20140701DeVol Direct.pdf

rN THE MATTER OF THE APPLICATION
OE IDAHO POWER COMPANY TO
IMPLEMENT SOLAR INTEGRATION
RATES AND CHARGES.
REEEIV[S
mlq JUL - | Ptl lrr 53
unl'B#lo&tfiil$s,**
rPC-E-14-18
BEEORE THE IDAHO PUBLIC UTILITIES COMMISSION
)
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) CASE NO.
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IDAHO PO9TER COMPANY
DIRECT TESTIMONY
OE
PHILIP B. DEVOL
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O. Pl-ease state your name and business address.
A. My name is Philip B. DeVol and my business
address is 7227 West Idaho Street, Boise, Idaho 83702.
O. By whom are you employed and in what capacity?
A. I am employed by Idaho Power Company ("fdaho
Power" or "Company") as the Resource Plannlng Leader
o Please describe your educatj-onal- background
and work experience with Idaho Power
A.In May of 1989, I received
Science Degree in Mathematics from Miami
Oxford, Ohio. I then received a Master
in Biostatistics from the University of
1991.
a Bachel-or of
University in
of Science Degree
Michigan in May of
O. Please descrj-be your work history at Idaho
Power.
A.I began my empJ-oyment with Idaho Power in 2007
as an Engi-neering Specialist in the Water Management
Department. In this position, I was responsible for
modeling of the Idaho Power hydroelectric system for the
Integrated Resource Plan ("fRP") and relicensing studies.
In 2004, T became a Water Management Operatj-ons Analyst,
where I continued to be responsible for hydroelectric
system modeling.
In 2005, I became a Planning Analyst in the Power
Supply Planning Department. In this position, I was
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responsible for the compj-1atj-on of Idaho Power's long-term
operating plan prepared on a monthly basis as part of the
Company's plan for managing risk. My duties in this
positi-on also expanded to include the study of wind
integration.
I became the Power Supply Planning Leader in 2010
and Resource Planning Leader j-n 2013. My duties in these
positions have included project management for the most
recent Idaho Power wind integration study.
I have been involved in regional and national
proceedings related to the study of wind integration. I
participated in methodology discussions for the 2001 Wind
Integration Action Plan produced by the Northwest Wind
Integration Forum. I have attended numerous Utility Wind
Integratj-on Group (*UWIG") workshops, and presented at UWIG
workshops in Oklahoma City in 2006 and Portland, Oregon, in
2007. I also presented to the Idaho Wind Working Group at
its September 2077 meeting. In November of 20L3, I
presented at a Centre for Energy Advancement through
Technol-ogical Innovati-on workshop focused on forecasting
uncertainties for renewable energy supply.
I am leading the sol-ar integration study on behalf
of Idaho Power.
What is the purpose of your testimony in this
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matter?
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A. The purpose of my testimony is to describe
Idaho Power's solar integration study ("Study" or *2014
Study" or "Sofar Study") and to provide the results. The
201-4 Sol-ar Integratj-on Study Report ("Study Report") is
attached hereto as Exhibit No. 1. This Study Report was
completed on June 76, 2074, and filed with the Idaho Public
Utilities Commission on June 17, 2014, in Case No. IPC-E-
1,4-09.
o.Can you provide a high level description or
summary of the Company's 201,4 Study?
A.Yes. Electric power f rom sol-ar generati-on
resources exhibits greater variabil-ity and uncertainty than
energy from conventional generation sources. The greater
variability and uncertaj-nty exhibited by solar resources
requires an electric utility integrating solar to modify
its operating practices by holding extra operating reserves
on dispatchabl-e generation resources. The effect of having
to hold operatj-ng reserves on dispatchable resources is
that the use of those resources is restricted and they
cannot be economj-caIIy dispatched to their ful-l-est
capability. The objective of the Study is to determine the
costs of the operational- modifications necessary to
integrate sol-ar generati-on.
The Company's Sol-ar Study determined solar
integration costs for four solar build-out scenarios at
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installed capacities of 100 megawatts ("MW"), 300 MW, 500
MW, and 700 MW. The Study utilized geographically
dispersed build-out scenarios with solar generation l-ocated
across the Company's service territory at Parma, BoJ-se,
Grand View, Twin Fal1s, Picabo, and Aberdeen. Pages 6 and
I of the Study Report provide additional- j-nformation
regarding the buj-ld-out scenarios.
The Company initlated the Study with the formation
of a Technical Review Commlttee ("TRC"), with the purpose
of providing input, review, and guidance for the Study. In
coll-aboration with the TRC, Idaho Power organized the Study
into four primary steps: (1) data gathering and scenario
development; (2) statistical--based analysis of solar
characteristics; (3) production cost sj-mul-ation analysis;
and (4) study concl-usions and resu1ts. The Study
determined sol-ar integration costs through paired
simul-ation of Idaho Power's system and each sol-ar build-out
scenario. Each pair of simulations consists of a test case
in which extra capacity in reserve is required of
dispatchable generators to a.l-low them to respond to
unplanned sol-ar variations and a base case in which no
extra capacity in reserve is required. The solar
integration costs indicated by the simulations are provided
bel-ow. These costs are al-so found in Table 2, page 3 of
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the Study Report, ds well as Tabl-e 8 and Table 9 on page 15
of the Study Report.
Average Integration Cost Per Mt[h
(2Ot4 cost and do].lars)
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Incremental Integration Cost Per M[iIh
(2OL4 cost and do].].ars)
Penetration Level 0-100 MW 100-300 Mlir 300-500 MW 500-700 MW
Integration Cost $0.40 $1. s0 $2.80 $4.40
I
9 Q. When did Idaho Power initiate the current
10 solar integration study?
11 A. The official- Study kick-off was on August 15,
72 20L3, with the first meeting of the TRC.
13 O. What is the TRC?
74 A. The TRC was formed during the summer of 20L3
15 with the purpose of providing input, review, and gui-dance
76 for the Study. It is made up of participants from outside
I7 of Idaho Power that have an interest and/or expertise with
18 the integration of intermittent resources onto utility
19 systems. The TRC consists of: Brian Johnson from the
20 Universj-ty of Idaho; Jimmy Lindsay from the Renewable
27 Northwest Project (*RNP") (now with Portl-and General-
22 El-ectric); Kurt Myers from the Idaho National Laboratory;
23 and Paul- Woods with the City of Boise (now self-employed as
24 a consultant). In addition to the members of the TRC,
25 Staff from both the Idaho and Oregon commissions are
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Build-out Scenarios 0-100 MW 0-300 MW 0-500 MW 0-700 Mv[
Integration Cost s0.40 s1.20 s1.80 $2.s0
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o.
A.
participants in the Study. Rick Sterling from the fdaho
Publ-ic Utilities Commissj-on Staff and Brittany Andrus and
John Crider from the Publ-ic Utility Commission of Oregon
Staff have participated throughout the Study. Although Mr.
Lindsay left RNP, he continued to participate as a TRC
member. Cameron Yourkowski was designated by RNP as Mr.
Lindsay's replacement for the TRC. Similarly, Mr. Woods,
although he left employment with the City of Boj-se and is
now a self-employed consultant, has continued to serve as a
member of the TRC.
How is the Study being conducted?
The conduct of the Study is guided by two
documents that were shared with and discussed with the TRC.
PrincipTes for TechnicaL Review (fRC) Invol-vement in
Studies of VariabLe Generation Integration into ELectricaT
Power Systems was produced by the National- Renewable Energy
Laboratory (*NREL") and Utility Variable-generation
Integration Group (*UVIG"). The NREL/UVIe principles
document provides guidance in defining the important role
of the TRC in the Study. The second report, The Evofution
of Wind Power Integration Studies: Past, Present, and
Future, was authored by five NREL researchers considered to
be at the forefront of the study of renewabl-e integration
and was published by the Instj-tute of Electrical and
Electronics Engineers ("IEEE"). Even though the report was
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written from the perspective of wind integration, the
principles remain the same for sofar j-ntegration. This
report is used as the roadmap for Idaho Power's sol-ar
intdgration study. So1ar, like wind, is variable and
uncertain and, consequently, the system of dispatchabl-e
resources has to be operated differently in order to
successfully integrate the generation without compromising
reliability.
0.
A.
What process is the Study following?
The Study is generally fol-lowing the process
outl-ined in the IEEE report, which includes: (1) data
gathering and scenario development; (2) study analysis-
a. statistical-based analysis of solar characteristics, b.
production cost simulation analysis, and c. reliability
assessment; and (3) study conclusions and results.
O. Has the TRC agreed with and been involved with
this process?
A.Yes. Idaho Power has comprehensively wal-ked
through both guiding documents, as wel-l- as the steps
outlined above, with the TRC. Additional-J-y, the importance
of the guiding documents was emphasized to participants at
a May 7, 2074, public workshop. The TRC was extensively
involved in the first step, data gathering and scenario
deveJ-opment. The TRC has been integrally involved with the
identification of suitable sources of solar production
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1 data, dS well as discussions leading to the development of
2 scenarios to be studied. The TRC has a leading rol-e in
3 advising as to the use of a technique to transform point-
4 source solar data to meaningful production data for a solar
5 farm. The technique is called wavel-et variability modeling
6 and is described on page 8 of the Study Report. The TRC's
7 counsel with respect to Idaho Power's use of the wavelet
8 technique was important and needed.
9 Q. Can you further describe how the Study
10 progressed to completion?
11 A. Yes. One of the larger tasks of step L, data
72 gathering and scenario deveJ-opment, as noted j-n the IEEE
13 report, is the undertaking invo1ved with comi-ng up with
t4 solar resource data that is needed to model- future power
15 output. In fact, the Study's biggest hurdle was obtaining
16 the solar resource data needed to model solar power output.
71 The solar build-out scenarios consj-der solar plants at six
18 locations in southern Idaho: Parma, Boise, Grand View,
19 Twin Fal-l-s, Picabo, and Aberdeen. The Study was able to
20 obtain solar data from the U.S. Bureau of Recl-amati-on
2l AgriMet network at the desired five-minute time step for
22 all- locations except Grand View. NREL maps indicate the
23 area surrounding Grand View and Glenns Ferry has the
24 highest annual solar intensj-ty in the state. For this
25 reason, fdaho Power and the TRC have felt it is important
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to model- a sofar plant at Grand View. Obtaining five-
minute sol-ar data for Grand View has required the
acquisition of data from SolarAnywhere, which is a web-
based service from Clean Power Research providing
satellite-derived solar irradiance data. The Study did not
recej-ve data for Grand View from SolarAnywhere until- April
2074, causing delay in the Study schedule.
With the acquisition of data for the Grand View
area, the Study progressed into the statistlcal--based
analysis of sol-ar characteristics. The intent of this
analysis is to translate the variability and uncertainty
present in the solar data to an incremental reserve
requJ-rement. The NREL authors of the IEEE report describe
this as an analysis to determine the increase in ancil-J-ary
services required by a given sol-ar scenarj-o, where NREL
defines ancillary services as services that help grid
operators maintain ba1ance on el-ectric power systems.
Idaho Power dj-scussed with the TRC and workshop
participants the next step in the Study was to take the
increase in reserve requirement, or ancill-ary servj-ces,
from step 2.a for any given sol-ar scenario and to input it
into the Study's production cost simulations to determine
the cost of carrying increased ancillary services, step
2.b.
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It was also at this point that the decision was made
to modify the build-out scenarios to include higher levels
of solar penetration. This decj-sj-on was based primarily
upon the increase in proposed sol-ar projects for Idaho
Power's system that outpaced the largest penetration l-evel-
initial-Iy contemplated by the Study. Initially, the Study
planned to analyze four build-out scenarios: dispersed 50
MW; dispersed 100 MW; dispersed 300 MW; and clustered 300
MW. However, with the emergence of over 500 MW of solar
generation seeking contracts with the Company, the need to
study beyond the 300 MW l-evel became apparent.
Consequently, in a May !6, 20L4, meeti-ng, the Company
communicated to the TRC the following four revised bui1d-
out scenarios: dispersed 100 MW; dispersed 300 MW;
dispersed 500 MW; and dispersed 700 MW. The proposal to
study these revised build-out scenarios was not
controversial with the TRC, and they recognized the need to
study expanded build-outs glven the potential- development
described in the Company's May 13, 20L4, fi11ng in Case No.
IPC-E-14-09 seeking a suspension of its obligation to
purchase Public Utility ReguJ-atory Poli-cies Act of 7918
solar generation until the Study coul-d be completed.
o.How was the statistical based analysis of
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solar characteristics conducted?
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A.Based on Idaho Power's review of the sol-ar
data from its buil-d-out scenarios, the Company focused its
analysis of varj-abiJ-ity and uncertainty in the context of
hour-ahead system scheduling. In this context, the hour-
ahead system scheduler requires for a given operating hour
a forecast for hourJ-y average sol-ar production, as well- as
forecasts for l-ower and upper bounds on instantaneous solar
production. The Study assumes these forecasts for sofar
productj-on need to be delivered to the system scheduler 45
minutes prior to the start of the operating hour being
scheduled. With this information, the system can be
scheduled according to the forecast for hourly average
solar production and, importantfy, also be scheduled in a
manner allowing dispatchable generators to respond during
the operating hour if solar production varies from the
forecasted leveI toward either bound. Discussion of the
regional electric power market and the Company's hour-ahead
scheduling activities is incl-uded in pages 8 through 11 in
the Study Report.
The hour-ahead hourl-y average solar production
forecast developed for the Study is based on persistence,
with an adjustment to account for the known changes in the
sun's position. The l-ower and upper bounds on sol-ar
production are established as percentages of the hourly
average solar production forecast, with adlustments made to
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narrow the bounds in response to periods of stable
production. The logic developed to make these adjustments
to the bounds was well received by the TRC, and has been
described in TRC meetings as an example of a "learning" or
"adaptive" modeI. The techniques fol-lowed to develop the
hour-ahead sol-ar production forecast and the accompanying
bounds on instantaneous solar production were described to
the TRC in a May 76, 20L4, meeting.
What was the next step in the Study process?
The next step was the production cost
simul-ations (step 2.b) . As described earlier in my
testimony, the Study followed the conventional design of
simul-ating two scenarios: a test scenario having
incremental amounts of solar-caused reserve and a base
scenario wi-thout the incremental reserve.
O. Could you describe the TRC invol-vement in the
l-ater stages of the Study?
A.Yes. The TRC schedule, including the meeting
dates and agenda items, is set forth on page 23 of the
Study Report. The final formal TRC meeting was held on May
29, 20L4. The intent of this meeting was to provj-de a
relatively high-l-evel description of the production cost
simulations. In response to TRC expressions of interest in
understanding how reserves influence system operations,
Idaho Power al-so provided an overview of operating
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reserves. The discussion during the NIay 29 meeting focused
on an explanati-on of the productj-on cost modeI, a
demonstration of the input of solar-caused reserve
requirements to the production cost model, and an
illustration of the effect of the solar-caused reserves on
simul-ated operatlons. The Company acknowl-edged the
complexity of the production cost simul-atj-ons to the TRC.
There were expressi-ons from some in the TRC to explore in
further detail-, specifically to explore additional water
year types (e.9., 1ow and high water year types). However,
the Company expressed that for this phase of the Study
further exploration of additional- water years was not
necessary, emphasizing the need for a timely completion of
the Study. Finally, the May 29 meeting ended with a
presentation of the integration costs found by the
productj-on cost simulations, which at the time were
considered preliminary.
A draft study report was circulated to the TRC on
June 2, 20t4. The Company indicated in its correspondence
with the TRC on June 2 the continued objective to complete
the Study by mid-June. The TRC members submi-tted comments
on the process and the Study. Several TRC members
identified items for further study, which are listed in the
Study Report on page 18.
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O. Can you describe the resul-ts of the Study?
A. Yes. The objective of the Study was to
determine the costs of the operational modifications
necessary to integrate solar generation. The integration
costs are driven by the need to carry extra capacity in
reserve to allow bidirectional response from dispatchable
generators to unplanned variations in sofar producti-on.
The simulations performed for the Study indicate the
followi-ng costs associated with holding the extra capacity
in reserve. The provided costs are the costs to integrate
sol-ar production for the calendar year 2014, and are not
costs averaged or levelized over the life of the solar
power p1ant.
Average Integration Cost Per MBlh
(2OL4 cost and dollars)
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Incremental Integration Cost Per Ml{h
(2OL4 cost and dollars)
O. Are the resul-ts of Idaho Power's Solar Study
consistent wj-th those conducted for other utility systems?
A. Yes. Idaho Power's Study resul-ts fall- within
the range reported by other utilities for the cost of
integrating solar generation. While the study of solar
integration is rel-atively young, especially when compared
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Build-out Scenarios 0-100 MW 0-300 MW 0-500 MW 0-700 MW
Integration Cost s0.40 $7.20 S1 BO $2.s0
Penetration Level 0-100 MW 100-300 MW 300-500 MW 500-700 MW
Integration Cost $0 40 s1. s0 $2 BO $4.40
1 to the study of wind integration, I am aware of solar
2 integration studies that have been conducted for other
3 utility systems. Notable among these studies are a 20LL
4 solar integration study for the NV Energy system, a 201,2
5 solar integration study for Arizona Public Servj-ce (*APS"),
6 and a 2014 solar lntegration study for Tucson El-ectric
7 Power (*TEP"). The NV Energy study reports integration
8 costs ranging from $3.00 to $8.00 per megawatt-hour (*MWh")
9 of integrated sol-ar generation. The APS study reports
10 integration costs ranging from about $1.50 to $3.00 per MWh
11 of integrated solar generation. The TEP study reports an
1,2 integration cost of $5.20 per MWh.
13 A. Does this conclude your testimony?
L4 A. Yes.
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Tt Philip B. DeVol, having been duly sworn to
testify truthfully, and based upon my personal knowledge,
state the following:
I am employed by Idaho Power Company as the Resource
Planning Leader in the Water and Resource Planning
Department and am competent to be a witness in this
proceeding.
I decl-are under penalty of perjury of the laws of
the state of Idaho that the foregoing pre-filed testimony
and exhibit are true and correct to the best of my
information and belief.
DATED this 25th day of June 2OL4
STATE OF IDAHO )
)
County of Ada )
SUBSCRIBED
June 20L4.
ATTESTATION OF TESTIMONY
SS.
AND SWORN to before me this 25th day of
or Idaho
Residing at:
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PhiIip
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My commission
BEFORE THE
IDAHO PUBLIG UTILITIES GOMMISSION
GASE NO. IPC-E-14-18
IDAHO POWER COMPANY
DEVOL, DI
TESTIMONY
EXHIBIT NO.
Solar lntegration Study Report
nlDll0rlptr;191-
June 2014
@ 2014 ldaho Power
Exhibit No. 1
Case No.|PC-E-14-18
P. DeVol,lPC
Page 1 of36
ldaho Power Company Solar lntegration Study Report
TneLe oF CoNTENTS
Page 1 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
Page 3 of 36
Solar lntegration Study Report ldaho Power Company
LIsT oF TABLES
Table I
Solarbuild-out scenarios studied.... ..................3
Table 2
Average integration cost per MWh for solar build-out scenarios.... ..........................3
Table 3
Solar build-out scenarios studied ......................6
Table 4
AgriMet site latitude, longitude, and elevation used in IPC's solar integration study .................7
Table 5
Forecast error for the hour-ahead solar production forecast .................10
Table 6
Forecasted incremental and decremental capacity held in reserve, water year 2012 .................11
Table 7
Inputs for the solar integration study production cost simulations............ ............-12
Table 8
Average integration cost per MWh for solar build-out scenarios.... ........................15
Table 9
lncremental integration cost results
LIST oF FIGURES
Figure I
AgriMet sites used in IPC's solar integration study ............7
LIST OF APPENDICES
Appendix I
Solar integration study appendix.... .................21
Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol,IPC
Page 4 of 36
Page2
ldaho Power Company Solar lntegration Study Report
ExeCuTME SUMMARY
Electric power from solar photovoltaic resources exhibits greater variability and uncertainty
than energy from conventional generators. The greater variability and uncertainty exhibited by
solar photovoltaic resources require an electric utility integrating solar to modi$ the operation of
dispatchable generating resources. The modified operation involves the sub-optimal dispatch of
generators to carry extra capacity in reserve for responding to unplanned solar excursions.
The objective of the ldaho Power solar integration study is to determine the costs of the
operational modifications necessary to integrate solar photovoltaic plant generation. This study
determines these costs for four solar build-out scenarios provided in Table 1.
Table 1
Solar build-out scenarios studied
lnstalled Capacity of Solar Build-Out Scenarios
Site 100 megawatts (MW)300 Mw 500 Mw 700 Mw
Parma, lD
Boise, lD
Grand Vieu lD
Twin Falls, lD
Picabo, lD
Aberdeen, lD
TotalMW
50
100
100
100
50
100
500
30
60
60
60
30
60
300
10
20
20
20
10
20
100
100
100
150
100
100
150
700
The study determines solar integration costs through paired simulations of the Idaho Power
system for each solar build-out scenario. Each pair of simulations consists of a test case in which
extra capacity in reserve is required ofdispatchable generators to allow them to respond to
unplanned solar excursions and a base case in which no extra capacity in reserve is required.
The solar integration costs indicated by the simulations are provided in Table 2.
Table 2
Average integration cost per MWh for solar build-out scenarios
0-100 Mw 0-300 i,lW 0-500 Mw 0-700 Mw
lntegration cost $0.40/MWh $1.20/MWh $1.80/MWh $2.so/[4vvh
Note: Costs are in 2014 dollars and rounded from simulation results to the nearest $0.'10.
Page 3 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
Page 5 of 36
Solar lntegration Study Report ldaho Power Company
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Page 4 Exhibit No. 1
Case No. IPC-E-I4-18
P. DeVol,lPC
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ldaho Power Company Solar lntegration Study Report
AcxttowLEDGMENTs
Idaho Power acknowledges the important contribution of the Technical Review Committee
(TRC) in this solar integration study. The TRC has been involved from the study outset in
August 2013 and has provided substantial guidance. Idaho Power especially thanks the TRC for
the collegial discussions of solar integration during TRC meetings. These discussions helped
shape the study methods followed and are consistent with the TRC guidelines as provided by the
Utility Variable-Generation Integration Group (UVIG) and the National Renewable Energy
Laboratory NREL) (UVIG and NREL n.d.). The following are members of the Idaho Power
solar integration study TRC:
o Brian Johnson, University of ldaho
o Jimmy Lindsay, Portland General Electric (formerly of Renewable Northwest Project)
o Kurt Myers, Idaho National Laboratory
o Paul Woods, (formerly of City of Boise)
o Cameron Yourkowski, Renewable Northwest Project (replacing Jimmy Lindsay)
Staff from the Idaho and Oregon regulatory commissions have participated as observers
throughout the process. The following staffhave been observers of the process:
o Brittany Andrus, Public Utility Commission of Oregon (OPUC) staff
o John Crider, OPUC staff
o Rick Sterling, Idaho Public Utilities Commission (IPUC) staff
TRC members and regulatory observers serve either voluntarily or are paid by their own
employers and receive no compensation from Idaho Power. The company is grateful for the
TRC's time spent supporting the study and recognizes this support has led to a better study.
lrurnooucrroN
Electric power from solar photovoltaic resources exhibits greater variability and uncertainty
than energy from conventional generators. Because of the gleater variability and uncertainty,
electric utilities incur increased costs when their other generators are called on to integrate
photovoltaic solar plant generation. These costs occur because power systems are operated less
optimally in order to successfully integrate solar plant generation without compromising the
reliable delivery of electrical power to customers. Idaho Power has studied the modifications it
must make to power system operations to integrate solar photovoltaic power plant generation
connecting to its system. The objective of this solar integration study is to determine the costs of
the operational modifications necessary to integrate solar plant generation. This report is
intended to describe the operational modifications and the resulting costs.
Page 5 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol,lPC
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Solar lntegration Study Report ldaho Power Company
In collaboration with the TRC, Idaho Power organized the study into four primary steps:
1. Data gathering and scenario development
2. Statistical-based analysis of solar characteristics
3. Production cost simulation analysis
4. Study conclusions and results
These steps were formulated based on an article published by the Institute of Electrical and
Electronics Engineers (IEEE) describing methods for studying wind integration (Ela et al. 2009).
While the IEEE article, which was authored by leading researchers at the NREL, was written
from the perspective of studying grid integration of wind generation, the principles underlying
the study of wind integration are readily transferrable to the study of solar integration. Both wind
and solar bring increased variability and uncertainty to power system operation, and a key
objective of an integration study for each is to understand how variability and uncertainty lead to
impacts and costs.
Dara GarnenrNc AND Scenenro DEVELopMENT
A critical element of the solar integration study is the solar generation data developed for the
studied solar build-out scenarios. For ldaho Power's solar integration study, the solar build-out
scenarios in Table 3 were studied.
Table 3
Solar build-out scenarios studied
lnstalled Gapacity of Solar Build-Out Scenarios
100 megawatts (MW)300 Mw 500 Mw 700 Mw
Parma, lD
Boise, lD
Grand View, lD
Twin Falls, lD
Picabo, lD
Aberdeen, lD
Tota! MW
10
20
20
20
10
20
100
50
100
100
100
50
100
500
100
100
150
100
100
150
700
30
60
60
60
30
60
300
The above build-out scenarios were developed in consultation with the TRC to represent
geographically dispersed build-outs of solar power plant capacity. The importance of geographic
dispersion in reducing integration impacts and costs is discussed in greater detail later in this
report. The sites from the solar build-out scenarios are part of the established United States
(U.S.) Bureau of Reclamation (USBR) AgriMet Network (AgdMet). AgriMet is a satellite-based
network of automated agricultural weather stations operated and maintained by the USBR.
The stations are located in irrigated agricultural areas throughout the Pacific Northwest and are
Exhibit No. 'l
Case No. IPC-E-14-18
P. DeVol, IPC
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ldaho Power Company Solar lntegration Study Report
dedicated to regional crop water-use modeling, agricultural research, frost monitoring,
and integrated pest and feftility management. The six sites are spread across southern Idaho
and cover over 220 miles from east to west (Figure 1). Sites represent elevations ranging from
2,300 feet to 4,900 feet (Table 4).
r.1
Tlrin Falls
Figure 1
AgriMet sites used in IPC's solar integration study
Table 4
AgriMet site latitude, longitude, and elevation used in IPC's solar integration study
Latitude (N)Longitude (west)Elevation (feet)Elevation (meter)
Parma
Boise
Grand View
Twin Falls
Picabo
Aberdeen
1 16.93
1 16.18
116.06
't14.35
114.17
112.83
43.18
43.60
42.91
42.55
43.31
42.95
2,305
2,720
2,580
3,920
4,900
4,400
702
829
786
1,195
1,494
1,341
All data used in the integration study are 5-minute interval global horizontal irradiance data from
each site. ldaho Power worked directly with the USBR Pacific Northwest Region AgriMet
manager to obtain data for the sites. AgriMet data was augmented with data from the University
of Oregon Solar Radiation Monitoring Laboratory when AgriMet data was incomplete. The use
of high-resolution (5-minute interval) data is critical to characterizing the variability of solar.
An altemative data-gathering approach was necessary for the Grand View site, for which only
l5-minute data was available. To acquire 5-minute data for Grand View, Idaho Power contracted
Page 7 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
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Solar lntegration Study Report ldaho Power Company
with SolarAnywhere to provide high-resolution modeled solar data. SolarAnywhere uses hourly
satellite images processed using the most current algorithms developed and maintained by
Dr. Richard Perez at the University at Albany (SUNY). The algorithm extracts cloud indices
from the satellite's visible channel using a self-calibrating feedback process capable of adjusting
for arbitrary ground surfaces. The cloud indices are used to modulate physically-based radiative
transfer models describing localized clear-sky climatology.
Wavelet-Based Variability Model
AgriMet solar data represents conditions at a single point. To better reflect conditions at a solar
plant size, the TRC recommended the use of the wavelet-based variability model (WVM)
developed by Dr. Matt Lave of Sandia National Labs (Lave etal.20l34b). WVM is designed for
simulating solar photovoltaic power plant output given a single irradiance point-sensor time
series. The application of the WVM to the point-sensor time series produces a variability
reduction reflecting an upscaling of the point-source data to a solar plant-sized area.
Research and use into the WVM showed it is not useable at time steps (intervals) greater than
l0 minutes and that times steps greater than 5 minutes may under-represent variability in
dispersed systems.
Solar Plant Gharacteristics
This study assumes solar plants comprising the build-out scenarios occupy 7 acres per MW
of installed capacity. Solar plant sizes in the build-out scenarios, as well as figures presented for
solar generation, are in terms of AC (alternating current) MW. Photovoltaic panels are assumed
to be of standard crystalline silicon manufacture. Panels are assumed to be fixed south facing and
tilted at latitude. While panel orientation and tracking capability are key factors in the
determination of avoided costs, these attributes are of lesser importance with respect to the
variability and uncertainty driving integration costs. Illustrations and data summarizing the solar
production of the studied build-outs are provided in Appendix 1.
SrRrsncAL-BASED Atrllysrs oF Solan CnanecrERISTIcs
The intent of the statistical-based analysis of solar characteristics is to translate solar's
variability and uncertainty into an increased requirement for ancillary services, where ancillary
services in this context relate to the electrical system's capacity to maintain a balance between
customer demand and generation. For the study, the variability and uncertainty associated with
solar generation were viewed from the perspective of hour-ahead scheduling of the Idaho Power
system. There are three critical elements from this perspective:
1. Forecast hourly average solar production for the operating hour being scheduled
2. Lower bound for instantaneous solar production during the operating hour
3. Upper bound for instantaneous solar production during the operating hour
From the perspective of real-time generation scheduling in practice, the lower and upper
bounds would be considered an interval or band on solar production, and the occurrence of
Exhibit No. 1
Case No. IPC-E-I4-18
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ldaho Power Company Solar lntegration Study Report
solar production outside the interval at any moment during the hour is highly unlikely.
Moreover, while under prudent operating practices the occurrence of solar production outside the
lower and upper bounds should be infrequent, occasional solar excursions outside these bounds
do not necessarily bring about events for which system reliability is jeopardized. Conversely,
the occurrence of solar production within the interval between the lower and upper bounds would
be considered likely enough to warrant the scheduling of dispatchable generators to have
capacity to respond if solar production varies during the hour from the forecasted level of
production toward either bound.
An understanding of Idaho Power's participation in the regional electric power market is
critical to this approach. Idaho Power primarily participates in the Pacific Northwest's
Mid-Columbia (Mid-C) electric power market. The company participates in the Mid-C market at
multiple time frames ranging from years or months in advance for long-term operations planning
to hour-ahead generation scheduling in real time.
The focus for this study is the real-time market activities occurring as part of hour-ahead system
scheduling. The study assumes hour-ahead schedulers require the delivery of forecast hourly
average solar production and the above-described lower and upper bounds 45 minutes prior to
the start of the operating hour being scheduled. Hour-ahead scheduling is assumed binding,
and unexpected conditions occurring during the operating hour being scheduled must be
managed by changing production for Idaho Power-owned dispatchable resources.
Idaho Power recognizes efforts to establish intra-hour trading in U.S. electric power markets.
However, company experience has shown the intra-hour market to be currently highly illiquid.
Therefore, the last opportunity to participate in the electric power market is at the hour-ahead
time frame; unexpected conditions occurring during the operating hour (e.g., unexpected levels
of solar production) cannot be managed through market activity at this time.
Hour-Ahead Solar Production Forecast
The hour-ahead solar production forecast was developed to predict hourly average solar
production for the operating hour being scheduled and lower and upper bounds for
instantaneous solar production during the operating hour. This forecast was developed using a
persistence-based technique that relies on observations from the previous hours to inform the
model about subsequent forecast hours. The results ofthe forecast are a unique set of
values (average production, upper bound, and lower bound) for every hour in the year.
The average production forecast is derived based on two components. The first component
accounts for the amount of generation the system observed from the last 20 minutes of the
preceding forecast hour. This component is referred to as the persistence component.
The persistence component serves as a mechanism to increase the average forecast during times
of high solar production and decrease the average forecast during times of low solar production.
These increases and decreases are made to the forecast hourly and account for changes in solar
production. [n general, the shape of the production from a solar photovoltaic system increases
before solar noon and decreases after solar noon. Every day ofthe year has a unique clear-day
shape. Generally, slunmer days are long and have a high potential for solar production while
winter days are shorter and have less potential. The forecast accounts for the uniqueness of each
Page 9 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
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Solar lntegration Study Report ldaho Power Company
day by applyrng an hourly shaping factor. This shaping component, or shaping factor, is a unique
value for every hour in the year. The shaping component is a ratio of the maximum solar
potential of the forecast hour divided by the maximum potential of the previous hour.
By utilizing a shaping component and a persistence component, the average production
forecast captures hourly changes due to atrnospheric conditions and seasonal effects. Table 5
provides the forecast error for the hour-ahead solar production forecast.
Table 5
Forecast error for the hour-ahead solar production forecast
100 Mw 300 Mw 500 Mw 7OO MW
Absolute Mean Hourly Error (MW)12.2
Table 5 reports the absolute mean error calculated on an hourly basis for water year 2012.
The absolute hourly error is calculated as the absolute difference between the average hourly
forecast and the average of S-minute observed production data for a given hour. It is noted that
the S-minute observed production data is the output of the WVM. The absolute mean hour errors
range from 1.9 MW to 12.2 MW for the 100 MW and 700 MW build-out scenarios, respectively.
The lower bound for instantaneous solar production during the operating hour is forecasted as a
percentage ofthe forecast average. In addition to the application ofa percentage ofaverage,
the forecasting tool adjusts the lower bound forecast upward if the previous lower bound forecast
was substantially too low. As a result of this secondary adjustment to the lower bound,
the amount of incremental capacity held in reserve for the coming hour is reduced.
Similar to the lower bound, the upper bound for instantaneous solar production during the
operating hour is forecasted as a percentage ofthe forecast average. In addition to the application
of a percentage of average, the forecasting tool adjusts the upper bound forecast downward if the
previous upper bound forecast was substantially too high. As a result of this secondary
adjustment to the upper bound, the amount of decremental capacity held in reserve for the
coming hour is reduced.
The upper and lower bounds are expected to capture the overwhelming majority of the variability
observed in solar production. The upper bound is forecasted in such away that only 2.5 percent
of all observations exceed the upper bound for the entire year. Similarly, the lower bound is
defined in such a way that only 2.5 percent of all observations are below the lower bound for the
entire year.
The hour-ahead forecast for the average production, lower bound for instantaneous solar,
and upper bound for instantaneous solar are calculated for every hour of the year. The amount of
incrernental capacity held in reserve for a given hour is calculated as the difference between the
average production forecast and the lower bound. The amount of decremental capacity held in
reserve for a given hour is calculated as the difference between the average production forecast
and the upper bound. The total amount of capacity held in reserve for a given hour is used by the
production cost model to calculate an integration cost. These reserve amounts, as well as the
hour-ahead forecast for solar production, are input to the production cost model on an hour-by-
hour basis, simulating the practice of real-time generation scheduling. Table 6 reports the
9.65.81.9
Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
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ldaho Power Company Solar lntegration Study Report
forecasted amount of capacity held in reserve for water year 2012. Further explanation of the
derivation of the hour-ahead solar production forecast and the lower and upper bounds is
provided in Appendix 1.
Table 6
Forecasted incremental and decremental capacity held in reserve, water year 2012
Solar Build-Out Scenarios
100 Mw 300 Mw 500 Mw 700 Mw
Average hourly production (MW)
Average hourly capacity held in
reserve-incremental (MW)
Average hourly capacity held in
reserve{ecremental (MW)
17.O
4.9
4.9
s2.5
13.2
15.2
89.0
21.2
26.9
118.2
27.6
34.8
PnooucroN Cosr Sruru lanoN ANALysrs
The production cost simulations are designed to isolate the effects on the system associated with
integrating solar. Under this design, production cost simulations are paired into a base case and
test case, with all inputs to the paired simulations equivalent except an amount of capacity held
in reserve in the test case simulation for integrating solar. The capacity held in reserve for the test
case varies hourly depending on the hour-ahead forecast ofsolar production for a given operating
hour and the lower and upper bounds on instantaneous solar production for the operating hour.
The derivation of the hour-ahead solar production forecast and the lower and upper bounds is
described in the previous section of this report.
Design of Simulations
The production cost simulations are set up on a water-year calendar, where by convention a
water year is from October I to September 30 and is designated by the calendar year in which the
l2-month period ends. For example, water year 2013 is the l2-month period from October l,
2012, throttgh September 30, 20 1 3.
The Idaho Power generating system as it exists at the time of issue of this report is assumed for
the production cost simulations. Critical elements of the simulated system of generating
resources include 17 hydroelectric facilities totaling 1,709 MW pf nameplate capacity,
3 coal-fred facilities totaling 1,118 MW of nameplate capacity, and 3 natural gas-fired facilities
totaling 762MW of nameplate capacity. An illustration of the generating resources is provided
inAppendix 1.
Idaho Power's critical interconnections to the regional market are over the Idaho-Northwest,
Idaho-Utah (Path C), and Idaho-Montana paths. For the solar integration study modeling,
the separate paths were combined to an aggregate path for off-system access. Purchases from the
regional market are treated separately from sales to the regional market. Net firm purchases from
the market are limited on a monthly basis to only the capacity and energy required to serve
Page 11 exniOit t,to. t
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Solar lntegration Study Report ldaho Power Company
Idaho Power's retail load. Sales to the market are limited to 500 MW in every hour. This profile
of purchases and sales reflects the current capabilities of Idaho Power's transmission system.
Idaho Power is pursuing the development of the Boardman to Hemingway Transmission Project
(B2H), which will increase ldaho Power's access to the Northwest to make additional purchases
and sales. However, the transmission line's current in-service date is at least five years into the
future. Previous integration studies have shown that unless there is a liquid capacity balancing
market, B2H will not significantly impact the solar integration cost. Idaho Power is actively
engaged in regional market discussions that could exist when B2H is completed, but the benefits
of a market are highly dependent on its design, and it is premature to speculate or incorporate in
this integration study.
Simulation Inputs
Table 7 provides key inputs to the solar integration study production cost simulations.
Table 7
lnputs for the solar integration study production cost simulations
lnput Assumed input level
Solar production
Snake River streamflows
Customer demand
Nymex-Natural gas prices
Mid-C-Electric power market prices
Non-wind PURPA1
Wind (PURPA and PPA)1
Geothermal PPAs
Waler year 2012
Water year 2012 (median-type streamflows)
Waler year 2012
Waler year 2012
Water year 2Q12
Water year 2012
Water year 2013
Waler year 2014
I PPA and PURPA represent facilities from which generation is contractually purchased as a power purchase agreement (PPA)
or under the federa,l Public Utility Regulatory Policies Act of 1978 (PURPA).
The selection of water year 2012 for the majority of the inputs was driven by the selection of
Snake River streamflows for water year 2012 (October 1, 201 l-September 30,2012) and the
objective to use time-synchronous input data to the greatest possible extent. Snake River Basin
streamflow conditions as observed in water year 2012 were selected because the observed water
year 2012 Brownlee reservoir inflow volume of 1 3.6 million acre-feet is representative of
median-type streamflow conditions. A graph of Brownlee inflow volumes for water years 1990
to 2013 is provided in Appendix 1.
The solar production data used in the production cost simulations are considered to be the solar
production that would have been observed during water year 20l2had the four studied solar
build-out scenarios existed. As described previously, the solar production data is developed by
applylng a wavelet smoothing transformation technique to S-minute interval AgriMet and
SolarAnywhere data. Importantly, the use of observed customer demand from water year 2012
allows time synchronization between solar and customer demand data in the study.
While customer demand has grown since 2012, the benefit of using time-synchronous
Exhibit No. 1
Case No. IPC-E-'|4-18
P. DeVol, IPC
Page '14 of 36
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ldaho Power Company Solar lntegration Study Report
customer demand and solar production data is considered to justi$ the use of 2012 customer
demand data. Monthly average customer demand used in the modeling is provided in
Appendix 1.
Water year 2012 Nymex natural gas prices and Mid-C electric power market prices are inputs to
the simulations. These prices, expressed as a monthly average, are provided in Appendix l.
Wind capacity under contract with Idaho Power grew by more than 60 percent during water year
2012, expatding from 395 MW of installed capacity to 638 MW. Because of the non-constant
amount of on-line wind capacity during water year 2012,the simulations used observed hourly
wind production data for water year 2013. The amount of on-line wind capacity during water
year 2013 changed only by the addition of a single 40 MW project added during December 2013
that brought wind to the current on-line capacity of 678 MW. Monthly energy production used in
the modeling is included in Appendix l.
The remaining energy purchased from non-wind PURPA quali$ing facilities is input into the
simulations as observed during water year 2012.The monthly energy from the non-wind PURPA
facilities in included in Appendix l.
Baseload generation from geothermal facilities contractually selling to Idaho Power under PPAs
is input as currently projected from these facilities. The amount of baseload generation delivered
from these facilities varies seasonally. The amount used in the production cost simulations
ranges fromZ2 MW to 32 MW.
Simulation Model
Idaho Power used an internally developed system operations model for the solar integration
study. The model determines optimal hourly scheduling of dispatchable hydro and thermal
generators with the objective of minimizing production costs while honoring constraints imposed
on the system. System constraints used in the model capture numerous restrictions governing the
operation of the power system, including the following:
o Reservoir headwater constraints
o Minimum reservoir outflow constraints
o Reseryoir outflow ramping rate constraints
o Generator minimum/maximum output levels
o Marketpurchase/saleconstraints
o Generator ramping rates
The model also stipulated that demand and resources were exactly in balance and importantly
that hourly reserve requirements were satisfied. The extra capacity in reserve held to manage
variability and uncertainty in solar production drives the production cost differences between the
Page 13 Exhibit No. 1
Case No.|PC-E-14-18
P. DeVol, IPC
Page 15 of36
Solar lntegration Study Report ldaho Power Company
study's two cases. The derivation of the extra capacity in reserve held for solar is described
previously in this report.
Wind and Load Reserves
Capacity in reserve to manage variability and uncertainty in load and wind is included in the
simulations in equivalent amounts for the study's two cases. By carrying equivalent amounts in
reserve for load and wind, the production cost differences yielded by the study's simulations can
be attributed to the extra capacity held in reserve for solar. Thus, while reserves carried for load
and wind are not drivers of production cost differences in the paired simulations, it is
nevertheless desirable in simulating the system as accurately as possible to incorporate reserve
levels for load and wind representative of levels carried in practice.
To manage variability and uncertainty in load, capacity in reserve equal to 3 percent of load is
held on dispatchable generators in the modeling for the solar integration study. The amount of
simulated capacity in reserve for balancing wind is based on an analysis performed for the
Idaho Power wind integration study as described in the February 2013 Wind Integration Study
Report (Idaho Power 201 3). The simulated reserves for the solar integration study are based on a
scaling of the reserves at the wind study's 800 MW wind build-out scenario to the water year
2013 wind build-out of 678 MW.
Conti ngency Reserve Obl igation
The study of integration impacts and costs focuses on the need to carry bidirectional capacity in
reserve for maintaining compliance with reliability standards. However, balancing authorities,
such as Idaho Power, are also required to carry unloaded capacity in reserve for responding to
system contingency events, which have traditionally been viewed as large and relatively
infrequent system disturbances affecting the production or transmission of power (e.g., the loss
of a major generating unit or major transmission line). System modeling for the solar integration
study imposes a contingency reserve intended to reflect this obligation equal to 3 percent of load
and 3 percent of generation, setting aside this capacity for both study cases (i.e., base and test).
Flexible Capacity Resources
As described previously, the focus of the production cost simulations for the solar integration
study is the real-time market activities occurring as part of hour-ahead system scheduling.
The study assumes hour-ahead schedulers require the delivery of forecast hourly average solar
production and the lower and upper bounds for solar production 45 minutes prior to the start
of the operating hour being scheduled. Hour-ahead scheduling is then assumed binding,
and unexpected levels of solar production occurring during the operating hour being scheduled
must be managed by Idaho Power's system.
To manage deviations in solar production from the forecast during the operating hour,
Idaho Power must schedule incremental and decremental capacity in reserve on dispatchable
generators. ln the modeling for the study, this capacity in reserve is scheduled on
Hells Canyon Complex (HCC) hydroelectric generators (Brownlee, Oxbow, and Hells Canyon),
natural gas-fired generators (Langley Gulch, Danskin, and Bennett Mountain), and Jim Bridger
Exhibit No. 1
Case No. IPC-E-14-'18
P. DeVol, IPC
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ldaho Power Company Solar lntegration Study Report
coal-fired generators. The allocation of reserve to these generators matches ldaho Power's
practice for balancing variations in wind production and load.
Resulrs
The objective of the Idaho Power solar integration study is to determine the costs of the
operational modifications necessary to integrate solar photovoltaic power plant generation.
The integration costs are driven by the need to carry extra capacity in reserve to allow
bidirectional response from dispatchable generators to unplanned excursions in solar production.
The simulations performed for the Idaho Power solar integration study indicate the following
costs associated with holding the extra capacity in reserve (Table 8). The provided costs are the
costs to integrate solar production for calendar year 2014, and are not costs averaged or levelized
over the life of a solar power plant.
Table 8
Average integration cost per MWh for solar build-out scenarios
0-100 Mw 0-300 Mw 0.500 Mw 0-700 Mw
lntegration cost $0.40/MWh $1.20l[4vvh $1.80/MWh $2.50/trwh
Note: Costs are in 2014 dollars and rounded from simulation results to the nearest $0.10.
The integration cost results in Table 8 are the cost per MWh to integrate the full installed solar
power plant capacity at the respective scenarios studied. For example, the integration cost results
indicate the total solar power plant capacity making up the 500 MW build-out scenario brings
about costs of $1.80 for each megawatt-hour (MWh) integrated.
Integration costs can be expressed altematively in terms of incremental costs. Integration costs
when expressed incrementally assume early projects are assessed lesser integration costs,
and later projects need to make up the difference to allow full cost recovery for a given build-out
scenario. For example, if solar plants comprising the first 100 MW build-out are assessed
integration costs of $0.40/MWh, then plants comprising the increment between 100 MW and
300 MW need assessed integration costs of $1.50/MWh to allow full recovery of the $1.2OllVIWh
costs to integrate 300 MW of solar plant capacity. lncremental solar integration costs are
provided in Table 9.
Table 9
lncremental integration cost results for solar build-out scenarios
0-100 Mw 100-300 Mw 300-500 Mw 500-700 Mw
lncremental integration cost $0.4O/tvfvvh $1.s0/tvlvvh $2.80/11/Wh $4.40/tvlvvh
Note: Costs ate in 2014 dollars and rounded from simulation results to the nearest $0.10.
Page 15 Exhib1 No. 1
Case No. IPC-E-14-18
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Solar lntegration Study Report ldaho Power Company
Study Findings
Hour-Ahead SoIa r Production Forecasting
Analyses suggest a persistence-based forecast with adjustment to account for known changes
in the sun's position provides a reasonable production forecast for hour-ahead operations
scheduling. The persistence-based hour-ahead solar production forecast used for the study is
based entirely on observed production and consequently could be readily adopted in practice.
While a day-ahead solar production forecast would be necessary in practice for a balancing
authority integrating solar, deviations from the day-ahead forecast can be managed through a
combination of market transactions and operations modifications, and consequently the study
imposes no reserve requirement to cover deviations for day-ahead solar production forecasts.
Compared to wind, system operators managing a balancing authority integrating solar would
have the benefit of at least six hours at the start of day with no or liftle solar production.
During this period of no or little solar production, system operators could evaluate the day-ahead
solar production forecast using information from updated weather forecast products and begin to
plan for necessary actions to manage deviations from the day-ahead solar production forecast.
In contrast, deviations from the hour-ahead solar production forecast can only be covered by
ldaho Power's dispatchable generators. The analysis for the solar integration study by design
determines the amounts of bidirectional capacity in reserve that system operators would need to
schedule to position dispatchable generators to cover possible deviations from the hour-ahead
solar production forecast. lntegration costs are a result of the sub-optimal scheduling of the
dispatchable generators associated with holding the solar-caused capacity in reserve.
Comparison to Wind lntegration
This study indicates solar plant integration costs are lower than wind plant integration costs.
The lower integration costs associated with solar are fundamentally the result of less variability
and uncertainty. As described in the preceding section, the study assumes deviations in solar
plant production from day-ahead forecast levels can be managed through a combination of
market transactions and operations modifications, allowing day-ahead generation scheduling to
avoid extra reserve burden. Therefore, reserves carried for solar generation can be focused on
readying dispatchable generators to respond to unplanned solar excursions from hour-ahead
production forecasts. Moreover, logic incorporated in the derivation of lower and upper bounds
on the hour-ahead production forecast, which can be readily adopted in practice, allows the
adjustrnent ofthe bounds in response to observed solar production patterns. ln effect,
the hour-ahead forecast is based on a persistence oflevel ofproduction (adjusted for the
known change in the sun's position), as well as a persistence of variability in production.
The consequence of these methods is that bidirectional capacity held in reserve on dispatchable
generators to respond to solar variability and uncertainty is less than that required for responding
to wind.
Qualitatively, solar is more predictable than wind. Sunrise and sunset times, as well as the
time of solar noon, are a certainty. The theoretical maximum level of production can be
Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol,lPC
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ldaho Power Company Solar lntegration Study Report
readily derived, reflecting patterns on daily, monthly, and seasonal time scales. Finally,
land requirements for a solar power plant are likely to promote a relatively high level of
dispersion, which is critical to the mitigation of impacts from severe and abrupt ramps in
production exhibited by individual panels in response to passing clouds. The effects of
geographic dispersion are discussed further in the following section.
Geographic Dispersion
Production for a single solar photovoltaic panel exhibits severe and abrupt intermittency
during variably cloudy conditions; a TRC member expressed during a meeting that for a single
panel, the drop in production from a cloud is effectively instantaneous. The effect of severe and
abrupt intermittency is commonly attributed to the absence of inertia in the photovoltaic process.
While the intermittency effect is severe for a single panel, dampening occurs when considering
the production from a solar plant-sized aggregation of panels, and even further dampening occurs
when considering the production from several solar plants spread over a region such as southern
Idaho. Therefore, geographic dispersion has significant influence on solar integration impacts
and is perhaps of greater importance for solar than wind.
The four studied solar build-out scenarios each have capacity installed at six southern Idaho
locations spread over more than220 miles from east to west. Because of the substantial
geographic dispersion, severe instantaneous ramps in solar production for the study data are
relatively infrequent. If solar plant development in southem Idaho occurs in a more clustered
fashion than assumed for this study, actual integration impacts and costs will be higher than the
results of this study.
Transmi ssion and Di stribution
The focus of ldaho Power's solar integration study is a macro-level investigation of the
operations modifications necessary to maintain balance between power supply and customer
demand for a balancing authority integrating photovoltaic solar plant generation. The objective is
to understand the impacts and costs of the sub-optimal operation of dispatchable generating
capacity. The study is not an investigation of integration issues related to the delivery of energy
from proposed solar photovoltaic power plants to the retail customer; these issues are addressed
in individual interconnection studies performed on a plant-by-plant basis.
Sprng-Season Integration
The production cost simulations suggest reserve requirements are particularly problematic when
hydroelectric resources are highly constrained, such as frequently occurs during spring-season
periods charactenzed by high water, low customer demand, and high generation from variable
generating resources, such as wind and solar. Experience has shown wind integration to be
particularly challenging during these periods, and the simulations suggest similar challenges
integrating solar. This study finding is corroborated by NREL in the Western Wind and Solar
Integration Study Phase 2 (Lew et al. 2013), which reports the need for flexibility is notably high
during the spring and that during these periods the curtailment of variable generation is one
source of flexibility enabling the balancing of generation and customer demand.
Page 17 Exhibit No. ,l
Case No. IPC-E-14-'t8
P. DeVol, IPC
Page 19 of 36
Solar lntegration Study Report ldaho Power Company
Gottcr-usroNS
The cost to integrate the variable and uncertain delivery of energy from solar photovoltaic power
plants is driven by the need to carry extra capacity in reserve. This extra capacity in reserve is
necessary to allow bidirectional response from dispatchable generators to unplanned excursions
in solar production. The simulations performed for Idaho Power's solar integration study indicate
the costs associated with holding the extra capacity in reserve (Table 8).
Further Study
The integration of variable generation, including the study of methods for determining
integration impacts and costs, continues to be the subject of considerable research. The breadth
of this research highlights the interest in variable-generation integration, as well as the evolution
of study methods. Idaho Power appreciates the level of interest in its study of integration of
variable generation and recognizes the likelihood of a second-phase study with expanded scope.
During the course of the solar integration study, in discussions with the TRC and participants of
the public workshop, Idaho Power has received suggestions for a second-phase study of solar
integration. Suggestions for a second phase include the study of the following:
o Alternative water-year types (e.g., low-type and high-type)
r lntra-hourtradingopportunities
o Shortening the hour-ahead forecast lead time from 45 minutes to 30 minutes
o Clustered solar build-out scenarios
o Smaller solar build-out scenarios (e.g., 50 MW of installed capacity)
. Other solar plant technologies (e.9., tracking systems or varied fixed-panel orientation)
e Distributed solar systems (i.e., rooftop systems)
o Correlation between solar, wind, and load variability and uncertainty
r Improved forecasting methods
o Energy imbalance markets
o Voltage/frequencyregulation
Idaho Power will consider these suggestions during the development of scope for a
second-phase study.
Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
Page 20 of 36
Page 18
ldaho Power Company Solar lntegration Study Report
LTeRRTURE Creo
Ela 8., M. Milligan, B. Parsons, D. Lew, and D. Corbus. 2009. The evolution of wind power
integration studies: Past, present, and future. IEEE Power & Energy Society General
Meeting,2009 PES '09.
Idaho Power.2013. Wind Integration Study Report. Boise, ID: Idaho Power.
Lave, M., A. Ellis, and J. Stein. 201 3a. Simulating solar power plant variability:
A review of current methods. Sandia report-SAND20l3-4757.
http://enersy. sandia. gov/wp/wp-contenVgallery/uploads/SAND20 I 3 -
4757-simulatine_Solar Power_Plant_Variability A_Review olCurrent_Methods FI
NAL.pdf. Accessed June 2014.
Lave, M., J. Kleissl, J. S. Stein. 2013b. A wavelet-based variability model (W\IM) for solar PV
power plants. IEEE Transactions on Sustainable Energy, Vol. 4, No. 2.
Lew, D., G. Brinkman, A. Ibanez, A. Florita, M. Heaney, B. M. Hodge, M. Hummon, G. Stark,
J. King, S. A. Lefton, N. Kumar, D. Agan, G. Jordan, and S. Venkataraman.2013.
Western Wind and Solar lntegration Study Phase 2. NREL/TP-5500-55588. Golden, CO:
National Renewable Energy Laboratory. http://www.nrel.gov/docs/fy13osti/55588.pdf.
Accessed June 2014.
[IVIG and NREL]. Utility Variable-Generation Integration Group and National Renewable
Energy Laboratory. No date. Principles for Technical Review Committee (TRC)
involvement in studies of variable generation integration into electric power systems.
http:llvaiablegen.org/wp-content/uploadsl2009l05/TRCPrinciplesJune2012.pdf.
Accessed June 2014.
Page 19 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
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Solar lntegration Study Report ldaho Power Company
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Case No.IPC-E-14-18
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ldaho Power Company Solar lntegration Study Report
Appendix 1
Solar integration study appendix
Table of Contents
lntroduction
Technical Review Committee
List of TRC Members
Regulatory Commission Staff Observers
TRC Schedule and Agenda
Public Workshop Schedule and Agenda
Data Inputs and Assumptions
Natural Gas Price Assumptions
Market Power Price Assumptions
IPC Customer Load Data
Idaho Power Existing Generation
Hydroelectric Generation Data
Run-of-River Projects
Wind Generation Data
Aggregate PPA and PURPA Projects
Non-Wind PLIRPA Generation Data
Solar Production Data
Derivation of Hour-Ahead Solar Production Forecast and Upper/Lower Bounds
Page 21 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
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Solar lntegration Study Report ldaho Power Company
!rurnooucnoN
This appendix contains supporting data and explanatory materials used to develop Idaho Power's
2014 Solar Integration Study.
The main document, the 2014 Solar Integration Study, contains a full narrative of Idaho Power's
process for studying solar integration costs. For information or questions concerning the study,
contact ldaho Power:
Idaho Power-Resource Planning
l22l W. Idaho St.
Boise,Idaho 83702
208-388-2623
Tecn rurcAL Reuew Gorvrlurree
The Technical Review Committee (TRC) was formed during summer 2013 to provide input,
review, and guidance for the study. It is comprised of participants from outside of Idaho Power
that have an interest and/or expertise with the integration of intermittent resources onto
utility systems.
As part of preparing the 2014 Solar Integration Study,Idaho Power held one public meeting and
four TRC meetings. Idaho Power values these opportunities to convene, and the TRC members
have made significant contributions to this plan.
List of TRC Members
Brian Johnson...................University of Idaho
Jimmy Lindsay.................Portland General Electric (formerly of Renewable Northwest Project)
Kurt Myers ....Idaho National Laboratory
Paul Woods ...(formerly of City of Boise)
Cameron Yourkowski......Renewable Northwest Project (replacing Jimmy Lindsay)
Reg ulatory Commission Staff Obseruers
Brittany Andrus................Public Utility Commission of Oregon (OPUC) staff
John Crider ....OPUC Staff
Rick Sterling....................Idaho Public Utilities Commission (IPUC) staff
Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
Page 24 of 36
Page22
ldaho Power Company Solar lntegration Study Report
TRC Schedule and Agenda
Meeting Dates
2013 Thursday, August 15
2013 Thursday, September 19
2014 Monday, January 6
2014 Friday, May 16
2014 Thursday, May 29
Agenda ltems
lntroductions and role of TRC
ldaho Power system overview
Formulation of basic study design
Establish solar futures
Techniques for building solar generation data
Closing thoughts and comments
Study design
Key study components
Hydro-WY 2011 vs. WY 2012 vs. WY 2013
Solar-WY 2011 vs. WY 2012 vs. WY 2013
Market power prices
Natural gas prices
Solar penetration levels
Review of Study Design
Solar Data Availability
Wavelet-based Variability Model
Analysis Conclusions
Review of lntegration Study Design
Review of IPUC Filing
Development of Reserve Requirement for solar scenarios
Review of Operating Reserves
Review of Production Cost Model
Agenda ltems
lntroduction of Technical Review Committee
ldaho Power system overview
Study objective
Study design
System modeling
Next steps
Public Workshop Schedule and Agenda
Meeting Dates
2014 Thursday, May 1
Page 23 Exhibit No. 1
Case No. IPC-E-I4-18
P. DeVol,lPC
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Solar lntegration Study Report
Natural Gas Price Assumptions
Table I
Actual monthly average Nymex price for waler year 2012
Average Monthly Price
2011
Dara lnpurs AND AssurrlpnoNs
$3.76
$3.52
$3.36
$3.08
$2.68
$2.4s
$2.19
$2.04
$2.43
$2.77
$3.01
$2.63
$26.02
$30.81
$30.13
$24.53
$23.50
$16.30
$8.9e
$s.81
$4.50
$12.05
$24.75
$24.47
2012
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
Market Power Price Assumptions
Table 2
Actual average Mid-Columbia dollars/megawatt-hour (MWh) for water year 2012
Average Monthly Price
2011
2012
Page24 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
Page 26 of 36
ldaho Power Company Solar lntegration Study Report
lPC Gustomer Load Data
Table 3
Actual average megawaft (MW) for water year 2012
Year Month Average Load
2011 October
November
December
January
February
March
April
May
June
July
August
September
2012
Hydroelectdc Fecllldes and
,{aneplate Capacltles
E xellsGnyon 391.5Mw
E OxUow Igo.OMW
1,403
1,563
1,729
1,680
1,597
1,457
1,504
1,742
2,108
2,388
2,197
1,679
ldaho Power Existing Generation
E growde
E c:sue
E 5mtalts
E c.r.st*e
E Sliss
I toner Malad
I upperMalad
@ l-owerSalmon 60.0Mw
E UppsSalmon 34.5Mw
@ IhourndSprings 8.8MW
E Oeer Late 2.5 MW
WASHINGTOI{
OREGOf,
E 5h6hon€ Falls 12-5 MW
@ Twin Falls
GI Milner
E Arerican Falls ?2:3 MlV
rotal 1,709,0-v!-

North \6lmy
,t.EVADA
Figure 1
Existing ldaho Power generating resources
Therma! Facllides And Capacltles
coal
A lim Bridg€r 770.5 MW'
A North Valrry 283.5 MW' Eoardm.n , q!]t!:
ror.l llt8,?]"r!
Natud G.s BennettMountain l72.tMW Danskin 270.9 MW
A bngley Gulch l1_8.s {ITotel 7.5?._2-Y!
Diesel
A S.lmon oie*l 5.0 MW
Totrl 1,885.4 MW
WYOMING
5t5.4 MW
12.i1MW
27.2 MW
E2.8 MW
75.0 MW
13.5 MW
8.3 MW
t"
*
52.9 MW
59.4 MW
Page 25 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol,lPC
Page 27 of 36
Solar lntegration Study Report ldaho Power Company
E zs.o!o(,ItE 2o.o
EEE 1s.o.oEE: 1o.otto
oE s.o-
ol}
1961-2O13rvcrIc
't I o WY2O12
o'01
1949 199{
Flgure 2
Brownlee Reservoir inflow by water year
October
November
December
January
February
March
April
May
June
July
August
September
447
418
415
358
365
380
388
2s2
337
292
251
208
Hydroelectric Generation Data
Run-of-River Projects
Table 4
Actual monthly average MW (aMW) for water year 2012
2011
2012
Page 26 Exhibit No. 'l
Case No. IPC-E-14-18
P. DEVOI,IPC
Page 28 of 36
ldaho Power Company Solar lntegration Study Report
Wind Generation Data
Aggregate PPA and PURPA Projects
Table 5
Actual monthly aMW for water year 2013
Year Month aMW
2011 95
190
120
194
167
191
172
166
163
144
131
't'16
2012
Non-Wind PURPA Generation
Table 6
Actual monthly aMW for waler year 2012
Year ailW
2011
October
November
December
January
February
March
April
May
June
July
August
September
Data
2012
October
November
December
January
February
March
April
May
June
July
August
September
96
52
45
43
43
54
104
135
131
140
130
111
Page 27 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol,lPC
Page 29 of 36
Solar lntegration Study Report ldaho Power Company
Solar Production Data
rm
DispoBod 100 MW: Seasonal Averago Daily Shape
90
80
-70o
=*5
E*
i*Iil30
N
10
0
-
Smmq Av6age: J0, JLa. Aug
-
Spring AsAe: Mr, Apr, May
- - Amud Asqe: Oct- Sep
Fall Avsage: S€p, Oct, tlov
1:m 2100 3:00 4:00 5:m 6:m 7:m 8100 9:00 10:m !1:00 12:m 13m
Tlm ot Day
Dispe6ed I00 MW-Production
14:00 15:00 16:00 17:m 1&m 2'l:0 2:N :fi
II
I
I
I
I
t;,
35
30
25
)n
15
10
(J
3-
"r;'i.:r1.:"1.Xl.:""1'*o"1;'"1.':1.;'*l'Figure 3
Dispersed 100 MW
-d -.s -.s ..s
.-"1iS*t"."';..".d
Page 28 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol,lPC
Page 30 of 36
ldaho Power Company Solar lntegration Study Report
Dispersed 300 ilW: Seasonal Ayorage Daily Shape
300
2fi
m
240
m
9*t3 rso
! 160t
! r+o
o2a.1tI ,00
80
60
40
20
0
-SmmAvsageJ6,
Jd, Aug
-
Spring Avsage: Ma, Apr, May
- - Am@lAsee:Ocl- S@
FallAvsage: Sfl, Ocl, Nov
WinlqAvereei D€, Jil, Feb
l
'..a--"
fi"'
0:00 1:00 2:00 3:00 4:00 6:m 7:00 8:00 9:00 10rm 11:m 12:00 13:00 14:m 15rm lom 17:m 18:00 1900 20:m 21:m Z:fi 23:n
Tim of Day
Dispersed 300 Mw+roduction
olI*i.l
Iloi go
2't 20ll
rol
I
,nl
I
I,tl
oL
"..::1
j"1..1i.:S.$...{]".:..""
Figure 4
Dispersed 300 MW
-$ -"6 $ "9"
,."::-{".":"""""'"
Page 29 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
Page 31 of36
Solar lntegration Study Report ldaho Power Company
DispeGed 500 mW: Seasonal Av.rage Daily Shape
500
450
400
_ 350o
=3:ooc
E*r*o!&r*.I{o- tso
100
-
Smms Avsage: Jm, Jd, Aug
-
Sprirg Avaage: Mer, Apr, May
- - AmEl Awage: Oct - S€p
FallAvs46: S€p, Oct, Nov
WntsAsage: Ds, Ja, Feb
6:00 7:00 8:00 9:00 10:m 11:m !2:00 13:m 14:m 15:m 1Cm 17:m 18:00 1900 20:00 21:00 Z:fi 23.@
Tlre of Day
Dispersed 500 Mw-Production
i oo- r,oo
o
=-
r80
160
't40
120
100
80
60
40
20
0
36 ir
IJ
-\Ps -$ -$ -..s -$ -S .S -'"6 -d -"Ps -'S -S -d
".""':"-';"".{.""*':i$""":""";t.."'t.1"*t"""":".""*J"
Figure 5
Dispersed 500 MW
-$ -"6 -,{f -.S
,".":-"o::.t:"t".""
Page 30 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
Page 32 of 36
ldaho Power Company Solar lntegration Study Report
Di3perced 700 ilW: Seasonal Average Daily Shape
-
SmmdAvsee Jn, Jd, Aug i
)
7@
650
600
s50
500
oIo,.
E.*![*o
;*I{ 250
*m
-
SprirE Averee Ma, Apr, May i
- - AnnGl Avsage: Oct- Sep ]
l
llFdlAvd46: Sa, Oct, Nov l
l
lWntsAvs4e:O€, Ja, Feb
l
'150
1m
50
00:00 '1:00 6:00 7:00 8:00 9:00 10:m 11:m 12:m 13:m 14:m 1500 16:m 17:00 18:00 19:m 20.@ 21tco 2:N 23tco
Timof Day
"';"i:::.:::.jlti..li$."..',"1.. ""$i.1;i.;1:1.r
Figure 6
Dispersed 700 MW
Page 31 Exhibit No. 1
Case No.|PC-E-14-'18
P. DeVol, IPC
Page 33 of 36
Solar lntegration Study Report ldaho Power Company
Derivation of Hour-Ahead Solar Production Forecast and
Upper/Lower Bounds
95
90
85
80
75
10
=r'Ero
o,-E,tu ,.
0,940
-g 3sotl30
20
15
10
0
Figure 8
Hour-ahead forecast example
The average forecast is shown on Figure 8 as the green series. For each hour ofthe day, the
forecast average is calculated by applying the follow equation:
F'orecast AugG) : Forecast Obs(MW)1r_r,oo-r_1:1s) *
Where:
/ : forecast hour
Avg CSISg:oo+ r:55)
Av g C S I S g_2:2o+r_1 :15)
CS/S: Clear Sky Index Surrogate
The Clear Sky Index Surrogate (CSIS) is an important measure of the maximum amount of solar
generation the system could experience in any given hour. The CSIS is a component of the
average solar production forecast and accounts for the seasonal changes that influence solar
photovoltaic generation. This value is unique for every hour of the year. The CSIS is calculated
using 5-minute, modeled production data from the wavelet-based variability model (WVM).
The CSIS is calculated by taking the maximum 5-minute observation for a given hour.
This maximum value is the absolute maximum for a given hour over a l0-day period.
After identifying the absolute maximum from water year 2011, the forecast also identifies the
absolute maxima for water years 2012 and 2013. With the three absolute maxima identified from
the three water years analyzed, the forecast applies the maximum CSIS observed in three years
Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol, IPC
Page 34 of 36
7l8l2OL2
Page 32
ldaho Power Company Solar lntegration Study Report
of data for a given hour. It is noted that the ratio of the CSIS values, described in the above
equation, result in the least amount of average production forecast error. Multiple variations of
this ratio were tested, and the final version of the ratio was the most accurate. The process
detailing the calculation of the CSIS is described in the equations below.
CSIs(r) = M ax ( [csls1r.r*, ear z07t)], [csts1r,,", y ear 2012)], [csrslr*-, ",, rorif)
Where:
cslSlwnt",v"o,zorr) = uax (lsminobs(MW)n,,.-r],[s*in lbs(MW)1qrr-r],...,[s*inoat{,raw)<o,r--,1)
Cslslwttcrvearzorz) = uatt (ls minobs(MW),u,r-rl , [s .in obs(MW) 1r1ro-r] , ..., [s .in oAr{,raw) ro,o-*,])
cslslwatervcorzors) = uax (fs min obs(MW),r,r-rl , [s tnin obs(MW) lqro-r] , ..., [s ,in oat{,uw) <o,r-,r])
Where:
f= forecast hour
d= forecast day
Figure 8 is a good example of how the persistence-based forecast does very well under the
majority of solar conditions and how a forecasting model struggles with extreme weather events.
Despite the limitations of a persistence forecast, within a short period of time the forecast
returned to accurate predictions. Figure 8 is a select, extremely variable generation profile.
The afternoon observations that fall beneath the lower bound forecast are included in the
2.5 percent of lower forecast error reported in the solar integration study. Generally, the forecast
does well capturing the variability in production due to solar. The forecast has the ability to
tighten the range between the upper and lower bounds. This ensures the amount of capacity held
in reserve is sufficient but not unduly large.
Page 33 Exhibit No. 1
Case No. IPC-E-14-18
P. DeVol,lPC
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Solar lntegration Study Report ldaho Power Company
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Case No.|PC-E-14-18
P. DeVol,IPC
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