CleanPowerSF 2022
Integrated Resource Plan
Preferred Conforming Portfolio Recommendation
October 24, 2022
What is an Integrated Resource Plan
(IRP)?
• An IRP is an energy planning
t
ool to support achieving policy
goals and meeting regulatory
requirements.
• State law requires retail sellers of
el
ectricity to develop an IRP that
evaluates electricity supply and
demand and identifies energy
resource options that can deliver
reliable and cost-effective energy
to customers.
• CCA IRPs are reviewed and
c
ertified by the California Public
Utilities Commission (CPUC),
every two years.
2 Integrated Resource Plan Portfolios
3 Conforming
Portfolio
Uses inputs and
assumptions provided by the
CPUC
Accounts for statewide goals
and targets
Minimal flexibility to
incorporate local
electrification goals and
targets
Alternative
Portfolio
Uses inputs and assumptions
determined by CleanPowerSF
Accounts for local goals and
targets:
Transportation electrification
Building decarbonization
Provides flexibility to
incorporate community
preferences
CleanPowerSF IRP Modeling:
Four Portfolios
4 1. Base Case: CleanPowerSF Goals by 2025
✓ 100% renewable and/or GHG-free by 2025
✓ Local resource prioritization
2. CleanPowerSF Goals and Time Coincidence by 2030
✓ 100% renewable and/or GHG-free by 2025
✓ Resource generation meets customer usage in real time CleanPowerSF analyzed the resource needs to meet both 90% and 95% time coincidence by 2030
✓ No system purchase 5-10pm
✓ Local resource prioritization
3. CleanPowerSF Goals and Climate Action Plan (Mayor’s EV and Building Decarbonization Targets Met)
✓ 100% renewable and/or GHG-free by 2025
✓ Local resource prioritization
✓ Emission-free passenger vehicle trips originating in, ending in, or passing through San Francisco by 2040
✓ Decarbonization of existing buildings by 2040
4. CleanPowerSF Goals and Local Resource Procurement
✓ 100% renewable and/or GHG-free by 2025
✓ Local content floor of 50% by 2030
Local Investment
5 • All portfolios analyzed
include 85.6 MW of
local solar, 150 MW of
local battery storage,
and 50 MW of local
geothermal
• This represents $600-
$700 million
commitment to local
projects*
*Estimated cost of local resource contracts through 2045.
INITIAL RESULTS
COMPARISON OF
CONFORMING PORTFOLIOS
6 Comparison of Conforming Portfolios: Total
Portfolio Capacity by Technology (2035)
7 1084
749
110
73 100
180
Base Case 2035
New Resource Capacity = 742 MW
Total Resource Capacity = 2,296 MW
1684
699
310
200
73 100
180
95% Time Coincident 2035
New Resource Capacity = 1,692 MW
Total Resource Capacity = 3,246 MW
1084
799
210
103
100
180
90% Time Coincident 2035
New Resource Capacity = 922 MW
Total Resource Capacity = 2,476 MW
Comparison of Conforming Portfolios:
Energy Generated by Resource Type (2035)
8 10%
56%
10%
10%
15%
Base Case 2035
6%
58%
12%
12%
6%
7%
95% Time Coincident 2035
9%
50%13%
14%
14%
90% Time Coincident 2035
4,060,895 megawatt-hours (MWh)
6,841,623 megawatt-hours (MWh)
4,517,730 megawatt-hours (MWh)
Comparison of Conforming Portfolios:
Average Portfolio Costs (2023-2035)
9 $-
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
$400,000
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 $2021 Annual Portfolio Cost ($000)
Base Case
95% Time Coincident
90% Time Coincident
CleanPowerSF 10-Year Financial Plan Supply Costs
Comprehensive Modeling Results
10 For more information and comprehensive modeling
results visit cleanpowersf.org/resourceplan :
IRP PORTFOLIO EVALUATION
AND RECOMMENDATION
11 12
Community Engagement & Feedback
Phase 2 Outreach Summary
• Written public comments were accepted online
from Sept 20th to Oct 14th
• 14 comments received using online form
• 2 letters received
• Two virtual community listening sessions held
(9/29, 10/5)
• Comments received expressed a range of
perspectives:
• Emphasis on electric system reliability and
affordability (i.e. rates)
• Interest in understanding the role of distributed
energy resources, including rooftop solar,
energy efficiency and demand-response
• Support for the 90% Time Coincident portfolio
with interest in a higher time coincidence in
future Integrated Resource Plans
Phase 1 (June-July)
• Digital survey
• Community Workshops
• Presentations
Phase 2 (Sept-Oct)
• Modeling Results
• Written Public Comment
• Listening Sessions
Plan Adoption
CleanPowerSF Portfolio Evaluation
13 Lead with Affordable
and Reliable Service
Provide Cleaner
Electricity Alternatives
Invest in Local
Renewable Projects
and Local Jobs
Invest in Local
Renewable Projects
and Local Jobs
While Providing for
Long-Term Rate and
Financial Stability
• Portfolio Cost ($/MWh)
• Portfolio Reliability
• Resource Diversity
• Portfolio Emissions
• Renewable Energy Content
• $ Invested Locally
• MW Developed Locally
• Job Development Potential
• % Long-term Contracted
• Market Exposure (Net Market
Pu
rchases)
Cases13 Base 90% Time Coincident 95% Time Coincident
Lead with Affordable Service
Cost 1 2 3
Reliability 3 2 1
Diversity 3 2 1
Provide Cleaner Energy Alternatives
Emissions Equivalent
Renewable Equivalent
Invest in Local Projects and Jobs
Local
Investment Equivalent
Provide for Long-term Rate and Financial Stability
% Long-term
Energy 1 2 3
Market Exposure 2 1 3
Total 10 9 11
25 MMT Preferred Portfolio Ranking
(1 = best, 3 = worst)
14 Preferred Portfolio Recommendation
Staff recommends the Commission adopt the 90% Time Coincident Portfolio because it best balances CleanPowerSF program goals:
Affordable
• The 90% Time Coincident Case has total portfolio costs comparable to C leanPowerSF’s 10- year financial plan supply costs
Reliable
• The 90% Time Coincident Case exceeds the CPUC- assigned annual reliability target and would improve the program’s ability to serve demand on a real time basis Cleaner
• The 90% Time Coincident Case achieves the City’s 100% renewable goals Supports Local Investment
• The 90% Time Coincident Case includes an amount of local resources that is comparable to t
he other portfolios analyzed
Supports Rate and Financial Stability
• The 90% Time Coincident Case provides long- term rate stability without over-building and creating unreasonable market risk
15 APPENDIX
16 Key IRP Terms and Acronyms
Term (Acronym) Meaning
Analytical Modeling Mathematical technique used for simulating, explaining, and making predictions about a complex system
California Independent
System Operator (CAISO)
Organization that manages California’s bulk electricity grid, transmission lines, and wholesale energy market.
California Public Utilities
Commission (CPUC)
State energy regulatory agency that oversees the IRP process for Investor-Owned Utilities (IOU), Energy Service Providers (ESP) and Community Choice Aggregators (CCA)
Capacity The maximum output that a generator can produce, it is typically expressed in terms of megawatts (MW) or kilowatts (kW)
Capacity Factor A measure of how much energy is produced compared to the resource’s maximum capacity over a set period time, expressed as a percentage Demand The amount of electricity usage met by a retail seller over a given period of time
Energy The ability to do work
Energy Storage A technology which captures energy produced at one time and discharges it for use at a later time
Greenhouse Gas Free
Resources
Electricity produced from generation sources that do not emit carbon dioxide or other greenhouse gas emissions as a by-product of power production, e.g., solar, wind, hydroelectric
17 Key Terms and Acronyms
Term (Acronym) Meaning
Hybrid Resources A generator that consists of two or more paired resource types eg., solar plus battery storage
Integrated Energy Policy
Report (IEPR)
A biennial report issued by the California Energy Commission that contains an assessment of major energy trends and issues facing California’s electricity sector, including the demand forecast used in CleanPowerSF’s Integrated Resource Plan
Integrated Resource Planning A process that evaluates future electricity demand and resource options over a long time horizon, typically 20 years, and optimizes the resource mix that meets set criteria at the lowest cost Investment Tax Credit (ITC) A federal tax credit available to investment in solar power facilities and co-located energy storage facilities
Job-years A job creation metric which is equivalent to one full time job (2,080 working hours) for one year
Load Serving Entity (LSE) A retail seller of electricity Long-duration Energy Storage Battery storage of various technology types which can discharge for 8 hours or more
Megawatt (MW) 1,000,000 watts (a unit of power demand or generating capacity) 18 Key Terms and Acronyms
Term (Acronym) Meaning
MMT CO2 Million metric tons of carbon dioxide Portfolio A collection of power supply resources used to serve electricity demand Preferred Portfolio Of the portfolios modeled, the one which best meets program goals and regulatory requirements that is approved and submitted to the California Public Utilities Commission
Preferred System Plan The portfolio developed by the California Public Utilities Commission with an aggregation of individual IRPs of all retail sellers subject to CPUC jurisdiction that includes the CPUC’s view of the optimal mix of resources for the state over the IRP planning horizon
Reliability Need Share of resource capacity needed to meet the CAISO managed coincident peak assigned to retail sellers by the California Public Utilities Commission
Renewable Portfolio
Standard (RPS)
California State program that requires a minimum amount of electricity in retail seller portfolios to come from eligible renewable energy resources Resource Adequacy (RA) A regulatory program intended to ensure that there are sufficient electricity generating resources to support reliable grid operation under peak demand conditions
19 Key Terms and Acronyms
Term (Acronym) Meaning
Revenue Requirement The total amount of money an electricity provider collects from its customers
RPS-Eligible Resources Solar, wind, geothermal, small hydroelectric, or biopower facilities that are located within the Western Electricity Coordinating Council (WECC) region, which encompasses fourteen western U.S. states and portions of Canada and Mexico. The majority of RPS-eligible electricity currently comes from solar and wind.
Sensitivity Analysis Analysis of the impact to the portfolio caused by a change to a variable in the analytical model
System Power Electric generation supplied by the grid at a particular time that is not associated with a specific generating facility
20 INITIAL RESULTS
MAYOR’S EV AND BUILDING
DECARBONIZATION TARGETS
MET PORTFOLIO
Alternative Portfolios
21 Mayor’s EV and Building Decarbonization
Targets Met Portfolio: Load Assumptions
22 0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Megawatt-hours (MWh)
CPUC Assigned Load Forecast
Mayor's EV and Building Decarbonization Targets Met Load Forecast The annual estimated increase in electricity usage resulting from the Mayor’s EV and Building Decarbonization Targets Met by 2040 averages 30% higher than CleanPowerSF’s assigned load forecast.
Mayor’s EV and Building Decarbonization
Targets Met Portfolio Results: New
Resource Capacity Build (2022-2035)
23 0
200
400
600
800
1000
1200
1400
1600
1800
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Capacity (Megawatts, MW)
Solar Storage Wind Wind Offshore Geothermal
730.6 MW 571.8 MW 200 MW 100 MW 79.3 MW
INITIAL RESULTS
LOCAL RESOURCE
PROCUREMENT PORTFOLIO
Alternative Portfolios
24 Local Resource Assumptions
• Local Resources are defined as
r
esources sited in the nine Bay
Area counties: Alameda, Contra
Costa, Marin, Napa, San
Francisco, San Mateo, Santa
Clara, Solano, and Sonoma.
• To meet the 50% local content
tar
get, the Local Resource
Procurement portfolio includes
995 MW of local resource
capacity by 2035.
25 Local Resource Procurement Portfolio
Results: Portfolio Content
26 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Solar Local Solar Wind
Local Wind Geothermal Local Geothermal
Existing Renewables Hydro CAISO System Power
CA RPS Requirement
The energy supply in the Local Resource Procurement portfolio provides a diverse energy mix, with local solar, wind, and geothermal supplying 50% of total energy supply by 2030.
Local Resource Procurement Portfolio
Results: Annual Portfolio Costs
27 The total projected revenue requirement in 2021 dollars for the Local Resource Procurement portfolio would be $2.75 billion vs. $2.32 billion projected in the 10-year financial plan during the 2023-2032 period (19% increase). $-
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Annual Portfolio Cost ($000)
Local Resource Procurement Forecasted Supply Costs ($2021) CleanPowerSF's 10-Year Financial Plan Supply Costs ($2021)
DEPARTMENT Power Enterprise
AGENDA NO.
14 MEETING DATE
October 24, 2022
APPROVAL:
COMMISSION
SECRETARY
Donna Hood
AGENDA ITEM
Public Utilities Commission
City and County of San Francisco
Approve CleanPowerSF’s 2022 Integrated Resource Plan Compliance Filing for Submission to the California Public Utilities Commission: Regular Calendar
Project Manager: Cheryl Taylor, Operations Manager, CleanPowerSF
Approve CleanPowerSF’s 2022 Integrated Resource Plan Compliance Filing
1 California Public Utilities Commission Decision 18-02-018. CleanPowerSF has filed two previous plans with the CPUC on August 1, 2018 and September 1, 2020.
Summary of
Proposed
Commission Action:
Approve CleanPowerSF’s 2022 Integrated Resource Plan Compliance Filing and authorize the General Manager to submit the final IRP Compliance Filing to the California Public Utilities Commission by the November 1, 2022 deadline.
Background: Senate Bill (SB) 350 (2015) required community choice aggregation (CCA) programs such as CleanPowerSF to prepare and file an Integrated Resource Plan for review by the California Public Utilities Commission (CPUC) (Public Utilities Code Section 454.52). An Integrated Resource Plan examines options for how a retail seller of electricity (retail sellers) optimally and reliably meets its future energy supply needs at the lowest cost while meeting environmental and other goals such as local economic development.
The CPUC requires that retail sellers file individual Integrated Resource Plans every two years.
1 The CPUC adopted requirements for retail sellers’
2022 Integrated Resource Plan filings in Decision 22-02-004, which updated and adopted a greenhouse gas emissions target for the electric sector of 30 million metric tons in 2035 along with a lower 25 million metric tons scenario requirement. Additionally, the CPUC released a series of rulings and documentation (the most recent occurring on June 15, 2022) providing further instructions for how CCAs and other retail sellers should prepare and file their Integrated Resource Plans. This documentation allows CleanPowerSF to submit one Preferred Portfolio to the CPUC if this portfolio meets its proportional share of the 25 million metric tons of greenhouse gas emissions target or less.
Agreement: Approve CleanPowerSF Integrated Resource Plan Commission Meeting Date: October 24, 2022
2 Local resources are those that are located within the nine Bay Area counties. 3 San Francisco’s greenhouse gas emission-free passenger vehicle trips and building decarbonization targets are included in the City’s 2021 Climate Action Plan:
https://sfenvironment.org/sites/default/files/cap_fulldocument_wappendix_web_220124.pdf.
The SFPUC retained a technical consultant to conduct an Integrated Resource Plan analysis and support the development of the report.
CleanPowerSF designed its analysis to help inform procurement decisions and identify resources and actions required to meet state mandates and City and County of San Francisco climate and renewable energy goals for 2035.
The technical consultant used industry standard production cost modeling software to perform this analysis and identify a mix of energy supply resources (power portfolios) that can reliably meet CleanPowerSF’s customer demand and policy objectives. The analysis accounted for CPUC requirements and guidelines for Integrated Resource Plan development.
CleanPowerSF’s analysis examined four different power portfolio scenarios – two Conforming Portfolios and two Alternative Portfolios – to achieve CleanPowerSF’s renewable energy targets and regulatory compliance requirements.
Conforming Portfolios are required to use inputs and assumptions provided by the CPUC with a focus on statewide emissions reductions targets. In these portfolios, for example, the demand forecast cannot be changed to account for local goals and targets. The two Conforming Portfolios analyzed are:
• Base Case: CleanPowerSF retail sales are supplied by 100% renewable and/or GHG-free energy, on an annual basis, by 2025, while prioritizing local resources;
2 and
• Time Coincident Case: CleanPowerSF retail sales are supplied by 100% renewable and/or GHG-free energy, on an annual basis, by 2025 and the generation supply meets a significant amount (>90%) of customer demand on an hour-by-hour basis, while prioritizing local resources.
Alternative Portfolios provide the flexibility to use CleanPowerSF-specific inputs and assumptions. For example, these include the forecasted electricity demand increases resulting from achieving more aggressive local transportation electrification and building decarbonization goals and targets.
The two Alternative Portfolios analyzed are:
• Climate Action Plan Case (also referred to as Mayor’s EV and Building Electrification Targets Met Case): By 2025, CleanPowerSF retail sales are supplied by 100% renewable and/or GHG-free energy, on an annual basis, while prioritizing local resources, and electricity supply meets the forecasted increase in electricity demand resulting from the achievement of San Francisco’s greenhouse gas emission- free passenger vehicle trips and building decarbonization targets; 3 and
• Local Resource Procurement Case: By 2025, CleanPowerSF retail sales are supplied by 100% renewable and/or GHG-free energy, on an Agreement: Approve CleanPowerSF Integrated Resource Plan Commission Meeting Date: October 24, 2022
4 See July 24, 2018 Agenda Item 17, https://sfpuc.org/sites/default/files/about-us/agendas- minutes/SFPUC%20Commission%202018%20July%2024%20Agenda%20Web.pdf [accessed on September 30, 2022] and August 25, 2020 Agenda Item 10, https://sfpuc.org/sites/default/files/about- us/agendas-minutes/Commission_August25- 2020_AgendaWEB.pdf [accessed on September 30, 2022].
5 See Agenda Item No. 7.b., https://sfpuc.org/sites/default/files/September%2013%20Agenda%20WEB.pdf [accessed on September 30, 2022].
6 See Agenda Item No. 9.c., https://sfpuc.org/about-us/boards-commissions-committees/sfpuc-commission/agendas-minutes- resolutions-log. [accessed on October 17, 2022].
annual basis, and by 2030, 50% of CleanPowerSF’s forecasted electricity demand is supplied with local resources.
Each portfolio meets all existing state electricity supply requirements (e.g., California State Renewable Portfolio Standard requirements, Resource Adequacy, and greenhouse gas emissions targets). All portfolios also meet City energy supply requirements (e.g., 100% renewable and/or GHG-free by 2025). In addition, each portfolio’s projected total costs allow comparison of each portfolio’s potential financial impacts on the CleanPowerSF program and its ratepayers.
The Integrated Resource Plan Compliance Filing details the results of the analysis Staff performed. The Compliance Filing serves as a planning document that will be updated every two years to reflect changing market and technology conditions as well as policy directives from the Commission, Mayor and Board of Supervisors.
The Commission has reviewed previous CleanPowerSF Integrated Resource Plan submissions to the CPUC and approved the 2018 Plan in Resolution No. 18-0130 and the 2020 Plan in Resolution No. 20-0182.
4 Description of Compliance Filing
Pursuant to CPUC instructions, Staff prepared the attached Draft Compliance Filing for approval by the Commission and for submission to the CPUC no later than November 1, 2022. An update on CleanPowerSF’s 2022 Integrated Resource Plan was provided to the Commission on September 13, 2022
5 and October 11, 2022.6
The Compliance Filing includes the following sections:
• Study Design—discusses the City policies and objectives that guide CleanPowerSF’s procurement planning and the methodology utilized; • Study Results—identifies the portfolios studied and discusses CleanPowerSF’s Preferred Portfolio;
• Action Plan—identifies activities to achieve the Preferred Portfolio and potential barriers; and
• Lessons Learned—discusses potential changes to the CPUC process.
Staff recommends the Commission adopt the Time Coincident Case as its Preferred Portfolio for CleanPowerSF’s Compliance Filing for submission to the CPUC under the 30 and 25 million metric tons of greenhouse gas emissions scenarios. Staff assessed the performance of each of the portfolios Agreement: Approve CleanPowerSF Integrated Resource Plan Commission Meeting Date: October 24, 2022
described above against the Commission’s goals for the CleanPowerSF program to determine its recommendation for a Preferred Portfolio.
Staff used the following criteria to evaluate and assign a ranking to each of the Conforming Portfolios: total portfolio cost, portfolio compliance with reliability requirements, portfolio risk, greenhouse gas emissions, renewable energy content, local investment, and % of portfolio under long-term contract. The portfolios were determined to be equivalent for the following metrics: greenhouse gas emissions, renewable energy content (each portfolio was 100% renewable and/or GHG-free by 2025 and thereafter), and local investment (each portfolio includes 85.6 megawatts (MW) of local solar, 150 MW of local storage, and 50 MW of local geothermal). Portfolio evaluation then focused on the remaining metrics.
Staff recommends that the Commission adopt the Time Coincident Case because it best balances all of the Commission’s goals for CleanPowerSF.
The Time Coincident Case meets the City’s renewable energy goals, offers the highest reliability value, yields the most resource diversity, while providing a comparable amount of local investment. The Time Coincident Case requires the development of more renewable energy capacity earlier in the planning horizon, allowing resource generation to meet 90% of CleanPowerSF’s forecasted hourly customer demand in real time by 2030.
The attached Compliance Filing, and the scenario analysis conducted as part of this 2022 Integrated Resource Plan, will provide a guide for CleanPowerSF’s future resource development and procurement activities.
Minor changes to the Draft may be made prior to filing to ensure clarity and compliance with all requirements.
Additionally, this Plan will become part of an on-going CPUC planning process in which the City and County of San Francisco has been and will continue to be actively engaged. Following CleanPowerSF’s Compliance Filing submission, the CPUC will take all of the Compliance Filings submitted by CCAs and other retail sellers during the Integrated Resource Plan process, aggregate the results, and then evaluate whether and how the combined plans achieve California’s statewide greenhouse gas reduction goals while maintaining electric system reliability. Based on the results of this evaluation, the CPUC may order (to the extent of its regulatory authority) retail sellers to procure energy supply resources to serve identified statewide resource shortfalls, if any.
Staff posted materials regarding the CleanPowerSF Integrated Resource Plan development process, including analysis results and recommendations, to CleanPowerSF’s website and invited public comment through the end of the day on October 14, 2022. These materials can be found at the following address: https://www.cleanpowersf.org/resourceplan. Additionally, Staff held informational question and answer sessions on September 29, 2022 and October 5, 2022. Written comments received from members of the public using the form posted by the end of the day on October 14, 2022 are posted on the same webpage.
Agreement: Approve CleanPowerSF Integrated Resource Plan Commission Meeting Date: October 24, 2022
Result of Inaction: Financial penalties for non -compliance with the requirement that all retail sellers submit an Integrated Resource Plan Compliance Filing to the CPUC by November 1, 2022.
Recommendation: SFPUC staff recommends that the Commission adopt the attached resolution.
Attachments: 1. IRP Portfolio Analysis Initial Results October 14, 2022
2. CleanPowerSF Draft 2022 Integrated Resource Plan Compliance Filing to be submitted to the CPUC (IRP Narrative Template)
3. CPUC Template and Instructions for 2022 Integrated Resource Plan Compliance Filing
PUBLIC UTILITIES COMMISSION
City and County of San Francisco
RESOLUTION NO. :
WHEREAS, The San Francisco Board of Supervisors established a Community Choice Aggregation (CCA) program in 2004 (Ordinance 86-04) and has implemented the program, called CleanPowerSF, through the work of the SFPUC in consultation with the San Francisco Local Agency Formation Commission (Ordinances 146-07, 147-07, and 232-09); and WHEREAS, The San Francisco Public Utilities Commission has adopted program goals for CleanPowerSF that guide CleanPowerSF’s procurement planning, including leading with affordable and reliable service, providing cleaner electricity alternatives, and investing in local renewable projects and jobs while providing for long-term rate and financial stability; and WHEREAS, California Public Utilities Code Section 454.52 requires CCAs to prepare and file an Integrated Resource Plan for review by the California Public Utilities Commission (CPUC); and
WHEREAS, An I ntegrated Resource Plan is a process engaged in by a retail seller to identify an optimal mix of energy resources needed to serve its customers’ electricity supply needs while meeting goals of reliability, affordability, greenhouse gas emissions reductions and renewable energy targets, and economic development; and WHEREAS, The CPUC requires CCAs and other retail sellers to file individual Integrated Resource Plans with the CPUC every two years with the third and current Compliance Filing due on November 1, 2022; and
WHEREAS, the CPUC has established instructions for how CCAs should prepare and file their plans, including the use of CPUC -specified inputs and assumptions and Compliance Filing templates; and
WHEREAS, The SFPUC retained a technical consultant to support the development of the Integrated Resource Plan; and
WHEREAS, The analysis examined two Conforming Portfolios, which are required to use inputs and assumptions provided by the CPUC with a focus on statewide emissions reductions goals and targets, and t wo Alternative Portfolios, which can use CleanPowerSF -specific assumptions and can focus on City goals and targets; and WHEREAS, CleanPowerSF must submit a Conforming Portfolio as its Preferred Portfolio in its Compliance Filing to the CPUC on November 1, 2022; and WHEREAS, All of the analysis performed for CleanPowerSF’s Integrated Resource Plans, including the Alternative Portfolios analyzed, will be used by Staff to inform power procurement activities and plan for future energy supply needs, as appropriate, given customer demand and electricity market conditions; and
WHEREAS, The SFPUC analyzed two Conforming Portfolios – a Base Case that achieves a 100% renewable and/or GHG-free power portfolio by 2025 and a Time Coincident Case that achieves a 100% renewable and/or GHG-free power portfolio by 2025 that meets at least 90% of customer demand on an hour -by-hour basis – and two Alternative Portfolios - a Climate Action Plan Case that achieves a 100% renewable and/or GHG-free power portfolio by 2025 and supplies the estimated increase in electricity usage resulting from the achievement of San Francisco’s emission-free trips and building decarbonization targets and a Local Resource Procurement Case that achieves a 100% renewable and/or GHG- free power portfolio by 2025 and supplies 50% of CleanPowerSF energy demand with local resources by 2030; and WHEREAS, CleanPowerSF’s Integrated Resource Plan serves as a planning document that will be updated every two years to reflect changing regulatory and market condi tions as well as City policy preferences; and
WHEREAS, CleanPowerSF staff have prepared a Compliance Filing in accordance with CPUC guidelines and CleanPowerSF program goals by using the CPUC’s templates and following CPUC instructions; now, therefore, be it
RESOLVED, That the Commission adopts the Time Coincident Case as CleanPowerSF’s Preferred Portfolio under the 2035 25 MMT and the 30 MMT GHG planning targets; and be it FURTHER RESOLVED, That the Commission approves the CleanPowerSF Compliance Filing; and, be it
FURTHER RESOLVED, That the Commission authorizes the General Manager to make necessary changes to finalize the compliance filing that are non -material to its conclusions and recommendations; and, be it
FURTHER RESOLVED, That the Commission directs the General Manager to submit CleanPowerSF’s Compliance Filing to the CPUC on or before the November 1, 2022 deadline for submission.
I hereby certify that the foregoing resolution was adopted by the Public Utilities Commission at its meeting of October 24, 2022.
Secretary, Public Utilities Commission
CleanPowerSF 2022
Integrated Resource Plan
Portfolio Analysis
Initial Results
October 14, 2022
Contents
• Presentation Updates
• Key Terms
• What is an IRP?
• IRP Assumptions and Analysis
• IRP Results
• IRP Evaluation
• Conclusions and Recommendations
2 Presentation Updates
Date Presentation Update
9/21/2022 Base Case Modeling Results Released
9/
21/2022 95% Time Coincident Case Modeling Results Released 9/28/2022 90% Time Coincident Case Modeling Results Released 9/28/2022 Mayor’s EV and Building Decarbonization Targets Met Case Modeling Results Released
9/28/2022 Local Resource Procurement Case Modeling Results Released 9/28/2022 Comparison of Conforming Portfolios
10/5/2022 Portfolio Evaluation and Staff’s Recommended Preferred Conforming Portfolio Summary
3 Key Terms
4 • Portfolio: collection of generation resources
us
ed to serve electricity demand.
• Conforming Portfolio: the specific mix of
el
ectric generation resources that meet
CPUC requirements.
• Alternative Portfolio: a portfolio developed
us
ing CleanPowerSF assumptions.
• Baseline Resources: resources that are
ex
isting or contracted to come online within
the planning horizon and assumed to be
fixed in each portfolio.
• Scenario: variations on a future state or
ob
jective that may influence the resources
included in a portfolio.
• Sensitivity Analysis: an analysis that
in
volves changing one assumption to
understand its influence on the portfolio.
WHAT IS AN IRP?
5 What is an Integrated Resource Plan
(IRP)?
• An IRP is an energy planning
tool to support achieving policy
goals and meeting regulatory
requirements.
• State law requires retail sellers of
electricity to develop an IRP that
evaluates electricity supply and
demand and identifies energy
resource options that can deliver
reliable and cost-effective energy
to customers.
• CCA IRPs are reviewed and
certified by the California Public
Utilities Commission (CPUC),
every two years.
6 Integrated Resource Plan Portfolios
7 Conforming
Po
rtfolio
Uses inputs and
assumptions provided by the
CPUC
Accounts for statewide goals
and targets
Minimal flexibility to
incorporate local
electrification goals and
targets
Alternative
Portfolio
Uses inputs and assumptions
determined by CleanPowerSF
Accounts for local goals and
targets:
Transportation electrification
Building decarbonization
Provides flexibility to
incorporate community
preferences
CleanPowerSF’s IRP Components
• Customer demand forecast,
with sensitivity analysis for
expected changes in demand
• Analyze portfolios for meeting
CleanPowerSF’s renewable and
GHG reduction targets and
investing locally
• All energy supplied being 100%
renewable & GHG-free by 2025
• Scenarios examining alternative
resource mixes and accelerated
decarbonization targets
• Optimize around a portfolio that
achieves program goals and
delivers competitively priced
energy products
8 What does the CPUC do with IRPs?
1. Before each electricity provider begins their IRP analysis, the CPUC sets a greenhouse gas (GHG) emissions reduction target for the CPUC-regulated electricity sector.
2. The CPUC performs electricity grid modeling to determine the amounts and types of new resources (e.g., wind, solar, and batteries) that are necessary to achieve the GHG emissions reduction target while meeting future electricity needs. This modeling is used to develop an overall plan for the CPUC- regulated electricity sector.
3. Electricity providers, like CleanPowerSF, create individual IRPs, illustrating how they plan to reduce GHG emissions as part of the CPUC plan.
4. The CPUC collects all the individual plans from jurisdictional electricity providers and develops what it calls a “Preferred System Portfolio” (PSP).
Th
e CPUC then compares their original plan (in Step 2) to this new plan to make sure that it will still meet its goals if the CPUC-jurisdictional electricity providers all follow their individual plans.
5. Lastly, the CPUC brings all this planning to life by implementing new procurement requirements and policies that support the development of new clean energy projects and overall grid reliability.
9 CPUC’s Integrated Resource Planning
Process
10 1. CPUC sets global
warming emissions
reduction target for
electric sector it regulates
2. CPUC creates a
plan
3. Electricity providers
create individual plans
4. CPUC aggregates
individual plans and
assess results
5. CPUC implements
new procurement
requirements and
policies
IRP ASSUMPTIONS &
ANALYSIS
11 Analytical Process
Develop
Inputs and
Assumptions
• Goal setting & scenarios
• B
aseline energy resources
& demand forecast
• Resource cost assumptions
• Local renewable energy
analysis
Develop
Energy
Portfolios
• Worked with consultant to conduct
po
rtfolio modeling
• Used industry standard electricity
production cost modeling software
Run
Sensitivity
Analysis
• Analyze impact of changes to
cust
omer demand
Evaluate
Results
• Assess results
ag
ainst program
goals
12 Portfolio Analysis Methodology
Developing the energy portfolios requires the key inputs, algorithms, and outputs illustrated below.
13 CPUC-required Assumptions
• Annual retail sales forecast through 2035 from State Integrated Energy Policy Report (IEPR)
• Load modifiers (i.e., demand-side changes to load like electrification, energy efficiency, behind-the-meter solar)
• Emissions accounting methodology
CleanPowerSF-specific Assumptions
• Hourly customer demand shape (must equal IEPR retai l sales forecast) • Existing renewable resource availability
• Project specific (e.g., local project) costs
• Minimum portfolio reliability requirements
CPUC-required and CleanPowerSF-
specific IRP Assumptions
The CPUC requires retail sellers to use a common set of assumptions in developing their IRPs to support aggregation and “apples-to-apples” comparisons of all IRPs.
14 CPUC GHG Emissions Target
Requirements for IRPs
• CleanPowerSF is required to submit a Preferred Conforming Portfolio to the CPUC under two 2035 statewide GHG
emissions targets: 30 and/or 25 million metric tons (MMT). • CleanPowerSF is planning to perform better than its
proportional share of both GHG emissions targets (below); it is permitted to submit one Preferred Conforming Portfolio as part of its individual 2022 IRP filing.
• Assigned targets are calculated based on CleanPowerSF’s proportional share of statewide electricity usage and represent CleanPowerSF’s allotted portion of the 2035 GHG emissions targets.
15 CleanPowerSF 30 MMT
CO2 Benchmark
CleanPowerSF 25 MMT
CO2 Benchmark
0.340 MMT CO2 0.272 MMT CO2
Additional CleanPowerSF-specific IRP
Portfolio Requirements
• CleanPowerSF required that all portfolios developed in its IRP meet the following additional requirements:
Be 100% renewable and/or GHG-free by 2025
Be at least 73% RPS-eligible by 2030
Meet at least 60% of projected Resource Adequacy obligation with long-term resources
Include 85.6 MW of local solar, 150 MW of local storage, and 50 MW local geothermal
Prioritize energy from California
New renewable resources not already under contract may be developed as soon as 2026 (project lead times)
Limit large hydro purchases to CleanPowerSF’s proportional share of statewide hydro availability
Provide long-term rate and financial stability; remain cost competitive with PG&E rates
16 Program Annual Power Content
Targets and Progress to Date
50% RPS-eligible renewable by 2020;
100% renewable and/or GHG-free by 2025
17 45% 48% 50% 52% 58% 63% 68% 73% 73% 73% 73% 73% 73%
37% 36 % 35% 35%
30%
31%
29%
27% 27% 27% 27% 27% 27%88%
96 %
91% 93%
0%
10 %
20%
30%
40%
50%
60%
70%
80%
90%
100%
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 CleanPowerSF RPS-Eligible Target CleanPowerSF Additional Renewable Target Expected Renewable Energy Resource Cost Assumptions
Levelized Cost of Energy ($/MWh)
18 *Source: Siemens PTI, NREL, HIS, EIA, EPA. Includes Capital Cost, Interconnection Cost, Investment Tax Credit, Periodic Replacement and Augmentation 0 20
40 60
80 100
120
140
160
180
2022 2025 2030 2035 2040
Dollars per Megawatt-hour ($/MWh)
Offshore Wind
Onshore Wind
Stand-alone Storage
Hydro
Geothermal
Solar Hybrid
Utility Solar PV
Baseline Resources for Conforming
and Alternative Portfolios
19 Resource Technology Total Capacity (MW)
Local Baseline
Resources
Solar PV 85.6
Storage 150.0
Geothermal 50.0
Subtotal 285.6
Non-Local Baseline
Resources
Solar PV 282.0
Wind 110.4
Storage 138.9
Geothermal 19.3
Subtotal 550.6
To
tal 836.2
CleanPowerSF IRP Modeling:
Four Portfolios
20 1. Base Case: CleanPowerSF Goals by 2025
✓ 100% renewable and/or GHG-free by 2025
✓ L
ocal resource prioritization
2. CleanPowerSF Goals and Time Coincidence by 2030
✓ 100% renewable and/or GHG-free by 2025
✓ R
esource generation meets customer usage in real time
CleanPowerSF analyzed the resource needs to meet both 90% and 95% time coincidence by 2030
✓ No system purchase 5-10pm
✓ Local resource prioritization
3. CleanPowerSF Goals and Mayor’s EV and Building Deca rbonization Targets MT
✓ 100% renewable and/or GHG-free by 2025
✓ L
ocal resource prioritization
✓ Emission-free trips originating in, ending in, or passing through San Francisco by 2040
✓ Decarbonization of existing buildings by 2040
4. CleanPowerSF Goals and Local Resource Procurement
✓ 100% renewable and/or GHG-free by 2025
✓ L
ocal content floor of 50% by 2030
INITIAL RESULTS
BASE CASE PORTFOLIO
Conforming Portfolio
21 Base Case Portfolio Results:
Portfolio Energy Content
22 The energy supply in the Base Case portfolio includes a diverse mix of generating technologies, with Solar being the most dominant of the technology types in all years of the planning horizon.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Solar Wind Geothermal Existing Renewables
Hydro CAISO System Power CA RPS Requirement
Base Case Portfolio Results: New
Resource Capacity Build (2022-2035)
23 The Base Case portfolio calls for 742 MW of new resou rce capacity by 2035, including 6 MW of new solar, 340 MW of new solar paired with storage (hybrid), 49 MW of new geothermal, and 347 MW of standalone storage.
0 200
400
600
800
1000
1200
1400
1600
1800
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Capacity (Megawatts, MW)
Solar Storage Geothermal
Base Case Portfolio Results:
Contributions to Electric Grid Reliability
The Base Case portfolio will meet CleanPowerSF’s share of system reliability needs with a small amount of short-term capacity purchases required in 2030 and 2031.
24 0
100
200
300
400
500
600
700
800
900
1000
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Effective MW
CPUC Allocations Short-Term Capacity Contracts
Wind Solar
Hydro Geothermal
Storage Reliability Need (MW)
Base Case Portfolio Results: Portfolio
Cost
25 This graph compares the Base Case Portfolio costs to CleanPowerSF's forecasted supply costs in its 2021 10-year financial plan. The graph shows that starting in 2026, the Base Case will increase the cost of energy in the portfolio relative to the 10-year plan. Notably, the market price forecast has increased sinc e the 10-year plan was developed, and new build project costs are up due to inflationary and supply chain issues. The total projected revenue requirement in 2021 dollars for the Base Case portfolio would be $1.72 billion vs. $1.57 billion projected in the 10-year financial plan during the 2023-2032 period (9.8% increase).
$-
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 $2021 Annual Portfolio Cost
($000)
Base Case Portfolio Cost ($2021)
CleanPowerSF 10-Year Financial Plan Supply Cost ($2021) Base Case Portfolio Results: California
ISO Market Purchases & Sales
26*Does not include line losses
The buildout required for the Base Case portfolio res ults in California ISO electricity market purchases and sales to balance CleanPowerSF’s supply and demand, represented as a percentage of CleanPowerSF’s annual retail sales. This information serves as a measure of portfolio market exposure as more purchases and sales on the wholesale electricity market means greater portfolio exposure to market price volatility.
-120%
-100%
-80%
-60%
-40%
-20%
0%
20%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Percent of Retail Sales
Market Purchases Market Sales Net Market Sales
INITIAL RESULTS
95% TIME COINCIDENT
POR
TFOLIO
Conforming Portfolio
27 95% Time Coincident Portfolio Results:
Po
rtfolio Content
28 The energy supply in the 95% Time Coincident portfoli o requires more new renewable energy supply, including offshore wind generation beginning in 2030. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Solar Wind Offshore Wind Geothermal
Existing Renewables Hydro CAISO System Power CA RPS Requirement 95% Time Coincident Portfolio Results:
Ne
w Resource Capacity Build (2022-2035)
29 The 95% Time Coincident portfolio calls for 1,692 MW of new resource capacity by 2035, including 306 MW of new solar, 790 MW of new solar paired with storage (hybrid),147 MW of standalone storage, 200 MW of new offshore wind, and 49 MW of new geothermal.
0 200
400
600
800
1000
1200
1400
1600
1800
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Capacity (Megawatts, MW)
Solar Storage Wind Offshore Geothermal
95% Time Coincident Portfolio Results:
Co
ntributions to Electric Grid Reliability
Due to the significant amount of new resources requir ed, the 95% Time Coincident portfolio will exceed CleanPowerSF’s share of system reliability needs in all years of the planning horizon.
30 0
200
400
600
800
1000
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Effective MW
Axis Title
CPUC Allocations Short-Term Capacity Contracts
Wind Offshore Wind
Solar Hydro
Geothermal Storage
Reliability Need (MW)
95% Time Coincident Portfolio Results:
Po
rtfolio Cost
31 This graph compares the Time Coincident Portfolio cos ts to CleanPowerSF's forecasted supply costs in its 2021 10-year financial plan. The graph shows that starting in 2026, the Time Coincident case will increase the cost of energy in the portfolio relative to the 10-year plan. Notably, the market price forecast has increased sinc e the 10-year plan was developed, and new build project costs are up due to inflationary and supply chain issues. The total projected revenue requirement in 2021 dollars for the Time Coincident portfolio would be $2.20 billion vs. $1.57 billion projected in the 10-year financial plan during the 2023-2032 period (40.2% increase).
$-
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 $2021 Annual Portfolio Cost
($000)
95% Time Coincident Portfolio Cost ($2021)
CleanPowerSF 10-Year Financial Plan Supply Cost ($2021) 95% Time Coincident Portfolio Results:
Cal
ifornia ISO Market Purchases & Sales
32*Does not include line losses
The buildout required for the 95% Time Coincident por tfolio relies on California ISO electricity market sales to balance CleanPowerSF’s supply and demand, represented in this graph as a percentage of CleanPowerSF’s annual retail sales. This information serves as a measure of portfolio market exposure as more purchases and/or sales on the wholesale electricity market means greater portfolio exposure to market price volatility. By design, this portfolio relies minimally on California ISO for supplying CleanPowerSF’s forecasted demand. However, it relies significantly on the California ISO to absorb excess supply generated by the portfolio. -120%
-100%
-80%
-60%
-40%
-20%
0%
20%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Percent of Retail Sales
Market Purchases Market Sales Net Market Sales
New!
INITIAL RESULTS
90% TIME COINCIDENT
POR
TFOLIO
33 To better understand the trade-offs between portfolio t
ime coincidence and affordability, CleanPowerSF
analyzed the resource needs required to meet a 90%
time coincident target by 2030.
Conforming Portfolio
90% Time Coincident Portfolio Results:
Po
rtfolio Content
34 The energy supply in the 90% Time Coincident portfoli o provides a diverse energy mix, with solar generation accounting for 50% of the energy supply beginning in 2026.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Solar Wind Geothermal Existing Renewables
Hydro CAISO System Power CA RPS Requirement
90% Time Coincident Portfolio Results:
Ne
w Resource Capacity Build (2022-2035)
35 The 90% Time Coincident portfolio calls for 922 MW of new resource capacity by 2035, including 6 MW of new solar, 340 MW of new solar paired with storage (hybrid), 397 MW of standalone storage, 100 MW of new onshore wind, and 79 MW of new geothermal.
0 100
200
300
400
500
600
700
800
900
1000
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Capacity (Megawatts, MW)
Solar Storage Wind Geothermal
90% Time Coincident Portfolio Results:
Co
ntributions to Electric Grid Reliability
Due to the significant procurement of energy storage, the 90% Time Coincident portfolio will exceed CleanPowerSF’s share of system reliability needs in all years of the planning horizon.
36 0
100
200
300
400
500
600
700
800
900
1000
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Effective MW
CPUC Allocations Short-Term Capacity Contracts Wind
Solar Hydro Geothermal
Storage Reliability Need (MW)
90% Time Coincident Portfolio Results:
Po
rtfolio Cost
37 This graph compares the 90% Time Coincident Portfolio costs to CleanPowerSF's forecasted supply costs in its 2021 10-year financial plan. The graph shows that starting in 2026, the 90% Time Coincident case will increase the cost of energy in the portfolio relative to the 10-year plan. Notably, the market price forecast has increased sinc e the 10-year plan was developed, and new build project costs are up due to inflationary and supply chain issues. The total projected revenue requirement in 2021 dollars for the 90% Time Coincident portfolio would be $1.80 billion vs. $1.57 billion projected in the 10-year financial plan during the 2023-2032 period (14.4% increase). $-
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 $2021 Annual Portfolio Cost
($000)
90% Time Coincident Portfolio Cost ($2021)
CleanPowerSF 10-Year Financial Plan Supply Cost ($2021) 90% Time Coincident Portfolio Results:
Cal
ifornia ISO Market Purchases & Sales
38*Does not include line losses
The buildout required for the 90% Time Coincident por tfolio relies on California ISO electricity market purchases and sales to balance CleanPowerSF’s supply and demand, represented in this graph as a percentage of CleanPowerSF’s annual retail sales. This information serves as a measure of portfolio market exposure as more purchases and/or sales on the wholesale electricity market means greater portfolio exposure to market price volatility. By design, this portfolio relies minimally on California ISO for supplying CleanPowerSF’s forecasted demand. However, it also relies on the California ISO to absorb excess supply generated by the portfolio. -120%
-100%
-80%
-60%
-40%
-20%
0%
20%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Percent of Retail Sales
Market Purchases Market Sales Net Market Sales
INITIAL RESULTS
MAYOR’S EV AND BUILDING
DECARBONIZATION TARGETS
MET PORTFOLIO
39 Alternative Portfolio
Mayor’s EV and Building Decarbonization
Targets Met Portfolio: Load Assumptions
40 0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Megawatt-hours (MWh)
CPUC Assigned Load Forecast
Mayor's EV and Building Decarbonization Targets Met Load Forecast Alternative Portfolios allow for the use of inputs an d assumptions that deviate from CPUC requirements. The annual estimated increase in electricity usage resulting from the Mayor’s EV and Building Decarbonization Targets Met by 2040 averages 30% higher than CleanPowerSF’s assigned load forecast.
Mayor’s EV and Building Decarbonization
Targets Met Portfolio: Load Shape
• Load shapes for electric vehicle charging and buildin g electrification demand are provided by the CPUC
• Electric vehicle charging is highest during the middl e of the day and increases again in the middle of the night
• The building electrification shape illustrates higher electricity demand (e.g., space heating/cooling and cooking) in the mornings and evenings. 41 0.000%
0.005%
0.010%
0.015%
0.020%
0.025%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hourly Percent of Annual Load
Hours
Daily Average EV
Load Shape
Daily Average of
Building
Electrification Load
Shape
Mayor’s EV and Building Decarb. Targets
Met Portfolio Results: Portfolio Content
42 The energy supply in the Mayor’s EV and Building Deca rbonization Targets Met portfolio provides a diverse energy mix, with more new renewable capacity beyond 2025 to serve increasing electricity usage. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Solar Wind Geothermal Existing Renewables
Hydro CAISO System Power CA RPS Requirement
Mayor’s EV and Building Decarbonization
Targets Met Portfolio Results: New
Resource Capacity Build (2022-2035)
43 The Mayor’s EV and Building Decarbonization Targets M et portfolio calls for 1,682 MW of new resource capacity by 2035, including 206 MW of new solar, 850 MW of new solar paired with storage (hybrid), 247 MW of energy storage, 200 MW of new onshore wind, 100 MW of new offshore wind, and 79 MW of new geothermal.
0 200
400
600
800
1000
1200
1400
1600
1800
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Capacity (Megawatts, MW)
Solar Storage Wind Wind Offshore Geothermal
Mayor’s EV and Building Decarbonization
Targets Met Portfolio Results:
Contributions to Electric Grid Reliability
Due to the significant procurement of both solar and
storage, the Mayor’s
EV and Building Decarbonization Targets Met portfolio will exceed CleanPowerSF’s share of system reliability needs in all years of the planning horizon.
44 0
100
200
300
400
500
600
700
800
900
1000
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Effective MW
CPUC Allocations Short-Term Capacity Contracts Wind
Offshore Wind Solar Hydro
Geothermal Storage Reliability Need (MW)
Mayor’s EV and Building Decarbonization
Targets Met Portfolio Results: Portfolio Cost
45 This graph compares the Mayor’s EV and Building Decar bonization Targets Met Portfolio costs to CleanPowerSF's forecasted supply costs in its 2021 10-year financial plan. The graph shows that starting in 2024, the Mayor’s EV and Building Decarbonization Targets Met case will increase the cost of energy in the portfolio relative to the 10-year plan.
Notably, the market price forecast has increased sinc e the 10-year plan was developed, and new build project costs are up due to inflationary and supply chain issues. The total projected revenue requirement in 2021 dollars for the Mayor’s EV and Building Decarbonization Targets Met portfolio would be $2.33 billion vs. $1.57 billion projected in the 10-year financial plan during the 2023-2032 period (48.8% increase). $-
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
$400,000
$450,000
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 $2021 Annual Portfolio Cost
($000)
Mayor's EV and Building Targets Met Portfolio Cost ($2021) CleanPowerSF 10-Year Financial Plan Supply Cost ($2021) Mayor’s EV and Building Decarbonization
Targets Met Portfolio Results: California ISO
Market Purchases & Sales
46*Does not include line losses
The buildout required for the Mayor’s EV and Building
Decarbonization
Targets Met Portfolio relies on California ISO electricity market purchases and sales to balance CleanPowerSF’s supply and demand, represented in this graph as a percentage of CleanPowerSF’s annual retail sales. This information serves as a measure of portfolio market exposure as more purchases and/or sales on the wholesale electricity market means greater portfolio exposure to market price volatility.
-120%
-100%
-80%
-60%
-40%
-20%
0%
20%
40%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Percent of Retail Sales
Market Purchases Market Sales Net Market Sales
INITIAL RESULTS
LOCAL RESOURCE
PROCUREMENT PORTFOLIO
Alternative Portfolio
47 Local Resource Assumptions
• Local Resources are defined as
resources sited in the nine Bay
Area counties: Alameda, Contra
Costa, Marin, Napa, San
Francisco, San Mateo, Santa
Clara, Solano, and Sonoma.
• To meet the 50% local content
target, the Local Resource
Procurement portfolio includes
995 MW of local resource
capacity by 2035.
48 Local Resource Procurement Portfolio
Results: Portfolio Content
49 The energy supply in the Local Resource Procurement p ortfolio provides a diverse energy mix, with local solar, wind, and geothermal supplying 50% of total energy supply by 2030.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Solar Local Solar Wind
Local Wind Geothermal Local Geothermal
Existing Renewables Hydro CAISO System Power
CA RPS Requirement
Local Resource Procurement Portfolio Results:
New Resource Capacity Build (2022-2035)
50 The Local Resource Procurement portfolio calls for 1, 437 MW of new resource capacity by 2035, including 106 MW of new solar, 1,110 MW of new solar paired with storage (hybrid), 97 MW of standalone storage, 25 MW of new onshore wind, and 99 MW of new geothermal.
0 200
400
600
800
1000
1200
1400
1600
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Capacity (Megawatts, MW)
Solar Storage Wind Geothermal
Local Resource Procurement Portfolio Results:
Contributions to Electric Grid Reliability
Due to the significant procurement of both solar-paire d storage (hybrid) resources, the Local Resource Procurement portfolio will exceed CleanPowerSF’s share of system reliability needs in all years of the planning horizon.
51 0
100
200
300
400
500
600
700
800
900
1000
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Effective MW
CPUC Allocations Short-Term Capacity Contracts Wind
Solar Hydro Geothermal
Storage Reliability Need (MW)
Local Resource Procurement Portfolio
Results: Portfolio Cost
52 This graph compares the Local Resource Procurement Po rtfolio costs to CleanPowerSF's forecasted supply costs in its 2021 10-year financial plan. The graph shows that starting in 2024, the Local Resource Procurement case will increase the cost of energy in the portfolio relative to the 10-year plan. Notably, the market price forecast ha
s increased since the 10-year plan was developed, and new build project costs are up due to inflationary and supply chain issues. The total projected revenue requirement in 2021 dollars for the Local Resource Procurement portfolio would be $2.21 billion vs. $1.57 billion projected in the 10-year financial plan during the 2023-2032 period (40.8% increase).
$0
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 $2021 Annual Portfolio Costs
($000)
Local Resource Procurement Portfolio Cost ($2021)
CleanPowerSF 10-Year Financial Plan Supply Cost ($2021) Local Resource Procurement Portfolio
Results: California ISO Market Purchases
& Sales
53*Does not include line losses
The buildout required for the Local Resource Procurem
ent portfolio relies on
California ISO electricity market purchases and sales to balance CleanPowerSF’s supply and demand, represented in this graph as a percentage of CleanPowerSF’s annual retail sales. This information serves as a measure of portfolio market exposure as more purchases and/or sales on the wholesale electricity market means greater portfolio exposure to market price volatility.
-120%
-100%
-80%
-60%
-40%
-20%
0%
20%
40%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Percent of Retail Sales
Market Purchases Market Sales Net Market Sales
INITIAL RESULTS
COMPARISON OF
CONFORMING PORTFOLIOS
54 Comparison of Conforming Portfolios:
New Resource Capacity Build
The 95% Time Coincident case (orange line) adds capac ity faster than the other cases, then levels off. The Base Case and 90% Time Coincident case requires 56% and 46%, respectively, less new resource capacity than the 95% Time Coincident case by 2035.
55 0
200
400
600
800
1000
1200
1400
1600
1800
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Capacity (MW)
Base Case 95% Time Coincident 90% Time Coincident
Comparison of Conforming Portfolios: New
Resource Capacity Build by Technology (2035)
The 95% Time Coincident Case
selects more than twice the
capacity of the Base Case and
84% more capacity than the 90%
Time Coincident Case. The 95%
Time Coincident Case also selects
offshore wind in 2035, while the
Base Case and 90% Time
Coincident portfolios include more
energy storage resources to meet
retail load.
56 201
492
49 Base Case 2035
Total New Capacity = 742 MW
801
442
200
200
49 95% Time Coincident 2035
Total New Capacity = 1,692 MW
201
542
100
79 90% Time Coincident 2035
Total New Capacity = 922 MW
Comparison of Conforming Portfolios: Total
Portfolio Capacity by Technology (2035)
The 95% Time Coincident case
requires significantly more
capacity than the other two
cases to meet the 95% time
coincident goal (41% more than
the Base Case and 31% more
than the 90% Time Coincident
Case), including significant
amounts of new solar (+600
MW) and wind (+300-400 MW)
capacity .
57 1084
749
110
73 100
180
Base Case 2035
Total Resource Capacity = 2,296 MW
1684
699
310
200
73 100
180
95% Time Coincident 2035
Total Resource Capacity = 3,246 MW
1084
799
210
103
100
180
90% Time Coincident 2035
Total Resource Capacity = 2,476 MW
Comparison of Conforming Portfolios:
Energy Generated by Resource Type (2035)
The portfolios have similar energy
mixes, although the 95% Time
Coincident Case generates
significantly more energy annually.
As a percentage of total energy
supplied, the 95% Time Coincident
case includes the most wind
energy, both on- and offshore,
while the 90% Time Coincident
case includes the most geothermal
generation in 2035.
58 10%
56%
10%
10%
15%
Base Case 2035
6%
58%
12%
12%
6%
7%
95% Time Coincident 2035
9%
50%13%
14%
14%
90% Time Coincident 2035
4,060,895 megawatt-hours (MWh)
6,841,623 megawatt-hours (MWh)
4,517,730 megawatt-hours (MWh)
Comparison of Conforming Portfolios:
Local Investment
59 • All portfolios include 85.6
MW
of local solar, 150 MW
of local battery storage,
and 50 MW of local
geothermal
• This represents $600-$700
million commitment to local
projects*
*Estimated cost of local resource contracts through 2045.
Comparison of Conforming Portfolios:
Portfolio Costs, 2023-2035
Million$ (2021$) Base Case 95% Time Coincident 90% Time Coincident 2023-2035 Portfolio Cost (NPV) $ 2,329 $ 3,031 $ 2,420 Difference from Base Case (NPV) $ 703 $ 91 Difference from Base Case (%) 30% 4%
60 $-
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
2023-2035 Portfolio Cost (NPV)
Net Present Value in $Million
($2021)
Base Case 95% Time Coincident 90% Time Coincident
Comparison of Conforming Portfolios:
Average Portfolio Costs (2023-2032)
61 0
20 40
60 80
100
120
140
160
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032
2021$/Megawatt-hour (2021$/MWh)
Base Case 95% Time Coincident
90% Time Coincident CleanPowerSF Projected Supply Costs
IRP PORTFOLIO EVALUATION
62 63
Environment
Cost &
Risk
The Sweet Spot:
CleanPowerSF’s
Preferred Portfolio
Local
Investment
Integrated Resource Plan Objectives
Balanced
Program
Design
Allows
De
livery
Across
Competing
Objectives
While
Providing
Financial
Stability
While Providing for Long-Term Rate
and Financial Stability
Lead with Affordable and
Reliable Service
Provide Cleaner Electricity
Alternatives
Invest in Local Renewable
Projects and Local Jobs
CleanPowerSF Goals and Objectives
64 CleanPowerSF Portfolio Evaluation
65 Lead with Affordable
and Reliable Service
Provide Cleaner
Electricity Alternatives
Invest in Local
Renewable Projects
and Local Jobs
Invest in Local
Renewable Projects
and Local Jobs
While Providing for
Long-Term Rate and
Financial Stability
• Portfolio Cost ($/MWh)
• Portfolio Reliability
• Resource Diversity
• Portfolio Emissions
• Renewable Energy Content
• $ Invested Locally
• MW Developed Locally
• Job Development Potential
• % Long-term Contracted
• Market Exposure (Net Market
Purchases)
CleanPowerSF Portfolio Evaluation:
Affordable, Reliable & Diverse
• The Base Case Portfolio is the lowest cost in 2035 and over the 2023-2035 period.
• In 2035, the cost of the Base Case Portfolio is estimated at $121.2 million less annually than the 95% Time Coincident Case and $9.9 million less than the 90% Time Coincident Case.
• The cost of the Base Case Portfolio is most inline with CleanPowerSF’s 10-year financial plan through 2032, estimated at 9.8% higher in total portfolio cost.
• Through 2032, the 90% and 95% Time Coincident Portfolios are 14.4% and 40.2%, respectively, higher than the energy portfolio costs estimated in CleanPowerSF’s 10- year financial plan.
66 Lead with Affordable
and Reliable Service
• Portfolio Cost ($/MWh)
• Portfolio Reliability
• R
esource Diversity
CleanPowerSF Portfolio Evaluation:
Affordable, Reliable & Diverse
• The 90% and 95% Time Coincident portfolios meet reliability criteria with long- term contracts, while the Base Case portfolio requires short-term capacity purchases in 2030-2031.
• The 95% Time Coincident Portfolio has the most diverse resource mix, but it also includes significantly more capacity than other cases, which drives up its cost and requires a significant amount of sales on the wholesale market. 67 Lead with Affordable
and Reliable Service
• Portfolio Cost ($/MWh)
• Portfolio Reliability
• R
esource Diversity
CleanPowerSF Portfolio Evaluation:
Cleaner/More Renewable
• All portfolios achieve the City’s goal of supplying
100% renewable and/or GHG-free energy by
2025.
68 Provide Cleaner
Electricity Alternatives
• Portfolio Emissions
• Renewable Energy Content
CleanPowerSF Portfolio Evaluation:
Local Investment
• All of the portfolios prioritize local resources and include an equivalent amount of new local resource development. • All portfolios include 85.6 MW of local solar, 150 MW of local battery storage, and 50 MW of local geothermal.
• This represents $600-$700 million commitment to local projects. • CleanPowerSF will continue to prioritize local resources in its renewable energy procurement work.
69 • $ Invested Locally
• MW Developed Locally
• Job Development Potential
Invest in Local
Renewable Projects
and Local Jobs
CleanPowerSF Portfolio Evaluation:
Rate and Financial Stability
• CleanPowerSF’s portfolio risk management framework limits long- term contracting to ~65% of total annual portfolio energy as a means of balancing various policy objectives, including affordability, reliability, cleaner energy, and rate and financial stability. • The ~65% long-term contracting limit prevents an overcommitted portfolio, mitigates short-term market fluctuations, and provides opportunities to benefit from emerging technologies.
• All Portfolios feature at least 56% long-term contracts (10 years or more) with new renewable resources.
• The 95% Time Coincident Portfolio is the most long-term contracted at 149% of total retail sales in 2030, but excess long-term resources significantly exceed program long-term contracting limits.
• The Base Case and 90% Time Coincident Portfolios are more comparably long-term contracted with 60-70% of total retail sales in 2030 and are more in-line with program risk management limits.
70 While Providing for
Long-Term Rate and
Financial Stability
• % Long-term Contracted
• Market Exposure (Net Market
Pu
rchases)
CleanPowerSF Portfolio Evaluation:
Rate and Financial Stability
• We defined energy market exposure as the reliance of a portfolio on the California ISO wholesale energy market for short-term or “spot” purchases (when the portfolio has
energy shortages) and sales (when the portfolio has
surplus energy).
• The Base Case Portfolio is the most reliant on market purchases and the least reliant on market sales.
• The 95% Time Coincident Portfolio is the least reliant on market purchases and the most reliant on market sales.
• The 90% Time Coincident Portfolio is most balanced and least reliant on both spot market purchases and sales.
71 While Providing for
Long-Term Rate and
Financial Stability
• % Long-term Contracted
• Market Exposure (Spot Market
Pu
rchases and Sales)
Cases13 Base 90% Time Coincident 95% Time Coincident
Lead with Affordable Service
Co
st 1 2 3
Reliability 3 2 1
Diversity 3 2 1
Provide Cleaner Energy Alternatives
Emissions Equivalent
Renewable Equivalent
Invest in Local Projects and Jobs
Local
Investment Equivalent
Provide for Long-term Rate and Financial Stability
% Long-term
En
ergy 1 2 3
Market Exposure 2 1 3
Total 10 9 11
25 MMT Preferred Portfolio Ranking
(1
= best, 3 = worst)
72 CONCLUSIONS AND
RECOMMENDATIONS
73 Preferred Portfolio Recommendation
Staff recommends the Commission adopt the 90% Time Coincident Portfolio because it best balances CleanPowerSF program goals:
Affordable
• The 90% Time Coincident Case has total portfolio costs comparable to CleanPowerSF’s 10- year financial plan supply costs
Reliable
• The 90% Time Coincident Case exceeds the CPUC-assigned annual reliability target and would improve the program’s ability to serve demand on a real time basis Cleaner
• The 90% Time Coincident Case achieves City’s 100% renewable and/or GHG-free goals Supports Local Investment
• The 90% Time Coincident Case includes an amount of local resources that is comparable to the other portfolios analyzed
Supports Rate and Financial Stability
• The 90% Time Coincident Case provides long-term rate stability without over-building and creating unreasonable market risk
74 Next Steps
• Written comment period available at
https://www.cleanpowersf.org/resourceplan will
close at 5pm PST on Friday, October 14th.
• Staff will present IRP modeling results to the
Commission on October 11th.
• St
aff will return to the Commission on October
25th to seek approval and adoption of a Preferred
Conf
orming Portfolio for submission of the IRP
Compliance Filing on November 1st.
75 APPENDIX
76 Key IRP Terms and Acronyms
Term (Acronym) Meaning
Analytical Modeling Mathematical technique used for sim ulating, explaining, and making predictions about a complex system
California Independent System
Operator (CAISO)
Organization that manages California’s bulk electricity grid, transmission lines, and wholesale energy market.
California Public Utilities
Commission (CPUC)
State energy regulatory agency that oversees the IRP process for Investor-Owned Utilities (IOU), Energy Service Providers (ESP) and Community Choice Aggregators (CCA)
Capacity The maximum output that a generator can produce, it is typically expressed in terms of megawatts (MW) or kilowatts (kW)
Capacity Factor A measure of how much energy is produced compared to the resource’s maximum capacity over a set period time, expressed as a percentage
Demand The amount of electricity usage met by a retail seller over a given period of time
Energy The ability to do work
Energy Storage A technology which captures energy produced at one time and discharges it for use at a later time
Hybrid Resources A generator that consists of two or more paired resource types eg., solar plus battery storage
77 Key Terms and Acronyms
Term (Acronym) Meaning
Integrated Energy Policy
Re
port (IEPR)
A biennial report issued by the California Energy Commission that contains an assessment of major energy trends and issues facing California’s electricity sector, including the demand forecast used in CleanPowerSF’s Integrated Resource Plan
Integrated Resource Planning A process that evaluates future electricity demand and resource options over a long time horizon, typically 20 years, and optimizes the resource mix that meets set criteria at the lowest cost Investment Tax Credit (ITC) A federal tax credit available to investment in solar power facilities and co-located energy storage facilities
Job-years A job creation metric which is equivalent to one full time job (2,080 working hours) for one year
Load Serving Entity (LSE) A retail seller of electricity Long-duration Energy Storage Battery storage of various technology types which can discharge for 8 hours or more
Megawatt (MW) 1,000,000 watts (a unit of power demand or generating capacity) MMT CO2 Million metric tons of carbon dioxide Portfolio A collection of power supply resources used to serve electricity demand
78 Key Terms and Acronyms
Term (Acronym) Meaning
Preferred Portfolio Of the portfolios modeled, the one which best meets program goals and regulatory requirements that is approved and submitted to the California Public Utilities Commission
Preferred System Plan The portfolio developed by the California Public Utilities Commission with an aggregation of individual IRPs of all retail sellers subject to CPUC jurisdiction that includes the CPUC’s view of the optimal mix of resources for the state over the IRP planning horizon
Reliability Need Share of resource capacity needed to meet the CAISO managed coincident peak assigned to retail sellers by the California Public Utilities Commission
Renewable Portfolio
Standard (RPS)
California State program that requires a minimum amount of electricity in retail seller portfolios to come from eligible renewable energy resources Resource Adequacy (RA) A capacity-based regulatory program intended to ensure that there are sufficient electricity generating resources to support reliable grid operation under peak demand conditions
Revenue Requirement The total amount of money an electricity provider collects from its customers
Sensitivity Analysis Analysis of the impact to the portfolio caused by a change to a variable in the analytical model
System Power Electric generation supplied by the grid at a particular time that is not associated with a specific generating facility
79 DRAFT
Standard LSE Plan
CLEANPOWERSF
2022 INTEGRATED RESOURCE PLAN
NOVEMBER 1, 2022
DRAFT – October 2022
1 Table of Contents
I. Executive Summary .................................................................................................................... 3 II. Study Design ................................................................................................................................. 6 a. Objectives .............................................................................................................................. 10 b. Methodology ....................................................................................................................... 12 i. Modeling Tool(s) ................................................................................................................ 12 ii. Modeling Approach ...................................................................................................... 16 III. Study Results .............................................................................................................................. 21 a. Conforming and Alternative Portfolios ..................................................................... 21 b. Preferred Conforming Portfolios ................................................................................. 29 c. GHG Emissions Results ........................................................................................................ 35 d. Local Air Pollutant Minimization and Disadvantaged Communities ........... 37 i. Local Air Pollutants ............................................................................................................ 37 ii. Focus on Disadvantaged Communities ................................................................ 38 e. Cost and Rate Analysis ..................................................................................................... 43 f. System Reliability Analysis ................................................................................................. 44 g. High Electrification Planning ......................................................................................... 46 h. Existing Resource Planning ............................................................................................ 51 i. Hydro Generation Risk Management ............................................................................ 51 j. Long-Duration Storage Planning .................................................................................... 53 k. Clean Firm Power Planning ............................................................................................ 53 l. Out-of-State Wind Planning ............................................................................................. 54 m. Offshore Wind Planning .................................................................................................. 55 n. Transmission Planning ..................................................................................................... 55 IV. Action Plan .................................................................................................................................. 57 a. Proposed Procurement Activities and Potential Barriers .................................. 57 ii. Resources to meet D.21-06-035 procurement requirements, including:
60 a. 1,000 MW of firm zero-emitting resource requirements .............................. 61 DRAFT – October 2022
2 b. 1,000 MW of long-duration storage resource requirements ...................... 62 c. 2,500 MW of zero-emissions generation, generation paired with storage, or demand response resource requirements ............................................. 62 d. All other procurement requirements ..................................................................... 62 iii. Offshore wind .................................................................................................................. 62 iv. Out-of-state wind .......................................................................................................... 63 v. Other renewable energy not described above .................................................. 63 vi. Other energy storage not described above ....................................................... 63 vii. Other demand response not described above ................................................. 63 viii. Other energy efficiency not described above................................................ 63 ix. Other distributed generation not described above ........................................ 63 x. Transportation electrification, including any investments above and beyond what is included in Integrated Energy Policy Report (IEPR) .................. 64 xi. Building electrification, including any investments above and beyond what is included in Integrated Energy Policy Report (IEPR) ................................... 64 xii. Other .................................................................................................................................... 65 b. Disadvantaged Communities ........................................................................................ 65 c. Commission Direction of Actions .................................................................................... 66 V. Lessons Learned ........................................................................................................................ 66
DRAFT – October 2022
3 I. Executive Summary
The City and County of San Francisco (City or San Francisco) has long championed clean energy.
Today, San Francisco is motivated by climate concerns to develop policies and programs that improve climate outcomes. The San Francisco Public Utilities Commission (SFPUC) is the City department identified in City code that is responsible for managing energy supplies. CleanPowerSF is the City’s Community Choice Aggregation (CCA) program operated by the SFPUC.
When it launched CleanPowerSF, the SFPUC adopted the following goals for the development and operation of the program:
1) Provide affordable and reliable service;
2) Offer cleaner energy alternatives;
3) Invest in local renewable projects and jobs; and
4) Ensure long-term rate and financial stability.
This Integrated Resource Plan (IRP) will guide CleanPowerSF’s resource development and procurement activities for achieving its abovementioned program goals. It also fulfills CleanPowerSF’s obligation to prepare and file an IRP for review by the California Public Utilities Commission (CPUC). [COMISSION DECISION HERE AFTER OCTOBER 25
th MEETING).
CleanPowerSF serves more than 380,000 customer accounts and provides San Francisco with an electricity supply from its default “Green” product that is at least 50% Renewables Portfolio Standard (RPS)-eligible. Additionally, CleanPowerSF offers “SuperGreen”, a 100% RPS-eligible electricity supply, that is available to customers for a small additional cost. The SFPUC has been tasked to provide San Franciscans with a 100% renewable and/or GHG-free electricity supply by 2025.1 The power supply portfolio scenarios CleanPowerSF analyzed in this IRP address this goal.
To prepare this IRP CleanPowerSF modeled four power supply portfolio scenarios, which are summarized in Table 1.
Table 1 CleanPowerSF Modeled Portfolios
NAME
GHG
TARGET
(MMT)
GOAL ACHIEVED
DATE GOAL
ACHIEVED
BY
Confo
i
Base Case 25
100% renewable
and/or GHG-free
product content.
2025
1 San Francisco Environment Code § 902(b)(3), available at https://codelibrary.amlegal.com/codes/san_francisco/latest/sf_environment/0 -0-0-928 [Accessed on 10/8 /2022] DRAFT – October 2022
4 Time Coincident Case 25
100% renewable
and/or GHG-free
product content, all
energy generation
meets <90% of
customer demand
on an hour-by-hour
basis with no system
power purchases
between 5-10pm.
2030
Alternative Portfolios
Climate Action Plan Case (also
referred to as Mayor’s EV and
Building Electrification Targets
Met Case)
25 100% renewable
and/or GHG-free
product content,
GHG emission-free
passenger vehicle
trips originating in,
ending in, or passing
through San
Francisco by 2040
and decarbonization
of existing buildings
by 2040.
2040
Local Resource Procurement 25
100% renewable
and/or GHG-free
product content and
50% of retail load
met by local
resource generation.
2030
CleanPowerSF submits one Preferred Conforming Portfolio because the Preferred Conforming Portfolio has GHG emissions below “CleanPowerSF’s “proportional share of both the 2030 30 million metric tons (MMT) benchmark and the 2035 25 MMT benchmark.”2 After evaluating each of the portfolios modeled on the basis of their performance against CleanPowerSF’s program goals, CleanPowerSF hereby submits the Time Coincident Case as its Preferred Conforming Portfolio under the 30 MMT and 25 MMT scenario. The resources selected in this case are illustrated in Figure 1. The Study Results section of this document describes CleanPowerSF’s modeled portfolio results, including a summary of the Preferred Conforming Portfolio’s GHG emissions and local air pollutants, impacts to disadvantaged communities, the estimated portfolio cost, contribution to
2 R.20-05-003, Administrative Law Judge’s Ruling Finalizing Load Forecasts and Greenhouse Gas Emissions Benchmarks for 2022 Integrated Resource Plan Filings, June 15, 2022 (“ALJ Ruling on Forecasts and GHG Benchmarks") p. 12.
DRAFT – October 2022
5 system reliability, and planning considerations for high electrification scenarios, existing resources, hydro generation risk, long-duration storage, clean firm power, offshore wind, and transmission needs.
The Time Coincident Case meets CleanPowerSF’s program objective of supplying customers with renewable and/or GHG-free electricity on an annual basis by 2025.3 It was also selected as the Preferred Conforming Portfolio because it provides a diverse energy supply, meets CleanPowerSF’s projected annual share of system reliability, and balances new resource builds to limit unreasonable market risk.
Figure 1 CleanPowerSF 25 MMT Preferred Conforming Portfolio (Time Coincident Case) Resource Capacity by Year
CleanPowerSF describes the activities it will pursue to meet its Preferred Conforming Portfolio targets in the Action Plan section of this document. These activities include issuing several solicitations in the near-term (end of 2022 and start of 2023. CleanPowerSF will continue to target procurement of new Bay Area solar and local storage projects on City-owned property in San Francisco and within the nine Bay Area Counties to support CleanPowerSF’s program goal of investing in local renewable projects and local jobs. Additionally, CleanPowerSF will procure new in-state solar, hybrid energy storage, standalone energy storage, and geothermal resources. We will also continue to engage in joint-CCA procurement efforts, as appropriate. To implement CleanPowerSF’s Preferred Conforming Portfolio, the Action Plan section of this document also describes CleanPowerSF’s progress toward D.19-11-016 and D.21-06-035 procurement obligations, additional procurement actions planned across resource types, and CleanPowerSF programs that address disadvantaged communities.
The IRP is a cyclical process, and all load-serving entities (LSEs) including CCAs such as CleanPowerSF have to update their IRP every two years. CleanPowerSF will update this IRP and adapt its procurement
3 San Francisco Environment Code § 902(b)(3).
0 500
1000
1500
2000
2500
3000
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Capacity (MW)
Solar Solar + Storage Hybrid Standalone Storage Wind
Geothermal Hydro Existing Renewables
DRAFT – October 2022
6 preferences to future changes in technology, market conditions, regulatory requirements and other factors to ensure it is able to consistently offer its customers clean, reliable, and affordable electricity supply services.
II. Study Design
CleanPowerSF’s long term procurement planning is guided by the goals and policies established by the City and County of San Francisco and the state’s regulatory requirements for LSEs. San Francisco has adopted a citywide goal of 100% renewable and/or GHG-free electricity supply by 2025.4 In our 2020 IRP, CleanPowerSF’s Preferred Conforming Portfolio exceeded the City’s previous planning targets by five years by planning to supply customers with renewable and/or GHG-free electricity on an annual basis by 2025.5 The annual planning targets for RPS-eligible renewable and additional renewable and/or GHG-free resources CleanPowerSF used in developing portfolios for this 2022 IRP are consistent with the 2020 IRP’s Preferred Conforming Portfolio and achieve CleanPowerSF’s power content goals over the 2022-2035 planning horizon, as summarized in Table 2.
Table 2 CleanPowerSF Planning Targets
POWER
CONTENT
GOALS
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
RPS-eligible
Renewable 58% 63% 68% 73% 73% 73% 73% 73% 73% 73% 73% 73% 73% 73% Additional
Renewable
and/or GHG-
Free
33% 31% 28% 27% 27% 27% 27% 27% 27% 27% 27% 27% 27% 27% Total
Renewable
and/or GHG-
free
91% 94% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
CleanPowerSF modeled and analyzed two Conforming Portfolios to arrive at the Preferred Conforming Portfolio submitted as part of this 2022 IRP:
4 San Francisco Environment Code § 902(b)(3).
5 CleanPowerSF 2020 Integrated Resources Plan Compliance Filing (Public Filing). https://sfpuc.sharefile.com/share/view/s61300b4056124fcda409799fd243d245 [accessed 10/12/2022].
DRAFT – October 2022
7 • Base Case – The Base Case portfolio models meeting the CleanPowerSF annual product content targets identified in Table 2 above, consistent with City goals. • Time Coincident Case – Th e Time Coincident portfolio adds a time coincidence constraint to the Base Case 25 MMT discussed above. In addition to meeting the product content targets outlined in Table 2, this portfolio’s generation matches ≥90% of demand on an hourly basis with no system power purchases between 5 and 10pm.
CleanPowerSF modeled the Base an d Time Coincident portfolios under market conditions for a statewide 25 MMT greenhouse gas target. Differences in the underlying assumptions between the two scenarios has allowed CleanPowerSF to better understand the resource availability and cost impacts the two scenarios may have on future CleanPowerSF procurement options and costs. CleanPowerSF’s Conforming Portfolios achieve emissions that are less than its proportional share of the 2035 25 MMT target and, therefore, submits only one Preferred Conforming Portfolio as part of this IRP filing.6 CleanPowerSF includes two additional Alternative Portfolios. The first Alternative Portfolio assumes the San Francisco Climate Action Plan’s electrification goals are met, and the second Alternative Portfolio includes a higher proportion of procurement from local resources, defined as projects located within the nine counties of the San Francisco Bay Area, that meet 50% of CleanPowerSF demand with local generation by 2030. Both Alternative Portfolios are modeled with CleanPowerSF’s behind-the-meter photovoltaic (BTM PV) forecast, which is significantly lower in capacity and total energy from the assumptions supplied by the CPUC and CEC.
• Climate Action Plan Case (also referred to as M ayor’s Electric Vehicle and Building Electrification Targets Met Case) – The Climate Action Plan portfolio models the additional generation needed to accommodate 100% GHG emission-free passenger vehicle trips originating in, ending in, or passing through San Francisco and decarbonization of existing buildings, all by 2040, as described in San Francisco’s 2021 Climate Action Plan.7 • Local Resource Preferences Case – The Local Resource Procurement portfolio includes a higher proportion of local resources in the resource baseline and establishes a local resource floor of 50% of energy supply by 2030.
Load Assignments for Each LSE
6 See (“ALJ Ruling on Forecasts and GHG Benchmarks") p. 12. 7 San Francisco Climate Action Plan 2021, page 41
https://sfenvironment.org/sites/default/files/cap_fulldocument_wappendix_web_220124.pdf [Accessed 10/13/2022]
DRAFT – October 2022
8 For this IRP analysis, CleanPowerSF used its load forecasts assigned by the CPUC.8 The demand forecast used to develop CleanPowerSF’s load assignment was derived from the 2021 Mid-Baseline – Mid Demand Case, in the California Energy Commission (CEC) Integrated Energy Policy Report9 (IEPR) through 2035 as summarized in Table 3 below.
Table 3 CleanPowerSF Assigned IRP Sales Annual IEPR Forecast
CLEANPO
WERSF
LOAD
FORECAS
T (GWH)
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Managed
Retail
Sales 2,944 2,970 2,993 3,015 3,039 3,062 3,090 3,119 3,153 3,181 3,215 3,247 3,293 Demand at
the
Generator
Busbar 3,179 3,205 3,232 3,254 3,282 3,307 3,337 3,365 3,405 3,436 3,472 3,507 3,552 The 2021 Mid-Baseline – Mid Demand Case forecast represents the CEC’s projections for electricity demand under a mid-range economic and demographic growth across California and mid-range of additional achievable energy efficiency, which is the projected energy savings resulting from programs and efforts that are likely to occur but have no official funding or planning commitments to date. These include updates of building standards, appliance regulations, or new and expanded energy efficiency programs. Additional Achievable Fuel Substitution (AAFS) is a new load modifier introduced in the 2021 IEPR forecast and represents incremental fuel substitution not included in the baseline demand forecast but is reasonably expected to occur.10 One example of incremental fuel substitution is the decarbonization strategy of replacing gas end uses with more efficient electric end uses.
The IEPR demand forecast represents CleanPowerSF’s managed retail sales. These annual volumes are then grossed up approximately 8% each year to account for transmission and distribution losses.
8 Administrative Law Judge’s Ruling Finalizing Load Forecasts and Greenhouse Gas Emissions Benchmarks for 2022 Integrated Resource Plans: https://docs.cpuc.ca.gov/PublishedDocs/Efile/G000/M485/K625/485625915.PDF [Accessed 10/7/2022]
9 Final 2021 IEPR available at: https://www.energy.ca.gov/da ta-reports/reports/integrated-energy-policy- report/2021-integrated-energy-policy-report [Accessed 9/6/22] 10 Final 2021 IEPR, Volume IV, Chapter 2. Available at: https://efiling.energy.ca.gov/GetDocument.aspx?tn=241581 [Accessed 10/13/2022]
DRAFT – October 2022
9 This is CleanPowerSF’s “Demand at the Generator Busbar,” which is the total demand modeled in each of the Conforming Portfolios described in this report.
CleanPowerSF opted to use a custom load, or demand shape due to significant observed differences between the load profile in the Clean System Power Calculator and CleanPowerSF’s internal demand forecast. This discrepancy occurs because the CPUC assigned load assumes a much higher rate of behind-the-meter solar generation for San Francisco, which impacts CleanPowerSF’s load shape. The GHG Emissions Results section of this plan provides more detail on how the custom shape was developed.
Required and Optional Portfolios
As authorized by the ALJ, CleanPowerSF submits the same Conforming Portfolio for both the 30 MMT and 25 MMT portfolios. This portfolio was developed using CPUC provided inputs and assumptions as well as additional inputs and assumptions developed by CleanPowerSF, where allowed by the CPUC’s guidelines. Both of the Conforming Portfolios achieve emissions below CleanPowerSF’s assigned GHG benchmark under the 30 MMT scenario and the 25 MMT scenario and meet San Francisco’s policy objectives to achieve a 100% renewable and/or GHG-free electricity supply.
Along with this narrative template, CleanPowerSF is submitting the same completed CPUC Resource Data Template and Clean System Power Calculator for its Preferred Conforming Portfolio under the 30 MMT and 25 MMT target as appendices to this narrative. A list of the appendices follows:
• Appendix A: 25 MMT Preferred Conforming Portfolio Resource Data Template • Appendix B: 30 MMT Preferred Conforming Portfolio Resource Data Template • Appendix C: 25 MMT Preferred Conforming Portfolio Clean System Power Calculator • Appendix D: 30 MMT Preferred Conforming Portfolio Clean System Power Calculator Additionally, CleanPowerSF is submitting two Alternative Portfolios in this IRP. These portfolios were developed using increased EV and building decarbonization load assumptions and BTM PV generation inputs developed by CleanPowerSF. Both of the Alternative Portfolios achieve emissions below CleanPowerSF’s assigned GHG benchmark under the 30 MMT scenario and the 25 MMT scenario and meet San Francisco’s policy objectives to achieve an electricity supply with zero GHG emissions.
CleanPowerSF’s Climate Action Plan Portfolio uses load inputs guided by San Francisco’s transportation and building electrification goals; these are reported using the standard IEPR filing form template in Appendix E.
GHG Emissions Benchmark
DRAFT – October 2022
10 CleanPowerSF was assigned a GHG emissions benchmark under both a 30 MMT and a 25 MMT statewide GHG emissions planning target for the 2035 planning year. These statewide targets represent the maximum levels of GHG emissions in 2035 that all California LSE portfolios may emit once aggregated into a statewide total. The 2035 targets were set by Administrative Law Judge’s (ALJ) ruling using the straightline projection between the 2021 Preferred System Plan’s (PSP) 2030 38 MMT GHG emissions planning target and the 2045 15 MMT GHG emissions planning target used in the modeling for the PSP decision.
The CPUC set CleanPowerSF’s individual benchmarks using CleanPowerSF’s proportional share of electricity demand in Pacific Gas & Electric Company’s (PG&E) territory; the two benchmarks are included in Table 4 below. GHG emissions associated with behind-the-meter combined heat and power (BTM CHP) were netted out at the system level and were not included in CleanPowerSF’s assigned GHG emissions benchmark. BTM CHP emissions, which total 4.4 MMT in 2035 for the entire electric sector, will be added to the electric sector emissions calculation by CPUC’s Energy Division when it aggregates all submitted 2022 Integrated Resource Plans.11
Table 4 CleanPowerSF GHG Benchmarks
2035 GHG PLANNING TARGET 30 MMT 25 MMT
CleanPowerSF 2035 GHG Benchmark
(MMT)
0.340 0.272
a. Objectives
Program Goals
The SFPUC adopted the following program goals for CleanPowerSF, which guide CleanPowerSF’s operations and the development of this IRP.
• Lead with Affordable and Reliable Service: CleanPowerSF is committed to providing service that is reliable and affordable for all San Franciscans. To do so, CleanPowerSF pursues the lowest cost energy supply possible that also satisfies its other program goals. Sourcing the most affordable energy requires both execution of desirable, low-cost contracts, and strategic management of its portfolio to allow CleanPowerSF to take advantage of market opportunities, while minimizing ratepayer risk.
• Provide Cleaner Electricity Alternatives: A critical element of CleanPowerSF’s mission is to provide cleaner energy alternatives to San Francisco. Today, CleanPowerSF offers a “Green”
11 ALJ Ruling Assigning GHG Benchmark available at:
https://docs.cpuc.ca.gov/PublishedDocs/Efile/G000/M485/K625/485625915.PDF [Accessed 9/7/2022] DRAFT – October 2022
11 electricity supply product that is at least 50% RPS-eligible renewable. CleanPowerSF’s second electricity supply product is called “SuperGreen” and features 100% RPS-eligible renewable energy. Consistent with San Francisco’s goal, CleanPowerSF plans to supply a 100% renewable and/or GHG-free power mix by 2025.
• Invest in Local Renewable Projects and Local Jobs: As CleanPowerSF continues to procure additional renewables to meet its program demand, it does so with a preference for local projects, where cost-effective.
• Provide for Long-Term Rate and Financial Stability: CleanPowerSF manages its program and its power supply portfolio to minimize rate increases while providing for long-term financial stability of the program.
City Policies
The City policies regarding energy supply product content were developed over time; the current targets guide CleanPowerSF procurement planning:
• Ordinance No. 81-08: In Ordinance 81-08, the Board of Supervisors articulated the goal of having a fossil-free electric system by 2030 .12
• Resolution 349-11: In this Resolution, the Board of Supervisors approved San Francisco’s 2011 Updated Electricity Resource Plan calling for a “City-wide plan to meet San Francisco’s zero-GHG goal by 2030” including through the development of Community Choice Aggregation.13 • Resolution 17-0102: On Earth Day 2017, the mayor of San Francisco announced a new City goal of a 50% renewable electricity supply by 2020.14 This goal was adopted for the CleanPowerSF program in San Francisco Public Utilities Commission (SFPUC) Resolution 17-0102, which directs that “renewable energy content of the Green (default) product from 35% to 50% by the end of 2020, or sooner if possible.”
15 • Ordinance No. 117-21: On Earth Day 2021, the mayor of San Francisco announced that CleanPowerSF will provide all customers with 100% renewable electricity by 2025.16 Ordinance
12 San Francisco Ordinance No. 81-08, (May 13, 2008) sec. 907, available at:
https://www.sfbos.org/ftp/uploadedfiles/bdsupvrs/ordinances08/o0081- 08.pdf [Accessed 10/10/2022] 13 San Francisco Resolution No. 349-11 (July 25, 2011) available at https://sfbos.org/ftp/uploadedfiles/bdsupvrs/resolutions11/r0349 -11.pdf [Accessed 10/10/2022] 14 City and County of San Francisco Office of the Mayor. Mayor Lee Announces San Francisco Exceeds New Greenhouse Gas Emissions Reduction Milestone. https://sfmayor.org/article/mayor-l ee-announces-san-francisco- exceeds-new-greenhouse-gas-emissions-reduction-milestone [Accessed 10/12/2022] 15 SFPUC Resolution 17-0102 (May 9, 2017) available at:
https://sfpuc.sharefile.com/share/view/s885b58732ca4f709 [Accessed on 10/10/2022] 16 City and County of San Francisco Office of the Mayor. Mayor London Breed Announces New Climate Commitments and Environmental Successes. https://sfmayor.org/article/mayor-lon don-breed-announces-new- climate-commitments-and-environmental-successes [Accessed 10/12/2022 ] DRAFT – October 2022
12 No. 117-21 set climate action goals for San Francisco, including the Mayor’s renewable electricity target.17
Using the targets established by the City’s policies, CleanPowerSF developed the annual product content targets for its Green product portfolio shown in Table 5.
Table 5 CleanPowerSF Green Product RPS-eligible Renewable and GHG-Free Power Content Goals POWER
CONTENT
GOALS
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
RPS-eligible
Renewable 58% 63% 68% 73% 73% 73% 73% 73% 73% 73% 73% 73% 73% 73% Additional
Renewable
and/or GHG-
Free
33% 31% 28% 27% 27% 27% 27% 27% 27% 27% 27% 27% 27% 27% Total
Renewable
and/or GHG-
Free
91% 94% 97% 100
%
100
%
100
%
100
%
100
%
100
%
100
%
100
%
100
%
100
%
100
%
To support further development of its portfolio, CleanPowerSF engaged consultants to develop modeling inputs, including costs and generation profiles for local resource options, and to develop portfolios under a range of different scenarios addressing the 2022 to 2035 time horizon. Guided by the adopted 2021 Preferred System Portfolio, CleanPowerSF and its consultants sought to determine the volume of existing renewable and GHG-Free energy resources it could reasonably procure as well as the most cost-effective new resource buildout for its portfolios. The overarching goal of the analysis was to identify the lowest cost portfolio mix that could meet the state’s RPS requirements, the City’s long-term electricity supply goals, and contribute sufficiently to maintaining systemwide reliability. b. Methodology
i. Modeling Tool(s)
CleanPowerSF engaged Siemens PTI as a consultant to support its IRP modeling. For CleanPowerSF’s 2022 IRP, Siemens PTI used version 14.0.1057 of the Aurora production cost modeling software from vendor Energy Exemplar.18 Siemens PTI tools fully integrate risk assessment into long-term energy and
17 San Francisco Ordinance No. 117-21 (July 27, 2022) sec. 902, available at:
https://sfbos.org/sites/default/files/o0117-21.pdf [Accessed 10/10/2022] 18 Further product information on Aurora version 14.0.1057 can be found on Energy Exemplar’s website here:
https://energyexemplar.com/solutions/aurora/ [Accessed 10/13/2 022].
DRAFT – October 2022
13 resource planning. The core component of our risk analysis system is the AURORAxmp® power dispatch and market price model, developed by EPIS and used extensively in the western U.S.
AURORA is a chronological unit commitment model which works to simulate the economic dispatch of power plants within a competitive market framework. The model uses a mixed integer linear programming (MIP) approach to capture details of power plant and transmission network operations, while observing real world constraints. Constraints include items such as emission reduction targets, transmission and plant operating limits, renewable energy availability and mandatory portfolio targets.
AURORA is widely used by electric utilities, consulting agencies, and other stakeholders for the purpose of forecasting generator performance and economics, developing IRPs, forecasting power market prices, assessing detailed impacts of regulatory and market changes impacting the electric power industry, and to generate financially optimized generating portfolios. The model can assess the potential performance and capital costs of existing and prospective generation technologies and resources, and make resource addition and retirement decisions for economic, system reliability, and policy compliance reasons on a utility system.
The CPUC used RESOLVE to develop the 2021 Preferred System Plan, which identifies the new resources needed to meet the GHG emissions planning constraint. CPUC uses SERVM as a separate tool to examine system reliability and simulate production cost. AURORA is both a long-term capacity expansion (LTCE) tool and a production cost model. AURORA and RESOLVE both optimize dispatch for a system under a given set of inputs. RESOLVE is a linear optimization model, which assesses dispatch based on representative days over a defined forecast horizon. AURORA differs in that it is a mixed integer program and hourly chronological dispatch simulation. Both RESOLVE and AURORA identify the optimal resources to meet needs based on the technology options offered including generation and storage. Both models also allow for the incorporation of different types of market and portfolio constraints including renewable generation, carbon emissions, reserve margin, and timing of new build requirements.
Table 6: Comparison of AURORA, RESOLVE, and SERVM Functionality
RESOLVE/SERVM AURORA
Groups resources into categories with
similar operational characteristics (e.g.,
nuclear, coal, gas CCGT, gas peaker,
renewables) and models them collectively.
Models each generator independently.
Linearized unit commitment where the
commitment variable for each class of
generators is a continuous variable rather
than an integer variable.
Models the operating cost and
performance parameters on a plant-level
basis, where the optimization method
uses a MIP to determine unit
commitment.
Run for a sampled 37 days in a year and
only for a few years, therefore, only
representative load and renewable
profiles were selected to reflect system
conditions.
In the LTCE process, Siemens used a
sampling of 104 days and every other hour
for each year of the 20-year study horizon.
In the final simulation of the system,
AURORA simulates plant operating and
DRAFT – October 2022
14 RESOLVE/SERVM AURORA
market conditions for every hour, every
day, and every year of the study horizon.
Generally, focuses on a single market,
reflecting high level interties and market
interaction with neighboring regions.
AURORA can be set up in several different
ways. For this analysis, AURORA was run
for the entire Western Interconnection.
Some key features of AURORAxmp® are:
• A dispatch algorithm that is similar to other production cost models like the RESOLVE model used by the CPUC;
• A unique bidding structure that simulates market incentives for investing in plants that can cause over or under building of assets. This feature provides a distinct advantage over equilibrium models, as power markets are rarely in equilibrium; • An ability to characterize market volatility and uncertainty in stochastic distribution representations for all fuels and the price movement correlations among these inputs; • An ability to conduct Monte Carlo simulations generated from these distributions to generate power dispatch and market price simulations;
• Direct links to nodal transmission models for accurate representations of LMP/nodal pricing and transmission congestion;
• Accurate modeling of SO
2, NOX, and carbon emission rate and emissions costs;
• Integrated evaluation of multiple portfolios in the context of one integrated model; • Easy downloads to graphics packages for representing inputs and results in easy to follow and understandable graphics.
Siemens PTI simulates the generation market prices and plant dispatch for a relevant market area over the range of inputs using its power dispatch model (AURORAxmp®). This model is a sophisticated market dispatch model that has every generating station (including its characteristics, regulatory requirements and transmission links) in the United States. Inputs to the model include operating cost and performance parameters, fuel options and costs and environmental costs and parameters.
Siemens PTI deploys AURORAxmp® to simulate the economic dispatch of power plants within a competitive framework. Representations of hourly regional demand profiles and plant-level supply characteristics are included, as well as detailed assessments on the fundamental drivers of power plant dispatch within each relevant market area. A summary of the methodology with key inputs, algorithms, and outputs is shown in the Figure 2 below.
Figure 2: AURORAxmp® Methodology Schematic
DRAFT – October 2022
15 There are some key similarities and differences between the Aurora model and the RESOLVE model.
AuroraAs indicated above, AURORA is both a production model and a LTCE optimization model. AURORA is an hourly, chronological production cost model with an integrated LTCE feature. The LTCE produces a resource expansion plan given resource options and constraints around those options. The options can include supply and demand generic resources, including energy storage, existing resources, and resources for economic retirement as desired. The full set of standard operational and cost parameters for new and existing resources are considered in the LTCE, providing a robust framework from which to evaluate different technologies with different operational (intermittent vs. baseload), cost and incentive profiles. The LTCE considers constraints such as reserve margin targets or requirements, RPS requirements, carbon limits, and operational constraints for providing ancillary services. Siemens’ LTCE logic is illustrated in the figure above.
The LTCE model makes use of an iterative logic to develop a regional capacity expansion plan. At the end of any given iteration, it has the information it needs to take retirement actions on existing uneconomic resources and to select economically viable new resource options. Convergence criteria reduce the total number of resource alternatives which are considered by the LTCE model through the iterations, with a converged solution being defined as one in which system prices remain stable even with changes in resource alternatives. In other words, the solution reflects an expansion plan that is at once both economically rational and stable.
With this approach, AURORA performs an iterative future analysis where:
1. Resources that have negative going-forward value (revenue minus costs) are retired;
2. Resources with positive values are added to the system on a gradual basis, whereby a set of resources with the most positive net present value are selected from the set of new resource options and added to the study;
3. AURORA then uses the new set of resources to compute all the values again; and
4. The process of adding and retiring resources is continually repeated until the system price stabilizes, indicating that an optimal set of resources has been identified for the study.
DRAFT – October 2022
16 Aurora differs in that it is an hourly chronological dispatch model. RESOLVE generally focuses on a single market, reflecting high level interties and market interaction with neighboring regions. Aurora can be set up in several different ways. Siemens PTI has developed Aurora as a national model reflecting all major zones and ISOs in the U.S. For this analysis, Aurora was run for the entire western interconnect.
Both RESOLVE and Aurora identify the optimal resources to meet needs based on the technology options offered including generation, storage and demand side resources. Both models also allow for the incorporation of different types of market and portfolio constraints including renewable generation, carbon emissions (or emission rates), reserve margin, and timing of new build requirements. ii. Modeling Approach
As noted above, CleanPowerSF’s IRP portfolios were designed to meet or exceed City and state power supply goals and were modeled to be CPUC Conforming Portfolios. To accomplish this, CleanPowerSF introduced a number of underlying assumptions, minimum requirements, and constraints to which all developed portfolios would adhere.
With the exception of local resources, CleanPowerSF’s 2022 IRP inputs and assumptions reflect these from the California Public Utilities Commission’s Preferred System Portfolio (CPUC’s PSP). These include updates released on June 15, 2022.19
The CPUC requires using their assumptions from the RESOLVE model for the base outlook but allowed LSEs to use different capital cost and financing information. CleanPowerSF elected to use capital cost and financing information developed by Siemens PTI. Key inputs include:
• Load forecasts
• Fuel costs
• Emission costs
• Technology costs
• Resource availability
• Transmission constraints
• Senate Bill 100 (SB100) increased the state’s renewable portfolio standard to 60% by 2030 • GHG target for the electric sector for 2030 is 30 MMT for the first Conforming Portfolio20 • GHG target for the electric sector for 2030 is 25 MMT for the second Conforming Portfolio The candidate resources’ capital cost, operating cost, and levelized cost of energy used in the analysis were derived from Siemens PTI and included below for reference. Cost values were taken from a combination of resources, including, but not limited to, National Renewable Energy Laboratory, IHS Market, Energy Information Administration, American Association of Cost Engineers, and Lawrence Berkeley National Laboratory, and were adjusted to reflect investment tax credit (ITC) and production
19 See LSE Filing Requirement RESOLVE Results available at: https://www.cpuc.ca.gov/industries -and- topics/electrical-energy/electric-power-procurement/long-term-procurement-planning/2022-irp-cycle-events-and- materials [Accessed 9/20/2022].
20 As mentioned above, CleanPowerSF is submitting one preferred portfolio with GHG emissions below CleanPowerSF’s proportional share of the 25 MMT benchmark.
DRAFT – October 2022
17 tax credit (PTC) benefits resulting from the Inflation Reduction Act of 2022.21 Additionally, CleanPowerSF conducted extensive analysis on local project options on City-owned properties within San Francisco and the nine Bay Area counties and used those project-specific costs for local resource options in its IRP modeling. An overview of the costs used are featured in Table 7 and Table 8 below.
Table 7 Average Resource Levelized Cost of Energy (2021$/MWh) 2022 2025 2030 2035
Geothermal $63 $50 $49 $53
Utility Scale Solar $39 $40 $31 $33
Onshore Wind $105 $40 $31 $33
Li-Ion Battery
(Standalone)
$92 $93 $71 $73
Solar PV +Li-Ion
Battery (Hybrid)
$62 $57 $45 $47
Offshore Wind $171 $170 $130 $135
Flow Battery $89 $92 $80 $52
Pacific Northwest
Hydro
$76 $81 $65 $56
In-State Large
Hydro
$76 $81 $65 $56
Table 8 Local Project Costs (2021$/MWh)
IN-CITY SOLAR
IN-CITY SOLAR PV +
LI-ION BATTERY
(HYBRID)
9 COUNTY BAY
AREA SOLAR PV +
LI-ION BATTERY
(HYBRID)
2-5 MW $153-$167 $274-$384 NA
5-20 MW NA NA $163-$187
CleanPowerSF staff used industry reports and conducted analysis to determine the incremental costs of existing renewable resources above projected energy market costs. These annual forecasts are shown in Table 9 below.
21 Resource cost assumptions were adjusted to reflect the technology neutral nature of PTCs and ITCs adopted pursuant to the Inflation Reduction Act of 2022.
DRAFT – October 2022
18 Table 9 Incremental Costs of Existing Renewable Resources (2021$/MWh)
YEAR RENEWABLE
2022 $9.00
2023 $8.50
2024 $8.00
2025 $6.75
2026 $6.00
2027 $5.00
2028 $3.90
2029 $3.00
2030 $2.00
2031 $1.50
2032 $1.00
2033 $0.25
2034 $0.00
2035 $0.00
Using the costs assumptions above, CleanPowerSF’s modeling approach aimed to develop the most cost-effective portfolios that also meet the following constraints. These were incorporated into the model to ensure each portfolio met CleanPowerSF environmental, cost, and local investment program goals:
• All portfolios have an RPS-eligible content of at least 73% and are 100% renewable and/or GHG- free by 2025.
• All portfolios prioritize local resources and contain 50 MW of local geothermal, 85.6 MW of local solar, and 150 MW of local battery storage.
• All portfolios meet at least 60% of CleanPowerSF’s projected System Resource Adequacy Requirements with long-term renewable contracts by 2030. • The earliest online date for new build resources not already under contract was 2026 to allow for contracting lead time.
• A hedging constraint was applied which limited long-term contracts to 62% of contracted energy in each portfolio.
• Future purchases of large hydro were limited to CleanPowerSF’s proportional share of what was included for in-state and Pacific Northwest large hydro in the CPUC’s 2021 Preferred System Plan.
• Net market purchases as a percentage of total demand were not permitted to exceed 20% in all portfolios.
• The Time Coincident case does not allow for market purchases of more than 10% on an annual basis beginning in 2030.
DRAFT – October 2022
19 CleanPowerSF modeled two Conforming Portfolios: CleanPowerSF’s Base Case and a Time Coincident Case. The Base Case was modeled as a baseline portfolio, one which meets CleanPowerSF’s established goals discussed above. The Time Coincident Case was modeled to achieve CleanPowerSF’s 2025 product content goals with renewable and/or GHG-free resources that meet <90% of projected customer demand on an hourly basis. Additionally, CleanPowerSF modeled two Alternative Portfolios.
The Climate Action Plan Case meets the additional demand projected from San Francisco’s 2040 GHG emission-free passenger trips and building decarbonization targets.22 The Local Resource Procurement Case meets 50% of energy supply with local resources by 2030.
Once the results were received, CleanPowerSF staff evaluated both of the portfolios using a set of metrics tied to CleanPowerSF program goals:
Figure 3: CleanPowerSF IRP Portfolio Evaluation Criteria
Climate Action Plan Portfolio
The advanced decarbonization goals of the City of San Francisco require CleanPowerSF to model a more aggressive schedule for decarbonization than that set in the Conforming Portfolio, which has led to the development of the Alternative Portfolio achieving the Mayor’s EV and building electrification targets. San Francisco’s Environment Code and its 2021 Climate Action Plan Update include targets to have zero emissions large commercial buildings by 2035 and all buildings zero emissions by 2040.23 San Francisco has also passed legislation that requires (as of June 2021) all new construction to be all-
22 See San Francisco Env. Code § 902(b)(2) and (5); San Francisco’s 2021 Climate Action Plan (page 41) available at:
https://sfenvironment.org/sites/default/files/cap_fulldocument_wappendix_web_2 20124.pdf [Accessed on 9/21/2022].
23 San Francisco Environment Code § 902(b)(5). San Francisco’s Climate Action Plan 2021, page 41.
DRAFT – October 2022
20 electric.24 Reflecting advanced transportation electrification goals, the Climate Action Plan Update puts forth that by 2040 100% of vehicles registered within San Francisco should be electric, achieving 25% electric vehicle registration by 2030.25 These targets have significant implications for CleanPowerSF long term planning and procurement.
CleanPowerSF staff commissioned a model built to review how the achievement of the Mayor’s building electrification targets is expected to impact CleanPowerSF’s total demand, peak demand and associated procurement requirements in each year.26 The model’s flexibility has allowed for review of how different variables – including therms of natural gas end use demand to be removed by rate class27, kWh-per-therm removed electrification rates by sector and subsector28, proportion of load electrifying to CleanPowerSF service, and adoption rates – impact the increase in total and peak demand. Iterative review of different sensitivities have led to development of a “most likely” mid case annual amount of increased energy demand due to building electrification (high and low cases reflecting sensitivities run have also been developed to inform CleanPowerSF procurement risk management). The total increase in each year has been developed into an hourly profile using the building electrification shapes provided in the Clean System Power calculator, after a modification to align with the monthly distribution of natural gas consumption in San Francisco for residential and commercial sectors and weighted to represent the percent of residential vs commercial load expected to be electrifying in each year.
CleanPowerSF staff also developed a model to project how the achievement of the Mayor’s vehicle electrification targets is expected to impact CleanPowerSF total demand and peak demand. The model separately calculates electricity usage estimates for an increasing amount of private electric vehicles and commercial electric vehicles. The private vehicle forecast (all fuels) derives three categories of private vehicle sales
29 calculated separately based on U.S. light duty vehicle (LDV) sales data and historic purchasing patterns. The resulting sales were multiplied by the average miles San Francisco vehicles are
24 San Francisco’s Building Code §106A.1.17.1.
25 San Francisco Environment Code § 902(b)(2); San Francisco’s Climate Action Plan 2021, page 41. 26 The adoption rate of the Mayor's Targets reflects the expected rates of building redevelopment and retrofit provided in the 2021 San Francisco Climate Action Plan Update (page 65) and discussions with the San Francisco Department of Environment.
27 The analysis used Item 16 natural gas utility data for San Francisco. 28 Analysis of the kWh-per-therm electrification rate considered a range of data, including the electrification rates used in electrification analysis within the California Energy Commission 2021 Additional Achievable Fuel Substitution data, 2019 IEPR Decarbonization Docket (19-IEPR-06), comparisons of CEC model building scenarios within cost effectiveness studies with data specific to each Climate Zone developed through the Statewide Codes & Standards Utility Program, and building data provided by the San Francisco Department of Environment. Climate Zone 3 and/or San Francisco-specific data was prioritized due to San Francisco’s mild climate and distinct HVAC and overall energy use profiles.
29 Light duty auto battery electric vehicles (BEVs), light duty auto plug- in hybrid electric vehicles (PHEVs), and light duty trucks.
DRAFT – October 2022
21 driven in a year30 and the fuel economy (Wh/mile) per category31 to identify registered vehicle annual energy use. A similar analysis was conducted for vehicles commuting into San Francisco.32 Commercial vehicle electric energy use was forecasted using EIA Annual Energy Outlook data due to incomplete local data. California and national commercial truck registrations from 2019 were used to estimate California and San Francisco commercial truck sales, and adoption rates specific to achieving the City goals were applied across commercial vehicle classes.33 The total annual energy requirements for all private and commercial electric vehicles were summed and shaped into hourly additional load using the managed EV hourly load shape provided in the 20-IEPR-03 report.
The hourly additional load data produced by the building electrification and the transportation electrification analyses were added to CleanPowerSF’s baseline hourly demand to generate the Climate Action Plan Portfolio demand forecast.
III. Study Results
a. Conforming and Alternative Portfolios
CleanPowerSF developed two Conforming and two Alternative Portfolios under the 25 MMT scenario. Each portfolio contains a mix of resource types with varying development statuses (e.g., online, in development, planned). The 2035 portfolios for each case are summarized in the tables below.
Base Case
Solar is the most cost-effective renewable resource, pairing it with storage allows its generation to be delivered in the evening hours when the sun is not shining. The new build in this portfolio consists mostly of solar and both hybrid and standalone storage, but also includes 30 MW of geothermal not currently under contract and 240 MW of existing resources which are included in all portfolios.
30 Uses data on vehicle miles traveled (VMT) from the Metropolitan Transportation Commission Plan Bay Area 2040, Land Use Modeling Report linked on pp. 44, available at https://mtc.ca.gov/planning/long-ra nge- planning/plan-bay-area-2040 [Accessed on 10/13/2022].
31 Fuel efficiency is assumed to improve through the forecast period. 32 The commuter traffic vehicle count was provided by the Mayor’s Electric Vehicle Working Group’s count of commuters, which was derived from a 2013 survey, available at https://sfenvironment.org/electricmobilitysf [Accessed on 10/12/2022].
33 Adoption rates for electric trucks was sourced from International Council on Clean Transportation (ICCT) Working Paper 2020-28, which assessed the charging infrastructure required in San Francisco to support 100% electric vehicle use. https://theicct.org/wp-content/uploads/2021/06/SF-EV-charging-infra-oct2020.pdf [Accesse d 10/14/2022].
DRAFT – October 2022
22 Table 10: Base Case 2035 Portfolio
RESOURCE CONTRACT STATUS
Existing Resources Under
Contract
San Pablo Raceway Solar (100 MW) Online
Blythe Solar (62.5 MW Solar, XX MW
Storage)
Online
Maverick Solar+Storage (100 MW Solar, 50
MW Storage)
Online
Voyager Wind IV Expansion (50.1 MW) Online
Oasis Wind (60.3 MW) Online
Geysers Geothermal (50 MW) Online
Sunset Reservoir Solar (5 MW) Online
New Resources
CleanPowerSF Has or Plans to
Invest In
Aramis Solar (75 MW Solar, 75 MW
Storage)
Development
Paulsell Solar+Storage (20 MW Solar, 20
MW Storage)
Development
University Mound – North Basin (3.5 MW
Solar)
Development
Sutro Reservoir (2.1 MW Solar) Development
Goal Line (10.8 MW Storage) Development
Tumbleweed (11.1 MW Storage) Development
Fish Lake (1.9 MW Geothermal) Development
Ormat Geothermal Portfolio (17.4 MW
Geothermal)
Development
Geothermal (30 MW) Planned New
Standalone Storage (325 MW) Planned New
DRAFT – October 2022
23 RESOURCE CONTRACT STATUS
Solar Hybrid (100 MW Solar, 50 MW
Storage)
Planned New
Existing Resources
CleanPowerSF Plans to
Contract With
In-State Large Hydro (60 MW) Planned Existing
Blended Renewable and GHG-Free Existing
Resources (180 MW)
Planned Existing
Time Coincident Case
The Time Coincident Case requires the development of 922 MW of new energy supply and storage capacity, to ensure CleanPowerSF demand is met in >90% of all hours with renewable and GHG-free resources. While this results in greater portfolio diversity, it also creates significant sales of excess generation to the market. This portfolio requires an additional 180 MW of new renewable and GHG-free resources as the Base Case to help meet the hourly constraint, again resulting in excess generation during certain hours that must be sold into the market.
Table 11 Time Coincident Case 2035 Portfolio
RESOURCE CONTRACT STATUS
Existing Resources Under Contract
San Pablo Raceway Solar (100
MW)
Online
Blythe Solar (62.5 MW Solar, XX
MW Storage)
Online
Maverick Solar+Storage (100 MW
Solar, 50 MW Storage)
Online
Voyager Wind IV Expansion (50.1
MW)
Online
Oasis Wind (60.3 MW) Online
Geysers Geothermal (50 MW) Online
Sunset Reservoir Solar (5 MW) Online
DRAFT – October 2022
24 RESOURCE CONTRACT STATUS
New Resources CleanPowerSF Has
or Plans to Invest In
Aramis Solar (75 MW Solar, 75
MW Storage)
Development
Paulsell Solar+Storage (20 MW
Solar, 20 MW Storage)
Development
University Mound – North Basin
(3.5 MW Solar)
Development
Sutro Reservoir (2.1 MW Solar) Development
Goal Line (10.8 MW Storage) Development
Tumbleweed (11.1 MW Storage) Development
Fish Lake (1.9 MW Geothermal) Development
Ormat Geothermal Portfolio (17.4
MW Geothermal)
Development
Geothermal (60 MW) Planned New
Standalone Storage (375 MW) Planned New
Solar Hybrid (100 MW Solar, 50
MW Storage)
Planned New
Wind (100 MW) Planned New
Existing Resources CleanPowerSF
Plans to Contract With
In-State Large Hydro (60 MW) Planned Existing
Blended Renewable and GHG-Free
Existing Resources (180 MW)
Planned Existing
Climate Action Plan Case (Alternative Portfolio)
The Climate Action Plan Case requires the most new resource development out of the four modeled portfolios, totaling 1,682 MW in new resource capacity by 2035. The greater capacity needs are a result of the additional customer demand projected by meeting San Francisco’s 2040 GHG emission-free passenger trips and building decarbonization targets. On average, CleanPowerSF forecasts annual DRAFT – October 2022
25 customer load to exceed its assigned IRP sales forecast34 by 23% over the planning horizon in this Alternative Portfolio. Additionally, the Climate Action Plan Case uses CleanPowerSF’s behind-the-meter solar (BTM PV) generation assumptions, averaging 74% less installed capacity than its assigned BTM PV forecasts annually over the planning horizon.35
Table 12 Climate Action Plan Case 2035 Portfolio
RESOURCE CONTRACT STATUS
Existing Resources Under Contract
San Pablo Raceway Solar (100
MW)
Online
Blythe Solar (62.5 MW Solar, XX
MW Storage)
Online
Maverick Solar+Storage (100 MW
Solar, 50 MW Storage)
Online
Voyager Wind IV Expansion (50.1
MW)
Online
Oasis Wind (60.3 MW) Online
Geysers Geothermal (50 MW) Online
Sunset Reservoir Solar (5 MW) Online
Aramis Solar (75 MW Solar, 75
MW Storage)
Development
Paulsell Solar+Storage (20 MW
Solar, 20 MW Storage)
Development
34 See CPUC-assigned load forecasts and GHG benchmarks dated June 28, 2022: http s://www.cpuc.ca.gov/- /media/cpuc-website/divisions/energy-division/documents/integrated-resource-plan-and-long-term- procurement-plan-irp-ltpp/2022-irp-cycle-events-and-materials/2022-final-ghg-emission-benchmarks-for- lses_public.xlsx [Accessed 9/21/2022].
35 CleanPowerSF forecasted total BTM PV generation in its service territory by using historical Rule 21 interconnection data for San Francisco and applying a growth factor to forecast incremental annual capacity through the planning horizon.
DRAFT – October 2022
26 RESOURCE CONTRACT STATUS
New Resources CleanPowerSF Has
or Plans to Invest In
University Mound – North Basin
(3.5 MW Solar)
Development
Sutro Reservoir (2.1 MW Solar) Development
Goal Line (10.8 MW Storage) Development
Tumbleweed (11.1 MW Storage) Development
Fish Lake (1.9 MW Geothermal) Development
Ormat Geothermal Portfolio (17.4
MW Geothermal)
Development
Geothermal (30 MW) Planned New
Standalone Storage (225 MW) Planned New
Solar Hybrid (430 MW Solar, 230
MW Storage)
Planned New
Wind (200 MW) Planned New
Offshore Wind (100 MW) Planned New
Existing Resources CleanPowerSF
Plans to Contract With
In-State Large Hydro (60 MW) Planned Existing
Blended Renewable and GHG-Free
Existing Resources (180 MW)
Planned Existing
Local Resource Procurement Ca se (Alternative Portfolio) The Local Resource Procurement Case requires 1,716 MW of new resource development by 2035, including 1,248 MW of local resource capacity. The Local Resource Procurement Case meets 50% of CleanPowerSF customer demand with local resource generation by 2030.36 Similar to the Climate Action
36 CleanPowerSF defines “local resources” as resources located in one of the nine Bay Area counties: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, and Sonoma.
DRAFT – October 2022
27 Plan Case, the Local Resource Procurement Case uses CleanPowerSF’s behind-the-meter solar (BTM PV) capacity assumptions, with an estimated 135 MW of installed BTM PV capacity by 2035.37
Table 13 Local Resource Procurement Case 2035 Portfolio
RESOURCE CONTRACT STATUS
Existing Resources Under Contract
San Pablo Raceway Solar (100
MW)
Online
Blythe Solar (62.5 MW Solar, XX
MW Storage)
Online
Maverick Solar+Storage (100 MW
Solar, 50 MW Storage)
Online
Voyager Wind IV Expansion (50.1
MW)
Online
Oasis Wind (60.3 MW) Online
Geysers Geothermal (50 MW) Online
Sunset Reservoir Solar (5 MW) Online
New Resources CleanPowerSF Has
or Plans to Invest In
Aramis Solar (75 MW Solar, 75
MW Storage)
Development
Paulsell Solar+Storage (20 MW
Solar, 20 MW Storage)
Development
University Mound – North Basin
(3.5 MW Solar)
Development
Sutro Reservoir (2.1 MW Solar) Planned New
Goal Line (10.8 MW Storage) Development
Tumbleweed (11.1 MW Storage) Development
37 Id at note 37.
DRAFT – October 2022
28 RESOURCE CONTRACT STATUS
Fish Lake (1.9 MW Geothermal) Development
Ormat Geothermal Portfolio (17.4
MW Geothermal)
Development
Standalone Storage (75 MW) Planned New
Local Geothermal (25 MW) Planned New
Local Solar (1.3 MW) Planned New
Local Solar Hybrid (483.6 MW
Solar, 460 MW Storage)
Planned New
Local Wind (25 MW) Planned New
Existing Resources CleanPowerSF
Plans to Contract With
In-State Large Hydro (60 MW) Planned Existing
Blended Renewable and GHG-Free
Existing Resources (180 MW)
Planned Existing
Local Geothermal (25 MW) Planned Existing
Compared to the 2021 Preferred System Plan, CleanPowerSF’s portfolios exceed CleanPowerSF’s proportional share of battery storage and geothermal, while only CleanPowerSF Alternative Portfolios exceeds CleanPowerSF’s proportional share of new build solar and offshore wind. The Time Coincident and Climate Action Plan portfolios exceed CleanPowerSF’s proportional share of wind resources. More new electricity supply and capacity build is needed to achieve San Francisco’s aggressive clean energy goals, as illustrated by the resource needs of CleanPowerSF’s Alternative Portfolios summarized above.
CleanPowerSF will closely monitor load growth within its service area and will conduct additional long- term renewable energy and capacity procurement, as appropriate, to meet its customer’s demand for these clean energy resources.
The greatest discrepancy between the 2021 Preferred System Plan and CleanPowerSF’s Preferred Conforming Portfolio involves the combined total capacity of wind, battery storage and geothermal. The Preferred Conforming Portfolio requires more than twice the capacity of these resources than CleanPowerSF’s 2035 proportional share in the 2021 PSP. The storage in these portfolios help shift the generation of lower cost intermittent renewable resources (like wind) while geothermal provides baseload generation that supplements intermittent renewables CleanPowerSF’s energy resource portfolio. These portfolio results indicate that future CleanPowerSF procurement will prioritize the acquisition of resources that compliment solar and other intermittent generation.
DRAFT – October 2022
29 Table 14 Comparison of CleanPowerSF Portfolios to 2021 Preferred System Plan 38 MMT 2021
PREFERRED
SYSTEM PLAN
CLEANPOWERSF’S
PROPORTIONAL
SHARE (MW)
BASE CASE
(MW)
TIME
COINCIDENT
(MW)
CLIMATE ACTION
PLAN
(MW)
LOCAL
RESOURCE
PROCUREMENT
(MW)
Utility Scale
Solar 377.7 200.6 200.6 530.6 585.5
Wind 49.4 0 100 200 25
Out-of-State
Wind 21.0 0 0 0 0
Battery Storage 247.1 491.1 541.9 571.9 651.9
Offshore Wind 24.2 0 0 100 0
Geothermal 16.2 49.3 79.3 49.3 44.3
b. Preferred Conforming Portfolios
CleanPowerSF staff assigned the ranking in Table 15 below to each of the Conforming Portfolios, using the portfolio evaluation metrics discussed in the Modeling Approach section above.
Table 15 CleanPowerSF Portfolio Evaluation
BASE CASE TIME COINCIDENT CASE
Lead with Affordable Service
Cost 1 2
Reliability 2 1
Diversity 2 1
Provide Cleaner Energy Alternatives
Emissions Equivalent
Renewable Equivalent
DRAFT – October 2022
30 BASE CASE TIME COINCIDENT CASE
Invest in Local Projects and Jobs
Local Investment Equivalent
Provide for Long-Term Rate and Financial Stability
% Long-Term Energy 1 2
Market Exposure 2 1
CleanPowerSF’s Time Coincident Case portfolio should be used by the CPUC when aggregating all Load Serving Entity portfolio submissions in development of the 2023 Preferred System Plan.
CleanPowerSF prefers this portfolio because it aligns with local climate and power content goals established by San Francisco. These targets are embedded in CleanPowerSF’s program goals, which CleanPowerSF intends to meet. The Time Coincident Case best accomplishes these goals and will more likely reflect CleanPowerSF’s actual procurement strategy than the Base Case.
The Time Coincident Case is the optimal portfolio for meeting CleanPowerSF’s program goal of a 100% renewable and/or GHG-free electricity supply on an annual basis by 2025. Ultimately, this portfolio was selected as the Preferred Portfolio because it meets CleanPowerSF’s program goals and best aligns with stakeholder preferences. While CleanPowerSF’s stakeholder preferences were based on achieving the highest percentage of time coincidence, both the Base Case and Time Coincident Cases were found to perform equally in meeting local climate and power content goals. This suggests that CleanPowerSF can seek to implement a portfolio catered to a time coincidence goal without sacrificing local climate and power content goals.
CleanPowerSF’s Preferred Portfolio will require the addition of 260 MW of new renewable resources and 350 MW of battery storage not currently under contract, before 2030. CleanPowerSF also has existing contracts for 100.6 MW of solar, 19.3 MW of geothermal, and 191.9 MW of battery storage that are expected to come online by that date.
The Time Coincident Case meets CleanPowerSF’s power content goals and exceeds California RPS targets for its managed retail sales. By 2035, the Time Coincident Case’s energy supply relies heavily on new resource generation; existing renewables generate less than 14% and in- and out-of-state hydro
generate less than 9% of total energy supply. The Time Coincident Case limited the quantity of available hydro it could procure to its proportional share of the California and Pacific Northwest future hydro generation from 2023-2035, consistent with the 2021 PSP.
DRAFT – October 2022
31 Figure 4: CleanPowerSF Preferred Portfolio (Time Coincident Case) Power Content Summary
The Time Coincident Case relies on system sales, with net market sales peaking in 2023 at 32% and accounting for 21% percent of CleanPowerSF’s total demand in 2035. This indicates that the portfolio is fairly well balanced in terms of the amount of energy it is expected to supply the CAISO system versus the amount of energy it is expected to draw from the CAISO system.
Figure 5: Time Coincident Case Net Market Sales
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Solar Wind Geothermal
Existing Renewables Hydro CAISO System Power
CA RPS Requirement
-40%
-30%
-20%
-10%
0%
10%
20%
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Percent of Retail Sales
Market Purchases Market Sales Net Market Sales
DRAFT – October 2022
32 The Time Coincident Case was estimated to be slightly more expensive than the Base Case portfolio, due to the extra capacity needed to meet the 90% time coincidence constraint. The Time Coincident Case normalized portfolio costs are slightly higher than CleanPowerSF’s supply cost projection used in its most recent 10-year Financial Plans, however, market prices have increased since those projections were developed, and new build project costs are also up due to inflationary and supply chain issues. In 2035, the total weighted average portfolio cost is expected to be $87.02/MWh (2021$). The steady projected portfolio costs indicate that CleanPowerSF ratepayers can expect their rates to follow a similar trajectory while receiving an increasingly clean and reliable electricity supply.
Figure 6: Time Coincident Case Energy Portfolio Costs (2021$)
As stated above, CleanPowerSF prefers that its Time Coincident Case portfolio be used for CPUC systemwide aggregation purposes. CleanPowerSF has evaluated how this portfolio may perform from a reliability perspective if other LSEs procure in a manner that is consistent with the 2021 Preferred System Plan and projects that it will contribute CleanPowerSF’s share of reliability needs.
A resource’s Effective Load Carrying Capacity (ELCC) represents its contribution to system reliability as a percentage of its nameplate capacity. This value is dependent of the mix of resources on the system and penetration of any one resource. This means that as the amount of a renewable resource on the grid increases, its incremental contribution to system reliability decreases. The table below demonstrates how annual ELCC assumptions differ between the 30 MMT and 25 MMT conforming portfolios.
0 10
20 30
40 50
60 70
80 90
100
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2021$/MWh
90% Time Coincident CleanPowerSF Projected Supply Costs DRAFT – October 2022
33 Table 16 Annual ELCC Summary
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 30 MMT
Solar
10% 10% 11% 10% 9% 8% 6% 6% 6% 6% 6%
6%
Wind
(N. CA) 30% 30% 31% 24% 17% 17% 16% 15% 13% 12% 10%
9%
4-Hour
Storage 89% 90% 92% 85% 77% 76% 75% 68% 61% 54% 47%
40%
8-Hour
Storage 89% 91% 93% 90% 87% 86% 85% 82% 79% 76% 73% 70% 25 MMT
Solar
12% 12% 12% 10% 8% 8% 7% 7% 7% 7% 7%
6%
Wind
(N. CA) 24% 27% 31% 21% 12% 15% 19% 17% 15% 13% 11% 9%
4-Hour
Storage 85% 86% 87% 85% 82% 85% 89% 79% 69% 60% 50%
40%
8-Hour
Storage 89% 89% 88% 87% 86% 87% 89% 85% 81% 77% 73% 70%
Solar and wind resources contribute less to system reliability over time in both conforming portfolios due to the increased penetration of these resources needed to achieve lower emissions.
CleanPowerSF evaluated how the system RA contributions of long-term resources in the Time Coincident Case differed under each set of assumptions. The results are in Figure 7, which shows that the long-term renewable portion of the portfolio contributes the most to system reliability needs under each of the two emissions targets. In the mid-term, the portfolio has a slightly higher RA value because the ELCC for storage is higher under the 25 MMT scenario (ELCCs are higher for 4-hour storage beginning in 2028 and for 8-hour storage beginning in 2029). Steeper declines in the value of solar ELCC in later years result in less RA value in later years. By pairing much of the solar in CleanPowerSF’s Time Coincident Case with storage, CleanPowerSF prevents significant drops in the RA value of the portfolio regardless of whether the rest of the system is more reflective of the 30 MMT or 25 MMT planning target.
DRAFT – October 2022
34 Figure 7: Time Coincident Case Long-Term System Reliability Contributions
The increased amount of storage in the Time Coincident Case also helps increase the time coincidence of the portfolio by shaping renewable generation to better meet demand. This increases the reliability of CleanPowerSF’s portfolio because it maximizes the amount of customer demand that can be met with renewable energy on an hourly basis.
CleanPowerSF’s Preferred Conforming Portfolio is consistent with the requirements for a LSE’s IRP listed in Public Utilities Code Section 454.52(a)(1) as it:
• Emits XX% and 59.6%% less carbon dioxide than CleanPowerSF’s emissions benchmark under the 30 MMT and 25 MMT statewide targets, respectively. • Exceeds the 60% RPS requirement, containing up to 100% RPS-eligible renewables and be 100% GHG-free on an annual basis beginning in 2025.
• Maintains costs associated with the supply portfolio at a level that is reasonable, given current market conditions and price forecasts, allowing CleanPowerSF to continue providing customers with affordable and stable rates.
• Meets 65% of its projected System Resource Adequacy obligation with long-term contracted resources containing bundled energy and capacity attributes by 2035. • Prioritizes resources within the Bay Area region, contributing local reliability and transmission benefits.
0 100
200
300
400
500
600
700
800
900
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Effective MW
CAM Allocations Short-Term Capacity Contracts
Wind Offshore Wind
Solar Hydro
Geothermal Storage
Reliability Need (MW)
DRAFT – October 2022
35 • Minimizes CleanPowerSF’s reliance on system power and emitting resources like natural gas plants which are disproportionately located in California’s Disadvantaged Communities. c. GHG Emissions Results
CleanPowerSF was assigned a GHG emissions benchmark under each of the statewide targets in a CPUC Administrative Law Judge Ruling.38 These GHG emissions benchmarks are included in the table below:
Table 17 CleanPowerSF GHG Emissions Benchmarks
2035 GHG PLANNING TARGET 30 MMT 25 MMT
CleanPowerSF 2035 GHG Benchmark
(MMT) 0.340 0.272
In addition to the emissions associated with its power supply, all Load Serving Entities submitting an IRP were assigned an allocation of non-dispatchable wholesale, (i.e., in front of the meter) Combined Heat and Power (CHP) facility GHG emissions based on each LSE’s load ratio share (i.e., their share of the system annual energy usage). These CHP resources are assumed to provide energy through 2035 because they are part of an industrial or commercial process and export excess electricity they generate to the grid. These processes help support system reliability, which benefits all LSEs. Additionally, Energy Division staff will add in behind-the-meter CHP emissions when calculating electric sector emissions of the aggregated LSE portfolios during the development of the next Preferred System Plan. These emissions had previously been attributed to the Industrial sector and are now attributed to the electricity sector to align with California Air Resource Board (CARB) statewide emissions accounting.
These emissions are not associated with specified purchases by CleanPowerSF. 39 Due to the assignment
of CHP emissions, CleanPowerSF’s submitted portfolios need to emit slightly less than their assigned benchmark to ensure that the electricity sector as a whole meets its GHG emissions targets.
The Time Coincident Case portfolio CO2 emissions are 0.11 MMT in 2035, which are 59.6% lower than CleanPowerSF’s assigned 25 MMT benchmark of 0.272 MMT.
Table 18 Time Coincident Case Clean System Power Calculator Results Emissions Total Unit 2024 2026 2030 2035
38 ALJ Ruling Assigning GHG Benchmark available at:
https://docs.cpuc.ca.gov/PublishedDocs/Efile/G000/M485/K625/485625915.PDF [Accessed 9/7/2022]
39 ALJ Ruling Assigning GHG Benchmark available at:
https://docs.cpuc.ca.gov/PublishedDocs/Efile/G000/M485/K625/485625915.PDF [Accessed 9/7/2022] DRAFT – October 2022
36 CO2 MMt/yr 0.25 (0.10) 0.10 0.11
PM2.5 tonnes/yr 15 (9) 1 1
SO2 tonnes/yr 4 (1) 0 0
NOx tonnes/yr 47 4 14 7
CleanPowerSF used a custom demand shape in its IRP analysis and in the Clean System Power Calculator. A normalized shape using CleanPowerSF’s internally developed load forecast was compared against the generic shape provided for all LSEs in the CPUC’s Clean System Power Calculator. Hourly differences of up to 49 percent between the two resulted in CleanPowerSF opting to use a custom shape in its 2022 IRP analysis. As Figure 8 demonstrates, CleanPowerSF is a winter peaking program and has lower demand than the CAISO system average during the summer months, due in part to San Francisco’s unique weather which has cooler than average summer months and lower load during this time of the year.
Figure 8: CleanPowerSF Load Shape
CleanPowerSF’s custom load shape was developed using 2021 actual energy consumption and makes several adjustments based on expected changes to energy demand in the years ahead.
CleanPowerSF’s 2021 actual energy consumption was adjusted for modest reductions in future load caused by Direct Access departing customers in the winter of 2021/22 and, based on an internal analysis of COVID recovery, a 0.5% annual load growth factor was applied. Demand for each hour of the year was normalized by dividing it by 8,760, the total number of hours in a calendar year. Using these inputs, the Aurora model applied the hourly shape to the monthly energy and peak forecast to automatically generate an hourly load forecast for each year of the study period.
0.0000%
0.0050%
0.0100%
0.0150%
0.0200%
0.0250%
Normalized Demand (% of Annual Demand)Jan Feb Mar Apr May Jun Jul Aug Sep Nov Dec Clean System Power Calculator Internal Forecast
DRAFT – October 2022
37 CleanPowerSF utilized a custom load shape for BTM PV in the development of its Conforming Portfolios.
CleanPowerSF’s assigned BTM PV forecast is based on CleanPowerSF’s pro-rata share of load consistent with IEPR forecasts and is significantly higher than CleanPowerSF’s BTM PV load forecasts. To reconcile the over-allocation of BTM PV generation, CleanPowerSF relied on the load shape of a baseload resource to smooth assigned BTM PV load evenly across all hours of the day to best mimic CleanPowerSF’s expected mid-day load. This approach is intended to reflect the relevant climate conditions in San Francisco while meeting the requirement to use BTM PV forecast assignments in the development of the two Conforming Portfolios.
d. Local Air Pollutant Minimization and Disadvantaged Communities i. Local Air Pollutants
The statutory requirement for CleanPowerSF’s IRP to minimize localized air pollutants and other greenhouse gas emissions “with an early priority on disadvantaged communities is effectively achieved by CleanPowerSF’s Time Coincident Case portfolio.40
Table 19 below shows the PM2.5, SO2, and NOx criteria pollutant emissions for the Time Coincident Case. The CPUC’s Clean System Power Calculator assigns system power emissions to LSEs on an hourly basis. Additionally, a portion of these system power emissions are assigned to CleanPowerSF regardless of its available energy supply, which means system power emissions are assigned during certain hours even if CleanPowerSF’s supply portfolio is meeting its demand with renewable resources in those hours.
This is because there are certain hours of the day when emitting facilities must run even if there is over generation of non-emitting resources on the system. To ensure that all system emissions are accounted for across LSE portfolios during such hours, emissions from system power are allocated to LSEs on a pro rata basis. These are the emissions that cannot be displaced by the addition of renewable resources on the system.
The second source of CleanPowerSF’s portfolio criteria pollutant emissions results are emissions from CHP resources. Even though CleanPowerSF does not contract with any CHP facilities, emissions from non-dispatchable wholesale (i.e., in-front-of-the-meter) CHP generators are automatically allocated in the CPUC’s Clean System Power Calculator to LSEs proportional to their load share. More detail on this process can be found in the CPUC’s Clean System Power Calculator Documentation. 41 CleanPowerSF plans to strategically procure resources to reduce the system power content of CleanPowerSF’s portfolio. These procurement strategies are discussed further in the Action Plan section of this plan.
40 See Public Utilities Code Section 454.52(a)(1)(H)).
41 The Clean System Power Calculator Documentation is available at: https://www.cpuc.ca.gov/-/me dia/cpuc- website/divisions/energy-division/documents/integrated-resource-plan-and-long-term-procurement-plan-irp- ltpp/2022-irp-cycle-events-and-materials/clean-system-power-calculator-documentation.pdf [Accessed 9/7/2022]
DRAFT – October 2022
38 Table 19: Time Coincident Case Criteria Pollutant Results (In Progress)
ii. Focus on Disadvantaged Communities
CleanPowerSF aims to address the disproportionate environmental burdens historically faced by typically low-income communities predominated by people of color by maximizing the renewable content of its portfolio and minimizing procurement from polluting facilities located in these communities.
The California Office of Environmental Health and Hazard Assessment developed the CalEnviroScreen tool to identify Disadvantaged Communities in the State. It assigns a score to each census tract in the state based on a range of environmental, health, and socioeconomic factors to help identify census tracts in California that are most impacted by pollution and where individuals are most vulnerable to its effects. A higher score indicates that a community is more impacted. A Disadvantaged Community, for the purposes of this IRP, refers to California census tracts in the top 25 th percentile of
CalEnviroScreen 4.0 scores.
CleanPowerSF identified 14 census tracts within San Francisco that are Disadvantaged Communities.
CleanPowerSF serves customers within 13 of these census tracts.42 Collectively, these census tracts have a population of approximately 55,114. Nine of these tracts are in the Hunters Point region of San Francisco, two are around the Portola/Sunnydale/Visitation neighborhoods, one rests in the Moscone Center/South of Market (SoMA) neighborhoods, and the remaining one is in the Civic Center area. The 14th census tract is on Treasure Island, which is entirely served by Hetch Hetchy Power, San Francisco’s public power utility. The map below identifies these census tracts in purple. Table 20 and 21 provide additional information on CleanPowerSF customers in each of these census tracts.
42 A twelfth census tract, located on Treasure Island is entirely served by Hetch Hetchy Power, San Francisco’s municipal electric utility.
DRAFT – October 2022
39 Figure 9: San Francisco's Disadvantaged Communities
Table 20 CleanPowerSF Disadvantaged Communities (CalEnviroScreen 3.0)
CLEANPOWERSF
RESIDENTIAL
CUSTOMER
TOTAL CENSUS
TRACT
CLEANPOWERSF
RESIDENTIAL
CUSTOMER
TOTAL
CLEANPOWERSF
COMMERCIAL
CUSTOMER
TOTAL
CLEANPOWERSF
CUSTOMER
TOTAL
6075012301 706 186 892
6075012502 1027 134 1161
6075017601 7172 912 8084
6075017801 1611 178 1789
6075017902 1 0 1
6075023102 1656 55 1711
6075023103 1181 129 1310
DRAFT – October 2022
40 6075023200 1266 407 1673
6075023300 1330 140 1470
6075023400 730 290 1020
6075061200 1214 150 1364
6075980600 180 20 200
Grand Total 18074 2601 20675
Table 21 CleanPowerSF Disadvantaged Communities (CalEnviroScreen 4.0)
CENSUS TRACT
CLEANPOWERSF
RESIDENTIAL
CUSTOMER
TOTAL
CLEANPOWERSF
COMMERCIAL
CUSTOMER
TOTAL
CLEANPOWERSF
CUSTOMER
TOTAL
6075012502 1027 134 1161
6075017802 4175 702 4877
6075017902 1 0 1
6075023001 1239 119 1358
6075023003 1146 47 1193
6075023102 1656 55 1711
6075023103 1181 129 1310
6075023200 1266 407 1673
6075023300 1330 140 1470
6075023400 730 290 1020
6075025702 1182 207 1389
6075061000 1576 69 1645
6075061200 1214 150 1364
6075980600 180 20 200
Grand Total 17903 2469 20372
CleanPowerSF serves 20,372 customers in San Francisco’s Disadvantaged Communities which represent 5.26% percent of total CleanPowerSF customers.
While CleanPowerSF does not currently make specified purchases of energy from emitting facilities in Disadvantaged Communities, it is committed to minimizing its system power emissions and providing the following benefits to Disadvantaged Communities in and outside of its service area:
• Reduction in Use of Polluting Power Plants in Disadvantaged Communities: Through procurement of California and Bay Area renewable and GHG-free energy at rates above the state RPS requirements, CleanPowerSF plays a part in moving away from reliance on natural DRAFT – October 2022
41 gas power plants and their emissions, which disproportionately affect Disadvantaged Communities. The City and County of San Francisco, which operates the CleanPowerSF program, has successfully collaborated with state energy planning agencies in the past to close down large in-city pollution-emitting power plants (in the Hunters Point and Potrero areas of San Francisco).
• Prioritization on Program Affordability and Stability: A s previously discussed, CleanPowerSF program goals include affordability and long-term rate stability. CleanPowerSF’s net revenues go back to ratepayers, either in the form of lower rates or customer-sided programming and incentives. CleanPowerSF’s financial reserves provide rate stabilization protection of its most economically vulnerable ratepayers. • Programs Accessible to All: As CleanPowerSF continues to develop new customer-sided programming, programs for Disadvantaged Communities and hard-to-reach populations – including financing and offerings for multi-family residential – are a top priority.
CleanPowerSF currently offers and is in the process of planning programs with the aim of providing ben
efits to low-income customers and customers located in Disadvantaged Communities:
• GoSolarSF, the SFPUC’s solar incentive program provided more than $1 million in financial incentives to support low-income access to on-site renewable generation at more than 120 customer sites since CleanPowerSF began service in 2016. While the GoSolarSF Program retired most all its solar incentive rebate categories, its one remaining solar rebate category supports exclusively Disadvantaged Communities – Single-Family Solar Homes (DAC-SASH) eligible customers receiving solar system through Grid Alternatives;43 • In 2022, CleanPowerSF began offering a Solar Inverter Replacement Program. The Solar Inverter Replacement Program provides up to $3,000 in rebates to CleanPowerSF CARE or FERA customers who previously installed solar through the GoSolarSF program.44 The program helps assure these systems can continue to operate by offering the bill reduction and environmental benefits intended for these low-income customers who likely need support for inverter repairs;
43 SFPUC. GoSolarSF. Available at: https://sfpuc.org/accounts-services/sign-up-for-savings/gosolarsf [Accessed 10/12/2022]
44 CleanPowerSF. Solar Inverter Replacement Program. Available at: https://www.cleanpowersf.org/solarinverter [Accessed 10/12/2022]
DRAFT – October 2022
42 • CleanPowerSF’s Budget Billing program helps low-income customers avoid big swings in their monthly payments by averaging their energy costs and providing a predictable and stable monthly bill;45
• CleanPowerSF launched its Disadvantaged Communities Green Tariff (DAC-GT) and Community Solar Green Tariff (DAC-CSGT) programs, enrolling eligible customers in the former program beginning June 2022.46 These programs are administered by the CPUC and reduce barriers to renewable energy adoption by residents in Disadvantaged Communities.
Both these programs allow customers in DACs to subscribe to up to 100% RPS-renewable energy at a 20% discount through their electricity provider; • CleanPowerSF is developing energy efficiency programming to support food services, including restaurants, grocery stores, refrigerated warehouses and community food services. The program would support energy efficiency measures for refrigeration, food preparation equipment, ventilation controls and other technologies. 47 New equipment and
energy savings will improve the financial viability and long-term operational efficiency of participating organizations, which we hope will include child & senior care centers, free dining rooms, grocery/food pantries, and other community support centers; • CleanPowerSF currently partners with other CCAs and the Bay Area Regional Energy Network (BayREN) to deliver an incentive program to promote adoption of heat pump water heaters (HPWHs). CleanPowerSF seeks to expand the use of HPWHs and similar technologies for low-income households and multifamily buildings through targeted outreach and education.48
• CleanPowerSF expects to launch EV Charge SF, an EV charging incentive program aimed at new and recent construction, in 2023. Affordable housing will be prioritized within the program through an additional 20% to all incentives and will be offered technical assistance to support the design of charging infrastructure and ensure compliance with state and local ordinances for less sophisticated customers.
CleanPowerSF is committed to continuing to address issues of equity when it comes to accessing renewable energy and distributed energy resources (DERs). CleanPowerSF established a working group
45 CleanPowerSF. Budget Billing Program. Available at:
https://www.cleanpowersf.org/budgetbilling#:~:text=Budget%20Billing%20is%20a%20free,a%20monthly%20budg et%20bill%20amount [Accessed 10/12/2022]
46 More information on CleanPowerSF’s Disadvantaged Communities Green Tariff program is available at:
https://www.cleanpowersf.org/supergreensaver [Accessed 10/13/ 2022] 47 See CPUC Resolution E-5180 certifying CleanPowerSF’s Energy Efficiency Program Administration available at:
https://docs.cpuc.ca.gov/PublishedDocs/Published/G000/M441/K154/441154671.PDF [Accessed 10/13/2022] 48 CleanPowerSF Heat Pump Water Heater Program. https://www.cleanpowersf.org/heat -pump -water-heater- program. [Accessed 10/12/2022]
DRAFT – October 2022
43 tasked with developing an equity framework to guide the development of additional customer programs, policies, and business practices that are inclusive and directly address the community’s needs. This equity framework is now being used in Power Enterprise to inform customer program development initiatives undertaken at the SFPUC.
e. Cost and Rate Analysis
CleanPowerSF’s Rate Setting Process
CleanPowerSF rates are subject to several levels of public oversight, and any changes must be approved by the SFPUC at a noticed public meeting. CleanPowerSF’s rates are created with the input, guidance and review of the Rate Fairness Board, an advisory group of ratepayers and City financial officers created under the voter-approved Proposition E (2002), to ensure rate stability, fairness and affordability. 49 Before each rate change, the Rate Fairness Board holds a public meeting, after which a report or recommendation is provided to the SFPUC.
The SFPUC sets CleanPowerSF retail rates pursuant to the San Francisco Charter Section 8B.125.50 All budgets, rates, fees, and charges presented by staff to the SFPUC must conform to the SFPUC Ratepayer Assurance Policy, which is guided by the key principles of: revenue sufficiency, customer equity, environmental sustainability, affordability, predictability, and simplicity. As required by San Francisco Charter section 16.112, a Notice of Public Hearing on the establishment of new rates is published in various forums. 51
If approved by the five member San Francisco Public Utilities Commission, pursuant to Charter Section 8B.125, the rates are subject to rejection by the San Francisco Board of Supervisors (BOS) within thirty (30) days of notification to the BOS.
IRP Cost and Rate Analysis
CleanPowerSF’s program goals include providing San Francisco residents and businesses with affordable rates and long-term rate stability. For its IRP analysis, CleanPowerSF modeled the most cost- effective portfolio for each scenario analyzed to ensure the portfolios developed align with program priorities.
49 More information on the Rate Fairness Board is available at: https://sfpuc.org/about-us/ boards-commissions- committees/rate-fairness-board [Accessed 10/13/ 2022]
50 See San Francisco Public Utilities Commission Comprehensive Annual Financial Report, Financial Authority and Policies, available at: https://sfwater.org/modules/showdocument.asp x?documentid=9975 (last viewed, August 15, 2020)
51 Charter Section 16.112 requires notices to be posted in the official newspaper, SFPUC website, and at the San Francisco Public Library prior to a public hearing.
DRAFT – October 2022
44 CleanPowerSF’s Preferred Portfolio is within a reasonable range of CleanPowerSF’sprojection of future supply costs in its most recent 10-Year Financial Plan52 (adopted in February 2022), as shown in Figure 10. The total cost of the Time Coincident portfolio is projected to be around $81.01/MWh (2021$) in net present value. The Time Coincident portfolio cost represents a less than 1.1% average annual increase in portfolio costs over the 13 years from 2023 to 2035. This is lower than trends for the past 10 years in PG&E’s service territory, which saw system average generation rates increase at an average annual rate of approximately 5.1%.53
Figure 10: Preferred Portfolio Costs Compared to CleanPowerSF 10-Year Financial Plan Supply Costs
f. System Reliability Analysis
In its IRP analysis, CleanPowerSF applied its marginal reliability need (MRN), as calculated in the Resource Data Template (RDT) using the annual CPUC-assigned managed peak share, as a floor for its modeled portfolios. CleanPowerSF’s annual MRN ensures enough dispatchable resources under contract to meet customer demand during specified periods of high demand. As such, CleanPowerSF has determined that projected contributions towards its annual MRN are an appropriate metric for measuring a portfolio’s contributions to systemwide reliability for this IRP cycle.
CleanPowerSF shares the concerns of state regulatory agencies regarding the continued reliability of the state’s electric system as an increasing number of variable energy resources are added to the
52 San Francisco Public Utilities Commission Resolution 22-032, adopted on February 8, 2022. 53 PG&E’s 2012 and 2022 Annual Electric True-Up Filings outlining revisions to electric tariffs are available at:
https://www.pge.com/rates/tariffs/tm2/pdf/ELEC_4096-E.pdf and https://www.pge.com/tariffs/assets/pdf/adviceletter/ELEC_6509-E-A.pdf [Accessed 10 /12/2022] $-
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Annual Portfolio Cost ($000)
Time Coincident Portfolio ($2021)
CleanPowerSF 10-Year Financial Plan Supply Costs ($2021) DRAFT – October 2022
45 grid. CleanPowerSF prioritizes its responsibility to provide reliable and clean electricity to its customers, especially during peak hours. CleanPowerSF plans the development of its energy resource portfolio to meet its RA obligations, and through this IRP analysis aimed to develop portfolios that contribute its fair share to forecasted systemwide reliability needs.
In developing its IRP, CleanPowerSF utilized the best available information to analyze the reliability of its IRP portfolios. Specifically, CleanPowerSF required that each portfolio developed meet at least 65% of CleanPowerSF’s annual MRN with new long-term contracts that provide energy and capacity.
CleanPowerSF also limited annual market purchases to no more than 20% of total annual energy all modeled portfolios.
Figure 11 below summarizes CleanPowerSF’s annual contribution to system reliability. As shown, the majority of CleanPowerSF’s capacity needs will be met by new and existing contracts.
Figure 11: CleanPowerSF Time Coincident Case Contribution to System Reliability by Year
Over the planning horizon, CleanPowerSF believes its RA needs will be met by a mix of additional CleanPowerSF procurement of long-term resources and Cost Allocation Mechanism (CAM) resources, as shown in Table 22 below and the Resource Data Templates in Appendices A and B.
Table 22: 25MMT Time Coincident Case Reliability Need and Capacity Position (Effective MW) 0 100
200
300
400
500
600
700
800
900
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Effective Capacity (MW)
Capacity Under Contract BTM PV
Planned Resources Marginal Reliability Need
DRAFT – October 2022
46 MARGINAL
RELIABILITY
NEED
CAPACITY
UNDER
CONTRACT BTM PV
PLANNED
RESOURCES
TOTAL
SUPPLY
NET
CAPACITY
POSITION
2024 490 409 14 92 516 26
2025 506 428 14 102 544 38
2026 524 496 14 253 763 239
2027 512 479 18 255 751 238
2028 504 461 22 278 761 258
2029 518 468 21 281 770 252
2030 533 263 19 293 575 42
2031 518 238 21 284 543 26
2032 504 213 23 344 580 76
2033 490 187 25 373 585 96
2034 477 162 27 398 587 110
2035 461 137 29 419 585 123
g. High Electrification Planning
As described in Sections II.b.ii and III.a., the Climate Action Plan Alternative Portfolio reflects resource planning that would achieve building and transportation electrification goals of the City of San Francisco, as described in the City’s 2021 Climate Action Plan.54 After review of the data behind the Additional Transportation Electrification (ATE) scenario recommended to the CAISO for study by the Commission, and the data higher additional achievable fuel substitution (AAFS) load scenarios considered in the 2021 IEPR, CleanPowerSF concludes that the Climate Action Plan Portfolio exceeds the load increases from high electrification that could be projected for its portfolio through these datasets.
CleanPowerSF therefore provides the incremental increases in renewable procurement found between the Climate Action Plan Portfolio and its Conforming Portfolio in CPUC-provided Table 23 below.
High Transportation Electrification Review Approach
Staff reviewed the Additional Transportation Electrification (ATE) Scenario within the RESOLVE Scenario Tool
55 and used the CAISO-wide ATE results to calculate the percent modification to the sum of Managed Net Load and behind-the-meter PV by electric vehicle load and calculate the estimated energy
54 San Francisco Climate Action Plan 2021, Page 41. Available at:
https://sfenvironment.org/sites/default/files/cap_fulldocument_wappendix_web_220124.pdf [ Accessed 10/13/2022]
55 See "CPUC IRP RESOLVE_Scenario Tool 2022-06-23_CEC2021_loads.xlsb" with the Filing Requirements RESOLVE package at https://www.cpuc.ca.gov/-/m edia/cpuc-website/divisions/energy-division/zipped-files/resolve-public- release-2022-06-23-lse-plans-filing-requirements.zip.
DRAFT – October 2022
47 increase in gigawatt-hours (GWh) due to ATE that would be assigned to CleanPowerSF. The first row of values in Table 23 below show the EV load modifier calculated within the Clean System Power calculator’s IEPR CAISO Load Modifiers tab. Review of the data behind the ATE Scenario within the RESOLVE Scenario Tool released June 23rd provided adjustments to these load modifiers, shown in row
2. Table 23: EV Load Modifiers
2024 2026 2030 2035
CSP EV Load Modifier 3.7% 5.1% 7.6% 10.9%
ATE Scenario EV Load Modifier 3.7% 5.1% 11.6% 26.4%
After applying the ATE-based EV load modifier to CleanPowerSF Managed Net Load and BTM PV, the resulting ATE Scenario’s projected impact on CleanPowerSF load was compared to the transportation electrification load projected by the San Francisco policy-focused Climate Action Plan Alternative Portfolio. The results in GWh, shown below in Table 24, conclude that the transportation electrification analysis in Climate Action Plan Alternative Portfolio exceed the projected impact of the Additional Transportation Electrification scenario in all years.
Table 24: Transportation Electrification Results: High Electrification Planning Energy Requirements Results (GWh)
Scenario 2024 2026 2030 2035
Transportation
Electrification
CSP EV Energy (CSP-Assigned BTM
PV56) 126.06 180.92 288.22 454.01
CSP EV Energy (Custom BTM PV) 109.33 154.09 236.52 359.02 Additional TE Estimated Energy
(Custom BTM PV) 109.33 154.09 361.87 870.46
Climate Action Plan Portfolio Energy
(Custom BTM PV) 261.6 405.9 690.8 970.9
High Building Electrification Review Approach
CleanPowerSF staff reviewed the assumptions used for the High Electrification scenario and per Slide 10 in the Narrative Template prompt’s linked presentation used the IEPR AAFS Scenario 4, 57 and
the relationship between the percent AAFS of Scenario 4 and the AAFS Load Modifiers provided in the CSP’s IEPR Load Modifiers tab (representing AAFS Scenario 3) to calculate the projected increase to CleanPowerSF’s demand.
56 Results are provided reflecting use of the BTM PV assignment to CleanPowerSF made within the Clean System Power calculator as well as CleanPowerSF’s own BTM PV data; note that the Climate Action Plan Alternative Portfolio uses CleanPowerSF’s own BTM PV data.
57 Available at https://efiling.energy.ca.gov/GetDocument.aspx?tn=243222 [Accessed 10/1 3/2022] DRAFT – October 2022
48 The Clean System Power calculator assumes AAFS adjustments as shown below in the first row of Table 25 to the aggregate of an LSE’s retail demand plus behind the meter solar PV production per its analysis of IEPR Mid Case (Scenario 3 AAEE, Scenario 3 AAFS) for the CAISO territory.
Table 25: AAFS Load Modifiers58
2024 2026 2030 2035
CSP AAFS Load Modifier 0.416% 0.736% 1.391% 2.169%
The following Table provides CleanPowerSF staff’s findings on the relationship between the CAISO Mid Case AAFS Scenario 3 and CAISO Mid Case AAFS Scenario 4:59
Table 26: CAISO Mid Case AAFS Scenario 3 and Mid Case AAFS Scenario 4 Comparison
CAISO Mid
Case AAFS
Scenario 3
Totals (GWh)
CAISO Mid Case
AAFS Scenario 4
Totals (GWh)
% Increase
Scenario 4 AAFS
Over CSP
Assumptions
2022 273 280 102.7%
2023 595 621 104.4%
2024 961 1,009 105.0%
2025 1,360 1,433 105.3%
2026 1,753 1,860 106.1%
2027 2,166 2,308 106.5%
2028 2,596 2,772 106.8%
2029 3,047 3,258 106.9%
2030 3,520 3,759 106.8%
2031 4,010 4,275 106.6%
2032 4,517 4,805 106.4%
2033 5,003 5,312 106.2%
2034 5,466 5,793 106.0%
2035 5,897 6,238 105.8%
Augmentation of the AAFS Load Modifier to reflect the Scenario 4 projected load increases results in an estimated increase in load shown in row 3 of the Table below. One can see that Climate Action Plan Alternative Portfolio building electrification assumptions result in a higher load increase in years 2026, 2030 and 2035 and are 0.2 GWh below the Scenario 4 projections in 2024.
Table 27: Building Electrification Results: High Electrification Planning
58 These are found in Row 48 of the IEPR CAISO Load Modifiers tab of the Clean System Power calculator. 59 GWh totals are aggregated from CAISO Mid Case Hourly Demand files located at https://www.energy.ca.gov/data-re ports/reports/integrated-energy-policy-report/2021-integrated-energy-policy- report/2021-1 [Accessed 10/13/ 2022].
DRAFT – October 2022
49 2024 2026 2030 2035
Building
Electrification
CSP AAFS GWh (CSP-Assigned BTM
PV) 14.3 26.1 53.0 90.6
CSP AAFS GWh (Custom BTM PV) 12.4 22.3 43.5 71.6
Scenario 4 Estimated GWh (Custom
BTM PV) 13.0 23.6 46.5 75.8
Climate Action Plan Portfolio GWh
(Custom BTM PV) 12.8 107.1 295.7 574.5
The results of the transportation high electrification analysis and building high electrification analysis are aggregated below in Table 28. This shows that when the load increases for building electrification and transportation electrification are aggregated, the Climate Action Plan Alternative Portfolio exceeds the projected load increases calculated in the aggregated impact of the increased transportation electrification of the ATE Scenario and AAFS Scenario 4. The full renewable resources required for building and transportation electrification, as estimated in the Climate Action Plan Alternative Portfolio, are shown in the final row of the table.
The incremental renewable resources beyond those of the Preferred Conforming Portfolio added for procurement for this Portfolio are shown below in Table 28.
DRAFT – October 2022
50 Table 28: Comparison of High Electrification Planning Load Projections and Climate Action Plan Portfolio, Aggregated
Scenario 2024 2026 2030 2035
Transportation
Electrification
CSP EV GWh (CSP-Assigned BTM PV) 126.06 180.92 288.22 454.01 CSP EV GWh (Custom BTM PV) 109.33 154.09 236.52 359.02
Additional TE Estimated GWh
(Custom BTM PV) 109.33 154.09 361.87 870.46
SF Policy Goals Portfolio GWh
(Custom BTM PV) 261.6 405.9 690.8 970.9
Building
Electrification
CSP AAFS GWh (CSP-Assigned BTM
PV) 14.3 26.1 53.0 90.6
CSP AAFS GWh (Custom BTM PV) 12.4 22.3 43.5 71.6
Scenario 4 Estimated GWh (Custom
BTM PV) 13.0 23.6 46.5 75.8
SF Policy Goals Portfolio GWh
(Custom BTM PV) 12.8 107.1 295.7 574.5
Aggregated
Total Additional Electrification
GWh per ATE Scenario and
Scenario 4
122.35 177.71 408.34 946.24
Total Additional Electrification
GWh, Climate Action Plan Portfolio 274.4 513.0 986.4 1,545.5 Total Additional Renewable GWh,
Climate Action Plan Portfolio 195.4 388.3 746.7 1,169.9
Table 29: Additional Resource Planning for High Electrification
RESOURCE
TYPE MWS
ANNUAL
GWH
2035
GHG
TARGET
TRANSMISSION
ZONE SUBSTATION/BUS
ALTERNATIVE
LOCATION NOTE
Utility Scale
Solar
100
MW
285.7
25 & 30
MMT n/a n/a n/a COD
1/1/2026
Geothermal 30 MW
239.7
25 & 30
MMT n/a n/a n/a COD
1/1/2030
Solar Hybrid
100
MW
Solar,
50 MW
Storage
285.6
(Solar)
25 & 30
MMT n/a n/a n/a COD
1/1/2027
DRAFT – October 2022
51 h. Existing Resource Planning
In CleanPowerSF’s 2020 IRP, 410 MW of existing resources were identified in the 38 MMT Preferred Conforming Portfolio, as outlined in Table 30 below. To limit overreliance on existing resources that were identified in the 2021 Preferred System Plan, CleanPowerSF’s limited existing resource availability to its share of Voluntary Allocation and Market Offer (VAMO) resources through the 2035 planning horizon in its 2022 IRP. 60 This constraint on existing resource availability is an appropriate proxy for existing resources available to CleanPowerSF since the VAMO resource pool represents an IOUs’ excess contracted RPS capacity.
For its 2022 IRP, CleanPowerSF’s Preferred Conforming Portfolio includes 60 MW of existing in-state hydro and 180 MW of blended renewable and GHG-free existing resources, which total 240 MW and make up less than 10% of the total portfolio capacity in 2035. CleanPowerSF does not anticipate significant challenges in procuring 240 MW of existing resources by 2035 and considers existing resources to be a component of a well-diversified portfolio. However, CleanPowerSF recognizes that competition, technology preferences, and resource availability are risks that should be considered in its planned procurement activities for existing resources.
Table 30: Summary of Existing Resources Identified in CleanPowerSF’s Preferred Conforming Portfolio
TECHNOLOGY
2020 IRP PREFERRED
CONFORMING PORTFOLIO
2022 IRP PREFERRED
CONFORMING PORTFOLIO
Existing Geothermal 50 MW
Existing In-State Hydro 60 MW 60 MW
Blended Renewable and GHG-Free
Existing Resources 300 MW 180 MW
Total 410 MW 240 MW
i. Hydro Generation Risk Management
CleanPowerSF has factored the risk of drought impacts on hydropower generation availability and the effects it may have on the emissions content of its portfolio. In 2021, hydro from California and the Pacific Northwest made up 38.8% of CleanPowerSF’s Green product, but that number is expected to decline as CleanPowerSF continues to invest in new renewable energy capacity.
60 See D.21-05-030 (authorizing a new VAMO process for RPS contracts subject to PCIA.) DRAFT – October 2022
52 In the near-term, CleanPowerSF manages hydro generation risk by contracting for firm energy volumes from hydro facilities across the West. This approach ensures that CleanPowerSF will receive a minimum, firm volume of hydro generation. CleanPowerSF’s suppliers regularly monitor weather forecasts and hydro generation conditions and notify CleanPowerSF in advance if they anticipate any shortfalls in hydro availability. To date, this has provided CleanPowerSF ample time to contract for any additional GHG-free supply that may be needed to meet its product content goals. To minimize the risk of regional droughts impacting CleanPowerSF’s hydro portfolio, CleanPowerSF also strives to execute contracts with multiple suppliers and with a range of facilities across the Western United States.
In this IRP analysis, CleanPowerSF limited the quantity of available hydro it could procure to its proportional share of the California and Pacific Northwest future hydro generation used to model the 2021 PSP. CleanPowerSF’s proportional share was determined to be 1.7% of generation available to the CAISO. As listed in the CPUC’s total installed capacity list used to model the 2021 PSP, in-state hydro capacity was estimated to be 6,619 MW.
61 CleanPowerSF’s share of this generation would be 112 MW.
Acknowledging that this full capacity may not be available each year due to existing commitments and drought, this share was risk adjusted down to 60 MW. In the RESOLVE model, a portion of Pacific Northwest (PNW) Hydro is available to CAISO as a directly scheduled import. 62 This represents 2,852
MW of capacity, of which CleanPowerSF’s share is 48 MW. This was adjusted down to 40 MW to account for hydro generation risks.
The table below shows the total amount of hydro capacity that is not already under contract CleanPowerSF’s Preferred Portfolio through the IRP planning horizon.
Table 31 Large Hydro Content in Preferred Portfolio
YEAR
TIME COINCIDENT
PORTFOLIO (MW)
2021 PSP PROPORTIONAL
SHARE (MW)
2023 100 160
2024 100 160
2025 100 160
2026 100 160
2027 100 160
2028 100 160
61 The total generator list is available on the CPUC’s 2022 IRP modeling datasets repository:
https://www.cpuc.ca.gov/industries-and-to pics/electrical-energy/electric-power-procurement/long-term- procurement-planning/2022-irp-cycle-events-and-materials/unified-ra-and-irp-modeling-datasets-2022 [Accessed 9/22/2022]
62 See ‘Planned Installed Capacities’ in the 2021 PSP RESOLVE Package available at:
https://www.cpuc.ca.gov/industries-and-t opics/electrical-energy/electric-power-procurement/long-term- procurement-planning/2022-irp-cycle-events-and-materials [Accessed 9/22/2022].
DRAFT – October 2022
53 2029 100 160
2030 100 160
2031 100 160
2032 100 160
2033 100 160
2034 100 160
2035 100 160
As shown in Table 31, CleanPowerSF plans to procure less than its proportional share of hydro in all years. The Time Coincident portfolio includes California and PNW hydro through 2035 and on average, hydro makes up less than 14% of annual energy supply in the Time Coincident portfolio. Given that hydro represents a modest portion of the portfolios presented in this IRP, CleanPowerSF does not expect drought to have a significant impact on its portfolio costs or its ability to meet its emissions benchmark.
j. Long-Duration Storage Planning
CleanPowerSF IRP modeling shows that storage is a beneficial resource for CleanPowerSF’s portfolio, it can contribute to California’s reliability needs , and provide environmental benefits. Storage resources can charge during curtailment hours and deliver generation from intermittent renewables like solar and wind during hours when energy may be less available. CleanPowerSF is actively contracting for renewables paired with storage, and already has 289 MW of both paired and standalone energy storage under contract, 21.8 MW of which have an 8 -hour discharge. CleanPowerSF’s Preferred Conforming Portfolio contains an additional 350 MW of long-duration storage (8-12 hours), exceeding CleanPowerSF’s share of the existing procurement requirements.
The 90% time coincident constraint in CleanPowerSF’s Preferred Portfolio favors the selection of more energy storage resources to provide the flexibility to shift generation to evening peak hours. Some risk considerations that CleanPowerSF weighs as it launches solicitations for battery storage include position in interconnection queue, project feasibility, deliverability, cost structure, project susceptibility to changes in State policy and legislation, among others.
k. Clean Firm Power Planning
CleanPowerSF IRP modeling shows that clean firm power is a beneficial resource for CleanPowerSF’s portfolio as it can contribute to California’s reliability needs and provide environmental benefits. Clean firm power resources deliver baseload generation that supplement intermittent renewables like solar and wind in CleanPowerSF’s energy resource portf olio. While clean firm generation provides valuable baseload renewable energy, CleanPowerSF will continue to evaluate and balance the relatively high cost DRAFT – October 2022
54 of clean firm resources, especially as opportunities to contract with emerging clean firm resource technology develop.
CleanPowerSF is actively contracting for clean firm generation and has recently contracted for 19.3 MW of new geothermal capacity. The 2021 PSP calls for 1,160 MW of new geothermal by 2032 and CleanPowerSF’s Time Coincident portfolio contains an additional 60 MW of new geothermal development, exceeding CleanPowerSF’s pro-rata share of new geothermal needs and individual clean firm generation procurement obligation.
CleanPowerSF’s new geothermal resources under contract are located outside of the California ISO’s transmission control area and will be pseudo -tied or dynamically scheduled into the ISO. CleanPowerSF , through California Community Power (CC Power), a Joint Powers Agency, has requested MIC expansion at the following Branch Groups to facilitate the import of power from its executed geothermal contracts.
The MIC expansion request noted that the developer will deliver and noted that there is flexibility in terms of the MIC Expansion needed at these Branch Groups.
Table 32: MIC Expansion Request Details
BRANCH GROUP QUALIFYING CAPACITY
(MW)
CleanPowerSF continues to make good faith efforts to secure Maximum Import Capability (MIC) allocation through the expansion process described above, annual elections, and long-term reservations.
However, contracting for resources outside of the ISO’s transmission control area prior to receiving M IC allocation adds risk and uncertainty to CleanPowerSF’s procurement process. Deliverability is a barrier to accessing clean firm generation located outside of the ISO’s transmission control area. The CPUC should work with CAISO to ensure deliverability for these resources by supporting LSEs’ MIC expansion efforts for these resources before CAISO.
l. Out-of-State Wind Planning
Use this section to discuss the approach that the LSE took to planning for out-of-state wind resources out to 2035. The LSE should discuss the potential it sees, how it arrived at the amounts included in its portfolios, and the benefits and risks of including this resource in its portfolio mix. The LSE should discuss the status of the transmission route the resources included in its portfolio mix will likely utilize to reach the CAISO border — will only existing, already online, transmission be needed or will any planned new transmission that is not yet online be required? DRAFT – October 2022
55 The CPUC’s 2021 Preferred System Plan included 1,500 MW of wind on new out-of-state transmission and CleanPowerSF acknowledges that new resource and transmission development may be needed to achieve the deeper statewide GHG reductions associated with the 25 MMT planning target. While out-of-state wind was not included in CleanPowerSF’s modeled portfolios, it may become a more viable resource post-2035. However, more analysis is needed to determine whether the state should prioritize investment in out-of-state wind transmission ahead of other resources. m. Offshore Wind Planning
The CPUC’s 2021 Preferred System Plan included 1,728 MW of offshore wind in 2035. While offshore wind was not included in CleanPowerSF’s conforming portfolios, it may become a more viable resource post-2035. However, more analysis is needed to achieve the planning goals required by Assembly Bill 525.
n. Transmission Planning
CleanPowerSF has executed additional contracted capacity since the IRP baseline63 was established in January of 2020. This consists of:
• Adding 50 MW of new battery storage to the 100 MW Maverick Solar 6 project • Adding MW of new battery storage to the 62.5 MW Blythe Solar IV project, • Paulsell S olar Energy Center (20 MW of new solar, 20 MW 4-hour storage), • Aramis Solar (75 MW of new solar, 75 MW of new 4-hour storage), • Tumbleweed (69 MW of new 8-hour storage, 11.1 MW under contract) • Goal Line (50 MW of new 8-hour storage, 10.8 MW under contract) CleanPowerSF has also executed additional capacity located in California and Nevada:
• Ormat Geothermal Portfolio (125 MW of new geothermal, 17.4 MW under contract) • Fish Lake Geothermal (13 MW of new geothermal, 1.9 MW under contract) These resources should be included in the baseline for modeling in the next CAISO Transmission Planning Process. These projects are hybrid solar plus storage, standalone storage, and geothermal resources. Maverick Solar 6 and Blythe Solar 6 are located in the Riverside and Palm Springs transmission zone. In addition to these two projects, CleanPowerSF has a number of resources under contract that have not yet reached their Commercial Operation Date. The CAISO interconnection queue position of these resources is summarized in Table 33.
63 Administrative Law Judge Ruling Finalizing Baseline for Purposes of Procurement Required by Decision 19- 11-016 available at: https://docs.cpuc.ca.gov/SearchRes.aspx?docformat=ALL&docid=323767159 [Accessed 10/7/2022] DRAFT – October 2022
56 Table 33 CleanPowerSF New Resource CAISO Interconnection Queue Positions
RESOURCE CAPACITY (MW)
TRANSMISSION
ZONE
CAISO INTERCONNECTION
QUEUE POSITION
Aramis Solar 75 MW Solar/75 MW
Storage Solano Cluster 10 - #1349
Paulsell Solar Energy Center 20 MW Solar/20 MW
Storage Los Banos Cluster 10 - #1350
Tumbleweed 69 MW Battery Storage Tehachapi CAISO Queue 1217 Goal Line 50 MW Battery Storage Greater Imperial Cluster 14 - #1832
CleanPowerSF examined all of the planned resources identified in its Preferred Portfolio not yet under contract and has no strong preference on the location where these resources may be built. Apart from the standalone storage resources, CleanPowerSF’s only locational requirement for the new generating resources is that the first point of interconnection must be with a California balancing authority or dynamically scheduled into a California balancing authority; these resources must also qualify for RPS compliance as PCC1 resources.
Although no specific new transmission lines or upgrades have been needed for the projects listed in Table 33 above, transmission upgrades triggered by other projects in the region may also provide benefits for CleanPowerSF’s projects. CleanPowerSF does not plan to seek approval for any transmission upgrades and instead will continue to monitor the status of different transmission projects in the CAISO’s Transmission Planning Process as CleanPowerSF develops new resources for its Preferred Conforming Portfolio.
The Ormat portfolio of geothermal projects are expected to mostly be import resources in northern NV
Energy territory or the Imperial Irrigation District. Projects are at various stages of maturity in their subsurface characterization, permitting, and interconnection. The RDT contains a representation of what the portfolio might look like (entered as 7 projects with potential substations). Ormat has limited ability to deliver at , so MIC expansion will likely be needed at to deliver up to 125 MW. One potential CAISO resource in the portfolio (at the Geysers) recently received Phase 1 results from its Cluster 14 study indicating that it is impacted by a costly network upgrade with a completion date no earlier than 2029—which may require it be substituted for an import resource.
DRAFT – October 2022
57 IV. Action Plan
a. Proposed Procurement Activities and Potential Barriers CleanPowerSF’s 2022 IRP modeling activities developed portfolios that could be feasibly implemented given current market conditions. This includes limiting new annual resource additions so they do not exceed what CleanPowerSF projects as could be reasonably available in any given year.
CleanPowerSF only includes its pro rata share of existing renewable and large hydro resources in Conforming Portfolios. These modeling constraints resulted in a 2022 Preferred Conforming Portfolio that includes a balanced mix of short- and long-term commitments with a range of resource types and online dates. The procurement identified in the Preferred Conforming Portfolio will allow CleanPowerSF to meet its aggressive product content goals and contribute to its procurement related CPUC obligations including those obligations associated with the Renewables Portfolio Standard, Resource Adequacy and IRP Procurement Track. The gradual rate of new resource additions will allow CleanPowerSF to plan for a reasonable procurement timeline and maintain the opportunity to capitalize on more favorable market conditions or emerging technologies in the outer years of the planning horizon.
Implementation of CleanPowerSF’s Preferred Conforming Portfolio will require procurement efforts to be conducted for the following broad resource categories:
• New build renewable resources
• New storage resources, both paired and standalone
• Existing renewable resources
• Large hydroelectric resources
The table below summarizes the procurement strategies and tentative schedules for each resource category.
DRAFT – October 2022
58 Table 34: CleanPowerSF Procurement Schedule
CleanPowerSF plans to issue annual all-source Requests for Offers (RFOs) through the IRP planning horizon to contract for utility scale renewable and storage resources. Regular RFOs will allow CleanPowerSF to have access to the most up to date resource availability and pricing options. Annual RFOs will also allow CleanPowerSF to make adjustments to its procurement practices in response to current needs and market conditions to help minimize procurement risk and maximize ratepayer value.
CleanPowerSF is also committed to investing in local resources, defined as those within the nine Bay Area Counties. In response to stakeholder feedback, including direction from the San Francisco Board of Supervisors, CleanPowerSF has been evaluating the feasibility of City-owned properties within San Francisco to support CleanPowerSF renewable energy projects. In 2020, CleanPowerSF released a Local Renewables Report identifying the most suitable sites and has continued deeper analysis of potential sites since publishing the report. Through this work, CleanPowerSF has selected two SFPUC-owned reservoirs as the sites with the highest near-term feasibility. The two reservoirs, University Mound North Basin and Sutro, represent approximately 6 MW of in-City solar capacity are included in the 2022 IRP Preferred Conforming portfolio. CleanPowerSF is in the process of developing technical specification and solicitation materials and plans to issue an RFO for this capacity by the second quarter of 2023 to bring the resources online by 2025.
In addition to long-term procurement, CleanPowerSF plans to conduct regular short-term procurement for renewable and large hydroelectric resources to maintain a balanced portfolio of resource types and contract lengths and support optimization. CleanPowerSF plans to contract for these resources through the following mechanisms: PCIA Voluntary Allocations process, PCIA Market Offer Process and short-term solicitations that are issued as needed. CleanPowerSF used its load ratio share of PG&E PCIA resources to inform the volume of existing renewable resources it could reasonably procure.
CleanPowerSF has made is 2023-2024 voluntary allocations and plans to participate in the IOUs’
DRAFT – October 2022
59 subsequent Market Offer processes. CleanPowerSF plans to continue participating in the VAMO at the start of future compliance periods to help close its short-term Preferred Conforming Portfolio’s renewable position.
CleanPowerSF will also seek to contract with existing renewables and large hydroelectric resources through short-term solicitations. CleanPowerSF regularly tracks its RPS and large hydro positions relative to the annual IRP portfolio content targets and issues solicitations for these resources as needed in accordance with its hedging policy. To minimize overreliance on existing resources, CleanPowerSF limited candidate hydro resources to its pro rata share of what was included in the 2020 Preferred System Plan. As discussed herein, existing renewable availability was limited to its share of VAMO resources.
Along with meeting City and program goals, CleanPowerSF procurement activities aim to meet mandated procurement obligations, including those ordered in D.19-11-016 and D.21-06-035.
While resources such as out of state and offshore wind were not selected in CleanPowerSF’s Preferred Conforming Portfolio, CleanPowerSF will continue to assess the suitability of these technologies for CleanPowerSF’s portfolio and accept bids from these resource types in its current and future renewable energy solicitations. At the time of 2022 IRP modeling activities, these were not the most cost-effective resource types to meet CleanPowerSF program goals, price and technology updates may make these resources more competitive with other technology types in the future. If a bid is submitted for out of state or offshore wind in a renewable resource solicitation, the price and delivery characteristics will be evaluated to determine the bid value and portfolio fit against other proposals received. CleanPowerSF will also continue to monitor the development of these resource types as they occur and will plan to include them as candidate resource types in future IRPs.
i. Resources to meet D.19-11-016 procurement requirements CleanPowerSF’s D.19-11-016 procurement obligation is outlined in Table 35 below:
Table 35: CleanPowerSF's D.19-11-016 Procurement Obligation
MINIMUM ONLINE BY
8/1/2021
MINIMUM ONLINE BY
8/1/2022
MINIMUM ONLINE BY
8/1/2023
MW NQC 28.5 42.8 57.0
CleanPowerSF has brought its full 57 MW share online as of June 2022, more than a prior to the Tranche 3 deadline. The entire obligation was met with renewable and storage resources. The details of the resources CleanPowerSF has used to meet its obligations are summarized in the table below.
DRAFT – October 2022
60 Table 36: Summary of Resources Used for D.19-11-016 Compliance
RESOURCE NAME RESOURCE TYPE SEPTEMBER MW NQC ONLINE DATE San Pablo Raceway Solar 14 August 2019
Blythe Solar IV Solar 8.75 September 2020
Voyager IV Expansion Wind 7.515 March 2021
Oasis Power Partners Wind 9.045 October 2021
Maverick Solar 6
Solar
17.69 December 2021
Storage June 2022
ii. Resources to meet D.21-06-035 procurement requirements, including:
CleanPowerSF continues to make progress on its Mid-Term Reliability (MTR) obligations, which are outlined in the following table.
Table 37: CleanPowerSF's Mid-Term Reliability Procurement Obligations
ONLINE BY
8/1/2023
ONLINE BY
6/1/2024
ONLINE BY
6/1/2025
ONLINE BY 6/1/2026
(LLT RESOURCES)
MINIMUM ZERO-
EMITTING
CAPACITY BY
2025
MW
NQC 31 93 23 31 39
To date, CleanPowerSF has successfully contracted to meet its 2023 obligation as well as its long- duration storage and firm clean resource requirements. CleanPowerSF entered into its long lead-time requirements as a member of the CC Power joint powers authority. Through CC Power, individual CCAs have been able to join efforts to contract for large, long lead time resources that may require larger off- takers to be feasible. CleanPowerSF can leverage its experiences contracting for these resources to procure larger projects or emerging technologies in the longer-term planning horizon.
In anticipation of the adoption of its 2022 Preferred Conforming Portfolio and to continue to make progress towards Mid-Term Reliability procurement obligations, CleanPowerSF issued an RFO for new and existing renewable energy supplies and demand response capacity on September 21, 2022. This solicitation is accepting bids for projects with online dates through the end of 2028. Depending on the outcome of the solicitation, CleanPowerSF may emphasize specific resource types, such as firm clean renewables or standalone storage in future RFOs to support contracting with the resources identified in the Preferred Conforming Portfolio.
CleanPowerSF continues to make efforts towards meeting its remaining MTR requirements and is prioritizing eligible resources, including demand response and renewables paired with longer duration storage, in solicitation discussed above. However, CleanPowerSF, like other LSEs, faces unprecedented challenging market conditions that may impact its ability to comply with MTR deadlines as adopted by DRAFT – October 2022
61 the California PUC and that may pose risks to the implementation of the Preferred Conforming Portfolio.
These potential barriers include:
• Project Delays: Projects originally slated to come online mid-decade have faced a host of supply chain challenges related to: the Covid-19 pandemic, global macroeconomic disruptions, and the Department of Commerce’s circumvention investigation. These challenges are pushing back project online dates as developers are struggling to meet key development milestones.
• Increased Costs: Recent inflation and the Department of Commerce’s solar anticircumvention investigation have increased commodities, materials and financing costs which has made it challenging for developers to deliver projects at the prices listed in Power Purchase Agreements. For projects not yet under contract, CleanPowerSF is seeing average market prices that are significantly higher than they have been in the past. • CASIO Interconnection and Deliverability Timelines: The CAISO Cluster 14 timeline has delayed the assessment of interconnection viability, identification of required network upgrades, and the awarding of deliverability for projects in the CAISO interconnection queue. This has created uncertainty regarding projects’ viability, online dates, costs of upgrades, and their ability to count towards MTR compliance. • Maximum Import Capability Uncertainty: CleanPowerSF’s firm clean resource, and other out of state projects may rely on obtaining long-term MIC or the success of MIC expansion requests to receive deliverability. The MIC allocation process cannot occur more than two years before project COD to ensure long-term import allocations. This uncertainty creates risks regarding a resource’s potential to deliver capacity into California and ability to contribute to both CleanPowerSF’s Preferred Conforming Portfolio and state mandated procurement requirements.
a. 1,000 MW of firm zero-emitting resource requirements CleanPowerSF has already contracted for new geothermal resources to apply to its share of the MTR order. Through CC Power, CleanPowerSF contracted with other CCAs for a new 13 MW geothermal plant in Nevada and up to a 125 MW portfolio of new geothermal projects in Nevada and California. Almost all these resources are expected to be outside the CAISO balancing authority in northern Nevada or the Imperial Irrigation District and will require Maximum Import Capability (MIC) to be secured to deliver energy and capacity. MIC at northern Nevada delivery points is limited, and suppliers indicate that transmission capacity on NV Energy to southern Nevada is constrained. MIC expansion at northern Nevada delivery points such as would considerably decrease the risk of these projects not being able to provide clean firm capacity to CAISO. Transmission projects that focus on better connecting CAISO with northern Nevada resources, such as alleviating the Control substation constraint for the Oxbow line, could also de-risk northern Nevada as a source of clean, firm resources and potentially reduce significant wheeling costs through other Transmission Providers.
DRAFT – October 2022
62 The CC Power 125 MW portfolio also may contain a new resource inside CAISO at the Geysers. However, the Phase 1 results of its Cluster 14 study indicate that required network upgrades are costly and not high priority—with a potential completion date no earlier than 2029, which is after the envisioned extension in the MTR order. This may result in substituting an import resource.
CleanPowerSF will continue to explore opportunities to add to firm zero GHG-emitting resources through 2035, including the 60 MW of new geothermal development identified in CleanPowerSF’s Preferred Portfolio that could be applied to CleanPowerSF’s MTR procurement obligations. As mentioned above, CleanPowerSF issued an RFO for new and existing renewable energy supplies, in which projects eligible to count toward CleanPowerSF’s firm zero GHG-emitting resource procurement obligation meet the RFO’s eligibility requirement.
While geothermal resources provide valuable baseload renewable energy, CleanPowerSF will continue to evaluate and balance the relatively high cost of this resource within the portfolio, especially as opportunities to develop new geothermal resources emerge. b. 1,000 MW of long-duration storage resource requirements CleanPowerSF has existing long-duration storage resources in its portfolio and will explore opportunities to add additional long-duration storage through 2035, including the 300 MW of new long- duration storage development identified in CleanPowerSF’s Preferred Portfolio that could be applied to CleanPowerSF’s MTR procurement obligations. As mentioned above, CleanPowerSF issued an RFO for new and existing renewable energy supplies, in which projects eligible to count toward CleanPowerSF’s long-duration storage procurement obligation meet the RFO’s eligibility requirement.
While long-duration storage resources compliment intermittent renewable generation, CleanPowerSF will continue to optimize the procurement of storage resources to balance intermittent generation in its portfolio.
c. 2,500 MW of zero-emissions generation, generation paired with storage, or demand response resource requirements
As mentioned above, CleanPowerSF plans to issue annual all-source RFOs through the IRP planning horizon to contract for zero-emission generation and storage resources. Annual RFOs will also allow CleanPowerSF to make adjustments to its procurement practices in response to current needs and market conditions to help minimize procurement risk and maximize ratepayer value while meeting MTR procurement obligations.
d. All other procurement requirements
All MTR procurement requirement are discussed above.
iii. Offshore wind
While offshore wind was not included in CleanPowerSF’s conforming portfolios, it may become a more viable resource post-2035. Although it was not selected as part of CleanPowerSF’s Preferred DRAFT – October 2022
63 Conforming Portfolio, offshore wind is a potentially beneficial resource that may come online beginning in 2026, as identified in the 2021 PSP, due to its higher capacity factor and energy delivery profile, which is complementary to solar.
iv. Out-of-state wind
CleanPowerSF recognizes the potential role new out-of-state wind resources across the Western United States could play as new sources of renewable energy supply over the long-term planning horizon. However, CleanPowerSF’s Preferred Conforming Portfolio did not include any new out-of-state wind resource development. While CleanPowerSF’s procurement strategy does not prohibit procurement from new out-of-state wind resources, it prioritizes procurement from new local and in- state renewable projects.
Through its regular procurement activities, CleanPowerSF will continue to solicit new and existing renewable energy supply, inclusive of out-of-state wind resources, so that it is able to identify the most cost-effective resources for its ratepayers. CleanPowerSF will continue to monitor the out-of-state wind market and project development and regularly evaluate the cost-effectiveness and compatibility of these resources with its program goals and its IRP Preferred Portfolio. As the market evolves, out-of- state wind may become a more suitable resource for CleanPowerSF’s portfolio in the future, and may then be included as a candidate resource in future IRP cycles. v. Other renewable energy not described above
CleanPowerSF has existing wind resources in its portfolio and will explore opportunities to add new wind resource capacity through 2035, including the 100 MW of new wind development identified in CleanPowerSF’s Preferred Portfolio. As mentioned above, CleanPowerSF issued an RFO for new and existing renewable energy supplies, in which wind projects would meet the RFO’s eligibility requirements.
While wind is an intermittent renewable resource, CleanPowerSF will continue to optimize the procurement of baseload and storage resources to balance intermittent generation in its portfolio. vi. Other energy storage not described above
N/A
vii. Other demand response not described above
N/A
viii. Other energy efficiency not described above
N/A
ix. Other distributed generation not described above
N/A
DRAFT – October 2022
64 x. Transportation electrification, including any investments above and beyond what is included in Integrated Energy Policy Report (IEPR) Due to the building electrification load requirements and the transportation electrification load requirements of the Preferred Conforming Portfolio representing only a percent of the electrification load requirements of the Climate Action Plan Alternative Portfolio, procurement for the Preferred Conforming Portfolio electrification load will be achieved by CleanPowerSF’s procurement planning for achievement of City goals. As discussed in Section II, CleanPowerSF has invested in developing building and transportation models to project the load requirements of the City’s goals based on San Francisco’s specific footprint and aggressive goals and targets – goals and targets that push required load projections beyond those of the High Electrification Planning reviewed in Section III.f.
In Section III.f above, CleanPowerSF identifies three resources needed to meet the anticipated increase in load resulting from three high electrification scenarios. Those resources include 100 MW utility-scale solar, 30 MW geothermal, and 150 MW solar paired with storage (100 MW solar, 50 MW storage). While these resources are incremental to those identified in the Preferred Conforming Portfolio, the products that CleanPowerSF seeks in its solicitations for new resources encourages bids for projects that share the same generation attributes described in the High Electrification Planning section above that could support the projected load increases for higher levels of electrification.
The results of these analyses will inform CleanPowerSF’s regular procurement for RPS-eligible renewable and GHG-free resources, as well as Resource Adequacy. CleanPowerSF staff will be using these projections, as well as monitoring consumption data to assess the impact of electrification and tighten projections, ahead of scheduled procurements to inform resource types, capacities, and total generation sought.
The increased load requirements due to building and transportation electrification are subjects of ongoing study by CleanPowerSF and SFPUC staff. Studies to date have shown that San Francisco is an outlier in its building energy use and electrification profiles (and may also be in transportation electrification due its high-density and high-commuter usage), driving CleanPowerSF staff interest in building and honing local data to inform procurement. CleanPowerSF staff will be monitoring changes to total consumption and hourly use shapes within different rate classes to hone projections.
CleanPowerSF staff will explore programming such as demand response, technology demonstrations such as customer-side installations and/or district-level virtual power plants (VPP), electrification- focused incentives, targeted technical assistance for low-income owners and tenants, and resources and incentives to ensure building electrification is appropriately incentivized and done efficiently to mitigate burdens to low-income ratepayers.
xi. Building electrification, including any investments above and beyond what is included in Integrated Energy Policy Report (IEPR)
Building electrification is addressed in the ‘Transportation electrification, including any investments above and beyond what is included in Integrated Energy Policy Report (IEPR)’ section above.
DRAFT – October 2022
65 xii. Other
The generation technologies identified in CleanPowerSF’s Preferred Conforming Portfolios have been addressed in the preceding sections. CleanPowerSF is not planning any procurement activities for generation technologies that are not captured in the preceding sections. b. Disadvantaged Communities
CleanPowerSF is committed to minimizing – and eventually eliminating – any harmful air emissions associated with its electricity supply portfolio, especially from plants located within the California’s Disadvantaged Communities. CleanPowerSF’s 25 MMT and 30 MMT Preferred Portfolios do not include specified purchases from fossil fueled resources, and the Time Coincident Case reduces its reliance on system power. The new build associated with the Time Coincident Portfolio includes a significant amount of new battery storage, which will help shape variable renewable generation to customers’ usage and CAISO system need, minimizing CleanPowerSF’s use of fossil-fueled resources to serve load.
As it begins to implement its Preferred Portfolios, CleanPowerSF will conduct outreach and seek input from Disadvantaged Communities that could be impacted by planned procurement. CleanPowerSF conducted local stakeholder engagement throughout its IRP process, including a series of webinars on the IRP process. CleanPowerSF also issued IRP modeling results and the proposed recommended Preferred Portfolio for public comment.64 CleanPowerSF will continue to engage communities that may be impacted by the IRP and related procurement activities.
In accordance with the SFPUC’s Community Benefits policy,65 CleanPowerSF has sought to include Community Benefits Agreements as part of its long-term renewable energy contracts. CleanPowerSF, as a program of the SFPUC, encourages responses to Requests for Proposals of $5 million or more to include social impact commitments to benefit communities directly affected by projects.66 The SFPUC was the first public utility in the nation to adopt community benefits and environmental justice policies, and through new long-term renewable commitments, CleanPowerSF aims to continue contributing to investments in workforce development, education, environmental justice, neighborhood revitalization, and the arts in communities impacted by its activities. 67 64 The CleanPowerSF IRP stakeholder engagement materials and public comments received will be made availa ble at: www.cleanpowersfl.org/resourceplan [Accessed 10/6/2022] 65 SFPUC Commission Resolution No. 11-0008 affirming the Community Benefits Policy is available at:
https://sfpuc.org/sites/default/files/about-us/policies-reports/CommunityBenefits%20Policy_JAN2011.pdf [Accessed 10/12/22]
66 See Social Impact Partnerships in Contracts Process Overview at:
https://sfpuc.org/sites/default/files/construction-and- contracts/Social%20Impact%20Program%20Information_2021.pdf [Accessed 10/12/2022] 67 More information on the SFPUC’s Community Benefits program is available at: https://sfpuc.org/about- us/policies-plans/community-benefits-policy [Accessed 10/12/ 2022] DRAFT – October 2022
66 c. Commission Direction of Actions
The CPUC does not authorize CleanPowerSF’s spending, programmatic goals, budgets or procurement. However, the CPUC procurement orders and potential future programmatic approaches to procurement will have significant impacts on procurement by LSEs, including CleanPowerSF.
With respect to the D.21-06-035 procurement order, CleanPowerSF requests that the CPUC provide more certainty around deliverability issues, especially as it relates to the MIC expansion process. As discussed in section III.k. above, the current process provides a significant barrier to procurement of firm clean power.
CleanPowerSF also requests that in future IRPs CPUC staff develops a process where LSEs can use their own forecasts of load modifiers, including BTM PV, in conforming portfolios assumptions. For this IRP, the discrepancy between CPUC and CleanPowerSF BTM PV forecasts were significant. This will result in the modeling results for CleanPowerSF’s conforming portfolios being less reflective of reality. While CleanPowerSF took steps to use custom load shapes to reduce the extent of the difference,68 it would be better for IRPs to be based on the best information possible.
V. Lessons Learned
CleanPowerSF thanks the CPUC IRP staff for all its efforts in this round of the IRP process.
CleanPowerSF staff appreciates the frequent communication by Energy Division staff and their responsiveness to questions and comments. CleanPowerSF offers the following suggestion to improve the process in the future:
Set a firm schedule that gives LSEs sufficient time to develop a comprehensive integrated resource plan: CleanPowerSF has repeatedly advocated for the CPUC to set, and stick to, a reasonable schedule which would allow LSEs sufficient time to prepare thorough and well detailed portfolios and plans.69 CleanPowerSF would like to start preparing its IRP filing early to provide sufficient time for analysis and public input. However, several critical inputs and filing requirements remained in flux for the 2022 IRP until the end of July 2022. Most importantly, finalized ELCC values were not available until July 29 which precluded LSEs from finalizing their model until late in the modeling process and IRP development.
These delays occurred despite the schedule adopted in D.22-02-004 setting June 15, 2022 as the date by which the filing requirements would be finalized.
68 See section III.C. supra.
69 See for example: Opening Comments of the City and County of San Francisco on the Administrative Law Judge’s Ruling Establishing Process for Finalizing Load Forecasts and Greenhouse Gas Emissions Benchmarks for 2022 Integrated Resources Plan Filings, Opening Comments of the City and County of San Francisco on the Proposed Decision Adoption 2021 Preferred System Plan, and Section V of CleanPowerSF 2020 Integrated Resource Plan.
DRAFT – October 2022
67 As CleanPowerSF suggested in the last 2020 IRP narrative, the CPUC should adopt a date by which all requirements and templates must be finalized.70 If delays occur past this date., then the IRP due date should be extended to ensure LSEs have sufficient time to conduct their planning process. CleanPowerSF requests that LSEs receive at least 6 months conduct their IRP analysis and perform extensive engagement with stakeholders and governing body members.
Glossary of Terms
Alternative Portfolio: LSEs are permitted to submit “Alternative Portfolios” developed from scenarios using different assumptions from those used in the Preferred System Plan with updates. Any deviations from the “Conforming Portfolio” must be explained and justified.
Approve (Plan): the CPUC’s obligation to approve an LSE’s integrated resource plan derives from Public Utilities Code Section 454.52(b)(2) and the procurement planning process described in Public Utilities Code Section 454.5, in addition to the CPUC obligation to ensure safe and reliable service at just and reasonable rates under Public Utilities Code Section 451.
Balancing Authority Area (CAISO): the collection of generation, transmission, and loads within the metered boundaries of the Balancing Authority. The Balancing Authority maintains load-resource balance within this area.
Baseline resources: Those resources assumed to be fixed as a capacity expansion model input, as opposed to Candidate resources, which are selected by the model and are incremental to the Baseline.
Baseline resources are existing (already online) or owned or contracted to come online within the planning horizon. Existing resources with announced retirements are excluded from the Baseline for the applicable years. Being “contracted” refers to a resource holding signed contract/s with an LSE/s for much of its energy and capacity, as applicable, for a significant portion of its useful life. The contracts refer to those approved by the CPUC and/or the LSE’s governing board, as applicable. These criteria indicate the resource is relatively certain to come online. Baseline resources that are not online at the time of modeling may have a failure rate applied to their nameplate capacity to allow for the risk of them failing to come online.
Candidate resource: those resources, such as renewables, energy storage, natural gas generation, and demand response, available for selection in IRP capacity expansion modeling, incremental to the Baseline resources.
Capacity Expansion Model: a capacity expansion model is a computer model that simulates generation and transmission investment to meet forecast electric load over many years, usually with the objective of minimizing the total cost of owning and operating the electrical system. Capacity expansion models can also be configured to only allow solutions that meet specific requirements, such as providing a minimum amount of capacity to ensure the reliability of the system or maintaining greenhouse gas emissions below an established level.
70 CleanPowerSF 2020 Integrated Resources Plan Compliance Filing (Public Filing). https://sfpuc.sharefile.com/share/view/s61300b4056124fcda409799fd243d245 [accessed 10/12/2022].
DRAFT – October 2022
68 Certify (a Community Choice Aggregator Plan): Public Utilities Code 454.52(b)(3) requires the CPUC to certify the integrated resource plans of CCAs. “Certify” requires a formal act of the Commission to determine that the CCA’s Plan complies with the requirements of the statute and the process established via Public Utilities Code 454.51(a). In addition, the Commission must review the CCA Plans to determine any potential impacts on public utility bundled customers under Public Utilities Code Sections 451 and 454, among others.
Clean System Power (CSP) methodology: the methodology used to estimate GHG and criteria pollutant emissions associated with an LSE’s Portfolio based on how the LSE will expect to rely on system power on an hourly basis.
Community Choice Aggregator: a governmental entity formed by a city or county to procure electricity for its residents, businesses, and municipal facilities.
Conforming Portfolio: the LSE portfolio that conforms to IRP Planning Standards, the 2030 LSE-specific GHG Emissions Benchmark, use of the LSE’s assigned load forecast, use of inputs and assumptions matching those used in developing the Reference System Portfolio, as well as other IRP requirements including the filing of a complete Narrative Template, a Resource Data Template and Clean System Power Calculator.
Effective Load Carrying Capacity: a percentage that expresses how well a resource is able avoid loss-of- load events (considering availability and use limitations). The percentage is relative to a reference resource, for example a resource that is always available with no use limitations. It is calculated via probabilistic reliability modeling, and yields a single percentage value for a given resource or grouping of resources.
Effective Megawatts (MW): perfect capacity equivalent MW, such as the MW calculated by applying an ELCC % multiplier to nameplate MW.
Electric Service Provider: an entity that offers electric service to a retail or end-use customer, but which does not fall within the definition of an electrical corporation under Public Utilities Code Section 218.
Filing Entity: an entity required by statute to file an integrated resource plan with CPUC.
Future: a set of assumptions about future conditions, such as load or gas prices.
GHG Benchmark (or LSE-specific 2030 GHG Benchmark): the mass-based GHG emission planning targets calculated by staff for each LSE based on the methodology established by the California Air Resources Board and required for use in LSE Portfolio development in IRP.
GHG Planning Price: the systemwide marginal GHG abatement cost associated with achieving a specific electric sector 2030 GHG planning target.
Integrated Resources Planning Standards (Planning Standards): the set of CPUC IRP rules, guidelines, formulas and metrics that LSEs must include in their LSE Plans.
Integrated Resource Planning (IRP) process: integrated resource planning process; the repeating cycle through which integrated resource plans are prepared, submitted, and reviewed by the CPUC Long term: more than 5 years unless otherwise specified.
DRAFT – October 2022
69 Load Serving Entity: an electrical corporation, electric service provider, community choice aggregator, or electric cooperative.
Load Serving Entity (LSE) Plan: an LSE’s integrated resource plan; the full set of documents and information submitted by an LSE to the CPUC as part of the IRP process.
Load Serving Entity (LSE) Portfolio: a set of supply- and/or demand-side resources with certain attributes that together serve the LSE’s assigned load over the IRP planning horizon.
Loss of Load Expectation (LOLE): a metric that quantifies the expected frequency of loss-of-load events per year. Loss-of-load is any instance where available generating capacity is insufficient to serve electric demand. If one or more instances of loss-of-load occurring within the same day regardless of duration are counted as one loss-of-load event, then the LOLE metric can be compared to a reference point such as the industry probabilistic reliability standard of “one expected day in 10 years,” i.e. an LOLE of 0.1.
Maximum Import Capability: a California ISO metric that represents a quantity in MWs of imports determined by the CAISO to be simultaneously deliverable to the aggregate of load in the ISO’s Balancing Authority (BAA) Area and thus eligible for use in the Resource Adequacy process. The California ISO assess a MIC MW value for each intertie into the ISO’s BAA and allocated yearly to the LSEs. A LSE’s RA import showings are limited to its share of the MIC at each intertie.
Net Qualifying Capacity (NQC): Qualifying Capacity reduced, as applicable, based on: (1) testing and verification; (2) application of performance criteria; and (3) deliverability restrictions. The Net Qualifying Capacity determination shall be made by the California ISO pursuant to the provisions of this California ISO Tariff and the applicable Business Practice Manual.
Non-modeled costs: embedded fixed costs in today’s energy system (e.g., existing distribution revenue requirement, existing transmission revenue requirement, and energy efficiency program cost).
Nonstandard LSE Plan: type of integrated resource plan that an LSE may be eligible to file if it serves load outside the CAISO balancing authority area.
Optimization: an exercise undertaken in the CPUC’s Integrated Resource Planning (IRP) process using a capacity expansion model to identify a least-cost portfolio of electricity resources for meeting specific policy constraints, such as GHG reduction or RPS targets, while maintaining reliability given a set of assumptions about the future. Optimization in IRP considers resources assumed to be online over the planning horizon (baseline resources), some of which the model may choose not to retain, and additional resources (candidate resources) that the model is able to select to meet future grid needs.
Planned resource: any resource included in an LSE portfolio, whether already online or not, that is yet to be procured. Relating this to capacity expansion modeling terms, planned resources can be baseline resources (needing contract renewal, or currently owned/contracted by another LSE), candidate resources, or possibly resources that were not considered by the modeling, e.g., due to the passage of time between the modeling taking place and LSEs developing their plans. Planned resources can be specific (e.g., with a CAISO ID) or generic, with only the type, size and some geographic information identified.
Qualifying capacity: the maximum amount of Resource Adequacy Benefits a generating facility could provide before an assessment of its net qualifying capacity.
DRAFT – October 2022
70 Preferred Conforming Portfolio: the conforming portfolio preferred by an LSE as the most suitable to its own needs; submitted to CPUC for review as one element of the LSE’s overall IRP plan.
Preferred System Plan: the Commission’s integrated resource plan composed of both the aggregation of LSE portfolios (i.e., Preferred System Portfolio) and the set of actions necessary to implement that portfolio (i.e., Preferred System Action Plan).
Preferred System Portfolio: the combined portfolios of individual LSEs within the CAISO, aggregated, reviewed and possibly modified by Commission staff as a proposal to the Commission, and adopted by the Commission as most responsive to statutory requirements per Pub. Util. Code 454.51; part of the Preferred System Plan.
Short term: 1 to 3 years (unless otherwise specified).
Staff: CPUC Energy Division staff (unless otherwise specified).
Standard LSE Plan: type of integrated resource plan that an LSE is required to file if it serves load within the CAISO balancing authority area (unless the LSE demonstrates exemption from the IRP process).
Transmission Planning Process (TPP): annual process conducted by the California Independent System Operator (CAISO) to identify potential transmission system limitations and areas that need reinforcements over a 10-year horizon.
Template posted by Commission on June 15, 2022
Standard LSE Plan
[NAME OF FILING ENTITY]
2022 INTEGRATED RESOURCE PLAN
[DATE]
1 Table of Contents
I. Executive Summary .................................................................................................................... 3 II. Study Design ................................................................................................................................. 3 a. Objectives ................................................................................................................................... 6 b. Methodology ............................................................................................................................ 6 i. Modeling Tool(s) ................................................................................................................. 6 ii. Modeling Approach ...................................................................................................... 6 III. Study Results ............................................................................................................................. 6 a. Conforming and Alternative Portfolios.......................................................................... 7 b. Preferred Conforming Portfolios ...................................................................................... 7 c. GHG Emissions Results ......................................................................................................... 8 d. Local Air Pollutant Minimization and Disadvantaged Communities ............ 9 i. Local Air Pollutants ............................................................................................................ 9 ii. Focus on Disadvantaged Communities ................................................................ 9 e. Cost and Rate Analysis ....................................................................................................... 10 f. System Reliability Analysis ................................................................................................ 13 g. High Electrification Planning ............................................................................................ 14 h. Existing Resource Planning ............................................................................................... 14 i. Hydro Generation Risk Management ........................................................................... 15 j. Long-Duration Storage Planning ................................................................................... 15 k. Clean Firm Power Planning ............................................................................................... 15 l. Out-of-State Wind Planning ............................................................................................ 16 m. Offshore Wind Planning ................................................................................................ 16 n. Transmission Planning ........................................................................................................ 16 IV. Action Plan ............................................................................................................................... 18 a. Proposed Procurement Activities and Potential Barriers ..................................... 18 i. Resources to meet D.19-11-016 procurement requirements ....................... 19 ii. Resources to meet D.21-06-035 procurement requirements, including:
19 2
a. 1,000 MW of firm zero-emitting resource requirements ................................ 19 b. 1,000 MW of long-duration storage resource requirements .................... 19 c. 2,500 MW of zero-emissions generation, generation paired with storage, or demand response resource requirements ............................................................... 19 d. All other procurement requirements ................................................................... 19 iii. Offshore wind ................................................................................................................ 19 iv. Out-of-state wind ......................................................................................................... 19 v. Other renewable energy not described above ................................................ 19 vi. Other energy storage not described above ...................................................... 19 vii. Other demand response not described above ................................................ 19 viii. Other energy efficiency not described above .................................................. 19 ix. Other distributed generation not described above ...................................... 19 x. Transportation electrification, including any investments above and beyond what is included in Integrated Energy Policy Report (IEPR) .................. 19 xi. Building electrification, including any investments above and beyond what is included in Integrated Energy Policy Report (IEPR) ................................... 19 xii. Other .................................................................................................................................. 19 b. Disadvantaged Communities ........................................................................................... 19 c. Commission Direction of Actions................................................................................... 20 V. Lessons Learned ........................................................................................................................ 20 Glossary of Terms............................................................................................................................... 21
3 How to use this template:
− All LSEs required to file a Standard LSE Plan must use this template, as well as the accompanying Resource Data Template and Clean System Power calculator provided by staff. − All LSEs filing a Non-Standard Plan may use this template. If Non-Standard LSE Plan filers choose to submit this template, they do not have to submit the Clean System Power calculator tool, the Resource Data Template, or address any of the requirements based on contracted or planned resource information.
− Instructions are provided in italics under each section. Delete all instructions before submitting the form, but preserve the numbered section headings.
− Complete each section. If the section is not applicable to the LSE, simply indicate “Not applicable” and provide a brief explanation.
− Definitions are provided in the Glossary of Terms at the end of this template.
I. Executive Summary
Use this section to provide an overview of the process used by the LSE to develop its plan and summarize the LSE’s findings, including a brief overview of the LSE’s Preferred Conforming Portfolio and Action Plan, and a brief summary of each section and sub-section of this template.
II. Study Design
Use this section to describe how the LSE approached the process of developing its LSE Plan.
Load Assignments for Each LSE
All LSEs should use the finalized energy and peak demand load forecasts, and behind-the-meter photovoltaic (BTM PV) information that has been developed pursuant to an ALJ June 15, 2022 Ruling.
The load forecast that each LSE shall use as the basis for its individual IRP planning horizon has been approved through an ALJ June 15, 2022 Ruling finalizing load forecasts and GHG benchmarks. Staff will finalize the peak capacity forecast and related behind-the-meter photovoltaic (BTM PV) component for use by each LSE by no later than July 1, 2022, and will distribute this information individually to each LSE.
LSEs may provide their own load or load modifier shapes in the Clean System Power (CSP) calculator, but, for “Conforming Portfolios,” the total annual energy volumes for both load and load modifiers must remain consistent with their assigned forecast. If using their own shapes, LSEs must provide detailed explanations as to how their load or load modifier shapes were developed, including data sources. If LSEs do not provide their own specific shapes, they will be automatically assigned the default hourly shapes in the CSP calculator, which reflects the 2021 Integrated Energy Policy Report (IEPR) “mid Baseline mid AAEE” hourly forecast for the CAISO system average.
LSEs are not permitted to use an annual load forecast (MWh) that differs from the one assigned to it in the IRP.
4 Required and Optional Portfolios
Each LSE must produce and submit at least two “Conforming Portfolios:" one that achieves emissions that are equal to or less than the LSE’s proportional share of the 38 MMT by 2030 and 30 MMT by 2035 GHG targets (the 30 MMT conforming portfolio), and another that achieves emissions that are equal to or less than the LSE’s proportional share of a 30 MMT by 2030 and 25 MMT by 2035 GHG targets (the 25 MMT conforming portfolio). If the LSE intends to go below its proportional share of both the 2030 30 MMT target and the 2035 25 MMT target, then that LSE will only be required to submit one conforming portfolio as part of its individual IRP filing. A Conforming Portfolio is one that utilizes the LSE’s assigned load forecast and is consistent with the Commission-adopted Preferred System Portfolio according to the following criteria:
• For the 30 MMT conforming portfolio, achieves emissions equal to or lower than the LSE’s 38 MMT 2030 GHG Emissions Benchmark and 30 MMT 2035 GHG Emissions Benchmark • For the 25 MMT conforming portfolio, achieves emissions equal to or lower than the LSE’s 30 MMT 2030 GHG Emissions Benchmark and 25 MMT 2035 GHG Emissions Benchmark • LSEs should use their individual load assignment as indicated above • Uses inputs and assumptions consistent with those used by staff to develop the Preferred System Portfolio as updated by IRP staff with more recent inputs and posted on the IRP website on June 15, 2022 (2021 PSP Portfolio with updates),
1 with the following exceptions based on updated
information:
o If the LSE has better capital cost and financing information that more accurately reflects its situation, the LSE is free to use those inputs and/or assumptions. For example, an LSE may have its own view of future resource levelized costs and it is free to use this information to develop its portfolio. The LSE should clearly identify, and provide an explanation for, instances where it used its own assumption in lieu of the default used by staff to develop the 2021 PSP Portfolio with updates.
• Completing all three filing items (Resource Data Template, CSP calculator, and Narrative template) according to completeness definition which has been provided in the "Filing Requirements Overview" document.
For a more comprehensive definition of a conforming portfolio refer to the "Filing Requirements Overview" document.
LSEs may study and report multiple Conforming Portfolios for each 2030/2035 GHG target. LSEs are required to select two "Preferred Conforming Portfolios" among all Conforming Portfolios developed and submitted: one that achieves emissions that are equal to or less than the LSE’s proportional share of the 38 MMT by 2030 and 30 MMT by 2035 GHG targets (the 30 MMT preferred conforming portfolio), and another that achieves emissions that are equal to or less than the LSE’s proportional share of a 30 MMT
1 Posted on IRP website on the following link and titled “LSE Filing Requirement RESOLVE Results”:
https://www.cpuc.ca.gov/industries-and-topics/electrical-energy/electric-power-procurement/long-term-procurement- planning/2022-irp-cycle-events-and-materials
5 by 2030 and 25 MMT by 2035 GHG targets (the 25 MMT preferred conforming portfolio). . LSEs should justify the selection of each GHG target, including why the portfolio is consistent with all state goals and is the best representation for how the LSE plans to meet state goals. As stated above, if the LSE intends to go below its proportional share of both the 2030 30 MMT target and the 2035 25 MMT target, then that LSE will only be required to submit one Preferred Conforming Portfolio as part of its individual IRP filing. However, LSEs submitting one Preferred Conforming Portfolio will still be required to submit that portfolio in each of the two required sets of Resource Data Templates and Clean System Power calculators required for each set of 2030/2035 GHG targets.
LSEs may also study and report additional "Alternative Portfolios" developed from different assumptions (including different annual levels of load modifiers) from the Preferred System Plan. LSEs may propose to meet their load and GHG requirements with both supply-side and demand-side investments and must explain how these resources meet or beat their assigned load levels and GHG target.
For all Alternative Portfolios developed, any deviations from the Conforming Portfolio must be explained and justified. If the LSE uses different
annual levels of load modifiers as part of any Alternative Portfolio the LSE should report that information using the standard IEPR filing form templates2 associated with that information. All Alternative and Conforming Portfolios must use the same assigned load forecast as a starting point, but Alternative Portfolios can use demand-side resources such as energy efficiency or electrification to deviate from the annual levels of load modifiers assigned to them for their Conforming Portfolios.
Community Choice Aggregators (CCAs) are permitted, in the Action Plan section of this template, to also describe a procurement strategy certified by their governing board if it differs from the one associated with their Preferred Conforming Portfolio.
Investor-Owned Utilities (IOUs) should assume no procurement on behalf of non-bundled customers would be needed unless specifically required by the Commission.
Additionally, each LSE should account for the costs and benefits of any resources subject to the cost allocation mechanism (CAM) and Modified CAM in its Conforming Portfolios.
GHG Emissions Benchmark
LSEs have been assigned new 2030 and 2035 GHG Emissions Benchmarks based on the results of the Preferred System Portfolio, specifically the 2030 GHG planning target adopted by the Commission for the electric sector, calculated using the same methodology from the previous IRP cycle, and as established by the California Air Resources Board (CARB) 2018 Staff Report, “Senate Bill 350 Integrated Resource Planning Electricity Sector Greenhouse Gas Planning Targets,” 3 and the 2035 GHG planning targets set
via ALJ Ruling. LSE GHG Benchmarks were assigned via ALJ Ruling on June 15, 2022 and are posted on the IRP website.
2 Forms used for the 2021 IEPR cycle are available here: https://www.energy.ca.gov/data-re ports/reports/integrated-energy- policy-report/2021-integrated-energy-policy-report/2021-iepr; see the March 2021 Webinar on Forms and Instructions to Collect Electricity Demand Forecast and Electricity Resource Plan Data 3 Available at https://ww3.arb.ca.gov/cc/sb350/staffreport_sb350_irp.pdf.
6 Because the IEPR does not include load forecasts for individual Electric Service Providers (ESPs), each ESP is required to calculate its own confidential GHG Emissions Benchmarks based on its 2030 and 2035 load shares within the host IOU’s territory. For any ESP that serves load in more than one IOU service territory, that ESP should add up the separate GHG Emissions Benchmarks calculated based on its share of direct access load for each IOU service territory to result in a single benchmark for 2030 and 2035. The CSP calculator includes a table for performing this calculation in the tab titled “ESP GHG Benchmark.”
LSEs filing a Standard LSE Plan should use the CSP methodology and calculator for estimating their GHG emissions across the IRP planning horizon. It is important to note that neither emissions from, nor demand met by, Behind-the-Meter Combined Heat and Power (BTM CHP) resources are included in the CSP calculator. While individual LSEs are not required to plan to reduce BTM CHP emissions, these emissions nevertheless count towards the electric sector emissions total and are included in LSE GHG Benchmarks. Commission staff plans to account for BTM CHP emissions when calculating electric sector emissions of the aggregated LSE portfolios during the development of the Preferred System Plan.
a. Objectives
Provide a description of the LSE’s objectives for the analytical work it is documenting in the IRP. b. Methodology
i. Modeling Tool(s)
Name all modeling software used by LSE to develop its IRP, if any, and include the vendor and version number. Provide an explanation of differences between the LSE’s modeling tool and RESOLVE and SERVM, and an explanation of how those differences should be considered during evaluation of the LSE’s portfolio(s).
ii. Modeling Approach
Describe the LSE’s overall approach to developing the scenarios it evaluated, and explain why each scenario was considered. Also describe any calculations, including post-processing calculations, used to generate metrics for portfolio analysis.
III. Study Results
Use this section to present the results of the analytical work described in Section 2: Study Design.
7 a. Conforming and Alternative Portfolios
Provide a list of all Conforming Portfolios and Alternative Portfolios developed. The portfolios should clearly identify and distinguish between the following:
• Existing resources that the LSE owns or contracts with, consistent with definitions provided in the Resource Data Template.
• Existing resources that the LSE plans to contract with in the future. • New resources that the LSE plans to invest in.
For new resources, LSEs should provide a description in table form of how those planned resources compare to the mix of new resources identified in the 2021 PSP Portfolio with updates and comment on the significance of the variances, if any.
LSEs should report all contracted and planned resources for each plan filed in the Resource Data Template and provide a narrative summary of those reported resources in this section.
For the Alternative Portfolios, deviations from the Conforming Portfolio need to be explained and justified.
b. Preferred Conforming Portfolios
Provide a detailed description of the two Conforming Portfolios—the 30 MMT Preferred Conforming Portfolio and the 25 MMT Preferred Conforming Portfolio—for which the LSE seeks Commission approval or certification. LSE should justify the portfolio selections for each GHG target. The LSE can provide a detailed description of just one Preferred Conforming Portfolio if it has used the same Preferred Conforming Portfolio to achieve both its 30 MMT Preferred Conforming Portfolio and the 25 MMT Preferred Conforming Portfolio requirements.
Explain the reasons for the LSE’s preference and how its selections are consistent with each relevant statutory and administrative requirement. Specifically, the LSE should address the following relevant statutory and administrative requirements in PU Code Section 454.52(a)(1) and explain how its Preferred Conforming Portfolios satisfy those requirements:
(A) Meet the greenhouse gas emissions reduction targets established by the State Air Resources Board, in coordination with the commission and the Energy Commission, for the electricity sector and each load-serving entity that reflect the electricity sector’s percentage in achieving the economywide greenhouse gas emissions reductions of 40 percent from 1990 levels by 2030 (I.e. meet the LSE’s GHG benchmarks).
(B) Procure at least 60 percent eligible renewable energy resources by December 31, 2030, consistent with Article 16 (commencing with Section 399.11) of Chapter 2.3.
8 (C) Enable each electrical corporation to fulfill its obligation to serve its customers at just and reasonable rates.
(D) Minimize impacts on ratepayers’ bills.
(E) Ensure system and local reliability on both a near-term and long-term basis, including meeting the near-term and forecast long-term resource adequacy requirements of Section 380. (F) Comply with paragraph (1) of subdivision (b) of Section 399.13 (I.e., at least 65 percent of the LSE’s RPS procurement for each compliance period shall be from its contracts of 10 years or more in duration or in its ownership or ownership agreements for eligible renewable energy resources).
(G) Strengthen the diversity, sustainability, and resilience of the bulk transmission and distribution systems, and local communities.
(H) Enhance distribution systems and demand-side energy management. (I) Minimize localized air pollutants and other greenhouse gas emissions, with early priority on disadvantaged communities identified pursuant to Section 39711 of the Health and Safety Code.
In providing its rationale, the LSE should assume that other LSEs procure in a manner consistent with the 2021 PSP Portfolio with updates. If the LSE submits a portfolio that achieves emissions reductions lower than its 25 MMT benchmark, the LSE should explain and justify its selection of that portfolio and explain whether and how that portfolio might operate differently, from a reliability perspective, depending on whether other LSEs procure in a manner consistent with a 30 MMT or 25 2035 MMT target. If the LSE has a preference, it should also state in its Narrative Template which Preferred Conforming Portfolio it prefers as a blueprint for its own procurement and justify that choice.
Lastly, if the LSE’s preferred confirming portfolios include new natural gas resources or re-contracting with existing natural gas resources for a term of five years or more, the LSE should describe why another lower-emitting or zero-emitting resource could not reasonably meet the need identified. This description is not required for any tariffed or must-take resources required by separately authorized Commission programs or decisions.
c. GHG Emissions Results
Use the CSP calculator to estimate the GHG emissions associated with each portfolio and report those results in this section. There are two versions of the CSP calculator, one for the 2035 30 MMT GHG target and another for the 2035 25 MMT GHG target. LSEs should use the associated version for each GHG target for their reporting. If the LSE submits a conforming portfolio that achieves lower than its 2035 25 MMT benchmark, it should estimate emissions for that portfolio using the 2035 25 MMT version.
9 If an LSE uses a custom hourly load shape or user-specified production profile in the CSP calculator for any portfolio, it must provide a detailed explanation as to how its load shape or production profile was developed, including the source of the data used. d. Local Air Pollutant Minimization and Disadvantaged Communities i. Local Air Pollutants
Use the CSP calculator to estimate the NOx, PM2.5, and SO2 emissions associated with the LSE’s Preferred Conforming Portfolios and report those results in this section. If the LSE’s only contribution to air pollutants are a result from reliance on system power, then the LSE should provide explanation in the Action Plan section of this document of how it plans to reduce reliance on system power.
ii. Focus on Disadvantaged Communities
Use this section to describe and provide quantitative evidence to support how the LSE’s Preferred Conforming Portfolios minimize local air pollutants with early priority on disadvantaged communities. The LSE must provide a description of which disadvantaged communities, if any, it serves. LSEs must also specify customers served in disadvantaged communities along with total disadvantaged population number served as a percentage of total number of customers served. This includes both residential and non-residential customers. The finest level of granularity would be census tracts, but LSEs may summarize at the zip code level if that is all that is available to them. If census tracts granularity is not being used, LSEs must explain the reason for the level of granularity they are providing. An ESP can report its total customers in disadvantaged communities divided by the total customers the ESP serves. Finally, the LSE must describe and provide specific details of outreach to disadvantaged communities undertaken prior to finalizing and submitting its IRP, summarize the feedback received from disadvantaged communities and their representatives, and describe how such feedback influenced development of the LSE’s Preferred Conforming Portfolios.
For purposes of IRP, a disadvantaged community is defined as following based on CalEPA’s designation:
4 I. Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 (1,984 tracts).
II. Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative pollution burden scores (19 tracts)
4 SB 535 Disadvantaged Communities | OEHHA (ca.gov)
10 III. Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 (307 tracts).
IV. Lands under the control of federally recognized Tribes.5
For the purposes of this IRP filing, LSE portfolio impacts on disadvantaged communities are not limited to impacts within the LSE’s own boundaries. Almost all LSEs have impacts on disadvantaged communities, at least indirectly, as a result of their reliance on some system power or other power with local pollutant or GHG emissions, which can still impact disadvantaged communities. In its response, the LSE must take an expansive view of its responsibilities in this area and describe in detail its efforts to minimize disadvantaged community air pollution impacts, not only in its own service area, but also in the state as a whole.
e. Cost and Rate Analysis
Describe and provide quantitative information to reflect how the LSE anticipates that its Preferred Conforming Portfolios will affect the costs for its customers. For this analysis, assume other LSEs procure resources in a manner consistent with the 2021 PSP Portfolio with updates.
Requirements for IOUs Only
Data must be provided showing the forecasted revenue requirement and system average rate for bundled customers for current (baseline)6 and all portfolios developed by the IOU. The costs should be forecasted consistently with the categories covered by each IOU in its general rate case and other Commission approved revenues and balancing accounts, and should incorporate all revenue requirements approved but not yet implemented as well as pending requests. 7 The data should
reflect the IOU’s assigned load forecast (for the conforming portfolio), and revenue requirements for each portfolio should be broken down by the following categories and include modeling assumptions for each category:
• Transmission
• Distribution (e.g. includes costs from distribution upgrades driven by customer-generation)
5 For purposes of this designation, a Tribe may establish that a particular area of land is under its control even if not represented as such on CalEPA’s DAC map and therefore should be considered a DAC by requesting a consultation with the CalEPA Deputy Secretary for Environmental Justice, Tribal Affairs and Border Relations . 6 The baseline scenario uses the same cost assumptions as the conforming portfolios but excludes all generic resource additions.
7 Pending revenue requests refers to revenue requests that have been filed and for which a decision by the Commission is pending.
11 • DSM Programs (e.g. includes costs of energy-efficiency, demand response, and other programs)
• Generation (e.g. includes costs of utility-owned generation, bilateral contracts, renewables contracts, and storage contracts, net of revenue from EDU allowances) • Other (e.g. includes nuclear decommissioning, DWR bonds, public purpose programs, and other miscellaneous)
In presenting revenue requirement data, IOUs should complete three tables to clearly distinguish between current (baseline) projected revenue requirement broken down by the categories above, and the incremental projected revenue requirement broken down by the same categories for the preferred conforming portfolios (one table for each conforming portfolio). For each new resource portfolio that the IOU is showing results for in its Plan report all assumptions used such as cost escalation rate, inflation rate, levelization period, discount rate, taxes, financing, etc.
IOUs should complete the following tables, adhering as closely as possible to the units and categories listed.
Revenue Requirements and Bundled System Average Rates for Baseline Scenario (2021 $) Line No. Cost Category 2023 … 2035
1 Distribution
2 Transmission
3 Generation
4 Demand Side Programs
5 Other
6 (sum
lines 1-5)
Baseline Revenue
Requirement
7 System Sales (GWh)
8 Bundled Sales (GWh)
9 System Average Delivery
Rate (¢/kWh)
10 Bundled Generation Rate
(¢/kWh)
11 Bundled System Average
Rate (¢/kWh)
12 Revenue Requirements and Bundled System Average Rates for 30 MMT Preferred Conforming Portfolio (2021 $)
Line No. Cost Category 2023 … 2035
1 Distribution
2 Transmission
3 Generation
4 Demand Side Programs
5 Other
6 (sum
lines 1-5)
Preferred Conforming
Portfolio Revenue
Requirement
7 System Sales (GWh)
8 Bundled Sales (GWh)
9 System Average Delivery
Rate (¢/kWh)
10 Bundled Generation Rate
(¢/kWh)
11 Bundled System Average
Rate (¢/kWh)
Revenue Requirements and Bundled System Average Rates for 25 MMT Preferred Conforming Portfolio (2021 $)
Line No. Cost Category 2023 … 2035
1 Distribution
2 Transmission
3 Generation
4 Demand Side Programs
5 Other
13 6 (sum
lines 1-5)
Preferred Conforming
Portfolio Revenue
Requirement
7 System Sales (GWh)
8 Bundled Sales (GWh)
9 System Average Delivery
Rate (¢/kWh)
10 Bundled Generation Rate
(¢/kWh)
11 Bundled System Average
Rate (¢/kWh)
Requirements for All LSEs
All LSEs should consider cost and rate impacts on their customers when planning and submitting their individual IRPs. At a minimum, LSEs should include a narrative description of how they approached the issue of affordability when developing their preferred conforming portfolios, the cost and (if applicable) rate findings of their analysis, and how each of their preferred conforming portfolios compare from a cost (and rate) impact perspective. Please explain the key assumptions used in the LSE’s cost (and rate) impact analysis and the LSE’s findings regarding the key drivers of cost and rate differences. If the LSE prefers one of its preferred conforming portfolios as a blueprint for its own procurement, the LSE should explain why its preference is justified from a cost (and rate) impact perspective.
f. System Reliability Analysis
Use the RDT to show that each portfolio contributes its fair share to system reliability and report those results in this section. The LSE should provide a Load and Resource Table by Contract Status, as well as a chart of this information, for each preferred conforming portfolio that includes total reliability need (effective MW), total supply (effective MW), and net capacity position (effective MW) for all study years.
14 g. High Electrification Planning
The Preferred System Plan decision delegated staff to work with the CEC and CAISO staff to explore the development of a portfolio with a 30 MMT emission limit in 2030 using high electrification assumptions for study as a policy-driven sensitivity in the 2022-2023 TPP. The lead staff of the Energy Agencies (CEC, CPUC, and CAISO) agreed to collectively develop inputs necessary for the CAISO to conduct an assessment of the transmission system impacts of a scenario representing a high electrification future. CEC has developed and adopted two demand side projections: High Electrification (HE) and Additional Transportation Electrification (Additional TE). 8 The CPUC is
developing the resource portfolio satisfying the higher load, using the Additional TE, and a 2030 30 MMT target and will transmit this portfolio to CAISO by the end of June to be studied as a policy- driven sensitivity in the 2022-2023 TPP. This portfolio goes through 2035.
Use this section to describe and provide qualitative and quantitative information on what additional resource planning and procurement the LSE would do under this high electrification scenario (ATE) to satisfy its assigned 2035 GHG Emission Benchmarks by 2035.
Please include a table that specifies resource type, MW amount, and what GHG target it is for (“30MMT”, “25MMT”, or “both”). Please fill Transmission Zone, Substation/Bus, and Alternative location after completing section III(N): Transmission Planning. Information provided in this table should be for additional resource planning by 2035.
Resource
Type
MWs Annual
GWh
2035 GHG
target
Transmission
Zone
Substation/Bus Alternative
location
Note
h. Existing Resource Planning
The 2021 Preferred System Plan found that in the 2019-21 IRP cycle, aggregated LSE Preferred Conforming Portfolios failed to meet GHG and reliability targets due to insufficient new capacity being planned for, and that this was caused in part by LSEs over-relying on existing resources. In other words, the quantity of contracted and uncontracted existing resources that LSEs included in their plans exceeded the amount of existing resources available on the system. This indicates that some LSEs may have trouble procuring the existing resources that they planned for in the previous IRP cycle.
Provide a narrative analysis and discussion of how this finding impacted the LSE’s approach to developing its 2022 Preferred Conforming Portfolios. In its response the LSE should describe the extent to which its Preferred Conforming Portfolios include existing resources, defined here as resources in the Resource Data Template with contract statuses of “online” and “planned existing.” If the LSE filed a 2020 IRP, it should compare the amounts of existing resources included in its 2022 Preferred Conforming Portfolios to the amounts of existing resources included in its 2020 38 MMT Preferred Conforming Portfolio. LSEs that did not file a 2020 IRP can just describe how this finding
8 https://efiling.energy.ca.gov/GetDocument.aspx?tn=243222
15 influenced their approach to developing their 2022 Preferred Conforming Portfolios without a comparison to their 2020 plans. The response should also include analysis explaining why the amounts of existing resources being planned for are appropriate, why the LSE believes that its planned amounts of existing resources will be available in the market, and what potential challenges the LSE might have in procuring existing resources (e.g., competition with other LSEs).
i. Hydro Generation Risk Management
Provide a narrative analysis and discussion of the risk that in-state drought poses to the LSE’s Preferred Conforming Portfolios, including the controls and strategies the LSE has in place to manage such risk. Using quantitative analysis, identify whether and how the LSE’s Preferred Conforming Portfolios differ from the 2021 PSP Portfolio with updates in terms of the amount of hydro generation proposed, and the level of risk thus incurred. Describe the degree to which the LSE’s expected costs, GHG emissions, and reliability are dependent on in-state hydro availability, and the controls such as hedging strategies or contingency plans. j. Long-Duration Storage Planning
Use this section to discuss the approach that the LSE took to planning for long-duration storage that is able to discharge at maximum capacity over at least an eight-hour period from a single resource to meet medium- and long-term needs. The LSE should discuss the potential it sees, how it arrived at the amounts included in its portfolios, the benefits and risks of including this resource in its portfolio mix, and whether the LSE plans to procure amounts additional to its ordered procurement requirements.
k. Clean Firm Power Planning
Use this section to discuss the approach that the LSE took to planning for clean firm generation (with an annual capacity factor of at least 80 percent) resources that are not subject to use limitations or are weather dependent. The type of resource described here must be a generating resource, not storage, able to generate when needed, for as long as needed, and may not have any on-site emissions, except if the resource otherwise qualifies under the Renewables Portfolio Standard (RPS) program eligibility requirements. The LSE should discuss the potential it sees, how it arrived at the amounts included in is portfolios, the benefits and risks of including this resource in its portfolio mix, and whether the LSE plans to procure amounts additional to its ordered procurement requirements.
If the LSE included any of this resource located outside of the CAISO balancing authority area in its portfolio, the LSE should describe the potential Maximum Import Capability (MIC) expansion or transmission upgrades needed to ensure the resource can be delivered to the CAISO.
16 l. Out-of-State Wind Planning
Use this section to discuss the approach that the LSE took to planning for out-of-state wind resources out to 2035. The LSE should discuss the potential it sees, how it arrived at the amounts included in its portfolios, and the benefits and risks of including this resource in its portfolio mix. The LSE should discuss the status of the transmission route the resources included in its portfolio mix will likely utilize to reach the CAISO border — will only existing, already online, transmission be needed or will any planned new transmission that is not yet online be required? m. Offshore Wind Planning
Use this section to discuss the approach the LSE took to planning for offshore wind resources out to 2035. The LSE should discuss the potential it sees, how it arrived at the amounts included in its portfolios, and the benefits and risks of including this resource in its portfolio mix. The LSE should address how its plan interacts, if at all, with the planning goals required by Assembly Bill 525 (Chiu, 2021) and also the procurement approaches discussed in Section 7.2 of D.22-02-004, that the Commission will explore with stakeholders in 2022. To the extent applicable, the LSE should differentiate between offshore located in the central coast and the north coast. n. Transmission Planning
Background information:
The CPUC transmits to the CAISO, resource portfolios to be used as an input for their Transmission Planning Process (TPP). The base case resource portfolio is used for CAISO to identify transmission needs in the ten-year timeframe. Because these transmission needs are then approved by the CAISO Board of Governors to accommodate planned resources, among other reasons, it is important that the CPUC use all available information, including LSE plans, when mapping the resources in the base case portfolio. For this reason, LSEs should carefully consider transmission planning when submitting IRP plans.
• Busbar mapping methodology9 criteria include consideration of commercial interest. This interest can be inferred from LSEs' plans, as well as interconnection queues. LSEs can identify which resources in their plans have been contracted since the IRP baseline was formed, and should therefore be included in the baseline for modeling in the transmission planning process.
Further, LSEs can identify which resources, whilst not yet contracted, have specific locations intended. The details of these resources should be included in the Resource Data Template (RDT), specifically by identifying the interconnection queue position. This section of the Narrative Template should summarize the data, and in the case of resources which do not yet have an
9 Available for "Modeling Assumptions for the 2022-2023 Transmission Planning Process" at:
https://files.cpuc.ca.gov/energy/modeling/Busbar%20Mapping%20Methodology%20for%20the%20TPP_V2021_12_21.pdf
17 interconnection queue position, provide as specific location as appropriate for the LSE’s stage of planning.
• Transmission upgrades may be cost-effective ways for LSEs to access new resources. The principles for aggregating LSEs’ plans10 include generally maximizing utilization of existing transmission capability where possible. For this reason, if LSEs are planning resources that will exceed existing transmission capability limits, LSEs need to demonstrate that they are actively planning for upgrades and can justify the costs, timeline, and risks.
As responses are provided to the questions below, describe what available information on transmission upgrades, interconnection, and resource planning was used. Suggested sources include, but are not limited to:
• CAISO White Paper11
• CAISO’s 2021-2022 Transmission Plan12
• Interconnection information such as that listed in CAISO’s Data Transparency Matrix13 • Busbar mapping results of the PSP Portfolio and the High Electrification Sensitivity Portfolio for the 2022-2023 TPP14
If the above sources of information are not utilized, please explain why. If additional information is utilized, please link and describe.
Required information:
a. For contracted resources, describe any known transmission upgrades needed to ensure interconnection of the resource, including online date. b. For planned resources with their locations specified in the RDT:
I. describe how transmission deliverability was ascertained; does the location align with current transmission systems with existing capability, upgrades currently under construction or approved by the CAISO board, or will it require additional new transmission upgrades?
II. The LSE should provide justification if in the RDT, the LSE noted it preferred such planned resources not be relocated to another location by staff in modeling or busbar mapping efforts. Please note whether this is investment grade information around which CAISO should be planning the transmission system and approving transmission 10 years out.
10 Available at: Methodology for Resource-to-Busbar Mapping & Assumption for the TPP 11 Available here: https://www.caiso.com/Documents/RevisedWhitePaper-20 21TransmissionCapabilityEstimates- CPUCResourcePlanningProcess.pdf
12 Available here: https://stakeholdercenter.caiso.com/RecurringStakeholderProcesses/2021-2022-Transmission-planning- process
13 Available at: Interconnection Process Enhancements 2021 - Data Transparency Matrix 14 Report available here: https://files.cpuc.ca.gov/energy/modeling/Modeling_Assumptions_2022-2023_TPP_V.2022-2-7.pdf; Dashboard available here:
https://files.cpuc.ca.gov/energy/modeling/BusbarMapping_Dashboard_38MMT_V2022_02_08_v2.xlsx
18 III. The LSE should provide justification of locations noted in the RDT for planned resources if the selected resource locations do not align with the LSE’s previously submitted 38 MMT by 2030 plan and/or with the resource locations either studied in CAISO’s 2021-2022 Transmission Plan or identified in the busbar mapping results of the PSP Portfolio and the High Electrification Sensitivity Portfolio for the 2022-2023 TPP noted above. -------------------------------------------------------------------------------------------------- For the additional procurement described under Section III(G): High Electrification Planning, please add transmission zone, substation/busbar location (if LSE has preference), and possible alternative locations (if any) in that section.
IV. Action Plan
Use this section to demonstrate to the Commission and to stakeholders the feasibility of the LSE’s planning and procurement strategy, what barriers it envisions to implementing its plan, and what actions the Commission should consider in order to facilitate plan implementation. a. Proposed Procurement Activities and Potential Barriers Describe all the activities the LSE proposes to undertake across resource types in order to implement its Preferred Conforming Portfolios, including any proposed procurement-related activities as required by Commission decision. Describe how each planned resource identified in the Study Results section corresponds to proposed activities. For each new resource identified, provide a narrative description of procurement plans, as well as potential market, regulatory, financial, and other resource viability barriers or risks associated with the LSE’s Preferred Conforming Portfolios, consistent with what is reported in the Resource Data Template. As applicable, describe the type of solicitation(s), when the solicitation(s) is/are expected to take place, the desired online dates of projects requested, and other relevant procurement information.
The LSE should provide responses for each of the following resource categories:
19 i. Resources to meet D.19-11-016 procurement requirements ii. Resources to meet D.21-06-035 procurement requirements, including:
a. 1,000 MW of firm zero-emitting resource requirements b. 1,000 MW of long-duration storage resource requirements c. 2,500 MW of zero-emissions generation, generation paired with storage, or demand response resource requirements
d. All other procurement requirements
iii. Offshore wind
iv. Out-of-state wind
v. Other renewable energy not described above
vi. Other energy storage not described above
vii. Other demand response not described above
viii. Other energy efficiency not described above
ix. Other distributed generation not described above
x. Transportation electrification, including any investments above and beyond what is included in Integrated Energy Policy Report (IEPR) xi. Building electrification, including any investments above and beyond what is included in Integrated Energy Policy Report (IEPR)
xii. Other
b. Disadvantaged Communities
Describe and provide specific details on any current and planned LSE activities/programs to address disadvantaged communities, including those located within the geographic area served by the LSE and beyond, and describe how the LSE’s actions and engagement have changed over time. Please describe any analysis or activities targeted at minimizing criteria air pollutants in disadvantaged communities and identifying feasible procurement opportunities to reduce reliance on fossil-fueled power plants, particularly those that are located within disadvantaged communities, including but not limited to those located within the geographic area served by the LSE. Describe specific metrics and scoring criteria that the LSE uses to prioritize the minimization of criteria air pollution in
20 disadvantaged communities, how those metrics and scoring criteria have been used in past procurement, and how those metrics and scoring criteria will be applied to planned procurement.
Also provide specific details on current and planned activities to conduct outreach and seek input from any disadvantaged communities, including those located within the geographic area served by the LSE and beyond, that could be impacted by procurement resulting from the implementation of the LSE’s Plan, as described in the LSE’s response to Section 4a. Include a list of any upcoming outreach activities and a description of how community members will be notified of opportunities to participate in these activities. Descriptions of current and planned outreach activities should address efforts to provide:
1. Accessible materials available in the languages spoken in the community;
2. Effective outreach and targeting to ensure that communities are aware of the opportunity to provide feedback;
3. Meaningful and transparent opportunities to provide input; and
4. Consideration of community input in the decision-making process.
If the LSE is not conducting targeted outreach directed toward disadvantaged communities, the LSE should explain why and discuss its plans for conducting such outreach in the future.
c. Commission Direction of Actions
If applicable, describe any direction that the LSE seeks from the Commission, including consideration in the IRP Procurement Track, new spending authorizations, changes to existing authorizations, or changes to existing programmatic goals or budgets. Draw clear connections between any requested direction and the study results, proposed activities, and barrier analysis presented above. In your response, please explain how you might have approached planning differently under the different IRP programmatic procurement options under consideration in 2022 in the IRP procurement track (per D.22-02-004 pages 152-156). If different program design options would have led to different amounts or types of planned resources in your Preferred Conforming Portfolios, please describe that difference and whether that influences how you think the Commission should design a programmatic approach to IRP procurement.
V. Lessons Learned
Document any suggested changes to the IRP process for consideration by the Commission. Explain how the change would facilitate the ability of the Commission and LSEs to achieve state policy goals.
21 Glossary of Terms
Alternative Portfolio: LSEs are permitted to submit “Alternative Portfolios” developed from scenarios using different assumptions from those used in the Preferred System Plan with updates. Any deviations from the “Conforming Portfolio” must be explained and justified.
Approve (Plan): the CPUC’s obligation to approve an LSE’s integrated resource plan derives from Public Utilities Code Section 454.52(b)(2) and the procurement planning process described in Public Utilities Code Section 454.5, in addition to the CPUC obligation to ensure safe and reliable service at just and reasonable rates under Public Utilities Code Section 451.
Balancing Authority Area (CAISO): the collection of generation, transmission, and loads within the metered boundaries of the Balancing Authority. The Balancing Authority maintains load-resource balance within this area.
Baseline resources: Those resources assumed to be fixed as a capacity expansion model input, as opposed to Candidate resources, which are selected by the model and are incremental to the Baseline.
Baseline resources are existing (already online) or owned or contracted to come online within the planning horizon. Existing resources with announced retirements are excluded from the Baseline for the applicable years. Being “contracted” refers to a resource holding signed contract/s with an LSE/s for much of its energy and capacity, as applicable, for a significant portion of its useful life. The contracts refer to those approved by the CPUC and/or the LSE’s governing board, as applicable. These criteria indicate the resource is relatively certain to come online. Baseline resources that are not online at the time of modeling may have a failure rate applied to their nameplate capacity to allow for the risk of them failing to come online.
Candidate resource: those resources, such as renewables, energy storage, natural gas generation, and demand response, available for selection in IRP capacity expansion modeling, incremental to the Baseline resources.
Capacity Expansion Model: a capacity expansion model is a computer model that simulates generation and transmission investment to meet forecast electric load over many years, usually with the objective of minimizing the total cost of owning and operating the electrical system. Capacity expansion models can also be configured to only allow solutions that meet specific requirements, such as providing a minimum amount of capacity to ensure the reliability of the system or maintaining greenhouse gas emissions below an established level.
Certify (a Community Choice Aggregator Plan): Public Utilities Code 454.52(b)(3) requires the CPUC to certify the integrated resource plans of CCAs. “Certify” requires a formal act of the Commission to determine that the CCA’s Plan complies with the requirements of the statute and the process established via Public Utilities Code 454.51(a). In addition, the Commission must review the CCA Plans to determine any potential impacts on public utility bundled customers under Public Utilities Code Sections 451 and 454, among others.
Clean System Power (CSP) methodology: the methodology used to estimate GHG and criteria pollutant emissions associated with an LSE’s Portfolio based on how the LSE will expect to rely on system power on an hourly basis.
22 Community Choice Aggregator: a governmental entity formed by a city or county to procure electricity for its residents, businesses, and municipal facilities.
Conforming Portfolio: the LSE portfolio that conforms to IRP Planning Standards, the 2030 LSE-specific GHG Emissions Benchmark, use of the LSE’s assigned load forecast, use of inputs and assumptions matching those used in developing the Reference System Portfolio, as well as other IRP requirements including the filing of a complete Narrative Template, a Resource Data Template and Clean System Power Calculator.
Effective Load Carrying Capacity: a percentage that expresses how well a resource is able avoid loss-of- load events (considering availability and use limitations). The percentage is relative to a reference resource, for example a resource that is always available with no use limitations. It is calculated via probabilistic reliability modeling, and yields a single percentage value for a given resource or grouping of resources.
Effective Megawatts (MW): perfect capacity equivalent MW, such as the MW calculated by applying an ELCC % multiplier to nameplate MW.
Electric Service Provider: an entity that offers electric service to a retail or end-use customer, but which does not fall within the definition of an electrical corporation under Public Utilities Code Section 218.
Filing Entity: an entity required by statute to file an integrated resource plan with CPUC.
Future: a set of assumptions about future conditions, such as load or gas prices.
GHG Benchmark (or LSE-specific 2030 GHG Benchmark): the mass-based GHG emission planning targets calculated by staff for each LSE based on the methodology established by the California Air Resources Board and required for use in LSE Portfolio development in IRP.
GHG Planning Price: the systemwide marginal GHG abatement cost associated with achieving a specific electric sector 2030 GHG planning target.
Integrated Resources Planning Standards (Planning Standards): the set of CPUC IRP rules, guidelines, formulas and metrics that LSEs must include in their LSE Plans.
Integrated Resource Planning (IRP) process: integrated resource planning process; the repeating cycle through which integrated resource plans are prepared, submitted, and reviewed by the CPUC Long term: more than 5 years unless otherwise specified.
Load Serving Entity: an electrical corporation, electric service provider, community choice aggregator, or electric cooperative.
Load Serving Entity (LSE) Plan: an LSE’s integrated resource plan; the full set of documents and information submitted by an LSE to the CPUC as part of the IRP process.
Load Serving Entity (LSE) Portfolio: a set of supply- and/or demand-side resources with certain attributes that together serve the LSE’s assigned load over the IRP planning horizon.
Loss of Load Expectation (LOLE): a metric that quantifies the expected frequency of loss-of-load events per year. Loss-of-load is any instance where available generating capacity is insufficient to serve electric demand. If one or more instances of loss-of-load occurring within the same day regardless of duration
23 are counted as one loss-of-load event, then the LOLE metric can be compared to a reference point such as the industry probabilistic reliability standard of “one expected day in 10 years,” i.e. an LOLE of 0.1.
Maximum Import Capability: a California ISO metric that represents a quantity in MWs of imports determined by the CAISO to be simultaneously deliverable to the aggregate of load in the ISO’s Balancing Authority (BAA) Area and thus eligible for use in the Resource Adequacy process. The California ISO assess a MIC MW value for each intertie into the ISO’s BAA and allocated yearly to the LSEs. A LSE’s RA import showings are limited to its share of the MIC at each intertie.
Net Qualifying Capacity (NQC): Qualifying Capacity reduced, as applicable, based on: (1) testing and verification; (2) application of performance criteria; and (3) deliverability restrictions. The Net Qualifying Capacity determination shall be made by the California ISO pursuant to the provisions of this California ISO Tariff and the applicable Business Practice Manual.
Non-modeled costs: embedded fixed costs in today’s energy system (e.g., existing distribution revenue requirement, existing transmission revenue requirement, and energy efficiency program cost).
Nonstandard LSE Plan: type of integrated resource plan that an LSE may be eligible to file if it serves load outside the CAISO balancing authority area.
Optimization: an exercise undertaken in the CPUC’s Integrated Resource Planning (IRP) process using a capacity expansion model to identify a least-cost portfolio of electricity resources for meeting specific policy constraints, such as GHG reduction or RPS targets, while maintaining reliability given a set of assumptions about the future. Optimization in IRP considers resources assumed to be online over the planning horizon (baseline resources), some of which the model may choose not to retain, and additional resources (candidate resources) that the model is able to select to meet future grid needs.
Planned resource: any resource included in an LSE portfolio, whether already online or not, that is yet to be procured. Relating this to capacity expansion modeling terms, planned resources can be baseline resources (needing contract renewal, or currently owned/contracted by another LSE), candidate resources, or possibly resources that were not considered by the modeling, e.g., due to the passage of time between the modeling taking place and LSEs developing their plans. Planned resources can be specific (e.g., with a CAISO ID) or generic, with only the type, size and some geographic information identified.
Qualifying capacity: the maximum amount of Resource Adequacy Benefits a generating facility could provide before an assessment of its net qualifying capacity.
Preferred Conforming Portfolio: the conforming portfolio preferred by an LSE as the most suitable to its own needs; submitted to CPUC for review as one element of the LSE’s overall IRP plan.
Preferred System Plan: the Commission’s integrated resource plan composed of both the aggregation of LSE portfolios (i.e., Preferred System Portfolio) and the set of actions necessary to implement that portfolio (i.e., Preferred System Action Plan).
Preferred System Portfolio: the combined portfolios of individual LSEs within the CAISO, aggregated, reviewed and possibly modified by Commission staff as a proposal to the Commission, and adopted by the Commission as most responsive to statutory requirements per Pub. Util. Code 454.51; part of the Preferred System Plan.
Short term: 1 to 3 years (unless otherwise specified).
24 Staff: CPUC Energy Division staff (unless otherwise specified).
Standard LSE Plan: type of integrated resource plan that an LSE is required to file if it serves load within the CAISO balancing authority area (unless the LSE demonstrates exemption from the IRP process).
Transmission Planning Process (TPP): annual process conducted by the California Independent System Operator (CAISO) to identify potential transmission system limitations and areas that need reinforcements over a 10-year horizon.