls (Report 1808_01)
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9. APPENDIX
Integrated Design Lab | Boise 17
Using Infrared Cameras in Building Controls (Report 1808_01)
Table 2: Camera Specifications
Specification FLIR C2
USB 2.0
Operating temperature range -10°C to +50°C
Color palettes
Iron, Rainbow, Rainbow HC,
Gray
Digital camera 3.07MP
Image Frequency 9 Hz
NETD, Thermal sensitivity 100mk, <0.10°C
Field of view 41°C x 31°C
IR resolution 320*240
Focus Focus free
Frame rate 9hz
Minimum focus distance Thermal: 0.15m (0.49ft)
Minimum focus distance with MSX 1.0m (3.3ft)
APPENDIX H
Two-Page Report: Energy Trading Phase II

Designing and Evaluating an Energy Trading System for
Prosumers – Phase II
Project Duration: 12 months Project Cost: Total funding $96,164
OBJECTIVE
The objective of this project is to analyze and
develop a prototype system that would enable
Avista prosumers and consumers to trade
power on-demand with utility oversight, or
with the utility while also ensuring the utility’s
network resilient operation. In phase I of this
project, we completed the analysis and
implementation of the first prototype of this
application. In Phase II, we propose to design,
implement, and integrate a transaction
prioritization and pricing algorithm into this
prototype. Such algorithm would have the goal
of maximizing the economic benefit, welfare,
and availability of the electric power grid in the
presence of prosumers, while adequately
incentivizing prosumer-offered services where
and when needed. Such system will enable
Avista to create a new market for prosumer-
consumer power trading and to plan, manage,
and control such market and the flow of power
through its network.
BUSINESS VALUE
Avista will benefit from such prototype in the
following ways:
Provide a platform for testing new
technologies and algorithms that would
enable larger-scale implementations of
such customer-driven trading markets.
Enable the collection of data on sharing
behaviors and trading patterns.
Enable data analytics that may lead to
increased efficiency in the distribution
system.
Enable the implementation of real-time
profitable and constraint-based electric
power pricing strategies.
INDUSTRY NEED
The adoption of consumer-level electric energy
production and storage and of smart consumer
appliances is accelerating. Examples of
technologies leading this rapid adoption are
photovoltaics, small wind, electric vehicles,
and smart water heaters and freezers, among
other technologies. Within such a new
prosumer-enabled smart grid, enabling the
managed, but timely and selective trading and
transmission of power between prosumers
(producer-consumer) and with the utility may
enable a more efficient use of the power grid
as well as provide utility savings on generation
and transmission. For example, a temporary
increase in demand from a smart-grid-enabled
customer could be served from a grid-near
prosumer with stored capacity or solar
generation.
BACKGROUND
The following is an example of how the
proposed prosumer-consumer power trading
may operate. One of Avista’s customers
installs solar panels or has a storage system
such as an electric vehicle and generates or
stores electricity. To sell some of this
prosumer’s excess electricity, this prosumer
uses the Avista-managed website and enters
the amount of energy that they want to sell,
the target price, its nature, and other
constraints such as timing. Another Avista
customer needs some additional electricity at
a scheduled time. Such customer uses the
same website and indicates an interest in
buying energy under certain conditions,
pricing, and time period. The proposed system
then creates a match between the prosumer
energy offers, requests, restrictions, timing,
and pricing. The system then informs the
customers and enables and records the
business transaction.
SCOPE
Task 1: Meet with Avista® to gather
system requirements.
Discussions have been productive in
determining the desired modeling system and
how to incorporate external transmission
Locational Marginal Prices (LMP) into the
distribution system.
Task 2: Review of the literature on
prioritization and pricing strategies at the
distribution level.
Research has been conducted in reviewing
past and current literature on LMP and
distribution Locational Marginal Price (DLMP).
Past project and transactive markets have also
been studied on how to incorporate a
prosumer-based market into the power system
for transaction support.
Task 3: Evaluate options for revenue or
objective prioritization and pricing
functions
Studies have been reviewed for customer costs
on the power system. Base prices and cost of
energy production have been evaluated for a
per customer basis on the power system.
Task 4: Enhance the simulated model of
the power distribution network developed
in phase I
The model developed within Phase I of the
project has been successfully enhanced from
the IEEE 13 bus system to the IEEE 34 Bus
system (OpenDSS). This IEEE 34 Bus system
has been modified to incorporate customers
with renewable resources.
Task 5: Implement the prioritization
algorithm in a modern programming
language
The prioritization algorithm will be developed
in a modern programming language such as
MATLAB or Python. Implementation is
progressing on schedule. Options to transfer
the algorithm to Python are being evaluated.
Task 6: Integrate the transaction control
system prototype with the updated
distribution model.
Transaction control from the database will
expand on the system implemented in the
Phase I portion of the project. This integration
is being implemented currently and requires
full system development from the database.
Task 7: Analyze the power grid condition
and economic benefits for different
scenarios
Different scenarios will be designed to study
the impact of different prices and operating
points of the model power system. Task 7 will
begin once Task 5 has completed.
Task 8: Design and perform tests for:
Functionality, Usability, and Security.
The team has the individual parts (SQL query,
OpenDSS, and Python) in the testing phases.
Once all the components are autonomously
interacting with each other, tests will be
developed to observe the systems
performance.
Task 9: Produce user and administrator
usage guides
This task will be completed once Task 8 has
been completed.
DELIVERABLES
The deliverables of the project include:
Written final report.
Interim reports and online conferences
with Avista, including one mid-term
report.
Proof-of-concept software and user and
administrative documentation.
Proof-of-concept evaluation within a
small-scale simulated distribution
system.
Outlines of proposals for follow-on
funding to further develop and refine
the prototype.
PROJECT TEAM
PRINCIPAL INVESTIGATOR
Name Dr. Yacine Chakhchoukh
Contact #(208)-885-1550
Email [e-mail]
CO-PRINCIPAL INVESTIGATOR
Name Dr. Daniel Conte De Leon
Contact #(208)-885-6520
Email [e-mail]
Name Dr. Herbert L. Hess
Contact #(208)-885-4341
Email [e-mail]
Name Dr. Brian Johnson
Contact #(208)-885-6902
Email [e-mail]
Name Dr. Hangtian Lei
Contact #(208)-885-0952
Email [e-mail]
SCHEDULE
TASK
TIME
ALLOCATED START
DATE
FINISH
DATE
Task 1 4.5 months 08/18/19 01/01/20Task 2 5.5 months 08/18/19 01/31/20
Task 3 5.5 months 08/18/19 01/31/20Task 4 5.5 months 08/18/19 01/31/20Task 5 4 months 12/01/19 03/01/20
Task 6 3 months 03/01/20 06/01/20Task 7 5 months 03/01/20 08/01/20
Task 8 3 months 06/01/20 08/31/20
Task 9 1 month 08/01/20 08/31/20
APPENDIX I
Two-Page Report: Gamification of Energy Use
Gamification of Energy Use Feedback
Project Duration: 12 months Project Cost: Total Funding $108,736
OBJECTIVE
In human systems, feedback is essential to
understanding the relationship between effort,
error, and optimal (or at least successful)
performance. If human users can be made
explicitly aware of the essential elements of
their performance, they can modify that
performance. The intent in this project is to use
the gaming metaphor to make attention to
feedback appealing and frequent. The most
effective feedback systems then offer
behavioral options, i.e., actions a user can take
to change his or her future outcomes. A slate
of options that affect usage, but also bring
users to educational modules and helpful
products will be recommended.
BUSINESS VALUE
Gamification can stimulate greater
attentiveness to energy use; attention can lead
to reductions in usage. A gamification system
will give the utility the ability to incentivize
conservation, and can also reward citizen
continuing education about energy systems
and products.
INDUSTRY NEED
To meet increasing demands and increasing
costs, the industry would benefit from
customers’ conservation behaviors. We believe
that the utility would benefit from better
monitoring of their energy usage by
customers. As meter systems become
“smarter”, information available to customers
can be more granular, more up-to-date, and
thus potentially actionable. Presumably, those
customer actions will lead to optimization of
their usage. Optimization can be in the service
of their own savings but could also serve their
other pro-social interests (donations of savings
to worthy causes, environmental protection,
etc.).
BACKGROUND
This is a new project. However, energy use
reduction is, essentially, a human performance
problem. There are extensive literatures on
feedback systems in general, and gamification
efforts in particular. Moreover, gamification
has been attempted in utility settings, albeit
with disappointing outcomes. We believe the
causes of those disappointments can be
identified, and that a system can be developed
that takes into account preferences for games
types, sustained play, action choices, and
incentive preferences.
SCOPE
To be consistent with our ongoing set of bi-
weekly short reports, we have consolidated
some of the tasks reported there into
superordinate tasks headings.
Task 1: --- A general review of incentive
systems and gamification. We will review past
and current attempts to use feedback-based
systems by utilities. These attempts have
been well-crafted but could not be sustained.
We want to learn from those attempts without
repeating the mistakes.
Task 2: -- We will review the literature on
successful incentive strategies. Then, survey
customers to create a user profile system that
will be the basis of customer gamification
choices.
The information contained in this document is proprietary and confidential.
Task 3: -- We will identify the utility data
collection capabilities that would allow
feedback to customers. E.g., what information
can be made available (taking into account
security concerns)? Then, we will provide
evidence of concept, as follows.
Game prototypes will be developed taking
into account gamification capabilities in
mobile and home devices. These will be linked
to incentives to monitor usage (informed by
incentive profiles). Potential customer actions
will be added in the course of development.
Task 4: We will user-test the system and
make appropriate modifications. Small
samples of potential users will be identified at
appropriate venues.
DELIVERABLES
1) A report will be prepared that updates
Avista about what systems and devices are
“out there” (with analyses of reasons for
success or failure).
2) We will have a detailed inventory of
incentive strategies, and an overview of
customer profile information linked to those
strategies.
3) We will have survey data on # 2, drawn
from actual and potential utility customers in
Avista’s service area.
4) We will create, and test, a prototypical
multiplatform application that will gamify
energy usage monitoring. By playing simple
entry-level games designed to entertain and
challenge, customers will find they have also
entered the “big game” (i.e., power usage).
Once there, they can be directed to power
savings advice, educational modules, and
product recommendations (and perhaps
discounts). They can earn points, badges, and
other rewards as they compete against others
or try to beat their own "personal bests." The
combination of usage awareness and
behavioral options within the “big game” can
foster behaviors that lead to more efficient
usage.
PROJECT TEAM
PRINCIPAL INVESTIGATOR(S)
Name Richard Reardon
Organization University of Idaho, Dept. Psychology/Comm
Contact #208-292-2523
Email [e-mail]
Name Julie Beeston
Organization University of Idaho, Dept. of Computer Science
Contact #208-292-2671
Email [e-mail]
RESEARCH ASSISTANTS
Name Kellen Probert
Organization University of Idaho, Psyc Human Factors
Email [e-mail]
Name Jode Keehr
Organization University of Idaho, Psyc Human Factors
Email [e-mail]
Name David Beeston
Organization Private Utility Consultant
Email [e-mail]
Name Undergraduate (to be named)
Organization University of Idaho, Dept. of Computer Science
Email tba
SCHEDULE
TASK TIME
ALLOCATED
START
DATE
FINISH
DATE
1. Research on incentive
systems and gamification; report
prepared
11 months (the bulk gathered by
the 5th month)
9/19 7/20
2. Survey developed,
deployed; data analyzed
4 months to develop/deploy;
2 months of analysis
9/19 3/20
3. Mockups/prototypes
developed and tested 11 months 9/19 7/20
4. User testing 4 months 4/20 8/20
APPENDIX J
Two-Page Report: IR Camera Phase II
Using IR Cameras in Building Controls – Phase II
Project Duration: 12 months Project Cost: Total Funding $52,500
OBJECTIVE
In 2019, the Integrated Design Lab (IDL)
demonstrated the technical feasibility of using
a low-cost infrared (IR) camera to provide
better temperature management of rooms.
The objective of the second phase is to bring
the concept of IR thermostats closer to
commercialization. The research team is
working to miniaturize, automate, and add
glare detection and occupant counts to the IR
thermostat. The research includes setting up a
test chamber to compare the new product to a
traditional thermostat, while developing a
business case.
BUSINESS VALUE
The IR thermostat functions as an all-in-one
sensor that is capable of measuring
temperatures and counting occupants. This
device can replace individual sensors, thus
limiting wiring costs and installation times. The
team is also working to add glare detection
algorithms so that this device can dim lights
and adjust motorized blinds. The lab aims to
commercialize the concept of combining three
sensors into one device to provide improved
comfort and energy savings.
INDUSTRY NEED
Most buildings manage their heating or cooling
based solely on zone air temperature, when in
reality human comfort is dominated by
surrounding surface temperatures. A control
system that incorporates the surface
temperatures of a zone would allow for a wider
range of supply air temperatures to better
meet the comfort needs of occupants. The
wider range of air temperatures has the
potential to reduce energy consumption and
can help to capitalize on operational features
such as natural ventilation, night-flush and
optimized set-points. Current occupancy
signals rely on CO2 sensors, which are
expensive and require frequent recalibration.
The IR thermostat will be able to count the
occupants in a room and provide accurate
levels of ventilation without the need for CO2
sensors.
BACKGROUND
The IDL measured the thermal comfort
parameters of several offices within Avista’s
climate zones. The findings indicated that
current thermostat controls fail to provide
adequate comfort in offices and waste cooling
energy while doing so. Last year, the IDL
explored the possibility of using a thermal
camera to incorporate surface temperatures
into thermostat controls. This process was
carried out with a large camera mounted on a
tripod and manual calculations. The research
showed that using the IR camera to estimate
comfort and detect occupancy is feasible.
SCOPE
The primary focus of this phase of research is
to automate and commercialize the IR
thermostat concept. To advance this concept,
the IDL will utilize a thimble-sized IR camera
on an automatic pan/tilt platform and some
micro processing boards. The research team
will set up communication between the IR
thermostat and a piece of HVAC equipment to
serve as a test-bed for analysis. This will close
the communication loop between the camera
and a heating or cooling device to condition a
space and provide a direct comparison to the
functioning of a traditional thermostat. The
team will convert the manual inputs of surface
temperatures and conditions into the standard
comfort formula to deliver better heating and
cooling signals through standard Building
Automation Control network (BACnet)
protocols. IDL has used open-source machine
learning codes to identify occupant outlines
from pixelated IR images. The team will modify
The information contained in this document is proprietary and confidential.
and enhance this approach to use the camera
to count the number of occupants in a space
and to detect direct sunlight (glare) coming
through any windows to adjust blinds and
optimize cooling controls. The research team is
working with the University of Idaho’s College
of Business and Office of Technology Transfer
on commercialization options.
Task 1: Project Planning and Reporting
Conduct team meetings and ongoing project
updates, reports and deliverables as required
by Avista staff, project management
contractors, and the PUC. This task continues
throughout the project.
Task 2: Set up HVAC Equipment
The team will link the IR thermostat to an
HVAC terminal unit that is able to respond to
BACnet control signals to condition the
experimental chambers.
Task 3: Automate IR Signal Processing
The camera will be set up to scan the room and
take pictures at automatic intervals and
process this into a control signal on a regular
schedule.
Task 4: Develop Glare Detection
Algorithm
In parallel with Tasks 2 and 3, IDL will use the
operational data collection to correlate the IR
camera files to readings of glare within the
space.
Task 5: Run Comparison with Traditional
Thermostat
The research team will compare the signal
response between the IR thermostat and a
traditional thermostat to gauge potential
energy savings.
Task 6: Document Results
The final task will be to prepare for
commercialization of this product and seek
outside investment. The team will provide a
final report and presentation to Avista as well
as pursue publication in an academic journal
such as Energy and Buildings.
DELIVERABLES
Project team has set up a piece of HVAC
equipment with a call and response to
the IR thermostat that can either heat
or cool the experimental chamber.
Project team has installed the device on
a pan/tilt mechanism and has
scheduled the photography and signal
processing for autonomous
deployment.
Project team has translated glare
readings to BACnet signal objects for
blind controls.
Project team has compared
performance of using an IR thermostat
versus a traditional thermostat to
manage the temperature of an
experimental chamber and can quantify
the difference in energy used.
Project team is able to articulate the
process, move towards
commercialization, and produce a final
report of findings for Avista. The team
has completed a current literature
review of the relevant technology in this
sector.
PROJECT TEAM
PRINCIPAL INVESTIGATOR(S)
Name Damon Woods
Organization University of Idaho Integrated Design Lab
Contact #[phone]
Email [e-mail]
RESEARCH ASSISTANTS
Name Jubin Mathai
Organization University of Idaho Integrated Design Lab
Email [e-mail]
SCHEDULE
TASK
TIME
ALLOCATED
(Months)
START
DATE
FINISH
DATE
Project planning 12 09/01/19 08/30/20
Set up HVAC equipment 2 09/01/18 01/01/20
Automate IR signal
processing 4 01/01/20 05/01/20
Develop glare detection algorithm 6 10/01/19 04/01/20
Run comparison with
traditional thermostat 2 05/01/20 07/01/20
Document results 2 07/01/20 08/30/20