20220304IPC to Staff 1-37.pdf

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LISA D. NORDSTROM
Lead Counsel
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Enclosures
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Lisa D. Nordstrom
An lD CORPComparry
. .ri:r'l'i1i/-'l:' ._:1,, .-'_alljiYMarch 4,2022
VIA ELECTRONIC EMAIL
Jan Noriyuki, Secretary
ldaho Public Utilities Commission
11331W. Chinden Blvd., Bldg 8,
Suite 2O1-A(83714)
PO Box 83720
Boise, ldaho 83720-0074
Re: Case No. IPC-E-21-43' !n the Matter of ldaho Power Company's2021 lntegrated Resource Plan
Dear Ms. Noriyuki:
Attached for electronic filing, pursuant to Order No. 35058, is ldaho Power
Company's Response to the First Production Request of the Commission Staff in the
above entitled mafter.
The confidential attachment wil! be provided under separate cover to the parties
who sign the Protective Agreement in this mafter.
lf you have any questions about the attached document, please do not hesitate to
contact me.
Very truly yours,
CERTIFICATE OF ATTORNEY
ASSERTION THAT INFORMATION CONTAINED IN AN IDAHO PUBLIC UTILITIES
COMMISSION FILING IS PROTECTED FROM PUBLIC INSPECTION
Case No. IPC-E-21-43
ldaho Power Company's 2O21 Integrated Resource Plan
The undersigned attorney, in accordance with RP 67, believes that Attachment
No. 1 to Request No, 33 to ldaho Power Company's Response to the First Production
Request of the Commission Staff dated March 4, 2022, may contain information that
fdaho Power Company or a third party clalms is confidential as describedin ldaho Code
S 74-101, et seg., and S 48-801, ef seg., and as such is exempt from public inspection,
examination, or copying.
DATED this 4th day of March 2022.
frr" !.fl,,t"t '.*,
Lisa D. Nordstrom
Counsel for ldaho Power Company
LISA D. NORDSTROM (lSB No. 5733)
ldaho Power Company
1221 West ldaho Street (83702)
P.O. Box 70
Boise, ldaho 83707
Telephone: [phone]
Facsimile: [phone]
lnordstrom @idahopower. com
Attorney for ldaho Power Company
BEFORE THE IDAHO PUBLIC UTILITIES COMMISSION
IN THE MATTER OF IDAHO POWER
COMPANY'S 2021 INTEGRATE D
RESOURCE PLAN.
CASE NO. IPC-E-21-43
IDAHO POWER COMPANY'S
RESPONSE TO THE FIRST
PRODUCTION REQUEST OF THE
COMMISSION STAFF
COMES NOW, ldaho Power Company ("ldaho Power" or "Company"), and in
response to First Production Request of the Commission Staff ('IPUC or Commission")
dated February 11, 2022, herewith submits the following information:
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 1
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REQUEST FOR PRODUCTION NO. 1: Page 38 of the 2021 lntegrated
Resource Plan ("lRP") states that ldaho Power operates 17 hydroelectric projects with a
total nameplate capacity of 1,773 MW. However, Table 4.2 shows 18 hydroelectric
projects with a total nameplate capacity of 1,798.8 MW. Please reconcile the two
amounts.
RESPONSE TO REQUEST FOR PRODUCTION NO. 1: The vAIue iN Table 4.2
of 1,798.8 megawatts ('MW") is correct and the value in the text of 1,773 MW is missing
the nameplate capacity change for the Brownlee plant of 22.4 MW as a result of the
upgrades at unit 5 and to a lesser extent Shoshone Falls increasing capacity 3.2 MW
due to general plant upgrades and the remainder due to rounding. Table 4.2 shows 18
different rows for hydroelectric facilities, but Upper Salmon A and B are generally
counted as one hydroelectric project.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company,
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.2
REQUEST FOR PRODUCTION NO. 2: Page 88 of the 2021 IRP states that,
given the lack of long-term firm transmission availability south of NV Energy, the
transmission path capacity into the ldaho Power system is not included within ldaho
Power's capacity planning margin; however, the path is expected to continue to be
heavily used for real-time transactions by the Energy lmbalance Market ('ElM"). Please
confirm that the transmission path still has non-firm availability for the ElM, despite the
lack of longterm firm transmission availability.
RESPONSE TO REQUEST FOR PRODUCTION NO. 2: Confirmed. The
transmission path can be used when capacity is available for non-firm usage by the
Energy lmbalance Market ("ElM'). ldaho Power continues to facilitate the provision of
Available Transmission Capacity ['ATC) for EIM transfers per Attachment O of its Open
Access Transmission Tariff.l
The response to this Request is sponsored by Curtis Westhoff, System
Consulting Engineer, ldaho Power Company.
http://www.oasis.oati.com/woaidocs/IPCO/lPCOdocs/lPC OATT lssued 2022-01-06.pdf
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.3
REQUEST FOR PRODUCTION NO.3: The Second Amended 2019 IRP used
different gas forecast levels and different carbon cost levels to develop portfolio
buildouts. See Chapter I of the Second Amended 2019 IRP. However, the 2021 IRP
only used the planning gas forecast and the planning carbon cost to develop portfolio
buildouts; different gas forecast levels and different carbon cost levels are used in the
Portfolio Cost Analysis. Please explain why different gas forecast levels and different
carbon cost levels are not used to develop portfolio buildouts in the 2021 lRP.
RESPONSE TO REQUEST FOR PRODUCTION NO. 3: The methodology to
develop scenarios in the 2019 lntegrated Resource Plan (.'lRP') included using
AURORA's Long Term Capacity Expansion C'LTCE") modelto develop portfolios using
planning conditions and different gas price and carbon price forecasts. The different
combinations of these forecasts produced 24 portfolios with different likelihoods of
occurrence, and many had very similar resource selections. The abundance of
portfolios generated under varying conditions made it difficult to analyze results
consistently.
For the 2021 IRP analysis, ldaho Power used a more deliberate branching
analysis to develop portfolios, as shown in Figure 9,1. Portfolios were developed using
the LTCE model under planning conditions. The portfolios were then costed with varying
natura! gas and carbon adder price forecasts. Additionally, a stochastic analysis was
performed on the portfolios to assess the cost sensitivity to varying shocks (stochastic
variables), shown in Figure 10.3. By creating resource portfolios under planning
conditions and then costing those portfolios under different gas and carbon price
forecasts, the analysis in the 2021 IRP allows for better comparison of portfolios.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 4
Portfolios were tested under a range of future conditions to determine which is the most
robust to typical uncertainty rather than building a poftfolio for each specific uncertainty.
New to lhe 2021 lRP analysis are "future" scenarlos as shown in figure 9.2,
including Rapid Electrification, Climate Change, and 100% Clean Energy scenarios.
These scenarios were developed based on feedback from and in consultation with
members of the !RP Advisory Committee. These scenarios involve several input
adjustments to explore various potential "futures" and yield a better understanding of the
resource selections (type,timing, and quantity) required to adequately serve demand in
those potential future environments. ldaho Power believes the branching analysis and
the comparison to future scenarios provides a better evaluation of cost and risk than the
previous methodology.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.5
REQUEST FOR PRODUCTION NO. 4: Table 9.2 in the 2021 IRP shows
regulation reserve requirements. Please answer the following questions.
a. How are the regulation reserve requirements determined? Please provide the
supporting workpapers.
b. Why is Wind RegDn OYoin the 2019 !RP, but it is not 0% in the 2021 IRP?
c. Why is Solar RegDn 0% (except for winter) in the 2019 lRP, but it is not 0% in
rhe 2021 IRP?
d, Why are regulation reserve requirements determined at the seasonal level in
the 2019 IRP, but they are determined at the monthly level in the 2021 IRP?
e. What is the "approximation" process in the 2021 IRP that extrapolates a
single year of regulation reserve requirements to the 20-year planning
horizon? Please provide the supporting workpapers with allformulas intact.
f. Staff expressed its concerns regarding the impacts of regulation reserve
shortfalls from a reliability perspective and a cost perspective in Staff s
comments in Case No. IPC-E-19-19. Please explain whether and how the
Company addresses these issues in the 2021 lRP. (Please note the
distinction between regulation reserve requirements and planning reserve
margins in answering this question.)
g. Page 6 of the Second Amended 2019 IRP states that Valmy Unit 2 was
modeled with the ability to provide regulation reserves, but an adjustment is
made that the unit cannot provide regulation reserves, Please explain why
Valmy Unit 2 cannot provide regulation reserves and whether Valmy Unit 2 is
modeled to not provide regulation reserves in the 2021 !RP.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.6
RESPONSE TO REQUEST FOR PRODUCTION NO. 4:
a. Regulation reserve requirements for the 2021 IRP were developed in the
2020Variable Energy Resource lntegration Study (2020 VER Study")
performed with the help of a Technical Review Committee and an externa!
consultant, Energy and Environmental Economics, lnc. ("E3"). The 2020 VER
Study is provided as an attachment to this request.
b. ln the 2019 lRP, Wind RegDn was 07o because it was netted into the Load
RegDn table. !n the 2021 !RP, the Wind RegDn reserve was captured
separately.
c. ln the 2019 lRP, Solar RegDn was 0% (except for winter) because it was
netted into the Load RegDn table. ln the 2021 lRP, the solar RegDn reserve
was captured separately.
d. Model enhancements in the 2020 VER study over the 2018 VER study
allowed for the modeling of regulation reserves for wind and solar on a
monthly basis. Those enhancements were utilized in the 2021 IRP analysis.
e. The approximation process was necessary to convert the VER findings into
usable inputs for the AURORA model. The VER study findings calculated
reserves for wind and solar based on a percentage of total load. However, the
AURORA mode! requires that the reserves for wind and solar be calculated
based on the generation amounts for wind and solar. The approximation
method first calculated the amount of reserves (in MWh) by month for wind
and solar based on the VER study reserve percentages and three years of
historical load data. That load data was combined with three years of
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 7
historical wind and solar generation data, which was used to solve for
percentages that created the same amount of reserves. The Excel
workpapers that convert the VER study findings into AURORA model inputs
are provided as an attachment to this request.
f. Regulation reserve requirements were entered in the 2021 IRP modelas a
constraint to approximate resource and reserve needs. The reserve
requirements vary based on the amount of load, wind, and solar generation
and are added to the resource requirement that is met by the model in the
capacity expansion process to help ensure the quantity and type of resources
selected would meet reserve requirements. The number of hours that have
regulation shortfalls are very low-just 21 hours over the 2}-year planning
horizon. All21 hours are regulation down violations, meaning that ldaho
Power has the option to curtail generation if a regulation reserve shortfal!
occurred. Given the type and quantity of reserve requirement shortfalls in the
model, no action was deemed necessary.
g. Consistent with the 2021 IRP and the operations of the plant, Valmy Unit 2 is
modeled without providing regulation reserves. Valmy Unit 2 is not capable of
providing regulation reserves to ldaho Power's Balancing Authority ("BA")
because the Company does not control the plant output directly and does not
have the ability to deploy reserves. Valmy is in NV Energy's BA and ldaho
Power uses a static interchange schedule to bring generation into ldaho
Power's BA at a set amount each hour.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY. S
The response to frris Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO FOWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.9
REQUEST FOR PRODUCTION NO. 5: Page 99 of Appendix C of the 2021 IRP
lists the average Electric Load Carrying Capacity ('ELCC") of existing resources and the
average ELCC of future resources. Please respond to the following.
a. Please describe how the average ELCC of existing resources and the
average ELCC of future resources are determined.
b. Please explain why Jackpot Solar is separated from Solar PV in determining
ELCC of future resources.
c. Page 138 of the 2021 IRP states that the Company utilized static ELCC
values for each resource type modeled, even though resource ELCC can vary
depending on the total resource makeup of a portfolio. PIease explain why the
Company believes that static ELCC values are appropriate.
RESPONSE TO REQUEST FOR PRODUCTION NO. 5:
a. For the Company's 2021 IRP , the average Effective Load Carrying Capability
('ELCC") of existing variable energy resources was determined by calculating
an ELCC value for each resource for each of the four historical test years
using ldaho Power's Loss of Load Expectation ("LOLE") too!; these four
ELCC values were then averaged to produce the final result. The ELCC
values applied to future variable energy resources ("VER) were determined
by using the Company's existing and committed resources as a base, and
then layering in potential future resources to calculate their ELCC based on
the time when those resources are forecast to come online.
b. Jackpot Solar was separated from Solar PV when calculating the ELCC of
future VERs because the project has already signed a Power Purchase
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 1O
Agreement, making it a committed resource that is considered part of the
Company's existing and committed resource stack.
c. The ELCC values are an input to the AURORA LTCE model, meaning all
portfolio buildouts consider the ELCCs of VERs when solving. This means
the portfolio buildouts are not known at the time the ELCC calculations are
performed. lt is infeasible to pre-determine ELCC values years into the future
because the resource buildout in outer years of the planning horizon can
fluctuate every IRP cycle. Additionally, the "last-in ELCC" concept, in which
the future resources being modeled only one resource is added at a time,
shows that the type of resource added to the system will affect the ELCC of
the next resource to be added. As a measure of assurance, poftfolios were
evaluated using ldaho Power's LOLE toolto ensure they met the establashed
reliability threshold on an annual basis.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
]DAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 1'I
REQUEST FOR PRODUCTION NO. 6: Page 145 of the 2o2'l IRP states that
the offsetting cost of selling wheeling service using the Boardman-to-Hemingway
('B2H') capacity is not factored into the poftfolio Net Present Value ("NPV"). Did the
Company include wheeling revenue from its transmission system in any of its modeled
NPV results? Please explain why it did or did not include wheeling revenue and which
scenarios/portfolios would be affected by not including it.
RESPONSE TO REQUEST FOR PRODUGTION NO. 6: Page 145 of the2021
!RP states:
The "PotentialOffsetting Costs Not Included" column represents the
possibility of selling wheeling seruice utilizing the BzH capacity that is not
being utilized by the company in the given scenario. This offsetting cost is not
factored into the portfolio NPV.
The paragraph referenced above refers to Table 10.8 in which the Company
evaluated B2H capacity sensitivities assuming the Company was able to access various
quantities of capacity from the Mid-Columbia market. For example, in the "Base B2H
Portfolio - 350 MW Planning Contribution" row, the assumption is that the Company
only utilizes 350 MW of B2H to meet load service needs, rather than the 500 MW
assumed in the Preferred Portfolio. Table 10,8 was a capacity sensitivity only, so total
portfolio costs assumed the Company would have the same B2H ownership and
capacity percentage, but that the B2H capacity was less usefu! toward meeting the
Company's planning margin requirements. The $51 million on the same row
corresponds with a high-level NPV of 150 MW of third-pafty wheeling sales over the
planning horizon (500 MW less the 350 MW contributing to the planning margin). The
$34 million and $17 million on the next two rows represent 100 MW and 50 MW of third-
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 12
party wheeling sales, respectively. The 150 MW, 100 MW, and 50 MW of sales
represent increased wheeling volumes, which were only quantified for these B2H
sensitivities.
For the 2021 IRP portfolio analysis, the Company included wheeling revenue
offsets for all portfolios that included B2H or Gateway West. Additional information
regarding the wheeling revenue offsets is provided in the Company's response to Staff
Request No. 33.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY. 13
REQUEST FOR PRODUCTION NO. 7: Page 162 of the 2021 IRP states that
the planning forecast for CSPP wind includes a renewal rate of 25o/o for contracts that
will expire during the IRP timeframe. Please respond to the following.
a. Please explain why 25o/o is used in the planning forecast and how it is
determined.
b. ls 25% assumption used in Table 10.7: July peak hour load and resource
balance table?
c. How does the Company determine which wind expiring contracts fall in the
25o/o of contracts that are renewed to include in the AURORA model and in
the existing load and resource balance, respectively?
RESPONSE TO REQUEST FOR PRODUCTION NO. 7:
a. !n the 2019 lRP, it was assumed that cogeneration and small power
producers C'CSPP) wind would not renew. Feedback from stakeholders
indicated they expected a renewal rate of more than 0 percent in the 2021
!RP. ln preparation of the 2021 IRP ldaho Power consulted with utility peers
including PacifiCorp, Avista, and Northwestern, all of which did not include
renewal assumptions for CSPP wind in their !RP planning cases. Additionally,
informal conversations with CSPP wind developers/owners do not give a
positive indication that the projects will renew. Regardless, ldaho Power
committed2 to include renewa! sensitivities in the 2021 IRP and discussed
both the renewal sensitivities and the planning case with the IRP Advisory
2 IPC-E-I9-19, ldaho Power Reply Comments at 70 (Feb 10,2021\
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 14
Council ('|RPAC").3 Feedback from the IRPAC led to ldaho Power's use of 25
percent CSPP wind renewal in the planning case and sensitivities at 0% and
looo/o (see 2021 lRP Report, Chapter 9, CSPP Wind Renewal Sensitivity
Studies, page 1 221. The results of the sensitivities indicate that both low and
high CSPP wind project renewal had minimal impact to the preferred poftfolio.
The Company anticipates aQjusting these scenarios in the future as more
information is known about which projects may renew. See the 2021 IRP
Report, Chapter 1 1, CSPP Wind Renewal Low, pages 162-163 and CSPP
Wind Renewal High, pages 164-165 for more information.
b. Yes. As the contracts expired the assumed percent of renewing capacity is
included in the load and resource balance table.
c. Rather than selecting certain expiring contracts to be renewed and not others,
the Company's methodology assumed that as CSPP wind contracts expired,
25 percent of their capacity would continue in the CSPP program.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
3 CSPP forecast methodology and modeling sensitivities were discussed at the March 11, 2021 and June
10, 2021 IRPAC meetings.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY. 15
REQUEST FOR PRODUCTION NO. 8: Page 170 of the 2021 IRP states that
"the developers of Jackpot Solar informed the Company that the global supply chain
disruptions have raised concerns regarding Jackpot Solar's ability to achieve
commercial operation by the dates identified in the approved agreement." Please
provide an update on the status of the project.
RESPONSE TO REQUEST FOR PRODUCTION NO. 8: IdAhO POWEr ANd
Jackpot have continued to discuss and exchange correspondence regarding the
potentia! delay; however, based on the information provided by Jackpot to date, ldaho
Power has not accepted Jackpot's characterization of this circumstance as a force
majeure event nor agreed to extend the Scheduled Commercial Operation Date in the
Power Purchase Agreement ("PPA"). The Scheduled Commercial Operation Date in
the PPA remains December 1,2022. The latest information from Jackpot indicates that
Jackpot currently anticipates a 40-day delay in project completion. The PPA requires
the seller (developer) to pay delay damages if the project does not achieve commercial
operation by the Scheduled Commercial Operation Date.
The response to this Request is sponsored by Donovan Walker, Lead Counse!,
ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 16
REQUEST FOR PRODUCTION NO.9: Please provide answers and
explanations to the following questions regarding Table 10.7: July peak hour load and
resource balance,
a. Does each year's largest deficit always occur in July?
b. Does the peak load reflect the haghest load amount for each month?
c. lf so, are the resource capacity amounts determined at the same point in time
when the highest load occurs each month?
d. Please confirm that the Energy Efficiency used to adjust for peak load in
Table 10.7 is determined in the Energy Efficiency Potential Study and
includes existing and future energy efficiency deemed to be cost-effective
from the Utility Cost Test.
e. Why are "EE Bundles" not used to adjust for peak load?
f. Why are "EE Bundles" included as a New Resource in Table 10.7 of lhe 2021
IRP but were not included as a New Resource in the Excel file in Response to
Staffs Production Request No. 1 in Case No. !PC-E-21-09?
g. Please explain the reasons for the differences in capacity amounts between
IRP Table 10.7 and the Excelfile in Response to Staffs Production Request
No. 1 in Case No. IPC-E-2I-09.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 17
Table 10.7 in 2021 IRP Response to Staff's
Production Request No. 1
in Case No. IPC-E-21-Og
Bridger 663MW 7O3MW
Valmy 121MW 136MW
Gas Peakers 365MW 416MW
Elkhorn 1sMW 5MW
:
a. Yes, historically the Company's peak load forecast and resulting deficit lands
in the month of July.
b. Yes, the peak load in the L&R represents a 50th percentile (1 in 2) peak hour.
c. No, the resource capacity amounts either represent nameplate capacity with
an equivalent forced outage rate applied, or they represent the ELCC value
associated with the resource.
d. Confirmed.
e. The "EE bundles" do adjust for peak Ioad and are listed in Table 10.7 on the
second row of Page 143 (the second page of Table 10.7).
f. EE Bundles are not included as a new resource in the Case No. IPC-E-21-09
Production Request No.1 workbook because EE Bundles were not selected in
the preferred portfolio for the 2019 lRP.
g. The response to production request No.1 in Case No. IPC-E-21-09 was the
fina! Load and Resource Balance ("L&RB") workbook from the 2019 !RP. With
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.lS
the 2021 IRP the Company further incorporated LOLE analysis for capacity
planning margin. The differences between Table 10.7 and the workbook
supplied for Case No. IPC-E-21-09 are mainly from the addition of Effective
Forced Outage Rates and the refinement of ELCC of resources. There were
also small refinements made to the peak capabilities of the coal and gas units
for the 2021 IRP analysis. The Table below shows how the Table 10.7
planning margin capacities were derived.
The response to this Request is sponsored by Jared Ellswofth, Transmission,
Distribution & Resource Planning Director, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO ]DAHO POWER COMPANY- 19
Resource EFORYELCC Capacity High Ambient
Temp
Shaping (July)
Planning
Margin
Capacity
Bridger 6.34o/"708 MW NA 663 MW
Valmy 9.180/o 133.5 MW NA 121 MW
Gas Peakers
Bennett Mountain 5o/o 164.2 MW 93.1o/o 145 MW
Danskin 1 4.40/o 171 MW 90.40/o 148 MW
Danskin 2 7.3o/"45.4 MW 90,3%38 MW
Danskin 3 7.3o/"45.1 MW 83.60/o 35 MW
Elkhorn 15%1OO MW NA 15 MW
REQUEST FOR PRODUCTION NO. 10: Please explain how the "Available
Transmission wffhird-Party Secured" are determined in Table 10,7 in lhe 2021 IRP and
provide workpapers that calculate the capacity amounts.
RESPONSE TO REQUEST FOR PRODUCTION NO. 10: A description of third-
party secured import transmission capacity can be found in the 2021 IRP Appendix D:
Transmission Supplement pages 1 3-1 4.
Please see the attached load and resource balance Excelworkbook from the
2021 IRP analysis. The tab labeled "ATC Backgnd" includes the transmission capacity
calculations. The "Available Transmission w/Third-Party Secured" in Table 10.7 is the
sum of ATC Backgnd Row 85 and Row 87. Row 85 is the amount of available set-aside
transmission on the paths from the Northwest market that have a corresponding third-
party firm reservation. Row 87 is the third-party reservation from the Desert Southwest
markets.
The response to this Request is sponsored by Curtis Westhoff, System
Consulting Engineer, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 20
REQUEST FOR PRODUCTION NO. 11: Please respond to the following
regarding Emergency Transmission (CBM) in Table 10.7 in the 2021 lRP.
a. Please explain in detail how Capacity Benefit Margin ('CBM') is used in the
Company's system. ln the explanation, please provide specific examples
when CBM is used, by whom (i.e, native load customers, transmission
customers, etc.), and if there are any restrictions or circumstances when it
isn't or should not be used.
b. How is the 330 MW of CBM determined? ls the amount of CBM capacity in
addition to the rated capacity of the transmission line or is it a portion of the
rated capacity of the line? Please explain.
c. How will CBM be used in the Northwest Power Pool's ("NWPP') Reserve
Sharing Program?
RESPONSE TO REQUEST FOR PRODUCTION NO. 11:
a. Capacity Benefit Margin ("CBM') is transmission capacity ldaho Power sets
aside on the Company's transmission system, as unavailable for firm use, for
the purposes of accessing reserve energy to recover from severe conditions
such as unplanned generation outages or energy emergencies. Reserve
generation capacity is critical and CBM allows a utility to reduce the amount
of reserve generation capacity on its system by providing transmission
availability to another market, in this case the Pacific Northwest. An energy
emergency must be declared by ldaho Power before the CBM transmission
capacity becomes firm. CBM can be used by Load Serving Entities within
ldaho Power's Balancing Authority, which includes ldaho Power native load
IDAHO POWER COMPANY'S RESPONSE TO THE FTRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.2l
and third-party network loads.
b. The 330 MW is based on ldaho Power's most severe single contingency,
which is either the loss of two Bridger Units or the loss of the Langley Gulch
plant. CBM is a portion of the ldaho to Northwest transmission path west-to-
east capability set aside for emergency use. The west-to-east Total Transfer
Capability ('TTC") of the ldaho to Northwest transmission path is presently
1,2O0 MW in the peak summer months with 330 MW set aside for CBM.
c. CBM is not a factor in the Northwest Power Pool ("NWPP")4 Reserve Sharing
Program because it is not a component of the program. The Company
believes the question may have been intended to ask about the NWPP's
future Western Resource Adequacy Program (.'WRAP"). At this point in
program design, it is unclear how CBM would be used within the WRAP. In
future IRP's, ldaho Power wil! continue to evaluate how CBM applies in the
context of ldaho Power's Load and Resource Balance, specifically if the
Company is a member of a regional resource adequacy program.
The response to this Request is sponsored by Curtis Westhoff, System
Consulting Engineer, ldaho Power Company.
a The NWPP is now the Western Power Pool ("WPP"), httos://www.westernpowerpool.org/.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 22
REQUEST FOR PRODUCTION NO. 12: The Company's Reply Comments in
Case No. IPC-E-I9-19 state that FERC determined it was important for the reliability of
the system for utilities to create two margins that are deducted from Available Transfer
Capacity ("ATC"): Transmission Reliability Margin ("TRM") and CBM. Please define
TRM, describe the situations in which TRM can be used, and explain why TRM is not
included in Ioad and resource balance.
RESPONSE TO REQUEST FOR PRODUCTION NO. 12: Transmission
Reliability Margin ("TRM") is transmission capacity that ldaho Power sets aside as
unavailable for firm use, for the purposes of grid reliability to ensure a safe and reliable
transmission system. ldaho Power's TRM methodology, approved by the Federal
Energy Regulatory Commission ("FERC") in 2002, requires ldaho Power to set aside
transmission capacity based on the average loop flow (i.e. unscheduled flow) on the
ldaho to Northwest path. In the west, electrical power is scheduled through a contract-
path methodology, which means if 100 MW is purchased and scheduled over a path,
that 100 MW is decremented from the path's total availability. However, actua! power
flow over the path (based on the path of least resistance) may vary from the scheduled
power flow, so actual flows don't always equal contract-path schedules. The difference
between scheduled and actualflow is referred to as unscheduled flow or Ioop flow. The
average adverse loop flow across the ldaho to Northwest path during the month of July
is 281 MW.
TRM is not included in the load and resource balance because it is non-firm
transmission set aside to account for non-scheduled loop flow that may flow across the
ldaho to Northwest path. The capacity is available as non-firm ATC if system conditions
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 23
do not prohibit its use.
The response to this Request is sponsored by Curtis Westhoff, System
Consulting Engineer, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO TDAHO POWER COMPANY.24
REQUEST FOR PRODUCTION NO. 13: Please respond to the following
questions regarding non-owned reserve capacity, which can be defined as a decrease
to resource capacity representing the capacity required to provide reserves for load and
generation by entities within the Company's balancing authority area ('BAA') such as a
municipal or co-operative.
a. Are there any wholesale customers that operate in the Company's BAA
purchasing non-owned reserve capacity?
b. !f so, what rates do these customers pay for the non-owned reserve capacity?
c, How is the non-owned reserve capacity modeled in the AURORA model and
in the load and resource balance, respectively?
:
a. Yes, network customers within the ldaho Power balancing authority area
("BAA') purchase ancillary services from the Company.
b. The rates that network customers can pay for reserve capacity are detailed in
Schedules 3, 5, and 6 of the ldaho Power Open Access Transmission Tariff.s
c. The reserve capacity for network resources or loads within the ldaho Power
BAA are not modeled directly in the AURORA or Load and Resource Balance
analysis. The reserve obligations are covered as part of the planning reserve
margin utilized in the 2021 IRP analysis.
The response to this Request is sponsored by Curtis Westhoff, System
Consulting Engineer, ldaho Power Company.
5 http://www.oasis.oati.com/woa/docs/IPCO/IPCOdocs/lPC OATT lssued 2022-01-06,pdf
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 25
REQUEST FOR PRODUCTION NO. 14: Direct Testimony of Jared Ellsworth in
Case No. IPC-E-21-12 states that the industry standard for resource planning is a Loss
of Load Event ('LOLE') of no more than 1 event in 10 years or an LOLE of 0.1 days per
year. What is the source for the current industry standard?
RESPONSE TO REQUEST FOR PRODUCTION NO. 14: ln its 2021 Long-Term
Reliability Assessment, the North American Electric Reliability Corporation ("NERC")
published the reliability methodology and threshold for the main system operators in the
country.l ln table 11 of the NERC document, the LOLE target for the main system
operators in the country is shown. While each system operator has its own methodology
and resulting reference margin level, most of them start from the target of 0.1 day/year
LOLE metric.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 26
REQUEST FOR PRODUCTION NO. 15: Page 117 of the2021 IRP and
Response to Staffs Production Request No. 20 in Case No, IPC-E-21-32 state that the
0.05 days per year threshold aligns with the reliability threshold used by the Northwest
Power and Conservation Council ('NWPCC"). Please provide the specific document or
report from the NWPCC to support this claim.
RESPONSE TO REQUEST FOR PRODUCTION NO. 15: The reliability target
used by the Northwest Power and Conservation Council C'NWPCC") can be found in its
latest Power Plan, "Seventh Power Plan" and is discussed in Chapters 10 and 11.6 The
council also references the reliability used in their analysis on the Resource Adequacy
site. z
The response to this Request is sponsored by Jared Hanse, Resource Planning
Leader, ldaho Power Company.
6 https ://www.nwcouncil.org/reports/seventh-power-plan
7 https ://www. nwcounci l. org/energy/energy-topics/resource-adequacy
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 27
REQUEST FOR PRODUCTION NO. 16: Response to Staffs Production
Request No. 9 in Case No. IPC-E-21-32 states that the 2021 IRP load and resource
balance is based upon 50th percentile load and 50th percentile hydro, while the method
behind the 2021 summer near-term operating plan utilized 95th percentile load and a
criticalwater year, and that, looking solely at the Company's assumed available existing
resources for July of 2021and excluding the applied planning margin and reserve
requirements to compare the two forecasts on equal grounds, the forecasted system
deficit increases by over 200 MW when utilizing the 95th percentile load forecast and a
critical water year. Please answer the following questions.
a. Does the Company always use the 95th percentile load and a criticalwater
year for near-term operating plans? Please explain.
b. How does the Company define a critical water year?
c. Why does the Company use 50th percentile Ioad and 50th percentile hydro
for load and resource balance in the lRP, but use 95h percentile load and a
critical water year for operational planning and how do they compare?
RESPONSE TO REQUEST FOR PRODUCTION NO. 16:
a. ldaho Power has used the 95th percentile load and critical water year for its
summer readiness analysis at least since 2009, This allows the Company to
prepare for a stressed case scenario with the intent of maintaining reliable
and affordable energy for customers during extreme conditions.
b. The Company defines a critica! water year forecast using its Enhanced
Forecast System ("EFS') hydrologic model encompassing the Snake River
Basin. From observed current conditions, the EFS modeling assumes future
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.2S
precipitation is approximately 6Spercent of normal throughout the entire
Snake River Basin. Subsequently, irrigation demand and aquifer flow to the
mainstem Snake River are also assumed to represent drought conditions.
The resulting summer streamflow forecast is intended to align with a 99th
percentile critical water condition, or a water supply that would occur with a 1
in 100 (1 percent) probability. The criticalwater case is updated monthly as
observations of snowpack and basin conditions are updated throughout the
year. ln a year where snowpack is healthy, the critical water case is higher. ln
a year where snowpack is weak, the critical water case is reduced. Please
note that the summer readiness analysis is designed to analyze peak hour
loads, meaning the Hells Canyon Complex hydro generation shown would
only be available for relatively short durations before needing to drop back
down during a criticalwater year (entirely dependent on the water supply
levels and expected maintenance of that specific year).
c. For the IRP planning cases, the 50th percentile assumptions are based on
median weather and streamflow conditions, meaning there is a fifty percent
chance that load could be higher or lower based on varying temperatures,
and a fifty percent chance that water flows could be higher or lower based on
weather-dependent streamflow conditions. These median assumptions are
reasonable given the intent of the model to capture energy conditions into the
future. The Planning Reserve Margin is added when calculating resource
capacity need to ensure sufficient resources are available during extreme
temperature and hydro conditions (among other contingencies). For near-
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 29
term operational planning, the Company uses 95th percentile peak load and
critica! water year assumptions. The Company uses these assumptions in the
near-term analysis because the purpose of this analysis is to assess system
conditions and readiness for the summer operating season. With that goal, it
is reasonable and appropriate to use assumptions that stress load and hydro
system capabilities.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 30
REQUEST FOR PRODUCTION NO. 17: Please respond to the following
questions regarding changes to the B2H Transmission Line project (Letter to IPUC
dated January 19,2022). Do the changes impact any modeling results for the 2021
IRP?
a. lf yes, please provide workpapers that quantify the impact the changes have
on capacity requirements.
b. lf yes, please provide workpapers that quantify the impact on Company's
capacity deficit date.
c. lf yes, please describe and quantify any changes to portfolios.
RESPONSE TO REQUEST FOR PRODUCTION NO. 17: No, the Company's
2021 IRP modeling assumptions are consistent with the details of the letter to the IPUC
The response to this Request is sponsored by Curtis Westhoff, System
Consulting Engineer, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.3l
REQUEST FOR PRODUCTION NO. 18: Please quantify the impact of NWPP
requirements on ELCC, LOLE, and planning reserve margin calculations and results in
the 2021 lRP.
RESPONSE TO REQUEST FOR PRODUCTION NO. 18: The NWPP dOES NOt
have any requirements in effect regarding ELCC, LOLE or planning reserve margin.
ldaho Power aligned its reliability threshold with the NWPCC by implementing an LOLE
of 1 in 20 (or 0.05) days per year.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 32
REQUEST FOR PRODUCTION NO. 19: PIease verify and provide evidence
that selected portfolios achieve a LOLE of 0.5 da\ys per year.
RESPONSE TO REQUEST FOR PRODUCTION NO. 19: Select portfolios were
chosen for review to ensure they met the LOLE reliability threshold of 1 in 20 (or 0.05)
days per year. Using ldaho Power's LOLE tool, the assurance check verified that the
selected portfolios met the reliability threshold when taking the average of the four
historical test years. lf any of the years in the planning horizon did not meet the
reliability threshold, a generator was added in that year and its size was increased until
the reliability threshold was met,
The figure below shows the reliability of the two base portfolios; the top plot
shows the poftfolios as created by AURORA, and the bottom plot shows the reliability
after the poftfolios had been modeled in the Company's LOLE tool with the additional
generation required to meet the reliability threshold included.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 33
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The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSTON STAFF TO IDAHO POWER COMPANY. 34
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REQUEST FOR PRODUCTION NO. 20: Please explain the analysis
represented by the graph and table provided in Response to staff Production Request
No. 20 in Case No. IPC-E-21-32.|n addition to the explanation, please provide the
following:
a. The workpapers (with formulas intact) used to calculate the amounts in the
table and the graph.
b. The sources of information and a description for all inputs used in the
workpapers.
c. The percentile(s) of the peak load forecast for each test year and for 2021
relative to the peak load forecast included in the 2021 !RP if it were backcast
to each of the test years.
RESPONSE TO REQUEST FOR PRODUCTION NO. 20:
a. The numbers shown in the referenced table and graph are a direct output of
the LOLE tool; no workpapers were used for the calculations.
b. The process details the Company's sources of information and description of
the inputs used in the reliability analysis is provided as an attachment to this
request.
c. Each of the four historical test years were scaled to have the same peak load
as the 2021 IRP forecasted fiftieth percentile peak load in 2023 (which was
used as the benchmark year for the analysis).
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.35
REQUEST FOR PRODUCTION NO. 21: ln the April22, 2021, workshop, the
Company said that regulation requirements may be relaxed for computing efficiency
resulting in potential capacity shortfalls and loss of load. Please explain how the
Company reconciled these shortfalls.
RESPONSE TO REQUEST FOR PRODUCTION NO. 21: ThC AURORA MOdCI
can relax the regulation requirement for computing efficiency and for the preferred
portfolio analysis it relaxed regulation requirements for four days during the entire
twenty-year planning period. No capacity shortfalls or loss of Ioad events were predicted
by the model.
Further analysis using the LOLE modeling too! identified a reliability deficit in
years 2037-2040. The cost of generation necessary to eliminate the reliability deficit
was added to the poftfolio.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.36
REQUEST FOR PRODUCTION NO. 22: ln response to Audit Request No. 2
dated July 7 , 2021 , the Company said that it has performed test scenarios with the 2019
!RP input to verify and validate that software enhancements will optimize for ldaho
Power and that it will continue to evaluate Long-Term Capacity Expansion ('LTCE')
results during lhe 20?1 IRP process to ensure the optimal portfolio is developed. Please
respond to the following.
a. How did the Company evaluate the 2021 IRP results to ensure that the
resources selected are cost-optimized for the Company's system?
b. What is specifically being optimized for the region versus what is optimized in
the Company's system through co-optimization?
c. Are there any conflicts that can occur between the two sets of objective
functions, and if there are conflicts, how the model resolves them.
:
a. Each of the sensitivities listed in Table 10,5 test the 2a21 IRP results to
ensure the LTCE resulted in an optimal portfolio. Each of these
sensitivities/tests are explained in the ModelValidation and Verification
section, pages 123 through 125 of the lRP.
b. The AURORA model attempts to find the minimum total cost, which is the
sum of total resource costs and the total wheeling costs over all hours after
accounting for constraints. !n lhe 2021 lRP, the solve representation for the
model was set to "Region" in which each pool (lPC and other Balancing
Authorities) solves its own commitment separately before the dispatch is
solved as a whole system (for the Company, the "whole system" is the
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.3T
Western Electricity Coordinating Council 'WECC") with the commitment
decisions Iocked in place. To summarize, the model optimizes first for each
region and then, based on those initial optimizations, optimizes for the
WECC.
c. The Company is not aware of conflicts in the objective functions that require
resolution.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 38
REQUEST FOR PRODUCTION NO. 23: ln response to Audit Request 4(f)
dated July 7, 2021, the Company responded with several potential ways it will verify that
generation capacity via B2H will be available to meet the Company's capacity deficits
after the line has been built. Please provide an update of the Company's efforts to verify
that generation will be available and provide a timeframe when the Company will begin
securing commitments from generators.
RESPONSE TO REQUEST FOR PRODUCTION NO. 23: The Company
continues to follow other modeling efforts, research, and studies to monitor liquidity and
market sufficiency in the Pacific Nofthwest. The Risk chapter in the 2021 Appendix D:
Transmission Supplement includes data points that address market sufficiency risk.
As noted in the response to Audit Request 4(0 following B2H construction
commitments, the Company intends to begin securing resources to flow across B2H
consistent with the lRP.
The response to this Request is sponsored by Curtis Westhoff, System
Consulting Engineer, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 39
REQUEST FOR PRODUCTION NO. 24: The natural gas forecast used to
develop the IRP has changed (see Case No. IPC-E-21-35). Please explain the
differences in the natural gas forecast and identify potential changes in the results of the
IRP based on these forecast differences.
RESPONSE TO REQUEST FOR PRODUCTION NO. 24: The Company
believes that Staff is asking about the differences between the gas forecast shared with
the IRPAC on March 11, 2021 and the gas forecast that the Company ultimately used in
the 2021 lRP. There are no differences between these forecasts, they are one and the
same (Plafts' Gas Long Term Prices forecast published on March 3,2021).
Regarding the gas forecast that Staff requested in Case No. IPC-E-21-35, which
combines a later Platts'forecast with actual Henry Hub settlements, the Company did
not utilize that forecast in the 2021 IRP and does not speculate how that forecast would
alter the results of the 2021 !RP.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, Idaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.40
REQUEST FOR PRODUCTION NO. 25: Please provide a status update on the
B2H project. ln the update, please identify the risks and probabilities that the
transmission line will not be completed by the timeframe included in the preferred
poftfolio and identify contingencies that the Company has developed if the project is
delayed.
RESPONSE TO REQUEST FOR PRODUCTION NO. 25: The most recent B2H
project updated was provided in Appendix D: Transmission Supplement filed on
February 16,2022. The Company remains focused on a 2026 in-service date and has
not identified a risk probability of not completing the project prior to the summer of 2026,
however, the Company believes it will know whether a delay is probable by mid-2023. lf
a delay were to occur, this would afford the Company three years to determine how to
meet summer 2O26 needs and bridge the gap until a B2H in-service date occurs.
A possible option could include a delay in the exit of Bridger Unit 3, which in the
Preferred Portfolio has been identified for early exit at the end of 2025. While the
Company also plans to exit Valmy Unit 2 at the end of 2025, it is unlikely this exit would
be delayed and additional resources would likely be needed to replace the gap caused
by the exit of Valmy Unit 2 at the end of 2025.
The response to this Request is sponsored by Jared Ellswofth, Transmission,
Distribution & Resource Planning Director, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 41
REQUEST FOR PRODUCTION NO. 26: Please provide a status update on the
Jackpot Solar PPA. In the update, please identify the risks and probabilities that the
project will not be completed and identify contingencies that the Company has
developed if the project is cancelled or delayed.
RESPONSE TO REQUEST FOR PRODUCTION NO. 26: For a status update on
the Jackpot Solar PPA, please see the Company's response to Staff Production
Request No. 8. Based on the latest information from Jackpot Solar, as described in that
response, ldaho Power believes there is a low probability that the project wil! not be
completed or that there will be significant delays in its completion.
ldaho Power anticipates Jackpot Solar coming online on or after December 1,
2022.ldaho Power's lntegrated Resource Plan and current and forthcoming Request for
Proposals (*RFP") assume Jackpot Solar will be online as of the current Scheduled
Commercial Operation Date of December 1, 2022. ldaho Power does not anticipate, in
either its lntegrated Resource Plan or its RFP processes, a situation in which Jackpot
Solar does not come online at all. ldaho Power will continue to monitor the situation to
determine if any additional resource or procurement actions are necessary.
The response to this Request is sponsored by Donovan Walker, Lead Counsel,
ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 42
REQUEST FOR PRODUCTION NO. 27: Please describe how storage costs are
forecasted considering the rapid decrease in utility-scale battery storage costs.
RESPONSE TO REQUEST FOR PRODUCTION NO. 27: Utility-scale battery
storage costs are forecasted primarily using the National Renewable Energy
Laboratory's 2020 Annual Technology Baseline report. For the cost curve used in the
2021 IRP for battery storage, please see table "Supply-Side Resource Escalation
Factors" on pages 44 and 45 of Appendix C: Technical Report.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 43
REQUEST FOR PRODUCTION NO. 28: Please provide a list of all storage
technologies that the Company considered and evaluated, Did the company include
internal historica! storage technology data?
RESPONSE TO REQUEST FOR PRODUCTION NO. 28: The Iist of storage
technologies modeled in the 2021 IRP can be found on page 115 of the IRP report. The
list of storage technologies includes Pumped Hydro, Compressed Air Energy Storage,
and three varieties of Battery Energy Storage based on Li-lon chemistry: four-hour and
eight-hour transmission connected and four-hour dastribution grid connected. ln addition
to these stand-alone technologies, the IRP also modeled solar paired with battery
energy storage, The 2021 IRP did not include internal historical storage technology
data.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 44
REQUEST FOR PRODUCTION NO. 29: Did the Company modeldegradation of
battery storage? lf so, please describe how it was modeled.
RESPONSE TO REQUEST FOR PRODUCTION NO. 29: The Company
originally considered modeling degradation of battery storage, but, after consultation
with multiple battery storage vendors, determined it would be inappropriate. Battery
storage vendors warranty battery installations for the rated capacity. They ensure
installations meet rated capacity by oversizing the installation and performing
augmentation over the life of the battery to maintain the rated capacity.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
]DAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 45
REQUEST FOR PRODUCTION NO. 30: Regarding the statement on page 51 of
the lRP, "The primary source of cost information for the 2021 lRP is the 2020 Annual
Technology Baseline report released by the National Renewable Energy Laboratory
(NREL) in July 2O2O." What other information sources were used or considered?
RESPONSE TO REQUEST FOR PRODUCTION NO. 30: The IRP document
correctly states that the primary source of inputs for cost information for future supply
side resources is provided by NREL's 2020 AnnualTechnology Baseline ("ATB") report
and accompanying data. To supplement and cross-validate some of the extensive data
provided by the ATB report, the Company incorporated data from additional sources.
These sources included peer utility lRPs, such as the 2021 lRPs from PacifiCorp and
Avista, generation developer data, industry analysis from Lazard, and governmental
sources such as the U.S. Energy Information Administration.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 46
REQUEST FOR PRODUCTION NO. 31: Have any candidate distributed storage
project locations been finalized or implemented?
RESPONSE TO REQUEST FOR PRODUCTION NO. 31: The first distributed
storage projects identified in the Preferred Portfolio are scheduled to be installed in Q4
2022 and during Q1-Q2 2023. A total of 11 MW is included at four separate substations
physically located in ldaho (Weiser, Melba, Filer, and Elmore substations). All battery
installations will occur inside the distribution substations and are anticipated to defer
substation transformer upgrades.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMM]SSION STAFF TO IDAHO POWER COMPANY- 47
REQUEST FOR PRODUCTION NO. 32: For the storage capacity included in
the 3 lowest cost portfolios, please provide a breakdown of the types of storage
included (battery, pumped hydro, etc.)
RESPONSE TO REQUEST FOR PRODUCTION NO. 32: AII three of the lowest
cost poftfolios include 4-hour and 8-hour Li-ion battery storage including a mix of
distribution connected, transmission connected, and solar paired systems. No pumped
hydro or other storage technologies were selected. For the total amounts of storage
selected in each portfolio please see Long-Term Capacity Expansion Results on pages
66 through 88 of Appendix C: Technical Repoft of the 2021 lRP.
The response to this Request is sponsored by Jared Hansen, ldaho Power
Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 48
REQUEST FOR PRODUCTION NO. 33: Please provide the amount of the
incrementa! transmission wheeling revenue credit that was included for 82H portfolios
and explain how the amount was developed. AIso, please provide the workpapers with
formulas intact that determined the amount of the credit,
RESPONSE TO REQUEST FOR PRODUCTION NO. 33: B2H was evaluated
similar to other resources in that the Company derived a levelized cost associated with
the project over the project's expected life, A levelized cost of $23.2 million per year was
utilized in the 2021 IRP for B2H. lf wheeling revenue were to be removed from the
levelized cost, the new Ievelized cost would be $44.9 million per year. Therefore, the
levelized payment was reduced by $21.7 million per year due to the incremental
wheef ing revenues. B2H has a 2026 in-service date, therefore, this $21.7 million per
year levelized cost over the 2O21-2O4O IRP planning horizon results in a total NPV
poftfolio cost saving of approximately $149 million.
To determine the incremental revenues, wheeling revenues were estimated
based on the current system to develop a base scenario wheeling revenue amount.
Next, for the B2H scenario, additional costs of the B2H project were added to
transmission rate base ($485 million) and changes in wheeling customer sales were
made by adding the additional sales from BPA (359 MW) and subtracting the loss of
sales from PAC (210 MW).
The difference in wheeling revenue between the base scenario (excluding B2H)
and the B2H scenario determined the level of incremental wheeling revenues
attributable to adding 82H. The Excel workpaper showing the calculation for the
incremental wheeling revenue is provided as a confidential attachment to this request.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 49
The response to this Request is sponsored by John Wondeflich, Finance Team
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO TDAHO POWER COMPANY, SO
REQUEST FOR PRODUCTION NO. 34: Regarding the impact of the additional
resource cost from the 2019 preferred poftfolio included in the 2021 sales and load
forecast, how much would the sales and load forecast change if the additional resource
cost from the 2021 !RP preferred portfolio was used instead of the resource cost from
the 2019 preferred portfolio?
RESPONSE TO REQUEST FOR PRODUCTION NO. 34: The Company has not
performed an analysis of the impact of the 2021 IRP Preferred Portfolio on the sales
and load forecast.
The response to this Request is sponsored by Jordan Prassinos, Load Research
and Forecasting Manager, ldaho Power Company,
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 51
REQUEST FOR PRODUCTION NO. 35: Please provide the tables on pages 66
through 88 in Appendix C of the 2021 IRP in Excelformat.
RESPONSE TO REQUEST FOR PRODUCTION NO. 35: P|ease see the ExceI
attachment for the requested information.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 52
REQUEST FOR PRODUCTION NO. 36: Please provide the total NPV and
annual cosUvalue streams over the 2O-year planning horizon for each of the portfolios
that were run through the portfolio cost analysis. For each of the annual cost and value
streams, please provide an annual breakdown by fixed and variable cost. For fixed cost,
please break down the costs further by fixed O&M and capital cost. For variable cost, at
a minimum, please provide a breakdown by purchased power net power cost ("NPC'),
PURPA NPC, company-generation NPC, surplus sales, demand response ("DR")
incentive payments, third-party transmission, and REC revenue. Please provide in Exce!
format with all formulas enabled.
RESPONSE TO REQUEST FOR PRODUCTION NO. 36: The Company did not
track all costs at the detail requested. Providing this data would be burdensome
because it would require a reconfiguration of the models and a rerun for each portfolio
to output the requested data,
lncluded in the Excel attachment are the cost streams captured in the 2021 IRP
analysis broken out in the detail available.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO ]DAHO POWER COMPANY- 53
REQUEST FOR PRODUCTION NO. 37: For each of the poftfolios that were run
through the portfolio cost analysis, please provide a breakdown of the annual Megawatt-
hours from each of the supply-side resources (including DR) included in the IRP across
the 2O-year planning horizon. Please provide in Excel format with allformulas enabled.
RESPONSE TO REQUEST FOR PRODUCTION NO. 37: Please see the Excel
attachment for the annual Megawatt-hours from each supply-side resource during the
2O-year planning horizon for the portfolios that were run through the poftfolio cost
analysis in the 2021 IRP.
The response to this Request is sponsored by Jared Hansen, Resource Planning
Leader, ldaho Power Company.
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 54
DATED at Boise, ldaho, this 4th day of March 2022.
&-!.ffr*t.-*,
LISA D. NORDSTROM
Attorney for ldaho Power Company
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY. 55
CERTIFICATE OF SERVICE
I HEREBY CERTIFY that on the 4th day of March 2022, I served a true and
correct copy of ldaho Power Company's Response to the First Production Request of
the Commission Staff to ldaho Power Company upon the following named parties by
the method indicated below, and addressed to the following:
Commission Staff
Dayn Hardie
Deputy Attorney Genera!
ldaho Public Utilities Commission
11331 W, Chinden Blvd., Bldg No. B,
Suite 201-A (83714)
PO Box 83720
Boise, lD 83720-0074
Kiki Leslie Tidwell
704 N. River Street, #1
Hailey, !D 83333
lndustria! Customers of ldaho Power
Peter J. Richardson
Richardson Adams, PLLC
515 N.27th Street
P.O. Box 7218
Boise, lD 83702
Dr. Don Reading
6070 Hill Road
Boise, ldaho 83703
ldaho Conservation League
Benjamin J. Otto
Emma E. Sperry
ldaho Conservation League
710 N. 6th Street
Boise, lD 83702
_Hand Delivered
_U.S. Mail
Overnight Mail
_FAX
FTP SiteX Emai!: [e-mail]
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Overnight Mail
_FAX
FTP Site
x Email: ktidwell2022 @qmail.com
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FTP SiteX Email: [e-mail]
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-FAX
FTP SiteXEmail: [e-mail]
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Overnioht Mail
_FAX
FTP SiteXEmail: [e-mail]
esperry@ ida hoconservation. org
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 56
Clean Energy Opportunities
Michael Heckler
Courtney White
Clean Energy Opportunities for ldaho lnc
3778 Plantation River Dr., Suite 102
Boise, lD 83703
Kelsey Jae
Law for Conscious Leadership
920 N. Clover Dr.
Boise, lD 83703
Micron Technology, Inc.
Austin Rueschhoff
Thorvald A. Nelson
Austin W. Jensen
Holland & Hart LLP
555 17th Street, Suite 3200
Denver, CO 80202
Jim Swier
Micron Technology, lnc.
8000 South FederalWay
Boise, lD 83707
STOP B2H Coalition
Jack Van Valkenburgh
Van Valkenburgh Law, PLLC
PO Box 531
Boise, !D 83701
_Hand Delivered
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FTP SiteX Email:
mike@cleanenergyopportunities. com
[e-mail]
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FTP SiteX Email: [e-mail]
tnelson @hollandhart.com
awje n sen @ ho I la n d h a rt, com
[e-mail]
qlqaroanoamari@holla ndhart.com
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FTP SiteX Email:[e-mail]
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FTP SiteX Email:
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY- 57
Jim Kreider
STOP B2H Coalition
60366 Maruin Rd.
La Grande, OR 97850
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FTP SiteX Email: [e-mail]
\to"-t &..-J.
Stacy Gust, Regulatory Administrative
Assistant
IDAHO POWER COMPANY'S RESPONSE TO THE FIRST PRODUCTION REQUEST OF THE
COMMISSION STAFF TO IDAHO POWER COMPANY.5S
BEFORE THE
IDAHO PUBLIC UTILITIES COMMISSION
cAsE NO. IPC-E-21-43
IDAHO POWER COMPANY
REQUEST NO.4a
ATTACHMENT NO. 1
Va riable Energy Resource
(VER) Integration Analysis
ldaho Power Company
December,2020
7 lr/
"/
@ Energy+Envl ronmental Economlcs
Va riable Energy Resource
(VER) Integration Analysis
ldaho Power Company
December 2020
@ 2020 Copyright. All Rights Reserved.
Energy and Environmental Economics, lnc.
tl4 Montgomery Street, Suite 1500
San Francisco, CA 94104
415.391.5100
www.ethree.com
Executive Summary
Energy and Environmental Economics, lnc. (E3) was retained by ldaho Power to
investigate the integration cost of variable energy resources in ldaho Power's
service territory. These costs are incurred due to increased dispatchable unit
cycling (from increased unit stops and starts; increased load following ramping)
and imperfect unit commitment and dispatch (resulting in higher average thermal
unit heat rates and/or lower net market earnings); and, in cases in which
economic VER curtailment is allowed, increased curtailment costs. E3's analysis
calculates both average and incremental integration costs on a S/tvtWh basis,
using the proposed unit additions and retirements to ldaho Powe/s2023 system
as a base case.
The study examines eleven cases of potential future VER builds in ldaho Power
territory. These cases are illustrated below in Table ES1. These include high wind
and high solar builds; cases in which low, average and high annual hydro energy
budgets are simulated; cases in which there is solar plus investment tax credit
(lTC)-enabled storage; cases in which solar can be economically curtailed; and a
case in which a planned unit retirement at the Bridger coal plant is not in effect
in2023. As can be seen in Table ES1, the overall incremental integration costs
were found to range from S0.64/Mwh-S4.55/MWh. Generally, these costs are
lower than those in the 2018 ldaho Power VER lntegration Analysis, although it is
ivlPage
notable that the method of deriving integration costs was substantially different
in the last study.l
Table ES1: Case Description and Results Summary
E3 believes that the integration costs in this study are lowerthan previous studies
primarily due to four factors: 1) Reduced need for modeled ancillary services, 2)
The fact that the remaining 2023 coal fleet is modeled as must-run (i.e. its
commitment decisions are not affected by VER penetration), 3) Access to the
Energy lmbalance Market (ElM) makes it easier to use market transactions to
thttps://docs.idahopower.com/odfs/AboutUs/PlanninpForFuture/wind/VariableEnersvResourcelntelrationAn
alvsis.odf
1 Base 2023 Case Retired 561 728 Normal 0 0 No 0 s 293
2
Base Case + First
BridPer Unit Online Onllne 561 728 Normal 0 0 No 0 s 3.61
3 Hish Solar Retired 561 728 Normal 7lA 0 No 0 s 3.86
4
High Solar, Low
ltudro Retired 561 7aa lil 7e!s 4.S50No0
5 Hirh Wind Retired 561 728 Normal 0 660 No 0 s 0.7,
6
High Solar, High
Wlnd Retired 561 7M 669 3 246728NormalNo0
7
Existing Solar Base
Cae Retired 310 724 Normal o o nleNo0
8
High Solar, High
Hydro Retired 561 728 Hkh 7g 0 No 0 S 4.6s
I
High Solar + 200
MW StoraEe Retired 561 728 Normal 7g 0 No z@ S 0;64
10
High Solar + 400
MW StoraEe Retired 561 728 Normal M 0 No tm s 0.93
11 Curtailable Solar Retired 551 728 Normal 79lil 0 Yes 0 s 3.1!l
New
Solar-
Coupleti
4-hr lr
lon
Battery
8u ilcl
C ase MW)
Fir st
Br idger
Unit
Tolal
Integr ation
Co st
llydr o
Year
AnloLrnt of N ew
VER Added to
trrstrng 2013
Builds
New
20t3
5olar
Brr ild
Proposed
txistlng
20)3
Solar
Capacity
Pr oposed
Ixist[19
2023
Wind
L apacity
NL,w
202 3
Wrnd
Brr ild
( N,r\ry )
Can New
Solar be
Curtarled?
integrate VERS (the EIM was not included in the pradous study) and 4) Allowing
addttionalsystern flexibility, in some cases (e.9. from batteries).
The integration costs calculated in thls curent effort specifically do not eonsider
fue! savlngs or capacfi contributions from, nor do they consider the capital costs
of new VERs. Therefore, this VER ifiegration cost study serv{$ as a valid basis for
calculating integration costs but may not include all economic and operational
factors required to integirte VERs on the ldaho Powersystem.
vllPage
Table of Contents
Executive Summary .....tv
1.1 Motivation and Background........... ...........1
2.1 Calculating VER lntegration Costs 2
2.2 Production Cost Mode|ing............... ..........5
2.3 Reserve Modeling.... ..............8
3 Data Gollection, Processing and Methods ....................10
3.1 PLEXOS Modeling.... ...........10
3.1.1 Load Profiles, VER Profiles and Dispatchable
Generation Fleet
3.1.2 External Market Representation
3.2 RESERVE Modeling....
3.2.1 Derivation ot2023 VER Profiles
3.2.2 Deriving Reserves Components
3.3 Case Matrix
4 Results
4.1 RESERVE Outputs......
4.1.1 Annual Average results
4.1.2 Detailed Reserve Results
10
13
L6
15
18
19
22
22
22
26
314.2 2019 PLEXOS to HistoricalCase Benchmarking......
42
4.4.4 High Solar \Mrth and \Mthout Storage 45
4.4.5 High Must Take Solar and Curtailable Solar Cases....49
5 Discussion 53
5.1 Discussion of Current Study Results 53
5.1.1 Effects of Binding Pmin Constraints on VER lntegration
Costs.......... ............53
5.1.2 High Solar \Mth Storage Cases...... ............ 56
5.2 Comparison to Data in Literature and 2018 ldaho Power VER
Study.......... ....... s9
5.3 Methodological Differences between2020and 2018 ldaho Power
Company Variable Energy Resource Ana|ysis............................. 60
5.3.1 Overview
5.3.2 Reserves
5.3.3 Treatment of Extemal Markets
5.3.4 Multistage vs. Single Stage Mode|..........
6.1 lntegrationCosts..........
7 Appendix 1: Process Document.
4.3 2023 Case Result Summary 33
4.4 System Dispatch Results 35
4.4.1
4.4.2
4.4.3
Existing Solar, 2023 Base Case and Jim Bridger Firct
Unit Online Cases ....................35
High Solar, High \Mnd, and High Solar + Wind Cases3g
High Solar with Low, Average and High Hydro Budgets
50
61
65
65
67
67
68
viii lPage
7.1
7.2
lntroduction .......68
Results Processing. .............74
lntroduction
L Introduction
1.1 Motivation and Background
ln 2019, ldaho Power committed to using 100 percent clean energy by 2045.
While more than 50 percent of ldaho Powe/s annual load was served by clean
resources in 2018 (primarily from hydroelectricity, with some additionalwind and
solar resources), ldaho Power may potentially add significant amounts of variable
energy resources (VERs), such as wind and solar power, to achieve this 2045 goal.
Energy and Environmental Economics (E3) was retained by ldaho Power to
perform a study of the cost of integrating new VERs into tdaho Powe/s system.
ldaho Power has periodically performed these studies and analyses to inform
regulatory proceedings, and to determine integration charges included in Public
Utility Regulatory Policies Act (PURPA) contracts. ldaho Power hired E3 to update
integration costs. E3 conducted this analysis by designing a suite of scenarios that
are relevant to the 2023 timeframe.
The following report details the modeling methodology, data collection and
assumptions, and results from E3's 2020 investigation of VER integration costs for
ldaho Power.
@ 2010 Energy and Environmental Economics, lnc.Page l1l
2020 ldaho Power VER lntegration Study
2 Methodology
2.L Calculating VER lntegration Costs
E3 used five metrics to estimate the total cost of VER integration to ldaho Powe/s
system. These were:
+ Start/Stoo Costs: The costs resulting from changes in unit start and stop
counts due to differing patterns of net load fluctuations caused by higher
VER penetration
+ Ramoinq Costs: The costs resulting from changes in unit ramping due to
differing patterns of net load fluctuations caused by higher VER
penetration
+ lmoerfect Unit Commitment and Dispatch Costs (Fuel Costs): The costs
resuhing from holding a greater amount of committed dispatchable
resources operating at part load and lower efficiency. These resources
operate at part load to provide reserves necessary to manage increased
VER-induced forecast error and subhourly net load variability
+ lmoerfect Unit Commitment and Dispatch Costs (Net lmoort Costs): The
costs resulting from suboptimal market transactions due to holding more
headroom and footroom on generators to account for increased VER-
induced forecast error and subhourly net load variability
+ Curtailment Costs: ln all cases, VERs are assumed to be contracted on a
take-or-pay basis (i.e. allVER energy is paid for whether it is consumed or
not). However, in the case in which solar can be economically curtailed,
ldaho Power would incur a cost from no longer generating a renewable
Page l2l
Methodology
energy credit (REC). This REC cost is factored into the integration cost for
that case.
The total VER integration cost for each case is calculated by summing each of
these costs.
To calculate these costs, E3 performed three model runs for each of the eleven
analyzed cases. ln the first model run, E3 ran a 2023 "base case" model that
served as the reference point for each ofthe subsequent investigated cases. The
base case included potential unit additions and retirements, the relevant hydro
budget, as well as projected load growth from 2019 through 2023. Next, E3 ran
an intermediate "perfect foresight" case in which any new VER additions beyond
the 2023 base case have perfect foresight (i.e. no new forecast error reserves are
held vs. the base case), and the new VER profiles are "smoothed" on a subhourly
timescale (i.e. no new regulation reserves are held vs. the base case). This perfect
foresight case is designed specifically to look at the effect of forecast error and
subhourly variability from VERs on integration costs, not factoring in savings from
extra energy provided by new VER additions. Finally, E3 ran a case with higher
VER-induced regulation reserves and higher net load forecast error reserves. The
formulae for calculating integration costs from these three cases are provided
below. ln the formulae, "Case j" refers to an individual case for which E3
calculated the VER integration costs. The "base case" is the 2023 base case
common to all but two of the evaluated cases. The remaining two cases are the
2023 base case and the base case with Bridger Unit l cases. These use the existing
solar case instead ofthe 2023 base case due to the need for an incremental VER
build to assess the integration costs in the equations provided below. The
resulting Total lntegration Costs pursuant to this study are calculated in units of
@ 2010 Energy and Environmental Economics, lnc.Page l3l
2020 ldaho Power vER lntegration Study
S/MWh. The graphical depiction of this three-part process is also shown below in
Figure 1.
Incremental Start Costs f or Case I
= Ent u^is, Start Costuris i * (Atmttal Start Coutttunit t,case j
- Anrual Start Countunttt,Base casel
IncrementalRamplng Costs f or Case J
= Eeuu,.ft, Rartptng Costunlsl
* (Crmrulative RTS MW Rampingunitt,case J
- Cumilative RTS MW Rampirtguniti,aasecase)
Intemental Impuf ect Unlt Commltment & Dispatch Cost f or Case j
= Enu u^ft, Fuel Costunis, * (Fuel U s€un1g 1,6ass i
- Fuel U seunis i,"p.rf.ct Foresighf casel) * (N et Import Costsas. i
- Net lrryort Cost"psTlsss Foresight" case J)
IncrementalCurtatlment Costs f or Case I
= Eaturist Curtatlment Costunisl
* (Cumtlative RTS MW Curtatlmentunlsl,ssss i
- Cumulative RTS MW Curtatlmenturl1t,"perf ect Forestght" case J)
T otal Inte gr atton C o st 6r.,6*" 1(Inc.Start Cost,sas.l * Inc.Ramptng Cost,s*. i *
= Incremetnal Imperf ect llnit Commitnent ond Dispatch Cost,srre 1 * Inc,Curt.Cost,sas. i)
Page l4l
Methodology
T ot. Inte gr atlon C o st 6",s*" 1 =
(Inc.Stmt Cost,s*.1 * Inc.Ro.ntping Cost,6*" i *
Inc.Imperf ect Unit Comm.ond Disp.Cost.sas. i
VER Energy Potential,case i -VER Energy Potential,Base case
Figure 1: VER lntegration Cost Calculation Methodology
3:l*I:
r'*
l'*I:
I*
J:l*"
+T A.lt/EndP.,tcft!.ail
t HouolDry ll(irof O.,
,
AdlrEist
tsD..HFolrilr
lloor of Dry
This methodology for deriving VER integration costs does not calculate various
costs and benefits from the VER additions. Additionally, this method does not
calculate fuel cost savings due to VER deployment, nor the capacity value of new
VERs in offsetting the need for firm generation unit additions. This method also
does not calculate capita! or PPA costs associated with contracting new VERs.
Therefore, the future use of these VER integration costs must be done with
knowledge and awareness of what costs and benefits they omit.
I rl,'i,
ii,lr '.lll
i,r: I | 1.,r 1..,
il-r::,' t '
ii..r;l
. i, .ir,
li itii
,.I I
@ 2010 Energy and Environmental Economics, lnc.Page lsl
2020 ldaho Power VER lntegration Study
2.2 Production Cost Modeling
E3 used Energy Exemplar's PLEXOS 7.2 Software2 to calculate the total production
costs associated with each evaluated case. The model uses load, VER, generator,
fuel cost and external market data provided by ldaho Power and other data
sources to calculate annual production costs for ldaho Power under varying
scenarios, which are then used to calculate VER integration costs. This is shown
schematically below in Figure 2.
ln orderto perform this modeling, E3 used a four-stage PLEXOS model. For each
day, the model sequentially solves the day-ahead (DA), hour-ahead (HA), 15-
minute (RT15) and S-minute (RTs) markets. ln each stage, the model is solved
completely (i.e. all units and transmission committed and dispatched). Then, any
unit commitment or other model decisions with a lead time longer than the next
phase's lead time to the real time are passed down to the next stage. ln this
manner, the model approximates the actual unit commitment and dispatch
constraints associated with the longer commitment times of thermal and
transmission markets. This captures the effects of greater average forecast error
and higher average reserves in model stages that are farther from the real time
on the ability of ldaho Power to efficiently commit long start units. This daily
sequential model execution process is depicted in Figure 3.
Page l6 |
Methodology
Figure 2: Using PLEXOS to Glculate VER lntegration Costs
i+
+
r)
+
Figure 3: PLEXOS Multistage Modeling
The change in start/stop cost, and the imperfect unit commitment costs are
calculated endogenously in PLEXOS. However, E3 used data from the 2013
National Renewable Energy Laboratory's (NREL) Western Wind ond Solor
l,L I..r. I,,
lLY:i
?I.EIOO llrulilon
D'aw
nnrE,..a*
-+-rlrI
VER
I ntegration
Cost
E+ErE+ttodollngBcqtrneo
r@
Efrmrltl.rt tlnta'lcfolr @ @@HA
@ 2010 Energy and Environmental Economics, lnc.Page l7l
2020 ldaho Power VER lntegration Study
tntegration Study: Phose 23 to estimate S/MW ramping costs for ldaho Powe/s
thermal units. The annual total ramping costs were calculated as a post-
processing step by calculating the total annual MW of ramping in the RT5 stage
for each thermal unit, and multiplying that by the per MW ramping cost from
NREL. The S/MW values that E3 used are shown in Table 2 below.
Table 2: Ramping Costs Used in Study (Sourced from NRELa)
2.3 Reserve Modeling
E3 used its RESERVE tools to model 2019 and 2023 levels of hourly reserves that
ldaho Power needs to hold in each PLEXOS interval. This is done to account for
the fact that tdaho Power needs to hold reserves to manage net load forecast
error and subhourly net load variations in its daily operations.
ldaho Powe/s participation in the California lndependent System Operator's
(CAISO's) Energy lmbalance Market (ElM) means that ldaho Power holds reserves
of CAISO's Flexible Ramping Producto (FRP). lt must do this so that it can trade in
the RT15 and RT5 EIM markets. Additionally, ldaho Power holds amounts of
regulation reserves and contingency reserves dictated by the North American
3 https ://www. nrel.sovldocs/fv1!lostl/55588.pdf
4 httpE ://www. nr€l.rovldocs/fy1!bstl/55588.odf
t
a
hRampimProduct.asox
Median Ramping Cost ($/IulW)$3 $2 $t
Value Coal Gas GT Gas CCGT
Page l8l
Methodology
Electric Reliability Corporation (NERC) and the Western Electricity Coordinating
Council(WECC).
While the derivation of contingency reserves is standardized (calculated as 3
percent of load and 3 percent of generation total, with at least half held as for
spinning reserves and the rest as non-spinning reserves), ldaho Powe/s future
CAISO FRP and regulation reserve needs are unknown. This is because future VER
additions and load growth will increase the level of net load forecast uncertainty
on ldaho Powe/s system relative to current conditions. Therefore, E3 used its
RESERVE tool along with ldaho Powe/s 2019 forecast and actual load and VER
data to simulate reserves that approximate the CAISO FRP and regulation needs.
E3 also used RESERVE to calculate CAISO FRP and regulation reserves in 2019 to
enable a consistent baseline for model comparisons.
These contingency, CAISO FRP and regulation reserves were input to the PLEXOS
model such that the reserves are held in alltime intervals. Further information on
the derivation of the 2023 load and VER profiles for each analyzed case are
provided in subsequent sections of this report.
@ 2010 Energy and Environmental Economics, lnc.Page lsl
2020 ldaho Power VER lntegration Study
3 Data Collection, Processing
and Methods
3.1 PTEXOS Modeling
3.1.1 LOAD PROFILES, VER PROFITES AND DISPATCHABTE GENERATION
FLEET
E3 collected forecast and actual gross load, wind and solar profiles for 2019 from
ldaho Powerforthe DA, HA, RT15 and RT5 phases. The VER data was on a plant-
level basis and covered most of ldaho Powe/s existing PURPA and ldaho Power-
owned facilities, with only a few smallwind and solar plants omitted from the
data collection process due to their small effect on net load forecast error. ldaho
Power also provided the total 2019 wind and solar nameplate build in ldaho
Power territory so that E3 could use a correct baseline VER build in its analysis.
ldaho Powe/s 2Ot9 average load was 1,980 aMW. To estimate 2023 loads, E3
used load growth projections from ldaho Power to uniformly increase 2019 loads
by approximately 5 percent totalto 2,081 aMW. The method for deriving new
2023 VER profiles is detailed below, but the 2019 historicalVER profiles were used
in all cases to derive the 2023 VER profiles.
ln all cases, E3 modeled existing and proposed solar, solar + storage and wind
plants as qualifoing facilities (QF) operating under PURPA. This means that, under
all circumstances except for one case, these resources are treated as must take
facilities.
Page 110 I
Oata Collection, Processing and Methods
E3 chose to use 2019 load and VER data to derive 2023 load and VER profiles in
order to capture the spatial and temporal correlations between load, wind and
solar production and forecast error, as well as the typical hourly and seasonal
distributions of load, and VER production. Most of ldaho Power's existing solar
capacity is modern, single-axis tracking utility solar, meaning that future
installations were likely to have similar annual capacity factors as existing arrays.
ldaho Power's solar and wind is mostly distributed across the Snake River Plain
and Eastern Oregon, as shown below in Figure 4, because this is where the
majority of existing ldaho Power transmission and load is, and it is also the best
solar resource in ldaho Powe/s service territory. ldaho Power stated that they
are likely to continue to add new VER resources within the Snake River Plain.
Therefore, E3's use of 2019 VER profiles to represent future profiles is reasonable.
ldaho Power proposed that, for the 2023 base case, it was reasonable to assume
that 251 MW of new solar was online in their service territory (131 MW of
unspecified PURPA contracts and 120 MW from the planned Jackpot Solar
facility). ldaho Power also proposed that the 2023 wind capacity remain the same
as that from 2019.
ldaho Power provided detailed information on each of its thermal (coal, natural
gas combustion turbine, natural gas combined rycle and diesel) plants, as well as
its hydroelectric fleet. Unit outages, heat rates, fuel prices and other relevant
data were collected. Coal plants are modeled as must-run units with seasonal
outages for ldaho Powe/s North Valmy Generating Station. Combined Cycle
plants (Langley Gulch) are committed in the hour-ahead timeframe and the gas
combustion turbine fleet has subhourly commitment intervals.
@ 2010 Energy and Environmental Economics, lnc.Page 111 |
2020 ldaho PowerVER lntegration Study
Figure 4: Existing ldaho Power VER Units for which E3 was Provided 2019 DA,
HA, RT15 and RTS Profiles
Given the large share of hydroelectricity on ldaho Power's system, E3 focused on
ensuring proper representation of the hydro fleet's capacity, ramping capability,
daily energy budgets, hourly maximum and minimum power ratings and other
such data. Additionally, E3 considered three hydro years, comprising
representative "low," "averager" and "highr" hydro years. These profiles were
determined by ldaho Power by choosing from historical data. The average daily
enerty profiles for these low, average and high hydro years are shown in Figure
5.
Planned future coal unit retirements through 2023 were modeled per ldaho
Power input. The overall planned change in fleet composition from 2019 to 2023,
as well as the total unit capacities by generation type are provided in Table 3.
Pagel12 l
Data Collection, Processing and Methods
ldaho Power's projected base case load and resource balance is shown below in
Figure 6.
Table 3: 2019 and 2023 Base Case Unit Capacities by Generator and Resource
Type
Boar*ncn
Bridger
Vrlmy
Bennett tountain
Danrkin I
Danskln 2
Dlnrkln 3
l"angley Gulch
ldahoWnd
ldaho Solar
HelbCanyon Complcr
Run-of-River
Coel
Coal
Coal
GarCT
GerCT
GarCT
GarCT
Gar CCGT
Wnd
Solar
lSro
Hydro
0
532
t30
173
180
45
a5
310
729
501
8at
sl0
e0
706
2Gt
173
t80
tts
15
3107n
310
843
530
60
-174
-130
0
0
0
0
0
0
+251
0
0
Figure 5: Daily High, Average and Low Hydro Energy Budget Profiles for ldaho
Power
^ t "'l 1
\
3,L.2 EXTERNAL MARKETREPRESENTATION
ldaho Power was modeled as being able to trade with external electricity markets
at the Palo Verde and Mid C hubs. ln the DA and HA stages of the model, ldaho
201 I
Capacity
(MW}
2023 i
C apac ity lC lra rrqe irr(MW) I CaoacitvUnit Nanre Unrt T e
@ 2010 Energy and Environmental Economics, lnc.Page 113 |
5,000
4.000
3.000
2.000
r,000
E
Bttaa,
Eatxul
2020 ldaho Power VER lntegration Study
Power can make bilateral trades with its neighbors, while incurring a hurdle rate
to do so.
Figure 5: Base Case Load and Resource Balance in ldaho Power through 2030
-lilhdN sdl.t
F oh.a
-Ca3
-&flrHrdo
.--',.. P.f Lad r PRll
E3 determined historical 2019 bilateral energy prices, hurdle rates, and transfer
limits through discussions with ldaho Power. ln the RT15 and RT5 stages, ldaho
Power can trade with its neighbors in a manner consistent with ldaho Powe/s
participation in the CAISO ElM, i.e. there are no hurdle rates, but there are
transfer limits. ln the RT15 and RTs, ldaho Power trades electricity at the RTPD
(RT15) and RTD (RTs) 2019 EIM prices for the DGAP_IPCO_APND node, which is
an aggregated node that averages ldaho Power prices. E3 benchmarked the 2019
DGACP_IPCO_APND node prices versus 2019 nodal prices for the Elkhorn, High
Mesa and Rockland plants and found that the aggregated node provided a
satisfactory representation of these various wind plants.
ln Ql of 2019, there was a natural gas pipeline outage in the Alberta Electricity
System Operator (AESO) service territory, which inflated market prices in the
Pacific Northwest. Accordingly, E3 replaced the Q1 2019 market prices with Q1
0
2txt0
I
NlBz0,tr,2t21M22020
Page l14 l
Data Collection, Processing and Methods
2020 market prices for the DA, HA, RT15 and RTS phases. Additionally, given the
2023 timeframe of the model, E3 used its AURORA Market Price forecasts to
create a month-hourly average basis differential between 2023 and 2019. This
was added to the historical market prices in order to reflect the effect of
anticipated growth of VERs and storage across the Western lnterconnection from
2019 through2023, among other changes.
E3 benchmarked the historical interaction of the Elkhorn, High Mesa and
Rockland wind plants with the ElM. E3 found its representation of ldaho Powe/s
interactions with the EIM to be reasonable.
Finally, E3 combined ldaho Power's multiple hydroelectric projects into two units
for modeling simplicity. One unit consisted of aggregated run-of-river (RoR)
plants, whose output is largely inflexible and in flat hourly blocks, and the other
consisted of the combined Hells Canyon Complex (HCC) units (consisting of the
Oxbow, Brownlee and Hell's Canyon dams), whose output can be varied within
certain time windows. This division of ldaho Powe/s hydroelectric assets into two
aggregated units was done to reflect the variation in flexibility, water storage and
dispatchability across ldaho Powe/s hydro fleet.
Planned future coal unit retirements through 2023 were modeled per ldaho
Power input. The overall planned change in fleet composition from 2019 to2023,
as well as the total unit capacities by generation type are provided in Table 3.
ldaho Powe/s projected base case load and resource balance is shown in Figure
5.
@ 2010 Energy and Environmental Economics, lnc.Page llsl
2020 ldaho PowerVER lntegration Study
3.2 RESERVE Modeling
3.2.I DERIVATION OF 2023 VER PROFITES
As new VER resources are added, the average forecast error and subhourly
variability of the aggregated fleet will decline on a per MW of installed resources
basis. This is due to well-known diversity effects (i.e. as solar and wind plants are
installed at different locations, the average forecast error and subhourly variation
across all units willtend to decline on a per MW basis). Additionally, based on
experience in other jurisdictions, E3 assumed that there will be improvements to
VER forecast error in the future.
ln order to capture these effects while using the 2019 VER data, E3 assessed the
reduction in forecast error and subhourly variability that ldaho Power has
observed to date. A similar approach was taken in ldaho Powe/s 2018 Variable
Energy Resource Analysis. E3 performed this through the following steps
+ Randomly order the forecast and actual profiles for existing wind and
solar that ldaho Power provided to E3
+ Sequentially add solar profiles or wind profiles, each time calculating the
average forecast error and regulation reserves ofthe aggregated solar or
wind profiles using RESERVE
+ Fit a polynomial trend to the average reserves versus the total MW of
online VERs for the solar and wind profiles
Pagellsl
Data Collection, Processing and Methods
+ From prior work in the CAISO Extended Day Ahead Market projectT, E3
assumed a 2 percent per annum improvement in VER forecasting
(average mean average percentage error reduction)
+ For each future VER build, linearly scale up the 2019 VER forecast and
actual profiles by the ratios of future VER build total online MW to 2019
online MW
+ Reduce the forecast error equally in all intervals using the polynomial
trend fit to forecast error data and using the estimated 2 percent per
annum improvement in forecast error from 20t9to2023
+ Reduce the subhourly interval-to-interval variation by the amount
derived from the polynomial trend fit to the regulation error data
+ Run RESERVE for this new set of profiles; and
+ lnput these new set of profiles to PLEXOS
Using this process, the average standalone (i.e. not net-load-based) HA forecast
error reserves and regulation reserves for wind and solar would decline as shown
below in Table 4. These data show the reduction in average forecast error and
regulation needs across all hours of the year, relative to a case with no diversity
benefits or forecast error improvements and the same VER unit additions.
As can be seen in Table 4, E3 projects that regulation reserves will drop more on
a percentage basis than CAISO FRP reserves needs will in the high solar and high
wind cases. This is consistent with the larger percentage increase in solar build
than wind build in the high solar versus high wind cases, respectively.
' https://stakeholdercenter.caiso.com/Stakeholderlnitiatives/Extended-day-ahead-market
@ 2010 Energy and Environmental Economics, lnc.Page 117 |
2020 ldaho Power VER lntegration Study
Table 4: Average Projected lmprovement in Forecast Error and Regulation
Reserves from Diversity and Forecasting lmprovements
3.2.2 DERIVING RESERVES COMFONENTS
The CATSO FRP's reserves for each interval consist of an uncertainty component,
plus a net load change from the previous interval, minus a credit component
based on the lesser of either the EIM-wide average footprint diversity or the
Balancing Authority's (BA) trading position-derived credit. E3 used the
information provided by ldaho Power on forecast and actua! load, wind and solar
to derive uncertainty requirements for the CAISO FRP. Given E3's simplified
representation of ldaho Powe/s external market transactions, E3 assumed that
the credit component of the reserve created a 40 percent reduction versus the
uncertainty component alone. This 40 percent value is an approximate value, and
was calculated using average historically-observed EIM footprint diversity in
Base 2023 Case Solar (251 MW
new solar added to 2019 build)
11.7 o/o 14.2 o/o
Base 2023 Wnd Case (0 MWnew
wind added to 2019 build)
7.8 o/o 0.0 o/o
2023 Hi Solar Case (794 MW new
solar added to 2019 build)
17.2 o/o 31.6 %
13.2o/o 19.1 0/o2023Hi \Mnd Case (669 MWnew
wind added to 2019 build)
Average CAISO FRP
Reserve lmprovement
Average Regulation
Reserve
lm provement
Case
Pagellsl
Data Collection, Processing and Methods
2019.8 This derivation, and its differences from the 2018 ldaho Variable Energy
Resource lntegration Study is further discussed in Section 5.3.2.
3.3 Case Matrix
E3 and ldaho Power worked together to derive a total of eleven 2023 cases to
examine, in addition to a 2019 base case, which are described below. Table 5
details the specifics of each case.
+ Case 1 is the 2023 base case for Cases 3-5 and Cases 8-11, which has
proposed unit additions and retirements and also includes the known
2019 through 2023 load growth
+ Case 2 explores the effect of not retiring one of the Bridger coal plant's
units, but is othenarise identicalto Case 1
+ Case 3 builds on Case 1 by exploring the effect of adding enough new
solar (794 MW of new solar) such that 10 percent of the 2023 ldaho
Power average gross load is provided by this new solar build
+ Case 4 extends the Case 3 analysis to a low, rather than average hydro
year
+ Case 5 builds on Case 1 and explores the integration costs of a high wind
build. Case 5 assumes a new wind build that can supply 10 percent of the
annual 2023 ldaho Power gross load (659 MW of new wind)
+ Case 5 builds on Case 3 and Case 5, including both high solar and high
wind builds (794 MW of new solar and 669 MW of new wind)
8 https://www.westemeim,com/Pages/About/Qua rterlyBenefi ts.aspx
@ 2010 Energy and Environmental Economics, lnc,Page lle I
2020 ldaho Power VER lntegration Study
+ Case 7 is identicalto Case 1, except that none of proposed solar additions
come online from 2019 to 2023, resulting in 251 MW fewer of solar than
Case 1 and lower reserves needs
+ @L extends the Case 3 analysis to a high, rather than average hydro
year
+ Case 9 extends the Case 3 analysis to have 200 MW of 4-hour, Federal
lnvestment Tax Credit (lTC)-enabled Li-lon battery storage
+ Case 10 extends the Case 3 analysis to have 400 MW of 4-hour, ITC-
enabled Li-lon battery storage
+ @!! extends the Case 3 analysis to allow economic curtailment of the
794 MW of new solar resource, while the 551 MW of existing and
proposed solar remain must-take resources
Pagel20 l
Data Collection, Processing and Methods
Table 5: Case Matrix for 2023 Cases
1
2
3
0
04
6
7
08
9
0
10
11
Basc 2023 Casc Rcdrd 561 728 Normal 0 0 l{o
Brtc C!!E r Flrlt
Brldrcr UnltOnltnG orterc s61 728 t{ormd 0 0 Ito
l{ormal 70tHkh Solar Rctlrcd s61 728 o No
High Solar, Low
Hvdro Retlred 561 728 lixf,79(0 No
lllrt Wlnd Rcdred s51 728 t{ormal 0 G6g ttlo
HEh Sobr, HBh
Wnd Redred 561 728 l{ormal ?er 689 No
Exbtlnt Sohr Brse
Cese Rcdred 8lo 728 Normal 0 0 No
Hlgtr Sohr, llfh
llvdro Retlr!d 561 7A ltlh ?tf 0 No
Hlgh Sotar + 200
MW Storarc Radrcd 551 728 Normrl ,94 0 No
Hlth Sohr + tl$
MW Storera Rctlrcd 561 728 Normal 't!r/,,0 No
CurEllablc Solff Retlrcd 551 728 Normal 794 0 Ycs
@ 2010 Energy and Environmental Economics, lnc.Page 121 |
2020 ldaho Power VER lntegration Study
4 Results
The following section provides detailed results from this work. A discussion of the
implications of these detailed results on VER integration in ldaho Power's system
is provided in Section 5.
4,L RESERVE Outputs
4,I.L ANNUAL AVERAGE RESUTTS
The average annual reserves for each of the cases is shown below in Table 6. lt
should be noted that actual reserves vary on an hourly or subhourly basis in all
stages. However, E3 provided these average annual reserves as a general
indicator of how reserves needs change from case to case. These same data are
displayed below for the hour-ahead forecast's CAISO FRP, regulation and
contingency reserves on a percentage of average monthly load basis for each
unique combination of solar and wind in Table 7, Table 8, Table 9, Table 10 and
Table 11. As observed in Table 6, wind reserves have more forecast error (CAISO
FRP reserves), whereas solar reserves have more subhourly variability. This trend,
observed here, is also expressed elsewhere in the lilerature.
Page 122 I
Results
Table 6: Average 2023 Case Reserves Needs
1.2023
Base
Case
728 s61 100 97 40 4',!1U 13 0h 7 o/o
2. Jim
Bridger
Online
728 561 100 97 40 41 1U 13%7 o/o
3. Hi
Solar 728 't3il 't47 142 71 72 1M 17 o/o 11 o/o
1,354 1U 17%11%
4. Hi
Solar,
Low
Hydro
728 '147 142 7'.!72
5. Hi
\ /[nd 1,396 561 152 147 50 52 1M 16%10%
6. Hi
Solar, Hi
\Mnd
1,396 1,354 193 186 79 81 104 19 o/o 't3Yo
7.
Existing
Solar
Case
728 561 87 86 32 33 104 11%6%
8. Hi
Solar, Hi
Hydro 728 1,354 147 142 71 72 1U 17%11%
9. Hi
Solar,
2OO MW
Battery 728 1,354 147 142 71 72 1U 17%11 %
10. Hi
Solar,
4OO MW
Battery 728 1sil 147 142 71 72 1(N 17%11 o/o
't't .
Curtail
Solar
728 1,354 '147 't42 71 72 104 17%11 %
Total
MW
Wind
(MW}
Total
MW
Solar
(MW)
Avg.
RTl 5
FRP
Up
(MW)
Avg.
RT1 5
FRP
Down
(MW)
Avg.
Reg.
Up
(MW)
Avg.
Reg.
Down
(MW)
Avg.
Conting.
Res.
(MW)
Avg.
Total
Res. Up
( P ercen t
of Avg.
Load )
Avg. Total
Reserves
Down
(Percent of
Avg. Load)
Case
@ 2010 Energy and Environmental Economics, lnc.Page 123 |
2020 ldaho Power VER lntegration Study
Table 7: 2023 Monthly Average, Load Normalized CAISO FRP, Regulation and
Reserves, Base 2023 Cases (Case 1 and Case 2)
I
2
3
1
5
6
7
8
9
10
l1
1.016
1.196
2,0t6
3.2t6
3.3*
t.T,t$
1.3t6
1.596
2,OrS
2.396
1.896
l016
3.0t6
3.69(
,.2*
r[.6rG
3.816
2.3tC
1.7t6
1.996
2.816
t.a%
a.0t6
il.616
1.7t5
r.5N
1.796
1.7t6
1.6r
1.516
1.4t6
1.5r
1.E (
1.6X
1.896
1.6t6
1.gtc 3.396 1.6t6
Table 8: 2023 Monthly Average, Load Normalized CAISO FRP, Regulation and
Contingency Reserves, Existing Solar 2023 Case (Case 7)
12.116
11.8ri
14.:tt6
l{.7r
r3.816
13.5t6
tt.ff
11.7I
l3.r[t6
t3.{96
13.196
11.4S
0.9t6
0.9tt6
2.996
2.916
2396
2.51$
1.9f6
2.0t6
2.1X
2.2r
2.8$
0.916
3.0t6
2.7.X
3.1t4
3.6t4
2.grs
2.Ct$
1.sf,
1.5t(
2.8X
2.!n6
2.sta
2.1Yt
8.Zr
8.:lta
8.rta
8.2r
a.2x
8.lt
&2*
8.21t
8.5r4
8.3r
&49a
8.1r
5.89a
5.4r
7.t96
9.616
8.16
5.596
4.5t6
4.996
6.6t6
8J96
7.@t
7,t*
12.916 g"3r2fi 2.6t6 agtt
L
2
3
4
5
6
7
8
9
10
11
L2
71.6%
tL.2%
12.e
13.3%
12,404
72.1%
!0.6%
r0.v
12.3%
12.2%
t2.L%
LO.9%
o.5%
0.5%
1.5%
L.60/0
1.6%
L.4%
T.UA
L.tr/o
t.t%
1.2%
\.2%
05%
2.9%
25%
3.@/o
3.504
2.7%
2.6%
L.4%
L.3%
2.7%
2.8%
7.5%
2.3%
8.2%
8.3%
8.2%
8.2%
8.2%
8.t%
8.2%
8.2%
8s%
8.3%
8.4%
8.!%
5.7%
5.8%
6.2%
A.V/o
7.4%
4.8yo
3.9%
4.1%
5.5%
7.2%
5.7%
6.3%
o.50/o
0.6%
Ls%
L.8%
2.0%
L.O%
o.a%
0.8%
L.Oo/o
13%
7.1%
O.5o/o
2.8%
3.5%
3.U/o
4.6%
3.4%
2.2%
1.7%
7.8%
2.70/6
4,30/o
3.4%
4.L%
t.7%
7,60/0
7,7o/6
7.6%
7.6%
1.6%
1.4%
1.5%
La%
1.6%
1.8%
t.6%
Avs.LL,86%8.2%11% 2.5%s.9%L.7% 3.2% L.6%
Hour Ahead HoLrr Hour llour Ahead lloLrr tlour llour Hour
FRP+Reg.+ AlreadlRP AheadFRP FRP+Reg.i AheadFRP Ah(,adFRP Ahe.rdFRP AheadFRP
Contirgeocy + Reg. + Reg. Cor)trn + Reg. + lteg. + Reg. + Reg.
Headroom, Headroonr, Headroonr, Headfoo[], Iootroor]r, Footroonr, lootroorn, Footroonr,
I otal Solar Wind Load Iotal Solar Wind toad
Month of t oad :'/" of Load oI t oad)), 01 I o.rd)o{i6 of Load of of Lo
Pagel24 l
Resutts
Table 9: 2023 Monthly Average, Load Normalized Regulation Reserves, High
Solar Cases
Table 10: 2023 Monthly Average, Load Normalized Regulation Reserves, High
Wind Case (Case 3)
1
2
3
4
5
6
7
8
9
10
11
1l
I
2
3
4
5
6
7
8
9
t0
11
12
16.0r5 2.2X 5.596
5.1/e
5.816
s.996
8"996
7.2*
4.sr6
3.s96
3.7%
5.316
8,396
7.lX
,.*$
9.996 ].rL_ 92e$_ 1.5t5 _
1.otr
r.1t6
2.t*
3.ax
3.fi
2.gx
t.5ta
t.7x
2.fi
2.1%
2.O%
1.0t6
l.tt6
L,7X
L.7X
1.716
1.6t6
1,6t6
1.4t6
1.5t6
1.8t6
1.616
1.816
rA!8.
t(.g*
Itt.Ota
19.5r
20.5t6
79.Ot6
t7,lr"6
ts.l,$
15.2t6
t7.t*
17.$6
15.5r
13.1t6
2,5r
2.ar
',,'rr$
8.216
7.6*
6.416
5,3t6
s.2t6
5.7'6
6.2X
5.a16
2.4x
3.3x
3.3t6
3.5t4
a.1r
3.016
3,4i
r,7L
t.n6
3.rr
3,21h
2.n6
2.6t6
8.rr
8.t9a
&3r
83r
&tra
8.29a
8.29a
&3r
&5r
&3r
&596
8.1t3
7.gt$
8.,'6
13.716
15.616
14.016
&6t6
7.tx
7.Sra
10.89a
12.6t5
r1.59C
10.196
2J*
3,1r
E.r(
&816
t.3t6
4.s93
3.8ta
a.or
5.816
5.2r1
tl.8t6
2.tN
3.4t3
a.o*
3.7'3
5.1r
4,0'6
2.4*
L,lrli
2.8
t.2x
4,8t4
tLSta
s.816
1.8fi
t.1L
r.893
r.7t6
1.7*
r.e'6
t.5t(
1.596
r.9ta
t.7t3
1.8r
r.ila
t6,7rt &3t3S.tlti 3.ort 10.716 5.2t6 ,.71x l.7r
15.596
t4.7%
L7.995
18.896
17.3X
L6.86
1:].516
L3.7X
15.n6
t6.n$
15.39i
Le.?%
1.196
0,9t6
3.096
3.116
3,2t5
2.AX
2.2%
2.216
2,ar$
2.316
2.s96
0.916
6.2l'6
55t6
6.n6
1.4%
5.996
5.3t6
3.296
3.396
5.895
6.1%
s.496
5.395
8.2tr
&3t6
&3r
8"216
&2ra
&1r
8.216
&216
&5t6
&316
8.tlt6
8.1r
8.495
9.696
10.a*
13.!rt6
12.sta
8.1t6
5.4%
6.E $
9.2*
t2.r%
ro996
10.st6
@ 2010 Energy and Environmental EconomicA lnc.Page l2s I
2020 ldaho Power VER lntegration Study
Table 11: 2023 Monthly Averagg load Normalized Regulation Reserves, High
Solar and Wind Case
4.L.2 DETAITED RESERVE RESUTTS
Whife additions of new solar and wind both cause a similar increase in overoge
reserves needs, the hours in which they increase reserves are very different. The
followi ng discussion illustrates these d ifferences.
As observed in Table 5, wind reserves have more forecast error (CAISO FRP
reserves), whereas solar reserves have more subhourly variability. This trend,
observed here, is also expressed elsewhere in the literature.e
Conversely, the incremental FRP needs from adding solar shown in Figure 11
indicate that CAISO FRP reserves increase primarily during solar hours. FRP
reserves do increase at night because caps on the level of uncertainty imposed
httos ://www.nrel.gov/docs/fylllosti/S5588.odf
I
2
?
a
5
6
7
8
9
10
11
17.113
16.rs
22.*
23.7X
lr.9t
NLTX
16.9r3t -o*
20.ofi
20.5t6
l9.at6
r3.9t6
2A,.
23r
7.5*
7.7X
7.6fi
6,4t6
5.3ff
s3t6
5.65
5.8*
s.0,6
2.3t6
6..X
6.lr?,*
7.71
6.rt6
6.2*
3.a$
3.5r
5,1r*
5,rx
s,lri
5,5r
8.2r
aas
8.3ta
E.tta
t.2r
E.2*
8.2*
,.2X
E.5r
8.3r
8.59at1I
10.5t4
tt,7ll
15.6r$.lr
L7.r*
rcat3
8.7t6
9.as
u.9t6
16.ora
t [.5t6
13.8tt
2.6tr
2.96
7.5t6
a.D5
8.1r4
tt.{'6
,.7X
a.116
5.3r
5.'.rx
a.st6
2.5.n
6.rr
7.rr
6.ar
9.Zt
7.7X
a.rt
3.6i3t*
5.7r
8.6fi
E.ri
9.fi
tfi
r.n6
1.7t6
t.7ta
r.7r
t.o3
1.5t6
1.Sta
1.tr
1,616
r.8t6
1.7t6
19.ttt6 &3ta5.:t* 5.8t3 1,!t.3S s.ot6 6.7* t.lx
Pagel26 l
Resuhs
by the CAISO FRP derivationlo (see further discussion in Section 5.3.2) also
increase. Similarly, solar regulation needs increase only during solar hours.
Because reserves can only be provided with dispatchable resources in the PLEXOS
model, it is important to compare the need for reserves with the availability of
dispatchable resources. Figure 13 and Figure 14 show month-hourly average
residual net load, calculated as load minus wind, solar, and RoR hydro forthe High
Solar and High Wind cases. This residual net load is the average load that must be
met by dispatchable resources and imports. lf the need for reserves is greater
than the residual net load, then the model must export power to the market to
be able to serve ldaho Powe/s reserves needs while not violating minimum
generation setpoints for online units. As discussed below, this can result in
exports to the market at a loss.
As can be seen from Figure 13, in the High Solar case, in March, April, May and
October, the residual net load is very low during the midday hours in which there
is high demand on reserves. Alternatively, as can be seen in the high wind case
for Figure 10, the residual net load is significantly higher during those midday
hours, and as shown earlier, average reserves needs are not especially high
midday.
10 See http6://bpmcm.caiso.com/Pases/BPMDetails.aspx?BPM=Martet oercentilooperations for a discussion of
these caps; E3 derives its own caps from P98 and P2 values ofthe seasonal forecast error.
@ 2010 Energy and Environmental Economics, lnc.Page l27 l
2020 ldaho Power VER lntegration Study
Figure 7: Average Month-Hourly CAISO FRR Headroom Needs for Base 2023
Case
Figure 8: Average Month-Hourly Regulation Reserves Headroom Needs lor 2023
Base Case
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Figure 9: High Wind Minus Base Case CAISO FRR Headroom
Dltr*saca H Wrd to EroG CIr;,CllSO fnn fllrdroont (lrlill
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Figure 10: High Wind Minus Base Case Regulation Headroom
Dlfietrnce, Hl Wnd to Base Gaee, Average Re$ladon Headroom (MWl
]burol Deyta ltt ,a t7
@ 2010 Energy and Environmental Economics, lnc.Pagel29 l
2020 ldaho Power VER lntegration Study
Figure 11: High Solar Minus Base Case CAISO FRR Headroom
Dlfference, Hl Solar to Base Case, Average CAISO FRR Headroom (MWl
Hour of Day
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Dlffurence, Hi Solar to Base C.ese, Ayerage Regulatlon Headroom (MW)
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Page l30 l
Resuhs
Figure 13: Residual Net Load, High Solar Case 3
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Figure 14: Residual Net Load, High Wind Case 5
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4.2 2Ot,9 PLEXOS to Historical Case Benchmarking
E3 and ldaho Power performed rigorous benchmarking to ensure that the PLEXOS
model was able to reasonably replicate actual 2019 historical behavior. E3 and
ldaho Power verified that the following were in line with historical 2019 behavior:
.irrarr
{rrrrit!
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3-:5
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@ 2010 Energy and Environmental Economics, lnc.Page 131 |
2020 ldaho PowerVER lntegration Study
+ Hydro and thermal unit flexibility (ramping rate) and dispatch
(distribution of ram ps);
+ Total generation by unit and technology class;
+ Market transaction behavior and external market prices;
+ Average ldaho Power nodalenergy prices;
+ Unit capacities;
+ Unit outages;
+ Number of unit starts; and
+ Average unit marginal operational cost
Particular attention was paid to the HCC to ensure its operation was reasonable.
This was critical because of the large amount of ldaho Powe/s energy from
hydroelectricity in a typical year, as well as the crucial role that this unit has in
providing flexibility. Figure 15 below shows a sample of the verification of the
model wherein actual dispatch of the PLEXOS HCC is shown to be within the daily
maximum and minimum power output ranges, and the dispatch of the HCC
adheres to the input daily hydro budget.
Additionally, after initial results were analyzed, the ldaho Power team thought
that EIM transactions were unrealistically high in the PLEXOS model, given that
the model operates a price taker for market transactions. ln reality, if ldaho
Power made particularly large sales or purchases in the ElM, prices would be
affected. Therefore, E3 and ldaho Power worked together to limit total net sales
and purchases in the EIM to +/- 300 MW in price taker mode. ln instances in which
the model traded between +/- 300 MW up to the line limits in the real time, the
model paid a hurdle rate of S150/MW, which was implemented to approximate
Page l32 l
Results
"price setting" behavior. Overall, there were few hours in which the model
accessed this additional EIM flexibility.
Figure 15: PTEXOS HCC Dispatch vs. Historical Power and Hydro Budget Bounds
Gold
trendline
displays
actual HCC
oulpul
Darly
RnadPmin
Bounde
4.3 2023 Case Result Summary
The lncremental specific integration costs for each of the cases is provided below
in Table 12. These results are discussed in greater detail below in Chapter 5.
ilarli,i-rC(- [jr]rrn [-'rr,l'.,rri] :l .,r.il,;t ',s
lltslot.tL ,l i()r A!'t t;t(iri lir,'(:r() \'t;;tr
@ 2010 Energy and Environmental Economicg lnc.Page 133 |
2020 ldaho Power VER lntegration Study
Table 12: Summary of lncremental VER lntegration Costs
1.2023
Base
Case
-$0.1 5 $0.22 $1.62 $0.00 1.69 $181 577 $2.93
2. Jim
Bridger
Online
-$0.17 $0.37 $1.88 $0.00 $2.08 $1 80 577 $3.61
3. Hi
Solar $0.80 $0.45 $5.78 $0.00 $7.04 $146 1,824 $3.86
4. Hi
Solar,
Low
Hydro
$0.60 $0.53 $7.16 $0.00 $8.29 $172 1,824 $4.55
s. Hi
Wind $0.35 -$0.07 $1.12 $0.00 $1.41 $143 1,823 $0.77
6. Hi
Solar +
Hi Vvind
$1.63 $0.33 $7.01 $0.00 $8.96 $109 3,647 $2.46
nla nla nla nla nla $193
7.
Existing
Solar
Base
0 nla
8. Hi
Solar, Hi
Hydro
$2.41 $0.19 $5.87 $0.00 $8.47 $75 1,823 $4.65
9. Hi
Solar,
2OO MW
Battery
$0.s8 $0.02 $0.56 $0.00 $1.16 $144 1,823 $0.64
't0. Hi
Solar,
4OO MW
Battery
$0.58 -$0.34 $1.46 $0.00 $1.69 $'142 1,823 $0.93
$0.39 $4.31 $0.29
11. Hi
Curtail.
Solar
$0.72 $5.71 $147 1,823 $3.13
ln c.
Ramping
Costs
(s
Millioni
vrl
Total
lnc.
lmperf .
Unit
Commit.
Dispatch
Costs
($Mill./yr)
Total
Curtail.
Costs
(SMillion/
va
Total
lnc.
lnteg rat.
Costs
(SMillion/
v0
Total
Prod uct.
Cost
($Million/
yr)
Total
ln c.
VER
Gen.
(GWh
tvt)
Total
lnc.
Specific
lnteg rat.
Costs
(siMwh)
Case lnc. Start
Costs
(9Million/
yt)
Pagel34 l
Resutts
4.4 System Dispatch Results
ln the following subsections, detailed day plots and other modeling results will be
used to illustrate how the ldaho Power system responds to adding different
capacities and kinds of VERs, and increasing or decreasing system flexibility. To
facilitate this, this study will examine the following case groupings:
+ Existing Solar (Case 7), Base Case (Case 1) and Jim Bridger First Unit Online
(Case 2)
+ High Solar (Case 3), High Wind (Case 5) and High Solar + Wind (Case 6)
+ High Solar with Low (Case 4), Average (Case 3) and High (Case 8) Hydro
Budgets
+ High Solar with (Cases 9 and 10)and without (Case 3) battery storage
+ Hi Solar with (Case 11) and without the ability to economically curtail
solar (Case 3)
4.4.L EXTSTTNG SOLA& 2023 BASE CASE AND JtM BRTDGER FIRST UNrr
ONTINE CASES
This case comparison illustrates the effect of adding successively more VERs, as
well as increasing the aggregate system thermal minimum power level (Pmin).
The salient differences between cases are outlined as follows
+ Total online solar
o Existing Solar (Case 7): 310 MW
o 2023 Base Case (Case 1):561MW
o Jim Bridger Online Case (Case 2): 561 MW
+ Jim Bridger Coal Plant Pmin/Pmax
@ 2010 Energy and Environmental Economics, lnc.Page l3sl
2020 ldaho Power VER lntegration Study
o Existing Solar (Case 7): 89 MW / 533 MW
o 2023 Base Case (Case 1): 89 MW / 533 MW
o Jim Bridger Online Case (Case 2): 118 MW / 707 MW
ln the modeled year of 2023, there will be periods during the daytime in the
spring and fall in which external electricity prices are low or negatively priced.
This is due to the growing penetration of solar across the WECC footprint and the
low net loads during these periods. Figure 16 illustrates the ldaho Power system
operation operating during a day (April 23,20231that exhibits these conditions.
Beginning with the "Existing Solar Case," which models the ldaho Power system
with the 2019 levels of wind and solar, the model will choose to purchase power
from the market rather than generate its own power during these periods. This is
shown by the purchase of electricity 4 am through 8 pm MST in Figure 16.
ln the 2023 base case, 561 MW of solar is assumed to be online, which increases
ldaho Power/s total VER Pmin during midday periods. This decreases ldaho
Powe/s ability to purchase negatively priced electricity from the market. This is
shown in Figure 16, wherein purchases are now only made in the morning and
evening periods.
Per discussions with ldaho Power, the Jim Bridger coal plant is modeled as a must-
run unit. As such, in the first Jim Bridger unit online case, the aggregate thermal
Pmin increases during all hours by 29 MW. Having both more solar and Jim
Bridger's first unit online further increases ldaho Power's aggregate Pmin. ln
Figure 1O this results the model no longer purchasing negatively priced electricity
in the afternoon.
Page l36 l
Resutts
Though not depicted here, during periods of high net load (e.g. during summer
peaking events), the addition of extra solar and the ability to dispatch more power
from Jim Bridger can prove beneficial in reducing system costs by displacing
expensive market purchases and/or natural gas combustion turbine (CI) and/or
combined cycle (CCGT) generation. Per Table 13, as more solar is added, and if a
Jim Bridger unit is not retired, total incremental specific VER integration costs rise
but total production costs fall.
@ 2010 Energy and Environmental Economics, lnc.Page 137 |
2020 ldaho Power VER lntegration Study
Figure 16: Existing Solar vs. 2023 Base Case vs. First Bridger Unit Online Daily
Dispatch Plots
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Page l38 l
Resufts
Table 13: Summary of Results for Existing Solar, Base Case Solar and Jim Bridger
Cases
4.4.2 HI6H SOLAR, HIGH WIND, AND HIGH SOTAR + WIND CASES
This set of cases illustrates the difference in the ease of integrating equivalent
amounts of new YER energy from solar and wind. Additionally, the effects of
combining these solar and wind additions is shown.
The salient differences in VER capacities between these cases are as follows:
+ TotalOnline Solar
o High Solar Case (Case 3): 1,355 MW
o High Wind Case (Case 5):561 MW
o High Solar + High Wind Case (Case 5): 1,355 MW
+ TotalOnline Wind
1.2023
Base
Case
-$0.15 $0.22 $1.62 $0.00 1.69 $181 577 $2.93
2. Jim
Bridger
Online
-$0.17 $0.37 $1.88 $0.00 $2.08 $180 577 $3.61
7.
Existing
Solar
Base
nla nla nla nla nla $193 0 nla
lnc.
Ramping
Costs
($
Million/
Y0
Total
lnc.
lmperf.
Unit
Commit.
&
Oispatch
Costs
($Million
tva
Total
Cu rta il.
Costs
($Million
tvo
Total
lnc.
lntegrat.
Costs
($Million
I vrl
Total
lnc.
VER
Gen.
(GWh
tva
Total
lnc.
Specific
lntegrat.
Costs
($/MWh)
Case Total
Product.
Cost
($Million
I yr)
lnc. Start
Costs
($Million/
vO
@ 2010 Energy and Environmental Economict Inc.Page l39 l
2020 ldaho PowerVER lntegration Study
o High Solar Case (Case 3): 728 MW
o High Wind Case (Case 5): 1,397 MW
o High Solar + High Wind Case (Case 5): 1,397 MW
This case builds on the phenomena observed in Section 4.4.1, wherein adding
more VERs reduces the model's ability to optimally perform market transactions
during low net load, springtime conditions. Figure 17 below depicts the high wind,
high solar, and high solar + high wind cases on the same low net load spring day
lApril27,2O23l.
Starting with the high wind case, one observes that during periods of low net load,
the system is fairly balanced in terms of imports and exports, only exporting to
the low to negatively priced EIM market in the afternoon when wind generation
begins to climb. Additionally, the system is able to provide the required reserves
for carrying wind with only the coal and HCC units. This is due to the relatively
low level of reserves required to integrate wind, as shown in Figure 9 and Figure
10.
ln the high solar case, the increased midday reserves needs shown in Figure 11
and Figure 12 coincide with high solar production. The increase in reserves needs
causes the model to start a CCGT unit, as the reserve can no longer just be
provided with hydro and coal. Bringing the CCGT unit online when there is high
solar production causes the modelto make significant exports to the EIM market
during low and negatively priced hours. This, along with the start costs of the
CCGT, increases the costs of integrating solar relative to the costs of integrating
wind.
Finally, adding both high solar and high wind further exacerbates the issues that
arise during the high solar case. Due to the increase in production of wind during
the afternoon, the model must make further exports to a low and negatively
Page I'Ol
Results
priced market. Additionally, the modelturns on a CT instead of a CCGT to provide
the additional reserves required due to wind and solar.
Figure 17 presents daily operations from the imperfect foresight cases. However,
as described in Section 2.1, the difference in total market transactions and
generator costs for each case are calculated using the difference between each
case's perfect and imperfect foresight cases. Though not shown here, on the day
shown in Figure 17, the model chooses to not start CCGTs or CTs in the respective
high solar and high wind + high solar cases in the perfect foresight cases. This is
due to the lower reserve need of the perfect foresight case.
Table 14: Summary of Results for High Solar, High Wind and High Solar + High
Wind Cases
As shown in Table 14, total incremental VER integration costs are highest in the
high solar + high wind case, followed by the high solar case and the high wind
case. However, the total specific incremental VER integration cost is lower for the
high wind + high solarthan the high solar case because, while the total integration
3. Hi
Solar $0.80 $0.45 $5.78 $0.00 $7.04 $146 1,824 $3.86
5. Hi
Wind $0.35 -$0.07 $1.12 $0.00 $1.41 $143 1,823 $0.77
6. Hi
Solar +
Hi\Mnd
$1.63 $0.33 $7.01 $0.00 $8.96 $109 3,647 $2.46
lnc.
Ramping
Costs
($
Million/
vr)
Total
lnc.
lmperf.
Unit
Comrnit.
&
Dispatch
Costs
($Million/
Yrl
Total
Curtail.
Costs
($Million/
yr)
Total
ln c.
lntegrat.
Costs
($Millioni
yr)
Total
lnc.
VER
Gen.
(GWh
tvi
Total
ln c.
Specific
lntegrat.
Costs
($/Mwh)
Case Total
Product.
Cost
($Million/
vr)
lnc. Start
Costs
($Million/
v0
@ 2010 Energy and Environmental Economics, lnc.Page 141 |
2020 ldaho Power VER lntegration Study
cost rises with morc VERs, there is also more total incrementalVER generation in
the high wind + high solar case versus the high solar case.
Figure 17: High Wnd vs. High Solar vs. High Solar + Hi Wind
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4.4.3 HIGH SOIAR W]TH tOW, AVERAGE AND HIGH HYDRO BUI'GETS
This set of cases compares the effects of varying hydro budgets under high solar
conditions. On a typical year, ldaho Power derives the majorfty of their power
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Page l42 l
Results
from their hydro fleet, but the total annual energy derived from hydro varies
considerably year-to-year. The simulated conditions considered in this set of
cases is depicted below in Figure 18.
Figure 18: Hydro Conditions in Low, Average and High Hydro Cases
ln the model, RoR hydro is treated as an inflexible, must take resource, whereas
HCC is dispatchable. The high hydro budget case capacity factor shown in Figure
18 indicates that both HCC and RoR hydro must operate near their Pmax
throughout the year in order to not violate daily hydro energy budgets, which
greatly reduces hydro system flexibility. tu shown in Figure 15, hydro conditions
are generally highest in the spring due to runofffrom snow melt. Figure 19 below
compares a spring day (April 20,20231in which the combination of low electricity
market prices, hydro availability and VERs interact with one another.
Starting with the high hydro case, the model must sell HCC and RoR output to the
market al! day, due to the high hydro budget. This includes sales during periods
of negative external market prices. Additionally, the model must start a CT to
provide solar reserves during midday. Conversely, during average hydro
conditions, this need to sell to the market at a loss is reduced, and the mode!
shifts HCC production to avoid selling hydro at a loss during the morning. The
model switches from using a CTto a CCGTto provide solar reserves. Finally, during
Run of Rhrer llell's Canyon Complex
1.9t6.1rl s7ral a,l?2rsxl 2J,6 Toxl t160StSl tlrao 92161 6,842
.1i!l,l,,r .rL'rrt
(r '.1" I I
i i,L
@ 2010 Energy and Environmental Economics, lnc.Page l43 l
2020 ldaho Power VER lntegration Study
low hydro conditions, ldaho Powe/s system can buy from the market during
negatively priced hours, but the model must run the CCGT more due to lower
hydro budgets.
Table 15: Summary of Results for High Solar with Low, Average and High Hydro
Budgets Cases
As shown in Table 15, total incremental specific VER integration costs are higher
in both the low and high hydro year cases. Moving from low to high hydro
conditions, market purchases and thermal generation decreases. This causes
production costs to decrease.
3. Hi
Solar $0.80 $0.4s $5.78 $0.00 $7.04 $146 1,824 $3.86
4. Hi
Solar,
Low
Hydro
$0.60 $0.s3 $7.16 $0.00 $8.29 $172 1,824 $4.55
8. Hi
Solar,
Hi
Hydro
$2.41 $0.19 $5.87 $0.00 $8.47 $75 1,823 $4.65
ln c.
Ramp
Costs
($
Million/
vr)
Total
ln c.
lmperf.
Unit
Cornnrit.
Dispatch
Costs
($Million
I vrl
Total
Curtail.
Costs
($Million
lvt)
Total
lnc.
lntegrat.
Costs
($Million
I yrl
Total
lnc.
VER
Gen.
(GWh
ly(l
Total
ln c.
S pec ific
lntegrat.
Costs
($/MWh)
Case lnc. Start
Costs
($Million/
Yt)
Total
P roduct.
Cost
($Million
t yrl
Page l44 l
Results
Figure 19: [ow, Average and High Hydro Case Comparison
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4.4.4 HIGH SOI.AR WITH AND WITHOUT STORAGE
This set of cases compares the cost of integrating solar with and without battery
storage. Because ldaho Power is a vertically integrated utility, there is no ancillary
services market for these PURPA facilities. Therefore, batteries do not provide
reseryes to the ldaho Power system in these cases. Additionally, the model treats
solar + storage systems having |TC-eligible battery storage. Per ITC regulations,
3ixEr*Er-tr-E ,r.P'-38
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if 'I t --';
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HVr irr r
@ 2010 Energy and Environmental Economics, lnc.Page l45 l
2020 ldaho Power VER lntegration Study
this requires storage to charge solely using solar power production. At the time
of this study's completion, compensation rate methodologies had not been
finalized for PURPA solar + battery storage facilities pursuing contracts with ldaho
Power. Thus, the model used a simplified approach of allowingthe batteryto only
discharge between 4 pm and 10 pm daily. However, the model allowed the
battery dispatch to minimize total ldaho Power production costs when during the
permitted charging and discharging periods. Finally, as shown in Table 6, the
reserves needs are modeled as identical in each of these cases.
ln all of these cases, the model uses a high solar build (1,355 MW of total solar),
but only the 794 MW of the solar (i.e. the incremental solar built vs. the 2023
Base Case) is coupled with an ITC-eligible battery. The differences in these cases
are as follows:
+ Total BatteryCapacity
o High Solar Case:0 MW
o High Solar + 200 MW Battery Case: 200 MW, 4-hour (800 MWh)
Li-lon Battery
o High Solar + 400 MW Battery Case:400 MW, 4-hour (1,600 MWh)
Li-lon Battery
As can be seen in Figure 20 and Figure 2L, on a typical medium-load spring day
(5lt0l2023l, the battery is used to move solar energy from morning and evening
solar production hours to increase net sales to the market and reduce ldaho
Power coalgeneration.
Page l45 l
Results
Figure 20: High Solarvs. High Solar + 2(Xl MW Battery Medium Load Spring Day
Cr.t'glODatI{nE
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Figure 21: High Solar vs. High Solar + 400 MW Battery Medium Load Spring Day
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I FEtEfia
--rEllt
cc.rc,oto,E
Hbh 8drf. re grs rd cot bil htrlufie nal arllir3 Dofr t5l.ra
ArfreDt, dqrdurog b adr
hcn a.n daclulrlg ha'ufa[ !n r
@ 2010 Energy and Environmental Economics, lnc.Page l47 l
2020 ldaho Power VER lntegration Study
The average month-hourly dispatch of charging and discharging for the ITC-
eligible storage is depicted in Figure 22. p$ can be seen in each of these figures,
having greater battery capacity does not fundamentally alter when charging and
discharging occur on a given day, or across the year.
Figure 22: Month-Hourly Average Battery Charge and Discharge Power for 200
MW and 400 MW ITC-Eligible Batteries
r
aso
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Table 15 shows the summary of results for these cases. The total production costs
are lowest for the 400 MW battery, increasing in the 200 MW battery case and
further increasing in the no battery cas+es. However, the total specific
integration costs are lowest for the 200 MW battery size. Both storage cases
exhibit dramatically lower VER integration costs than the high solar without
storage case. This is discussed in greater detail in Section 5 of this report.
Page l48 l
Resuhs
Table 15: Summary of Results for High Solar with and without Storage
4.4.5 HIGH MUSTTAKE SOTAR AND CURTAITABTE SOTAR CASES
ldaho Power is not able to perform economic solar curtailment of PURPA
facilities. The high must take solar and high curtailable solar cases were therefore
implemented to show how being able to economically curtail PURPA solar would
change the cost of integrating VERs.
ln the high solar case, the model can only perform reliability-based curtailment,
i.e. the model will curtail VERs only when the alternative is to have unserved
energy or face some other infeasibility. ln the curtailable case, the model may
economically curtail power for the incremental 794 MW of solar installed vs. the
2023 base case. This allows the modelto curtail power to reduce ldaho Powe/s
total production costs. There would be no difference in short-run marginalenergy
3. Hi
Solar $0.80 $0.45 $5.78 $0.00 $7.04 $146 1,824 $3.86
9. Hi
Solar,
200
MW
Battery
$0.58 $0.02 $0.56 $0.00 $1.16 $144 1,823 $0.64
10. Hi
Solar,
400
MW
Battery
$0.58 -$0.34 $1.46 $0.00 $1.69 $142 1,823 $0.93
lnc.
Ramping
Costs
($
Million/
Yr)
Total
ln c.
lmperf.
Unit
Commit.
&
Dispatch
Costs
($Million/
vr)
Total
Curtail.
Costs
($Million/
yr)
Total
ln c.
lntegrat.
Costs
($Million/
vr)
Total
lnc.
VER
Gen.
(GWh
lvrl
Total
lnc.
Specific
lntegrat.
Costs
($/MWh)
Case lnc. Start
Costs
($Million/
yr)
Total
Product.
Cost
(SMillion/
vd
@ 2010 Energy and Environmental Economics, lnc.Page 149 |
2020 ldaho Power VER lntegration Study
costs from economically curtailing PURPA solar, however ldaho Power may have
to pay for the lost renewable energy credit (REC) due to curtailing solar.
Therefore, the model assumes a S2O/MWh curtailment penalty, which is a typical
REC price in WECC. Similarly to the solar with storage cases, the VER reserves
needs are modeled as identical between the must take and curtailable cases.
Figure 23 and Figure 24 respectively show the difference between the must take
and curtailable cases on a low net load spring daV @12U2O23)and a high net load
summer daV 17l2tl2o23). ln Figure 23, the modelchooses to curtail power both
when the external market price is below the curtailment penalty (i.e. below
-S20/MWh), as wel! as during the middle of the day. The modelchooses to curtail
power midday because, while the market price is not below -S20/MWh, the
model performs reliability curtailment of solar in the must take case as well. ln
other words, this low net load day requires VER curtailment of some sort. Total
annual curtailment in the curtailable solar case is 3.8% of potential generation for
the 794 MW of new solar. This curtailment is largely confined to spring hours,
when the net load is very low.
Alternatively, Figure 24 shows that the model does not curtail solar when solar
helps reduce total production costs. This is because solar increases net sales to a
high-priced market.
Pagels0 l
Results
Figure 23: High Must Take Solar and High Curtailable Solar, Low Load Day
CrE 3
C*l'l
Or{y rntreirreal6l
sdar nndelsl il!
rudoililble Or lhrs l
Cny. soliir is il(fi.i,rE.rll, irJrlijjl.-hsd durvr l
lir lli6 {iusl tale
lovel
-htnl
tfld
lrlofr
rqn cds-
-36T. , ccgr
-llCC
-RC
-A H
"'.3o1,
--- tod . La 8.5r
---bd rrn!|b
-Clru30
-ECCrccqt
-mC
-CdH
'' tcrt
---ld.I(S*r
-|mld
0!
3:rXrnI'-lHe16Q'sl.-JE
th
a6ts
IE
t5
aors
rD
yp
o
IrotE
@t6
Ds
t
1l@ tro
-Il.{a^ A ^ r-L- flf -.rfr BMl, Rh.(l/lftVt)
Figure 24: High Must Take Solar vs. High Curtailable Solar, High load Day
C.s.3^ at003 +-ooE ,.r-
.E ,.*l.-t r.m$'*-!'*
==;.e
!IacliCI
OE lfi r2s f,o
Cam tl
lrs f,6
No tiurlnrln rcrrl rs crhssrrvr:tj on hiqir clr:rnand
rlayr. lir:r :ar ist. rlrti,rrJy f I rrr, gtrlar is virltt;tl llt,
tlur irrg periods,rl higfr dt:riliind
a500
a,o
Ll0
a.oo
2.500
,.000
r.t0
t.06
gD
oc@
a in strainti
Cudailrrtr:nl 11:serve'i It lrltr,: oi hrt
Eili'l pnces and/of i,,].rlir,-vrele ottre,
@ 2010 Energy and Environmental Economics, lnc.Page ls1 |
2020 ldaho Power VER lntegration Study
Table 17 shows that while the total incremental specific integration cost is lower
in the curtailable solar case than the must take solar case, the total production
costs are essentially identical between the two cases.
Table 17: Summary of Results for High Must Take and Curtailable Solar
3. Hi
Solar $0.80 $0.45 $5.78 $0.00 $7.04 $146 1,824 $3.86
$4.31 $0.29 $5.71 $147 1,823 $3.1 3
11. Hi
Curtail
Solar
$0.72 $0.39
ln c.
Ramping
Costs
(s
Million/
Yd
Total
lnc.
lmperf.
Unit
Comrnit.
&
Dispatch
Costs
($Million/
v4
Total
Curtail.
Costs
($Million/
vr)
Total
lnc.
lntegrat.
Costs
($Million/
vtl
Total
ln c.
VER
Gen.
(GWh
tvil
Total
ln c.
Specific
lnteg rat.
Costs
($/MWh)
Case Total
Product.
Cost
($Million/
Yi
lnc. Start
Costs
($Mrllion/
Y0
Page ls2 l
Discussion
5 Discussion
5.1 Discussion of Current Study Results
E3's results provide several high-level insights about integrating VERs:
+ lntegration costs are driven by the need for procuring system flexibility
on dispatchable generators during periods of low net load
+ lntegrating solar is more expensive that integrating new wind resources
+ VER integration costs can be lowered by adding flexibility to the ldaho
Power system, such as battery storage, allowing economic curtailment
and reducing the must-run thermal Pmin of the system
+ VER integration costs increase during abnorma! hydro conditions (low or
high annual budgets)
+ The integration costs found in this 2020 ldaho Power VER integration
study are lower than the 2018 ldaho Power Variable Energy Resource
Analysis
These results are discussed in more detail below.
5.1.1 EFFECTS OF BINDING PMIN CONSTRAINTS ON VER INTEGRATION
cosTs
As discussed in Section 3.2, as more VERs are added to ldaho Power's system, the
aggregate reserve and flexibility needs tend to increase. Only HCC, coal, CTs and
CCGTs are modeled as eligible to provide reseryes. Because allthese generators
have a non-zero Pmin, the aggregate thermal Pmin grows when more generators
@ 2010 Energy and Environmental Economics, lnc.Page ls3 I
2020 ldaho Power VER lntegration Study
are brought online to provide reserves. ldaho Power has a large penetration of
PURPA VERs, which are treated as must take units by ldaho Power. When these
must take resources produce large amounts of power, the net load on ldaho
Powe/s system can fall to very low values. ln order to maintain supply-demand
equilibria on ldaho Powe/s system, ldaho Power must export power to the
market when the aggregate system Pmin, plus the required system footroom, is
greater than the system net load. This is depicted schematically below in Figure
25.
Figure 25: Effects of Additional Solar on Unit Commitment and Market
Transactions
Orr*
----!eege'----- H --
----_!.,!pgg.---II
0
lclr, lrr r' a't.
Lrtlnr,( rrrc rrrr(9
x
During these "binding Pmin" events, exporting power to the market does not by
itself cause VER integration costs to rise. However, due to the growing
penetration of solar across the EIM footprint, 2023 EIM market prices are
projected to be, on average, below typical marginalthermal unit generation costs
during daytime hours in the spring and fall, as shown in Figure 26. These periods
of low EIM prices are also when ldaho Powe/s solar generators will be producing
enough power to significantly lower ldaho Powe/s net load to binding Pmin
No VER -> Iv4trst Take VER
,1,
J,i l.ri , rr
Page ls4 l
Discussion
levels. Therefore, under high solar builds, ldaho Power is often exporting power
at a financial loss to a low- or negative-priced EIM market. At other times, ldaho
Power may have to shift its hydro production to non-optimal hours (e.g. away
from times when hydro could earn the greatest amount of export revenues) in
order to provide sufficient flexibility on HCC while adhering to the HCC daily
energy budget.
Figure 26: Month-Hourly Average2O2S EIM Market Prices
As shown in Section 3.2, in contrast to the High Solar case, in the High Wind case,
the reserves profile is more uniform across time. Additionally, the period of
highest reserves needs do not necessarily coincide with low net loads resulting
from high ldaho Power wind production because ldaho Power wind production
2a1t2325 25 25 2rt 2a 24 23 2lzl212rzazfru
27 2t z$ ao rs zrfi u6 z. z, z zo affi lt33:l3rrlt363125:E53ata!5!1254tg :ta t2 tr 28 25 21 ro282525L13224t922222120202120l!)zo r9 D rg to zr l'frr zlt282?a2t2929ilt?f272$26252t2s26
?ofrfr?5n7a?a?62527,838292726a
71
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:B 30 r E 75 22 17 t!' t6 17 20
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2lut:t aal7
:r4 3l
25 2a 2it 21 23 23 22 19 90 tt lt tt t6 ll l5 ll t7 20
21 20 20 2r a 2r r r za'i I 7 t i E_ r , 9
28 26 26 26 15 25 9 lt 22 15 17 19 17 18 t5 U t5 2l? 2!,28 u 27 ulr*'8.u I22 15 13 rr u !oB 53 it6 tU a7 a5 t2 21' 37 tt t7 33 t6 al t9 $ 33 Al56 sir 5r $ iE t3 A B 22 2a 2a 25 16 27 4t r C ia
a7 a. a3 a !s 29 2t t9 20 m n 2a 2, 26 26 16 Zr 51
2r
B25 l7 25 2. 2a 2? 27 2' A 27 S I ll 'l. !r :r
20 20 20 21 2t 13 28 ,l .l rr EltE* !n r :E8 22 22 21 2a 29 17 tt !O O S I l0 al 16 3at tl 2t z? 21 25 25 ar a2 el tlo /p is * a n
19 rt zo 2r zo 20 u lII 2. 3e 5l 2. zt 2! u.rf,EEG rE. 17 l, uI zl ro u zl zr rr28 26 23 ?3 24 25 25 25 Zr 38 :15 t7 26 3r '0 31
26 2a 2t 25 25 24 21 2' 2t 25 20 t!' A A A E
15 25 2s*22nu27?82:$3r
6OGA5a6:55a!laa16:I, 3t 3t
12 /l2 2315 t9 rE73?{x,tB22?6
Month- Hour Avtrai;r, ?02 1 Rl l ; P' r(. (S,/fvlWh l
f\1onth llolrr Avr'tagu 202 1 RI5 PrrLrr i5ll"l\!h)
@ 2010 Energy and Environmental Economics, lnc.Page lss I
2020 ldaho Power VER lntegration Study
tends to be highest during wintertime evenings. This results in fewer binding Pmin
intervals in the High Wind case that force suboptimal market transactions.
Not retiring a Bridger unit and high hydro conditions increases the cost of
integrating new solar. ln these cases, having higher levels of must run coal or must
take hydro has the effect of decreasing the solar production level at which these
binding Pmin events take place.
As shown in
Table 12, the VER integration costs are typically dominated by the costs of
imperfect unit commitment and dispatch costs. Therefore, the reader can largely
focus on periods in which these binding Pmin events occur when seeking to
understand what drives integration costs for the different cases.
5.L.2 HIGH SOLAR WITH STORAGE CASES
A paradoxicalfinding of this analysis is that the totalspecific integration cost of solar
is lower for the High Solar + 200 MW Battery case than the High Solar + 400 MW
Battery case.
The reason for this is due to the way in which this study calculates VER integration
costs. As discussed in Section 2.1, the VER integration costs are calculated as the
sum of the ramping and start costs, plus the total imperfect unit commitment and
dispatch costs. The total imperfect unit commitment and dispatch cost is calculated
for each case as the difference of production costs for the imperfect foresight and
perfect foresight cases. The only difference between these cases is how much VER
forecast error, subhourly VER variability and reserves are carried for the
Page ls6 l
Discussion
incremental VER build. Due to its greater capacity, the larger 400 MW battery
allows for a greater production cost savings than the 200 MW battery when moving
from the imperfect foresight to the perfect foresight case. This larger savings is
added into the integration cost. Therefore, the apparent integration cost is higher
for the 4OO MW battery than the 2OO MW battery. However, there are limitations
to how this study was able to model a PURPA solar + ITC-enabled solar fleet in
PLEXOS. These limitations are discussed below.
The PLEXOS model used to calculate ldaho Powe/s VER integration costs has
muhiple stages that reflect different levels of uncertainty the DA, HA, RT15, and
RT5 time intervals. Storage dispatch can change between the stages due to
different grid conditions and solar forecasts. lf storage provides more flexibility
ahead of realtime, it can leave real-time dispatch with lower levels of flexibility, or
vice versa. The difference between storage dispatch in perfect and imperfect
foresight cases, propagated through multiple modelingtime horizons, results in the
potential for small, unexpected swings in VER integration costs. Considerations
with respect to storage scheduling include:
+ Storage scheduling between different commitment timeframes will
evolve as more storage is deployed. Currently, there is not a standard
practice for battery storage scheduling
+ The scheduling of PURPA-contracted storage over multiple timeframes is
especially uncertain given the lack of experience with this type of
resource
@ 2010 Energy and Environmental Economics, lnc.Page lsTl
2020 ldaho Power VER lntegration Study
+ The scheduling of PURPA-contracted storage in a perfect foresight
counterfactualwill never be known with any precision because grids are
not operated with perfect foresight.
The impact of storage sizing on unit commitment may be non-linear - a bigger
battery may cause a large ldaho Power unit to alter its commitment schedule
whereas a small battery would not be able to cause as big of an impact.
Additionally, the interaction between storage dispatch and ldaho Power market
revenues can create significant swings in the VER integration cost. The extent to
which ldaho Power has control over PURPA-contracted battery operations can
impact market revenues, especially during periods of extreme EIM prices.
The considerations above imply that there is uncertainty around future PURPA-
contracted storage dispatch and VER integration costs. E3 has included many of
the relevant dynamics in the PLEXOS model, and believes that the two integration
cost calculations for storage are within reasonable bounds of error given what is
known currently about PURPA-contracted storage. However, E3 believes it is
appropriate to use the results from these two cases to derive an overoge solar +
storage VER integration cost, ratherthan assign discrete values to dffierent storage
capacities.
Pagelssl
Discussion
5.2 Comparison to Data in Literature and 2018 ldaho
Power VER Study
ln its Western Wind ond Solor lntegrotion Study: Phose 271, NREL calculated
integration costs for up to 33 percent penetration of wind and solar in the
Western lnterconnection. The summary integration costs by scenario from the
NREL study, the 2018 ldaho Power VER integration study and this study are shown
below in Table 18, in 2020 dollars. Generally, it can be seen that the values from
this study vary considerably more than the values from the NREL study. The NREL
study integrated wind and solar across the Western lnterconnection versus a
small individual balancing area, and did not use the same reserves derivation
process as this study. Modeling the entire Western lnterconnection meant that
NREL did not assess the effects of suboptimal market trades on integration costs
at the interconnection footprint level. Additionally, the greater resource diversity
across the entire Western lnterconnection likely reduces specific VER integration
costs. However, the generaltakeaway from this modeling is that VER integration
costs in the 2018 and 2020 ldaho Power VER integration studies are generally
higher than those from prior NREL work.
11 https://www.n rel.gov/docs/fo 13osty55588. pdf
@ 2010 Energy and Environmental Economics, lnc.Page ls9 l
2020 ldaho Power VER lntegration Study
Table 18: Comparison ol2020ldaho Power VER Study Results to Other VER
lntegration Cost Results
5.3 Methodological Differences between 2020 and
2018 ldaho Power Company Variable Energy
Resource Analysis
5.3.1 OVERVIEW
The incremental integration costs shown in this study are lower than those from
the 2018 ldaho Variable Energy Resource Analysis. While it was not in scope for
E3 to perform a detailed analysis of the 2018 study and how its methodology
differed from that of this analysis, several things stand out as important
differences between the two studies.
NREL High \Mnd 33 o/o $0.25-0.75
NREL High Solar 33 o/o $0.22-0.56
NREL Mixed Resources 33 o/o $0.16-0.43
28 o/o $3.864.652020 ldaho Power VER Study High Solar Cases
(no storage or curtailment allowed)
2020 ldaho Power VER Study High \lVind Case 28 o/o $0.77
2020 ldaho Power VER Study High Wnd and
Solar Case 38 o/o $2.46
2018 ldaho Power VER Study't,000 MWof
Wnd Case 14 o/o $6.17
Total percent of
Annual Load
Supplied by VERs
(TotalVER
Generation/Gross
Load)
Specific
lntegration
Cost, Low
Bound
(2020$/MWh
vER)
Case
Page l60 l
Discussion
5.3.2 RESERVES
The 2018 study calculates reserves in a very different manner than in the 2020
study. The resulting average reserves levels are higher in the 2018 study than
those investigated in the 2020 study. The 2020 study includes CAISO FRP reserves,
regulation reserves and contingency reserves. The 2018 study included regulation
reserves and contingency reserves, but the regulation reserves were calculated
differently.
ln the 2020 study, to derive the CAISO FRP reserves, E3 used a method that
approximates the method used to derive the CAISO FRP within reasonable
bounds.12 The CAISO FRP has RT15 and RT5 stages. For the RT15 stage, E3
calculated the uncertainty component of the FRP using the difference between
2019 HA forecast net load and RTS actual net load. Similarly to CAISO's derivation
methodology, E3 then binned this net load forecast error by month-hour and
used a 95 percent confidence interval (as does CAISO) to determine headroom
and footroom components of the uncertainty reserves. After capping these net
load-based reserves using P98 and P2 values for footroom and headroom,
respectively, E3 assumes a 40 percent diversity credit to reduce the uncertainty
component by the same percentage in all hours, based on historical levels of EIM
footprint diversity. This 40 percent value approximates the caps and "credit"
system that the CAISO FRP uses.l3 Finally, E3 calculates the RT5 CAISO FRP using
12 See, e.g. http://www,caiso.com/lnitiativeDocuments/DMMResourcesufficiencvEvaluationPresentation-
Ene]gvlmbalanceMarketofferRulesTechnicalworkhoo.odf for a description of CAISO FRR components.13 See, e.g. http://wwucaiso.com/lnitiativeDocuments/DMMResourcesutficiencvEvaluationPresentation-
EnersylmbalanceMarketofferRulesTechnicafworkshop.pdf for a dessiption of CAISO FRR components.
@ 2010 Energy and Environmental Economics, lnc.Page 161 |
2020 ldaho PowerVER lntegration Study
historical data derived from the ratio of 2019 CAISO RTs FRP uncertainty reserves
to the 2019 CATSO RT15 FRP uncertainty reserves.la
E3 calculates regulation reserves for the individual load, wind and solar profiles
using a persistence forecast of the S-minute data. Solar data are then binned by
season, hour and percent output, whereas load and wind are binned by percent
of maximum observed load and output, respectively. A 95 percent confidence
interval is then used to derive headroom and footroom needs forthese reserves,
and they are then combined using a root mean square, assuming that the load,
wind and solar regulation components have no covariance on this short
timescale. Finally, spinning contingency reserves are calculated at 3 percent of
load. This results in the average reserves shown below in Table 19.
Table 19: Reserves Summary for Different 2020 ldaho Power VER lntegration
Cost Cases
14 http://oasis.caiso.com/mrioasis/ogon.do
561 100 97 40 4'.|1U 13 o/o 7 o/o
't.2023
Base
Case
728
2. Jim
Bridger
Online
728 561 't00 97 40 41 1U 13 o/o 7 o/o
3. Hi
Solar 728 1,354 '147 142 71 72 104 17 o/o 11 o/o
Total
MW
Online
Wind
(MW)
Total
Online
Solar
(MW)
Avg.
RT1 5
FRP
Up
(MW)
Avg.
RT1 5
FRP
Down
(MW)
Avg
Reg
Up
(MW)
Avg.
Reg.
Down
(MW)
Avq.
Conting
Rcs.
(MW)
Avg.
Total
Res. Up
(Percent
of Avg.
Load)
Avg.
Total
Reserves
Down
( Perc e nt
of Avg.
Load )
Case
Page l62 l
Discussion
ln the 2018 study, ldaho Power calculated the regulation reserves using 2HA
forecasted wind and load, and l-minute actual wind and load data. These data
were then binned by percentage of wind output or maximum load. lt is not clear
from the study if confidence intervals are subsequently applied to this data, but
the resulting reserves, as a percentage of normalized load, are shown below as
Table 20 and Table 21. Spinning reserves are calculated as 3 % of the hourly load,
which is identicalto the method E3 used.
4. Hi
Solar,
Low
Hydro
728 1,354 147 142 71 72 104 17 o/o 11 o/o
5. Hi
Wind 1,396 561 '152 147 50 52 104 16 o/o 10 o/o
6. Hi
Solar,
HiWnd
1,396 1,354 193 186 79 81 104 19 o/o 13 o/o
7.
Existing
Solar
Base
Case
728 561 87 86 32 33 104 11o/o 60/o
8. Hi
Solar,
Hi
Hydro
728 1,354 147 142 71 72 104 17 o/o 11 o/o
9. Hi
Solar,
200
MW
Battery
728 1,354 147 142 71 72 104 17 o/o 11 o/o
10. Hi
Solar,
400
MW
Battery
728 1,354 147 142 71 72 104 17 o/o '11 0/o
11. Hi
Curtail.
Solar
728 1,354 147 142 71 72 104 17 o/o '11 0/o
@ 2010 Energy and Environmental Economics, lnc.Page 153 |
2020 ldaho Power VER lntegration Study
Table 20: 2018 Idaho Power VER lntegration Study Wind Reserves
As shown in Table 20 and Table 21, the 2018 study had much higher reserves than
the 2020 study, particularly for VERs. This likely results in higher costs for
integrating VERs in the 2018 study, due to the high reserves levels causing more
binding Pmin constraints for a given VER penetration level.
Table 21: 2018 ldaho Power VER lntegration Study Load Reserves
E3 believes that its 2020 reserve derivation methodology is closer to standard
practice than the method used in the 2018 study. There was negligible observed
unserved energy in E3's models. Similar normalized levels of reserves (MW per
Vvlnd
Ouartiled
Forec.
Outsut
Reg
UPo/o
ofAvS
Wnd
Forec.
Reg Doivn
o/o of Avg
(Namdate
- Forec.)
Reg
up oh
ofAvg
\Mnd
Forec.
Rog Dofln
0/6 of Avg
(Namplate
- Forec.)
ReS Up
o/o d
AW
Wnd
Fore.
Reg Dofln
0/6 dAW
(Namplate
- Forec.)
Reg Up
Vod
AW
\Mnd
For6c.
Reg Dorvn
9o of Avg
(Namdate
- Forec.)
,|1000h 28 o/o 1000 62%100 %48 o/o 100 o/o 66 06
2.86 o/o 51 o/o 94 0A 79 Yo 93 7o 75 o/o 80 o/o 65%
71 Vo3.55%65%81%68%85%76 o/o 75 o/o
U o/o 43Yo 69%59%82%39%43 o/o4.49%
Reg
DoYvn%d
AW
Load
UpReg
%of
Avg
Load
Reg
Dorn Yo
ofAW
Load
Reg
Doiln%
ofAW
Load
Load Quartile
of Forccast
Maximum
ReS Up
o/o d
AW
Load
ReS Up%d
AW
Load
Reg
DoYvn
%d
AW
Load
Reg Up
Yod
AW
Load
4.9 o/o 9.1 olo 8.1 0h 10.5%7.9 Yo 11.5%8.0 %10.6 %1
2.9.3 %6.8 %6.8 %11.3%8.1 0/o 6.0 %7.5%8.9 %
3.9.5 %5.8 %9.9 %6.7 %9.7 %9.8 olo 9.9 %8.5 olo
4.7.9%6.9 %8.3 %7.0%6.2o/o 13.3 Yo 7.3%7.1 Yo
Spring Sumnrer FallWinter
Pagel64 l
Discussion
MW of installed VERs) and confidence intervals of historical forecast error have
been used elsewhere.ls 16 17
ln both the 2018 study and the 2020 study, there were a significant number of
hours in which the AURORA and PLEXOS models were unable to hold sufficient
reserves to meet the requirements outlined above. ln the PLEXOS model, the
reserve violation penalties were set up such that regulation reserves were
typically not met whereas CAISO FRP reserves and contingency reserves were
nearly always met.
5.3.3 TREATMENT OF EXTERNAL MARKETS
The 2020 study is modeled with an EIM market, whereas the 2018 study is not.
Because ldaho Power joined the EIM in Q2 2018, this omission was reasonable in
the 2018 study. ln the 2020 study, the presence of the EIM market allows the
model to balance forecast error from the DA and HA intervals to the real time.
The 2018 model had less flexibility in its ability to trade, which likely reduces the
ability of ldaho Powe/s system to buy and sell from the market to enable
procuring reserves relative to a scenario with the ElM.
5.3.4 MULTISTAGE VS. SINGLE STAGE MODET
The 2020 study used a multistage PLEXOS model, which contains information
about typical net load forecast error and subhourly net load variability, whereas
ts Z. Zhou, T. Levin, G. Conzelmann, "survey of U.S. Ancillary Services Markets."
https://publications.anl.gov/anlpubV20l 6 l0l I l242l7.Nf15http://www.ercot.corlicont€nywcm/key_documents_listsll3791819-019_Methodology_for_DetermininlMini
mum_Ancillary_Service_Requirements.pdf
17 http://www.caiso,com/Documents/Addendum-DraftFinalTechnicalAppendix-FlexibleRampingftoductpdf
@ 2010 Energy and Environmental Economics, lnc.Page l6sI
2o2O tdaho Power VER lntegration Study
the 2O18 study used a single hourly stage AURORA model that did not reflect
forecast error. ln executing its multistage PLEXOS model, E3 did not observe
signfficant levels of unsenaed energy. Therefore E3 believes its reserves derivation
method provldes reasonable reserve lerrels.
Page l66 l
Conclusions
5 Conclusions
6.1 lntegration Costs
Overall, it was found that integration costs for new VERs on ldaho Powe/s system
vary from S0.64lMWh up to $4.65/MWh. Generally, solar integration costs are
significantly higher than those for new wind. Adding more must-run resources,
such as hydro operating at very high capacity factors, or keeping must run thermal
units online, increases VER integration costs. lncreasing system flexibility, such as
by pairing solar with dispatchable storage, or by allowing solar to be economically
curtailed, reduces VER integration costs.
Additionally, the VER integration costs found herein are significantly lower than
those from the 2018 ldaho Power VER integration study. This is due to multiple
factors, but likely the single greatest cause is the reduction in growth in reserves
per unit of online wind and solar capacity in the 2020 study versus the 2018 study.
Finally, the results from this study are contingent upon VERs being must take; coal
units being committed as baseload, must run units; maintaining strategies for
deploying ldaho Powe/s HCC hydroelectric resources; storage paired with solar
not being able to provide reserves; and other assumptions about current
practices that may change in the future.
@ 2010 Energy and Environmental Economics, lnc.Pagel6Tl
2020 ldaho Power VER lntegration Study
7 Appendix 1: Process
Document
7.L lntroduction
This Appendix is provided as a guide to further understand how E3 developed its
PLEXOS modelfor this study.
The production cost simulation software, PLEXOS, was used to calculate VER
integration costs in this study. This was done by using PLEXOS to generate the
outputs necessary to derive the VER integration costs: start/stop costs, ramping
cost, imperfect unit commitment and dispatch fuel costs, imperfect unit
commitment and dispatch net import costs and curtailment costs.
To yield results, PLEXOS requires various inputs into E3's four stage model. The
inputs to the PLEXOS model were developed by E3, ldaho Power, and in some
instances in collaboration between ldaho Power and E3. These include:
+ Load Profiles: The 2019 profiles were developed by ldaho Power and E3
and consist of 4 comma separated value (CSV) files to represent load
forecasts at the DA, HA, and RT15 stages with the RTS profile seen as the
actual load profile, and these were scaled to 2023load profiles by E3.
+ Renewable Profiles: Solar and wind profiles were developed by E3 using
ldaho Powe/s data and consist of 4 CSV files to represent generation
forecasts at the DA, HA, and RT15 stages with the RT5 profile seen as the
actual output.
Pagel6sl
Appendix 1: Process Document
+ Hydro Profiles: Daily hydro budgets were created by E3 using ldaho
Powe/s historical hydro data, and Pmax/Pmin levels were derived using
ldaho Power input. These are fed into the model using separate CSVs for
daily HCC maximum power, daily HCC minimum power, daily HCC energy
budget and daily RoR power outputs
+ Generator Characteristics: Generator characteristics were provided by
ldaho Power as E3's part of the data collection process and include
properties such as maximum and minimum capacity, ramp rates, start-up
costs, VO&M, as well as any must-run flags or particular generating
patterns. These are input for each generator using the PLEXOS Ul.
+ Reserve Policies and Profiles: Reserve profiles for the "perfect foresight''
and "imperfect foresight" cases were developed using E3's RESERVE tool,
along with the renewable and load profiles provided by E3. Each case has
its own set of reserve profiles, which are in the form of CSVs read in for
the flexible ramping requirement and the regulation needs. Contingency
reserves are enforced within the PLEXOS Ul.
+ Topology and Transmission: The transmission and zonal topology of the
model was created by E3 with input from ldaho Power towards
transmission capacityto the Mid C and PV market nodes. These limits and
the topology were input to the PLEXOS Ul.
+ Markets: Market transaction limits were provided by ldaho Power for the
two markets nodes, Mid C and PV, represented within this model.
Fonrvard Q2-Q4 2019 and Q1 2020 market prices were provided to E3 by
ldaho Power, and E3 downloaded historicalQ2-Q4 2019 and Q12020 EIM
market prices. These prices are then modified using E3's in-house
AURORA price forecasts to adjust them to 2023 expected market prices.
These adjusted prices are fed into the model using CSVs for each market
and modelstage.
@ 2010 Energy and Environmental Economics, lnc.Page l69 l
2020 ldaho Porer vER lntegration Study
+ Fuel Prloes: Fuel priees were prodded to E3 for each of the generatorc,
and arc enforced within the PI,EXOS Ut.
When running a case within PLEXOS, lt ls important to ensure that the appropriate
reneweble proflles are added as data ffles ln the model. These are found in the
'\Alind Proflles' and 'Solar Proftles' sub$olders within the 'Data' dlrectory and 'Deta
Filed folder lllustrated in Figure 27. In addition, ff need be, updatad reserue
profihs must also be added to the PTEXOS model. These data files are named to
corrcspond with the rehvant case thcy wlll be used for and can be fiound under
the'Reserves ldaho Powe/ subfo er in the'Data'dlrectory and within the'Data
Files' folder. Daily hydro budget proflles can be added or adJusted within the
'Hydro Budgets'subfolder within the 'Data Files'folder.
Figure 27: PtEl(O6 Dffi Dfuuctory
Creatirg a new case or edttlng an existing case's properties can be done wfthin
the PTEXOS Ul's 'Scenarbs' folder seen ln Flgure 28 under 'ldaho Power Core
PagelT0 l
Appendh il Proce$ Doqment
Cases'. Each Scenario represents an individual case. The properties that are
tagged with this case'Scenario'will onfu be used if this case is being run.
Flgure 28: PI,EXOS Sconario Direcory
A speciftc case is only run if the 'Scenario' associated with it is included in the
'Membemhiy' of each monthly stage model (DA, HA, RT15, RT5) and can be
ldentifled as shown in Figure 29. Only one 'ldaho Power Core Cases' 'Scenario'
can be linked to the models at any one time. lf muhide case ScenarioJ are
included in the model 'Memberships', errors may occur while attempting to
execute the full model or may yield incorrect resufts.
O 2010 EneEy and Environmental Econornks, lnc.Page 171 |
2020 lfiho PowervER lnt€gratlon Study
Figure 29: PIEXO,S Membershlp ulew
To derive VER integration costs, the overall PLBOS model is run twke for each
case, once using the perfiect foresight proffles forthe relwant VER resources and
res€n es, and then once using the imperfect foresight resen e and VER proftles.
The individual cases are exprersed as indivldual FTEXOS models with custom
modiftcatlons and, in some instances, C,SVflles. The primarydifferenceE between
the cases are described below.
* Case I is the 2023 base case for Cases 3{ and Cases 8-11, which has all
known unit additions and retirements and also includes the known 2019
through 2023 load growth. The Solar and Wind objects are scaled to the
appropriate size for Case 1
PaBe l72 l
Appendix 1: Process Document
+ Case 2 explores the effect of not retiring one of the Bridger coal plant's
units, but is otherwise identical to Case 1. The Bridger coal plant Pmin
and Pmax are increased to reflect this change
+ Case 3 builds on Case 1 by exploring the effect of adding enough new
solar (794 MW of new solar) such that 10 percent of the 2023 tdaho
Power average gross load is provided by this new solar build. This is done
using the existing aggregated solar plant from Case 1
+ Case 4 extends the Case 3 analysis to a low, rather than average hydro
year. The hydro budgets and daily Pmin/Pmax levels are updated using
the CSVs fed into the model
+ Case 5 builds on Case 1 and explores the integration costs of a high wind
build. Case 5 assumes a new wind build that can supply 10 percent of the
annual 2023 ldaho Power gross load (559 MW of new wind). This is
performed using the existing wind object from Case 1
+ Case 5 builds on Case 3 and Case 5, including both high solar and high
wind builds (794 MW of new solar and 659 MW of new wind). This is done
using the existing solar and wind objects from Case 1
+ Case 7 is identicalto Case 1, except that none of proposed solar additions
come online from 2019 to 2023, resulting in 251 MW fewer of solar than
Case 1 and lower reserves needs. This is done using the existing solar
object from Case 1
+ Cases 8 extends the Case 3 analysis to a high, rather than average hydro
year, and as in Case 4, this is accomplished by feeding in different CSVs
to adjust the energy budgets and Pmax/Pmin levels
+ Case 9 builds on Case 3 by adding a 200 MW 4-hour Battery object with
a roundtrip efficiency of 85% and can only charge from the additional 794
MW of new solar
@ 2010 Energy and Environmental Economics, lnc.Page 173 I
2020 ldaho Power VER lnte8ration Study
+ Case 10 adds a 400 MW 4-hour Battery object with an 85% roundtrip
efficienry and is only able to charge from the additional 794 MW of new
solar
+ Case 11 splits the solar object in Case 3 into two distinct generator
objects: an 'ldaho Solar' and 'ldaho Solar Curtailable'. The 'ldaho Sola/
resource is modeled as must-take, while the 'ldaho Solar Curtailable'
object is allowed economically curtail
7.2 Results Processing
The results viewer enables us to display annual PLEXOS ST data in a more user-
friendly format and consists of several different tabs. Below, we explain how to
navigate and manipulate each tab in the order of their use when processing
results:
+ Cover: this tab provides a high-leveloverview of the workbook and is not
of any practical use in processing results
+ Params: The Params tab is used as a library that the embedded excel
macro will read and use to pull outputs from individual properties in the
PLEXOS solutions zip files. The 'ParentClassName' column corresponds to
the tabs within the PLEXOS Ul either'System'or'Simulation' as seen in
Figure 29. The 'ParentName' is the system name within PLEXOS which is
given as 'lPC in this model. 'ChildClassName' is the subfolder name
within any of the 'Production', 'Transmission', 'Generic', 'Data' folders.
For example, 'Generators' or 'Lines'. The 'PropertyName' column is the
name of the propefi to be output to the results viewer. 'ChildName' is
the name of the object that the output property belongs to. lf the
generation of a generator called 'GENI' needed to be brought into the
PagelT4 l
Appendix 1: Proc€ss Document
results viewer then the 'PropertyName' would be 'Generation' and the
'ChildName' would be'GEN1'.
Figure 30: PLEXOS Ul
Sl+{n 'i$ffiruE$,r'*,:lrc.
r lfrqqild[bft
,r I(iqtaditu
D
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rced
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rilffitri:
e flBqsrnr
e aMffir {Erffiits
r fTrarsmffi
D aBsirqip afih&r.r Jr.tu
lf pulling in individual object properties, the 'AggregrationEnum_type'
column by default should be input as 'AggregationEnum_None' and the
'aglcategory' column should be left blank; however if it is more
beneficial to load properties from all objects within a subfolder of the
'ChildClassName'folders such as 'lPC Sola/ as seen in Figure 30, then it is
possible to do this by leaving the 'ChildName' column blank, changing the
'AggregrationEnum_type' column entry to'AggregationEnum_Category',
t, frKsolr
@ 2010 Energy and Environmental Economics, lnc.Page lTsl
2020 l&ho Pouer VER lntegr*ton Study
and changlng the'aggeategorf entry to'lPC Sola/. Flnally, the 'Untts'
column shsuld contain the units of the property that ls being selected.
One should ensure that the properties that are being llsted in the Params
tab in the resulB vienrer are being output by the PLEXOS model. !t ls
possible to verfr and, if need be, add the property to be output as part
of the PTEXOS solution zip file through the PLEXOS Ul. As seen in Fflure
3L Wclickingonthe Simulatlon'tab inthe PLEXOS Uland double clicking
on the obfect withln tho'Reports'subfolder, the'Field Lisf tab willshow
the entlre list of possible outpuB from the model.
Flgure 3l: PlEl(oS RqxrrG
Ensurc th6t thr desirud outpuB hove the'Perlod', or'Flat Flle' bolres
checked. PI"EXOS Help documentation is extrcmeff thorough in providirg
additional detail in understanding the full amsunt of avallable output
propefties. This must be done beforc running the models to ensure that
the sehcted outputs are created in the PLEXOS solution zip ffle.
Controh Onoe the desiled outputs ar€ set in the'Params'tah the resuhs
vhwer cen be run. The 'gontrof tab contains a ftw celb that must be fflhd
Page 176 I
Appendix 1: Process Document
out before running the Macro. The 'Start Solution Month' and 'End
Solution Month' allows the flexibility to run the results viewer for one
month or a set of months if need be, though use caution as the results
viewer capacity factor calculations are set up to calculate over the whole
year so may yield incorrect results if not run over the whole year. ln
addition, ensure that the 'Stage Name'and 'Model Name Constant' inputs
are aligned with the model names as seen in Figure 32, where the 'Stage
Name' is'RealTime5'and the'Model Name Constant'is'lPC. The rest of
the values within the 'Control' tab should not be touched. Ensure
calculations within the workbook are set to manual and then click the 'Do
allthe PLEXOS things NOW!' button to start the results viewer.
Figure 32 PLEXOS Mode! Naming Convention
"$ffi Simulation
t )Etr€ute
r f llodelr
+ TimeSeries Data: Once the results viewer is finished compiling the
PLEXOS outputs these will all appear in the 'TimeSeries Data' tab.
+ Plot: The 'Plot' tab provides dispatch plots, price plots, and market
transaction plots of a user-selected date. The day chosen can be toggled
between any days represented within the output data. The'Plot'tab also
I'
F
IJ
I
@ 2010 Energy and Environmental Economics, lnc.PagelTTl
2020 ldaho Power VER lntegration Study
provides an annual look at capacity factor, cost, generation, number of
starts by generator and provides annual cost and generation figures
associated with market transactions to provide an overall production cost
for the system over the year.
+ Month-Hour Summary: This tab converts the S-minute data within the
'TimeSeries Data'tab to hourly average values which is then used to
create heat maps.
+ Month-Hour: This tab is used as a data visualizing tool to display output
data as month-hour average heat maps. The data being shown in the heat
map can be toggled by the user via the dropdown menu.
+ SummaryAll: The 'SummaryAll' tab offers a quick average value of each
of the properties listed in the 'Params' tab.
+ Hydro Budget: This tab provides information on Hells Canyon Complex
hydro budgets.
+ Conversion: This tab provides conversion figures within the workbook.
Page l78 l
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t.
i:ti:'
I tnputfilc
I Mett-eatuncuon
I ouguttocsvnb
I ouputtowkrpre
2. Function descriotions:
Function: Data
Description: This function reads a CSV file that contains hourly historical values for the
Company's system adjusted load, wind, solar, Run of River (ROR) hydro and
cogeneration and outputs a timetable to the MATLAB workspace.
Function: Load
Description: This function utilizes the timetable created by the "Data" function to create
a net load by taking the system adjusted load and netting it with any non-firm resources
(allwithin the MATLAB workspace).
Function: DRYESS Dispatch
Description: This function takes the previously calculated net load and creates a
dispatch pattern that maximizes net Ioad reduction with given constraints. The given
constraints are variables that can be modified to model a Demand Response (DR)
program or an Energy Storage System (ESS). Constraints that can be varied include:
o Staft date
. End date
o Start hour
o End hour
o Events per week
o Events per season
. Weekday versus weekend
. Holidays
o Group size (MW)
o Energy (MWh) - for ESS only
The algorithm coded in MATLAB sorts the days from high to low based on their net load
peak. Starting on the day with the highest net load, a target for each is set based on the
net load peak and the size of the system. The function first checks if the day is within
the predetermined start and end dates, and then checks whether the day occurs on a
weekend or a holiday. lf the day is within the given constraints, the function iterates over
each hour of the day and compares the net Ioad with the target. lf the net load is above
the target, the function will dispatch the program/resource. For DR, the code dispatches
all MW assigned to that group for that hour. For ESS, the model dispatches the
difference between the net load and the target. The function will continue to iterate over
the remaining hours in each day until it has maxed out the number of events per day for
DR or exhausted the energy available for the ESS. The function willthen move to the
next day and perform the same checks and hourly iterations to create a dispatch
pattern. All the groups are added at the end of the loop and combined into a single load
shape that is written to a CSV file.
Function: DR/ESS Read
Description: This function uses MATLAB to read the load shape created by the
"DR/ESS Dispatch" function and rearranges said load shape data to the proper format
so that it may be included in the net load calculation.
Function: Dates
Description: This function separates the net load data into monthly vectors within the
MATLAB workspace.
Function: Gen Cap
Description: This function reads a CSV file that contains the monthly capacity for each
dispatchable generator and the corresponding Equivalent Forced Outage Rate (EFOR)
values. The function then outputs two MATLAB vectors, one with the monthly capacities
and one with EFORS.
Function: Outage Table
Description: This function takes the monthly capacities and EFOR vectors from the
"Gen Cap" function and creates an outage table for each month in the MATLAB
workspace. The outage table is comprised of the following four components:
Capactty In = Capactty available to serve load (MW)
Capacity Out = Capactty in f orced outage (MW)
Individual Probabtltty = Probabtltty that a specif tc euent will occur
Cumulative Probabtlity = Cumulative distrtbution of the indivtdual probabtlities
Function: LOLP Calculator
Description: This function calculates the Loss of Load Probability (LOLP) for each hour
during the study period. Inputs include the outage table and the net load, and the output
is the corresponding LOLP for the given hour in the MATLAB workspace. The process
is repeated for each hour of the analysis.
Function: Wrapper
Description: This function takes the LOLP from the previous function and calculates
the Loss of Load Expectation (LOLE) in the MATLAB workspace. The function acts as
the "wrapper"; all previously described functions are called within this function.
3. Process inputs:
Function: Data
lnputs: The following are inputs to the "Data" function described in point b) of this
request:
System adjusted Ioad: For Effective Load Carrying Capability (ELCC) calculations,
the system adjusted load is the historical hourly load the
estimated system would have experienced if no DR had
been deployed; the Company's Load Research and
Forecasting group provides this data. For LOLE portfolio
calculations, the forecasted load is used. The forecasted
load is also provided by the Company's Load Research and
Forecasting group.
Wind generation: Hourly time-weighted average wind generation output data
is obtained from Pl Data Historian.
Solar generation: Hourly time-weighted average solar generation output data
is obtained from Pl Data Historian.
ROR hydro generation: Hourly time-weighted average run of river hydro generation
output data is obtained from Pl Data Historian.
Cogeneration: Includes all non-solar and non-wind third party generation
in ldaho Power's system. Hourly time-weighted average
cogeneration output data is obtained from Pl Data
Historian,
Function: Load
lnputs: The "Load" function takes a timetable as an input. This timetable is created by
the "Data" function.
Function: DRYESS Dispatch
lnputs: The "DR/ESS Dispatch" function takes the net load as an input. The net load is
created by the "Load" function.
Function: Dates
lnputs: The "Dates" function takes the net load as an input. The net load is created by
the "Load" function.
Function: Gen Cap
lnputs: The "Gen Cap" function takes the monthly capacity for each dispatchable
generator and the corresponding EFOR values as inputs. The monthly capacities for
existing generation come from the Company's Power Supply team. The corresponding
EFORs also come from the Company's Power Supply team (or from the Generator
Availability Data System (GADS) when the data is not available in-house)
Function: Outage Table
lnputs: The "Outage Table" function takes the monthly capacity and EFOR of the
dispatchable generators. This data comes from the "Gen Cap" function.
Function: LOLP Calculator
lnputs: The "LOLP Calculator" function takes the outage table and the net Ioad as
inputs. The outage table comes from the "Outage Table" function and the net load
comes from the "Load" function.
Function: Wrapper
lnputs: The "Wrapper" function takes the LOLPs as input. The LOLPs come from the
"LOLP Calculator" function.
4. Process outputs:
Function: Data
Outputs: The "Data" function outputs a timetable with the hourly data for system
adjusted load, wind, solar, cogeneration, and run of river hydro.
Function: Load
Outputs: The "Load" function outputs the net load by taking the system adjusted load
and netting it with non-firm resources.
Function: DR/ESS Dispatch
Outputs: The "DR/ESS Dispatch" function outputs an 8760 by 1 vector that contains
hourly DR/ESS dispatch values,
Function: Dates
Outputs: The "Dates" function outputs 12 timetables with the same columns as "Data"
(one timetable for each month in the year).
Function: Gen Cap
Outputs: The output of the "Gen Cap" function is a 12 by n matrix that includes the
monthly capacities, where "?r" is the number of dispatchable resources. The function
also outputs a 1 by n vector with the corresponding EFORs, where "n" is once again
the number of dispatchable resources.
Function: Outage Table
Outputs: The "Outage Table" function outputs an outage table with dimensions 4 by m
where the 4 columns are capacity in, capacity out, individual probability, and cumulative
probability. The number of rows, m, will depend on the number of resources and their
correlating sizes; the number will be 2m ,f all resources are different sizes, and it will be
equal to the binomial probability if all resources are equal sizes.
Function: LOLP Calculator
Ontputs: The "LOLP Calculatof function ouputs the LOLP for each hour of the study
period; if the study period is a year, the function will output 8760 LOLPs.
Function: Wrapper
Oupnts: The "Wrapper" function outputs the LOLE for the study period; if the study
period is one year, the function willoutpnt a singular LOLE value. lf the study period is
20 years, the function willoutput 20 LOLEs (one for each yeafl.
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