Riverside City Council
June 11, 2024

The SHARE program has been in place since 1989 and enhanced several times over the last few years. Changes have been reflected in the assistance amounts offered as well as an extension of the income qualification criteria and include the following:

• In July 2020, a revision was made to the SHARE program guidelines to allow for an urgent notice to qualify customers for the $150 emergency assistance; an increase was made to the SHARE electric monthly credit, taking it from $14.50 to $15.00, and to the SHARE water monthly credit, taking it from $2.50 to $2.75.
• In March 2021, the SHARE emergency/deposit credit was increased from $150 to $250. • In August 2021, a change was made to increase the SHARE electric monthly credit to $15.50 and increase the SHARE water monthly credit to $3.00.

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• In May 2022, the income eligibility criteria for the program were expanded from 200% of the Federal Poverty Guidelines to 250% and the SHARE electric monthly credit was increased to $16.00, and the SHARE water monthly credit was increased to $3.25. • Future increases are anticipated to align with changes to rates.

RPU’s ESAP program was paused during COVID and reestablished in 2022. The program provides no-cost energy efficiency home improvements based on income and household size.

Households may be eligible for the program based on participation in certain public assistance programs, such as RPU’s Sharing Households Assist Riverside’s Energy (SHARE) program, which assists income-qualified, residential customers with their electric utility bills, deposits, and urgent notices.

Finally, RPU offers a wide range of energy efficiency rebates that are available to all customers.

These programs are regularly reviewed to ensure that all customers can access ways to reduce their energy consumption. Details on all programs are discussed in Chapter 6 of this IRP.

17.2.5 RPU Low-Income Clean Transportation Assistance

The CARB’s Barriers Study: Part B analyzes barriers to accessing clean transportation in low- income communities and offers recommendations and actions to address these barriers. One of the priorities listed in the report was identifying and expanding funding as a way to address the barrier of accessibility for low-income and disadvantaged communities. Specifically, CARB recommended using revenue from the Low Carbon Fuel Standard (LCFS) program to develop EV rebates for low-income residents and disadvantaged communities.

RPU currently offers rebates to customers for the purchase of used electric vehicles and the installation of EV chargers. The rebate program is funded by LCFS revenue and was started in 2022. The latest program revisions were approved by the City Council on December 6, 2022, and went into effect January 1, 2023. Through the updated program, low-income customers enrolled in RPU’s low-income assistance program called SHARE are eligible to receive a used EV rebate of up to $2,500. Customers are also able to receive an EV Level 2 charger rebate of up to $500 as well as a rebate of up to $805 to cover the cost of installing an EV meter adapter so they can use a time-of-use EV rate when charging their electric vehicle. As of April 1, 2023, six (6) SHARE customers had applied for and received a Used EV Rebate. Details of these programs are discussed in Chapter 16 of this IRP.

17.2.6 New and Upcoming Decarbonization Incentive Programs

In addition to the variety of low-income assistance programs RPU currently offers, there are several programs being developed or newly in effect at the state and federal level to assist households, particularly of low-income, with building decarbonization improvements. Once these programs are fully established, RPU may be able to participate or direct its customers to these assistance programs.

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One incentive is the updated Energy Efficient Home Improvement Credit. This allows homeowners to receive a federal tax credit of up to $3,200 for qualified energy-efficient home improvements annually from 2023 to 2033.

Another program is the Home Energy Rebate Program, which was funded by the Inflation Reduction Act in August 2022. States will receive formula funding for two rebate programs. The first program is the Homeowner Managing Energy Savings (HOMES) rebate. California is expected to receive approximately $292 million to provide performance-based rebates for whole-house energy efficiency upgrades for single-family homes and multifamily buildings. Up to $8,000 for single-family homeowners and up to $400,000 for multifamily buildings will be available depending on energy savings and household income. The second program is the High-Efficiency Electric Home Rebate Program (HEEHRA).

California is expected to receive approximately $290 million to provide point-of-sale rebates for the purchase and installation of qualified Energy Star appliances such as electric heat pumps for space heating and cooling. Households below 150% of area median income are eligible to participate and may receive up to $14,000 depending on their qualifying purchases.

At the state level, the CEC is currently working to develop the Equitable Building Decarbonization Program which will consist of a Statewide Direct Install Program and a Statewide Incentive Program. The Statewide Direct Install Program will offer low- or no-cost retrofits for low- and moderate-income households throughout the state. The Statewide Incentive Program will offer incentive to promote low-carbon technologies in homes and reduce GHG emissions with at least fifty percent of the funds required to benefit under-resourced communities. This program is anticipated to begin its initial rollout in 2024 or 2025.

The CEC also offers technical assistance and financial incentives to builders and developers. The Building Initiative for Low-Emissions Development (BUILD) program authorized by SB 1477 provides $60 million for new, all-electric homes for low-income residents. Up to 300 hours of free building electrification technical assistance and $2 million in incentives is available for each eligible developer.

The California Electric Homes Program (CalEHP), named in statute as the Building Initiative for Low- Emissions Development Program Phase 2 by AB 137, provides $58 million for new, market-rate homes with all-electric appliances and equipment. In 2023, incentives start at $1,750 per residential unit for multifamily dwellings and $3,500 per residential unit for single-family dwellings on a first-come, first- served basis with additional incentives available based on location and above-code measures.

RPU will continue to monitor these new and upcoming decarbonization incentives to verify that the Utility’s efforts are complementary to state and federal programs. Additionally, RPU will continue to actively inform customers about the variety of incentives available to them.

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18. Potential Future Studies
While staff believe that this 2023 IRP document has proposed a viable strategy for RPU’s continuing efforts towards deep decarbonization, several of the planning issues examined in this document will most likely require further investigations and additional studies. In this chapter we explore some of these issues in greater detail, specifically with respect to the results presented in chapters 10-14 and 16. The discussions presented here are primarily focused on how future resource planning assessments on these chapter topics might be strengthened, improved, and/or expanded.

Topics that are briefly discussed in this chapter include (a) the role of future generation technologies in RPU’s resource planning efforts, (b) improved methodologies for performing more comprehensive distribution system ICA studies, (c) potential future DR and/or EE/DSM efforts, and (d) the value and benefits of a more comprehensive and integrated future TE planning effort.

18.1 Future Generation Technologies
As discussed in detail in Chapters 11 and 12, staff have proposed a set of new renewable / carbon-free resource contracts for the 2030 to 2045 time frame that will ensure that RPU achieves a zero-carbon portfolio by 2040 (see Table 11.2.1). Central to this modeling assumption is a “Baseload Resource Tranche” of unspecified generation assets that begin delivering carbon-free energy in 2034.

As stated in Chapter 12, the lineup of resource technologies that will be commercially available in the middle of the next decade is currently unknown. Therefore, this resource tranche could represent a known renewable and/or carbon-free technology such as nuclear or solar/wind + long dura�on storage, or an emerging technology like green hydrogen.

From a high-level, preliminary planning perspec�ve, this assump�on works fine; the proposed Baseload Resource Tranche (Resource 2) serves as a generic placeholder for resources that have yet to be iden�fied. However, ul�mately these resources will need to be iden�fied and studied in greater detail, similar to how the baseload geothermal (Resource 1a) versus solar + storage (Resource 1b) assets were examined in Chapter 12. Likewise, a robust analysis of future technology op�ons should include the most promising emerging technologies, such as long dura�on batery energy storage applica�ons, proposed offshore wind developments, 2nd genera�on nuclear facili�es, and/or green hydrogen thermal applica�ons, etc.

While RPU is aware that such an analysis will need to be undertaken in the future, staff currently do not possess the necessary subject mater exper�se to adequately specify the various cost components associated with most of these emerging technology op�ons. Furthermore, since a significant amount of research may be required to even begin to develop and specify such cost components, it might make more sense to outsource this type of future study to a qualified 3 rd party
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or suitable governmental research agency. Regardless, RPU may wish to seriously consider this op�on in support of such future resource planning efforts.1

18.2 Distribution System ICA Studies
As part of the current Integrated Resource Plan, RPU has evaluated the distribution grid’s sensitivity to customer adoption of distributed energy resource (DER) technologies. Specifically, Chapter 13 presented a capacity analysis study of each RPU distribution system feeder. Recall that in industry literature this is sometimes referred to as an Integration Capacity Analysis, or ICA.

The intent of this chapter’s study is exploratory in nature, rather than a formal forecast used for resource planning or capital investment planning purposes. As stated in Chapter 13, while similar methods can be applied to create a forecast, RPU does not currently have all the technology tools available to complete a formal forecast ICA. RPU has not yet developed a comprehensive method to forecast on the distribution feeder or geospatial level when and/or where such DER adoption may occur.

Therefore, this study does not currently identify the costs of specific items that might require upgrading on RPU’s distribution feeders and substations, nor does it identify specific years when such upgrades may need to occur.

For future IRPs, RPU will need to pursue more refined studies and methodologies to disaggregate the expected growth of these alternative DER technologies to develop organic growth models and forecast geospatial adoption rates.
2 However, such studies can be developed at various
spatial resolutions. For example, future studies might apply load growth assumptions or DER forecasts to circuit level data, like the data analyzed in this ICA. In principle, RPU staff should be able to develop and perform such analyses internally, subject to certain simplifying assumptions about how future DER assets are distributed across the circuits.

Unfortunately, while simpler to implement, this approach will only yield a “low level spatial resolution” understanding of how RPU’s distribution system might be expected to be impacted. In contrast, a more intensive effort could be made to model DER forecasts down to either a 2 nd stage
transformer level or even the individual customer meter level. This latter approach would yield a “high level spatial resolution” view of distribution system impacts and perhaps prove to be far more flexible for simulating various DER adoption scenarios. Of course, while this latter approach seems far more appealing, it will also require far more work and staffing resources.

1 This last point is especially relevant should there be a desire to explore any hydrogen- based internal generation technology options for future reliability needs. RPU Resource Planning staff are currently unable to assess or study such a scenario, due to a lack of sufficient subject matter expertise in the emerging field of green hydrogen generation applications.

2 Future IRPs may assess projections of DER adoption in the RPU territory using NREL’s dGen TM tool, a geospatial model that simulates the potential adoption of DERs for residential, commercial, and industrial entities.

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Which of these approaches RPU should pursue in future studies remains an open question.

Developing high resolution IDP models would represent a paradigm shift away from how RPU staff currently develop distribution capital investment plans – and perhaps require the support of 3rd party consultants or research organizations. Nonetheless, the answer to this question may ultimately depend in part on how important it becomes for RPU to produce comprehensive Integrated Distribution Planning (IDP) studies in support of future IRP activities.

18.3 Future EE, DSM and/or DR Programs
RPU is committed to making Riverside a greener place to live by supporting renewable energy, responsible purchasing and design, and sustainable living practices. An important portion of RPU’s future resource strategy is to cost effectively support both Energy Efficiency (EE) and Demand Side Management (DSM) programs. Chapter 6 presents an overview of RPU’s EE and DSM programs. RPU offers a variety of EE and DSM programs that both reduce customer bills and help support Citywide environmental and sustainability goals.

Following up on Chapter 6, Chapter 14 presents a review of RPU’s analysis of the costs to increase energy efficiency (EE) targets with respect to various EE measures and the value that these measures provide to the utility. While Chapter 6 summarizes RPU’s adopted and forecasted EE targets that are included in the power supply analysis, Chapter 14 focuses on the costs of these programs and what the impacts would be to RPU and its customers if higher targets are sought. More specifically, Chapter 14 examines the costs associated with the three broad types of RPU EE measures across four customer classes and compares them to the avoided costs of energy to determine their overall cost effectiveness.

As discussed in Chapter 14 (see Table 14.5.1 and Figure 14.5.1), eight out of twelve EE program categories exhibit positive net unmet revenue estimates and thus have benefit to cost ratios < 1. This is not that surprising, since RPU’s energy rates are designed to collect all the utility’s fixed operating costs (i.e., infrastructure, personnel, and O&M), in addition to its variable power supply costs. However, these results also indicate that the HVAC EE programs appear to be the most cost effective with respect to the type of EE measure being implemented. (HVAC EE programs exhibit relatively small but negative net unmet revenue estimates in three of RPU’s four customer classes.) This suggests that RPU might want to consider directing proportionally more of its EE efforts towards these HVAC EE measures.

Historically, RPU has found most EE and nearly all Demand Response (DR) programs to not be cost effective, especially when compared to many supply-side resource options. However, given the current market prices for new capacity, it may be worthwhile to reexamine the avoided cost calculations for certain EE and DR programs in greater detail, specifically for those programs that have the potential to materially reduce RPU’s future peak load serving needs. RA prices have nearly tripled over the last four years and it appears unlikely that RA cost pressures will abate anytime soon. Thus, implementing additional DR and/or EE programs that are specifically targeted towards reducing peak load levels might now make financial sense, even if these same programs were found to not be cost effective in the past.

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Hence, RPU may want to seriously consider examining and implementing new DR and/or EE/DSM programs that are specifically targeted towards reducing summer peak load levels. Furthermore, given current CAISO market capacity prices, the utility may wish to consider implementing at least some of these programs on an expedited schedule.

18.4 More Comprehensive TE Planning Efforts
Chapter 16 presents an overview of RPU’s and the City of Riverside’s efforts to support increasing levels of electric transportation. Included in this overview is a discussion addressing the anticipated energy demand and reduced greenhouse gas (GHG) emissions that will result from the forecast transition of vehicles from using internal combustion engines (ICE) to electric motors supported by plug-in battery-electric vehicles (BEV or EV). Chapter 16 also reviews the policy and regulatory environment around transportation electrification and the status of electrification in the RPU service territory. Additionally, multiple forecasts for EVs and their associated annual loads within the RPU service territory are examined, along with the forecasted GHG emissions reductions corresponding to these various EV penetration scenarios.

RPU coordinates closely with the City of Riverside and is working to expand access to electric vehicle charging infrastructure as well as meet Citywide environmental and sustainability goals. RPU and the City of Riverside have taken several actions to support increasing EV adoption rates, including:
• Streamlining the permitting process for installing EV charging infrastructure at residential, commercial, and multi-family buildings.
• Supporting a robust, city-wide Clean Fleet program.
• Supporting a robust public EV Education and Awareness campaign. • Implementing various EV rebates using revenue from CARB’s Low Carbon Fuel Standard (LCFS) program, including rebates for purchasing used EVs and rebates for residential and multi-family Level 2 chargers.
• Implementing whole-house and EV-only TOU rates that residential customers can optionally opt into.

Finally, RPU is also developing a public access Level 2 Charger Program throughout the City with the City General Services Department, with the goal of increasing the availability of publicly accessible EV charging at locations such as libraries, community centers, public parking lots, and garages.

These all represent important actions taken by the utility within the last five years to help make EV ownership a more convenient and cost effect option for RPU customers. However, with that said, RPU has not yet developed a long-range, comprehensive EV transportation plan that incorporates and integrates with its current Integrated Distribution and/or Resource Planning efforts (see the discussion in Section 18.2). In the absence of such a plan, it becomes more difficult to develop investment strategies to support expanded charging infrastructure or mitigation strategies to minimize distribution system impacts. Additionally, it also makes planning efforts for other types of transportation RPU 2023 Integrated Resource Plan

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electrification more challenging, such as developing longer range plans for accommodating medium- and heavy-duty vehicle electrification or public transit electrification.

Currently, staff anticipate needing outside assistance to help develop a robust and comprehensive EV transportation plan for the City of Riverside. While this could remain a utility led effort, RPU staff do not currently possess enough overall transportation related subject matter expertise to undertake this effort in isolation. Alternatively, and like the future resource technology issue, it might make more sense to outsource this type of future study to a qualified 3rd party commercial energy consulting firm or suitable governmental research agency.

For now, it remains an open question as to how to best go about developing a long-range, comprehensive EV transportation plan. Nonetheless, it seems fairly obvious that the utility needs to develop such a plan, ideally before the 2028 IRP process gets underway.

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19. Conclusion
As stated in the Introduction, this 2023 Integrated Resource Plan (“IRP”) has provided an impact analysis of Riverside’s acquisition of new power resources, specifically towards meeting the state of California’s aggressive carbon reduction goals; along with the effect these resources will have on Riverside Public Utilities future projected power supply costs. The six primary goals of this IRP were broadly summarized as follows:
 To provide an updated overview of RPU’s (a) energy and peak demand forecasts, (b) current generation and transmission resources, and (c) existing electric system.

 To review and assess the impact of important legislative and regulatory mandates imposed by various state or regional agencies (California Energy Commission, California Air Resources Board, South Coast Air Quality Management District, etc.), along with the impact of important active or proposed California Independent System Operator (CAISO) stakeholder initiatives on RPU’s power system and supplies.

 To summarize and assess the utility’s current set of Energy Efficiency (EE) and Demand Side Management (DSM) programs and assess the overall cost-effectiveness of these EE/DSM programs with respect to both the utility and all utility customers (i.e., both participating and non-participating customers).

 To review and quantify the most critical intermediate term power resource forecasts, specifically with respect to how RPU intends to meet its (a) projected energy, capacity, and resource adequacy requirements, (b) renewable portfolio standard (RPS) mandates, (c) carbon emission goals and mandates, and (d) power resource budgetary objectives.

 To examine and analyze certain critical longer term power resource procurement strategies and objectives, specifically those that will help RPU reach its 2030 and 2040 carbon reduction goals and quantify how such strategies and objectives might impact the utility’s future power supply costs.

 To begin to assess how various emerging technologies may concurrently impact RPU’s existing distribution system, so that staff can better define future distribution system improvements that continue to support the utility’s fundamental objective of providing reliable electrical services at competitive rates.

The chapter organization and layout has sequentially followed the general goals discussed above; i.e., background information has been presented in Chapters 2-4, legislative and regulatory mandates and initiatives were discussed in Chapter 5, EE and DSM programs were reviewed in Chapter 6 and assessed for their cost-effectiveness in Chapter 14, forward market views were discussed in Chapter 7 and RPU’s intermediate term portfolio forecasts were discussed in detail in Chapter 8, longer term resource planning and issues and carbon reduction strategies have been analyzed in detail in Chapters 9- RPU 2023 Integrated Resource Plan
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12, and a preliminary assessment of the impacts of emerging DSM technologies on RPU’s existing distribution system was presented in Chapter 13. Additionally, RPU’s most recent electric rate plan has been reviewed in Chapter 15, an overview of RPU’s efforts to support increasing levels of TE was presented in Chapter 16, and the utility’s commitment to serving its disadvantaged community members has been discussed in Chapter 17. Finally, Chapter 18 presents a broad review of the key resource planning issues that require further investigations and additional studies. Overall, staff has attempted to compile and present information in these chapters that address these six primary IRP goals in a comprehensive and analytical manner.

This final chapter provides a high-level review of each of these primary goals, specifically with respect to the data and analyses presented in this IRP. Succinct summaries of the most important staff findings are presented below.

19.1 RPU Background Information
An overview of RPU’s long-term energy and peak demand forecasting methodology was presented in Chapter 2. This overview included a discussion of staff’s econometric forecasting approach, key input variables and assumptions, and pertinent model statistics. Chapter 3 provided an overview of RPU’s long term resource portfolio assets, including the utility’s existing resources, future renewable resources (currently under contract), and recently expired contracts. This chapter also reviewed RPU’s transmission resources, along with the utility’s transmission control agreements with the CAISO. Finally, a brief review of RPU’s existing electric distribution system was given in Chapter 4, along with a description of its operational constraints and planned enhancements. The key highlights from these background chapters are as follows:
 Econometric forecasting models were used to produce RPU’s baseline 2023-2045 output energy and peak demand forecasts. These forecasts call for system loads to grow at about 1.4% annually and peak demand to grow at approximately 0.6% annually over the next 20+ years.  RPU currently either owns or has contracts for nineteen (19) different generation resources that are based on multiple types of thermal or renewable technologies. Altogether, this current resource portfolio provides RPU with about 665 MW of nameplate capacity; by 2027 this number should increase to about 675 MW of capacity, after the Pattern wind resource replaces RPU’s retired IPP coal contract and the last component of the Coso geothermal portfolio comes online.
 RPU is a vertically integrated utility that operates electric generation, sub transmission, and distribution facilities. The Utility receives over 90% of its system power through the regional bulk transmission system operated by the CAISO. Undoubtedly, the Riverside Transmission Reliability Project (RTRP) represents the most important anticipated change to the distribution system. If built, this project will provide Riverside with a second source of transmission capacity to import bulk electric power. In turn, this could significantly alter the utility’s long-term RPU 2023 Integrated Resource Plan
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internal resource procurement needs to ensure that RPU has sufficient electricity to cover summer peak loads while simultaneously working towards meeting State and City renewable and zero-carbon electricity mandates.

19.2 Important Legislative and Regulatory Mandates
Chapter 5 provided an overview and discussion of relevant legislative, regulatory and stakeholder issues that will significantly impact the California electric energy industry in the foreseeable future, specifically the markets run by the CAISO. Not surprisingly, there are several legislative, regulatory, and CAISO mandates and initiatives that are expected to significantly impact RPU.

With respect to CA legislation, the most important Assembly bills (AB) and Senate bills (SB) effecting RPU include AB 32 (California greenhouse gas reduction mandate), SB 1368 (baseload generation emission performance standards), SB 1037 and AB 2021 (EE/DSM programs and 10-year targets), AB 398 (greenhouse gas Cap-and-Trade program extension), SB 350 (clean energy and pollution reduction act of 2015), SB 100 (the 100% clean energy act of 2018), SB 1020 (clean energy targets for 2035, 2040, and 2045) and SB 1028, SB 901, and AB 1054 (legislation relating to wildfires). Additionally, Executive Order B-55-18 mandates the achievement of state-wide carbon neutrality by 2045 and Executive Order N-79-20 mandates that 100% of in-state sales of new passenger vehicles to be zero- emission by 2035.

Concurrent with these legislative activities, the CAISO has been and continues to promote market enhancements and initiatives that are designed to both improve system reliability and better accommodate the increasing levels of renewable energy generation assets. Some of the most important CAISO initiatives include the launching of the Energy Imbalance Market (EIM), the proposed Extended Day-Ahead Market (EDAM) and corresponding EDAM enhancements, the Resource Adequacy, Energy Storage, and Maximum Import Capacity enhancements, and the recently completed initiatives that focused on improving the efficiency of the Congestion Revenue Rights Auction and Transmission Service and Scheduling protocols.

Finally, it is critically important to note that the Envision Riverside 2025 Strategic Plan was created and approved by the Riverside City Council on October 20, 2020. This Strategic Plan sets forth the Strategic Priorities and Goals of the City Council to advance Riverside’s potential and to frame the work efforts of staff through 2025. The Riverside City Council adopted six strategic priorities and associated goals for each priority. One of these adopted priorities was the Environmental Stewardship priority. Under the Environmental Stewardship priority, the first foundational goal adopted by the City Council was Goal 4.1: Rapidly decrease Riverside’s carbon footprint by acting urgently to reach a zero- carbon electric grid with the goal of reaching 100% zero-carbon electricity production by 2040 while continuing to ensure safe, reliable, and affordable energy for all residents. This City Council goal has in turn served as the fundamental driving guideline for the goals and strategies proposed in this 2023 IRP.

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19.3 EE/DSM Programs
RPU is committed to making Riverside a greener place to live by supporting renewable energy, responsible purchasing and design, and sustainable living practices. An important portion of RPU’s future resource strategy is to cost effectively support both Energy Efficiency (EE) and Demand Side Management (DSM) programs.

Chapter 6 presented an overview of RPU’s EE and DSM programs. RPU recognizes the important role that DSM and EE play in planning for resources. RPU offers a variety of programs and education to customers about efficiently using energy and managing energy usage to reduce bills and meet Citywide environmental and sustainability goals. With the passage of Senate Bill 350 and the requirement to develop and submit an IRP to the California Energy Commission (CEC), RPU is also required to specifically address the procurement of energy efficiency in this IRP. As such, Chapter 6 reviewed the methodologies for determining the cost effectiveness of DSM and EE programs overall, as well as the officially adopted EE targets reflected in RPU’s demand and peak demand forecasts.

Following up on Chapter 6, Chapter 14 presented a review of RPU’s analysis of the costs to increase energy efficiency (EE) targets with respect to various EE measures and the value that these measures provide to the utility. While Chapter 6 summarized RPU’s adopted and forecasted EE targets that were included in the power supply analysis, Chapter 14 focused on the costs of these programs and what the impacts would be to RPU and its customers if higher targets were sought. More specifically, Chapter 14 examined the costs associated with the three broad types of RPU EE measures and compared them to the avoided costs of energy. Avoided cost analyses were differentiated between residential and commercial/industrial (CI) customer measures as well as whether the EE measure was for Baseload, Lighting, or Air Conditioning (HVAC).

As discussed in Chapter 14 (see Table 14.5.1 and Figure 14.5.1), most of the EE program categories exhibit positive net unmet revenue estimates and thus have benefit to cost ratios < 1. This should not be that surprising, since RPU’s energy rates are designed to collect all the utility’s fixed operating costs (i.e., infrastructure, personnel, and O&M), in addition to its variable power supply costs.

While four EE program categories do exhibit marginally negative net unmet revenue estimates, only two EE measures (CD – HVAC and TOU – HVAC) exhibit negative net unmet revenue estimates that are less than -$0.01/kWh, and there are no EE measures with estimates less than -$0.02/kWh. These results imply that there are probably not a lot of current opportunities for the utility to significantly increase the adoption of many EE measures in a cost-effective manner. However, as discussed further in Chapter 18, it may be worthwhile to reexamine the avoided cost calculations for these EE programs in greater detail, specifically for programs (like HVAC) that have the potential to materially reduce RPU’s future peak load serving needs.

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19.4 Intermediate Term Power Resource Forecasts
Chapter 8 presented a detailed overview of RPU’s most critical intermediate term power resource forecasts. These forecasts quantified the metrics that the Planning Unit routinely analyzes, monitors, and manages in order to optimize RPU’s position in the CAISO market and minimize the utility’s associated load serving costs. The following metrics were discussed in detail:
• Renewable energy resources and projected RPS %’s
• Primary Resource Portfolio metrics
• Net Revenue Uncertainty metrics
• Internal Generation forecasts
• Forecasted Hedging %’s and Open Energy positions
• Forecasted GHG Emission profiles and net Carbon allocation positions • Five-year Forward Power Resource Budget forecasts

All the analyses presented in this chapter were performed using the Ascend Portfolio Modeling software platform and referenced late August 2023 CAISO market conditions.

Based on the forecast data presented in this Chapter, the following conclusions can be drawn concerning RPU’s intermediate term resource positions.
 RPU continues to procure a significant amount of excess renewable energy, above and beyond the state’s minimum mandated amounts. Since 2017, RPU has begun to accumulate excess renewable energy credits which can be used to meet future RPS compliance obligations, should any significant (and extended) future renewable resource outages occur. Currently, the utility expects to remain above a 44% RPS in 2024 and exceed the “60% by 2030” mandate three years early.
 Over the next five years, 62% to 83% of the utility’s expected system energy needs will be served using fixed-price contracts within the resource portfolio (including optional IPP natural gas energy), while another 4% to 5% will be served using internal generation assets (primarily during summer). The remaining 22% to 33% of energy needs will need to be acquired from the CAISO market, either via forward purchases or day-ahead market transactions. These open energy positions will need to be forward hedged via longer term forward market purchases or through the contracting of additional resources. Currently, these unhedged positions can cause RPU’s forecasted net power supply budget costs to potentially either increase or decrease as much as $21.6 million per year due to weather, load and/or market price volatility.  RPU is expected to have more than enough carbon allowances to fully meet its direct emission compliance needs through 2028. Staff currently forecasts having an excess allowance balance of 550,000 to 800,000 credits annually; however, these excess allowance volumes might be substantially reduced under the CARB 2025 rulemaking process. Nonetheless, whatever excess RPU 2023 Integrated Resource Plan
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credits remain are expected to be monetized through the CARB quarterly auction process, with a significant portion of the proceeds used to help offset RPU’s excess renewable energy costs.  The utility’s gross power supply costs are projected to be $254.2 million in FY 23/24, which represents a $27.1 million cost increase over the budget projections produced in October 2021.

Unfortunately, these power supply cost forecasts are expected to remain elevated throughout the five-year planning horizon due to the impact of macro market conditions beyond the utility’s control.

In summary, the utility is reasonably well positioned to meet its load serving needs over the next five years, but additional efforts will need to be undertaken to control RPU’s power supply costs. With respect to energy needs, additional systematic forward hedging activities will be required to maintain cash flow stability. Additional resources may also need to be procured in the immediate term time horizon (i.e., in the next three to five years) to reduce the utility’s net revenue uncertainty.

19.5 Critical Longer Term Power Resource Issues
The fundamental purpose of the 2023 IRP process is to identify and assess the most cost- effective means for RPU to continue to reduce its GHG emissions, such that the utility can meet or exceed its specified 2030 emission target. As such, Chapters 9 through 12 examined the critical longer term power resource planning issues surrounding this goal.

This discussion was initiated in Chapter 9 by first reviewing the utility’s latest 2030 GHG emission targets, as specified in CARB’s August 2023 GHG Planning Targets: Draft 2023 Update report.

In their report, CARB listed individual utility targets for the 16 largest POUs based on two different electricity sector targets: 30 and 38 MMT CO
2-e. CARB has endorsed this target range for the POUs and proposes that each POU should choose one or more targets within this range for integrated resource planning purposes. Hence, for planning purposes RPU has elected to focus on these target levels.

Under the 38 MMT sector target, RPU’s utility specific target is 349,000 MT CO2-e. Likewise, under the 30 MMT sector target, RPU’s utility specific target is 275,000 MT CO2-e. RPU is electing to use the higher 349,000 MT target for official planning purposes, but the results presented in Chapter 9 show that the lower target could also be easily reached if RPU contracts for one additional medium sized renewable energy asset by or before 2030. Table 19.5.1 summarizes these two GHG planning targets, respectively.

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Table 19.5.1. The two RPU GHG planning targets analyzed in this IRP.

GHG Planning Target Description MT CO2-e
Emission Value

38 MMT Sector Goal Official RPU target 349,000
30 MMT Sector Goal More aggressive GHG reduction scenario 275,000

Next, in Chapter 10 staff reviewed RPU’s future capacity needs for the 22-year time horizon from 2024 through 2045. Ultimately, these needs will be primarily influenced by Riverside’s future load growth rate and the potential expiration of various capacity resources currently under contract.

Additionally, staff also analyzed and discussed RPU’s “Net-Peak” demand forecasts for 2022 and 2028 in this chapter as well.

Having thoroughly reviewed RPU’s future capacity expansion needs, in Chapter 11 staff examined all the utility’s current resource contracts that are scheduled to end before December 2045; the goal being to identify resources with contracts that are likely to be extended versus replaced (at least for integrated resource planning purposes). Battery energy storage resources were considered as replacements for RPU’s gas-fired Springs and RERC Generation Facilities once they reach their anticipated end-of-life retirement dates. More importantly, a set of five potential future portfolio resource additions were presented in this chapter that are consistent with RPU’s long-term carbon reduction goals. Four of these proposed resource additions represented specified carbon-free renewable resources, while the fifth was defined to be an unspecified, carbon-free baseload resource tranche. As originally shown in Table 11.2.1 and reproduced here as Table 20.5.2, the acquisition of these proposed resources (1a or 1b plus 2, 3, and 4) will allow RPU to successfully exceed the utility’s lower (30 MMT Sector) 2030 emission target and achieve full carbon neutrality by 2040.

Table 20.5.2. Proposed new renewable resource contracts for 2030-2045.

New Resource Resource Description COD Annual
MWh
1a Baseload Geothermal 50 MW baseload geothermal resource (84% CF) 2030 367,920 1b Solar + Storage 120 MW Solar PV (35% CF) +
50 MW / 200 MWh BESS 2030 367,920
2 Baseload Resource
(Renewable and/or
Carbon-Free)
Baseload Resource Tranche (90% CF) (1)
• 50 MW
• 60 MW
• 20 MW

2034
2038
2043

394,200
473,040
157,680
3 Solar PV-2037 75 MW Solar PV (35% CF) 2037 229,950
4 Solar PV-2041 75 MW Solar PV (35% CF) 2041 229,950
Note (1): The additional 60 MW and 20 MW may come from new assets or be incremental to the original 50 MW asset.

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Finally, Chapter 12 examined the projected budgetary impacts of the Baseline Portfolio, specifically focusing on comparing two resource options in 2030. Additionally, this chapter examined the projected budgetary impacts of battery energy storage replacement options for the Springs and RERC generation facilities upon their assumed retirements, as well as the impacts of ±30% deviations in the price marks for resources 2, 3, and 4. The budgetary assessments considered both the expected values and simulated standard deviations of RPU’s resource portfolio cost over the 2024-to-2045 time horizon. Hence, both Net Value calculations and Risk Integrated Portfolio Costs (RIPC) for the proposed resources across the various scenarios were also provided. A few of the more pertinent findings presented in Chapter 12 can be briefly summarized as follows.  The Baseline Portfolio satisfies all GHG reduction and RPS mandates through the 2045 timeframe. Under either the Baseline A or B portfolio, RPU achieves (a) a 2030 emission level of 152,065 metric tons, which is below its more aggressive reduction target of 275,000 metric tons, (b) a 2030 RPS of 80.5%, and (c) carbon neutrality by 2040.

 Between 2024 and 2042, these IRP studies suggest that RPU’s power resource costs will grow at about 1.9% to 2.0% annually under the two proposed Baseline Portfolios.

 Deploying BESS as replacements for Springs in 2028 and 2030 resulted in essentially no impact to RPU’s RIPC in those years. Beyond 2030, the BESS produces about a 1% decrease in the RIPC compared to the Baseline A Portfolio. These results suggest that a more detailed future analysis of battery replacement options for Springs is warranted.

 Deploying BESS as a replacement for RERC after it retires in 2039 results in RIPC estimates for 2042 and 2045 that are about 3% and 6% lower, respectively, compared to the Baseline Portfolio which does not include a RERC replacement. These results show that it will be important to replace RERC with some type of energy storage technology after 2039, to continue mitigating the financial impacts of high price energy hours.

 Most importantly, the price sensitivity studies presented in section 12.4 confirm that it will be very important for RPU to continue to secure cost-effective resources as the utility builds out its portfolio over the next 15 years. Securing cost-effective resource pricing will be essential for RPU to control its RIPC growth rate, which in turn will directly impact the magnitude of future rate increases.

19.6 Preliminary Distribution System DER Impact Studies As part of this 2023 Integrated Resource Plan, RPU staff evaluated the distribution grid’s sensitivity to customer adoption of renewable and alternative technologies. Accordingly, Chapter 13 presented a preliminary Integration Capacity Analysis study of each RPU distribution system feeder. The intent of this chapter’s study was exploratory in nature and was not a formal forecast used for resource planning or capital investment planning purposes. While similar methods can be applied to create a RPU 2023 Integrated Resource Plan
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forecast, RPU does not currently have all the technology tools available to complete a formal forecast ICA. With this study, staff primarily intended to show threshold levels of adoption that will require upgrades to distribution substation or feeder equipment.

The study results illustrated the ability for RPU’s distribution system to accommodate more generation from Distributed Energy Resources (DERs) like rooftop solar photovoltaic (PV) installations and loads from Fuel Switching (FS) or DERs like electric vehicle (EV) charging across most RPU distribution feeders. For each feeder, Capacity Utilization Factors (CUF’s) were developed by comparing the SCADA data to engineering estimated PV, FS, and EV generation or charging patterns, which in turn defined the amount of additional PV, FS, and/or EV penetration that the circuit could theoretically accommodate. These CUF’s were then analyzed and summarized both statistically and spatially, to begin to develop a better understanding of the potential timing and location of various future distribution system upgrades.

Finally, section 13.5 discusses three critical OT platforms and technology efforts that RPU is relying on to better optimize the deployment of DER technology. These platforms and efforts include:
(1) the successful deployment of the OSI Pi platform, which serves as RPU’s Operational Data Management System, (2) the distribution wide rollout of AMI technology, and (3) the consolidation and restructuring of the GIS workflow process. Each of these OT systems had been previously identified as part of an integrated Operational Technology/Information Technology Master Plan strategy to improve RPU’s organizational efficiency and to better manage, integrate, and optimize DER assets within RPU’s distribution system.

19.7 Additional Important Topics Covered in Chapters 15 Through 18 The latter part of this IRP reviewed some additional specific topics directly related to RPU’s Integrated Resource Planning efforts. These topics include Retail Rate Design (Chapter 15), Transportation Electrification (Chapter 16), Disadvantaged and Low-income Communities (Chapter 17) and Potential Future IRP Studies (Chapter 18).

Chapter 15 briefly reviewed and summarized the utility’s latest electric rate proposal, including its justification for why the new electric rate plan is fair and reasonable. The Riverside City Council adopted RPU’s proposed new five-year (fiscal years 2023/24 through 2027/28) electric utility rate plan in September 2023 and the relevant rate increase documents and information about this new rate plan can be found online at: https://www.riversideca.gov/utilities/electric-proposed-rates . This chapter also
described the new Self Generation rate tariff that the utility introduced in 2022, as well as new enhancements to RPU’s low-income and fixed-income assistance programs.

Chapter 16 presented an overview of RPU’s and the City of Riverside’s efforts to support increasing levels of electric transportation. The discussion addressed the anticipated energy demand and reduced greenhouse gas (GHG) emissions that will result from the forecast transition of vehicles from using internal combustion engines (ICE) to electric motors supported by plug-in battery-electric RPU 2023 Integrated Resource Plan
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vehicles (BEV or EV). RPU works closely with the City of Riverside and is working to expand access to electric vehicle charging infrastructure as well as meet Citywide environmental and sustainability goals.

Chapter 16 also reviewed the policy and regulatory environment around transportation electrification and the status of electrification in the RPU service territory. Additionally, multiple forecasts for EVs and their associated annual loads within the RPU service territory were examined, along with the forecasted GHG emissions reductions corresponding to these various EV penetration scenarios. These forecasts for EVs and their associated loads were derived from the CEC Transportation Electrification Common Assumptions 3.0 model, as well as the 2021 and 2022 Integrated Energy Policy Reports (IEPR) developed by the California Energy Commission (CEC).

RPU and the City of Riverside have long been committed to implementing the best existing and emerging sustainability practices, particularly in the areas of reducing air pollution and greenhouse gas emissions. Along these lines, Chapter 17 discussed disadvantaged and low-income communities in Riverside and then presented the utility’s efforts to minimize local air pollutants and greenhouse gas emissions; focusing specifically on disadvantaged communities as required by Senate Bill 350.

Additionally, RPU’s efforts that specifically address the CEC 2016 Barriers Study report recommendations were also presented at the end of this chapter.

Finally, while staff believe that this 2023 IRP document has proposed a viable strategy for RPU’s continuing efforts towards deep decarbonization, several of the planning issues examined in this document will most likely require further investigations and additional studies. Therefore, Chapter 18 explored some of these issues in greater detail, specifically with respect to the results presented in chapters 10-14 and 16. These discussions primarily focused on how future resource planning assessments on these chapter topics might be strengthened, improved, and/or expanded. Topics that were briefly discussed in this chapter include (a) the role of future generation technologies (including hydrogen), (b) improved methodologies for performing more comprehensive distribution system ICA studies, (c) potential future DR and/or DSM efforts, and (d) the value and benefits of a more comprehensive and integrated future TE planning effort.

In summary, a significant number of diverse resource planning issues have been discussed and analyzed in detail this 2023 Integrated Resource Plan. More detailed discussions of key results have been presented throughout the various chapters, along with staff recommendations for further analyses and studies that should be undertaken. Additionally, staff has also proposed certain strategies that RPU can implement now to continue to provide the highest quality electric services at the lowest possible rates to benefit the Riverside community. The analyses, findings and recommendations presented in this 2023 Integrated Resource Plan can and should help RPU to continue to achieve this goal in a proactive, intelligent, and cost-effective manner.

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19.8 An Index of SB 350 IRP Requirements for CEC Staff This last section identifies where specific narratives and information pertaining to Senate Bill 350 IRP requirements can be found throughout this document. Please note that this information has been included for the benefit of CEC staff and these order of index requirements follow the CEC’s POU IRP Checklist. Additionally, the following RPU 2023 worksheet datasets are being submitted to the CEC concurrently with this IRP and are referenced in some of the checklist items below.  Capacity Resource Accounting Table (CRAT)
 Energy Balance Table (EBT)
 GHG Emissions Accounting Table (GEAT)
 Resource Procurement Table (RPT)

GHG Reduction Targets
[PUC code 9621(b)(1), 9621(2)(B)]
Chapter 9 discusses RPU’s GHG emission targets and forecasts through 2030 in detail. RPU already expects to get below its official 2030 GHG target of 349,000 MT CO2e using its current portfolio of resources. Additional reductions will occur if the Utility contracts for and receives any further new renewable resource energy before 2030 (see section 9.5 and Chapter 11, section 11.2). Longer term GHG emission forecasts (through 2045) are discussed in Chapter 12, section 12.1. Mid-term 1st importer emissions (through 2028) are also discussed in Chapter 8, section 8.6. Finally, all CEC requested GHG emissions information for RPU has also been provided in our concurrently submitted GEAT workbook.

60% RPS Targets by 2030
[PUC code 9621(b)(2), 399.11 (50% RPS mandate), SB 100 (60% RPS mandate)] Mid-term RPS forecasts (through 2028) are discussed in Chapter 8, section 8.1. Longer term renewable energy procurement plans are discussed in detail in Chapter 11 and the resulting RPS forecasts through 2045 are presented in Chapter 12, section 12.1. Additionally, all CEC requested RPS forecast information for RPU has also been provided in our concurrently submitted RPT workbook.

It should be noted that Riverside has met all CEC RPS compliance obligations for Compliance Periods 1, 2, and 3 (CP1, CP2, CP3). Furthermore, the Utility expects to meet/exceed all CP4, CP5, and CP6 obligations using generation resources that are already under contract. RPU does not currently use or claim any category 2 or 3 RECs and almost all our existing renewable resource contracts qualify as long-term contracts.

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Just and Reasonable Rates / Minimize Impact on Ratepayer Bills [PUC code 9621(b)(3), 454.52(a)(1)(C), 454.52(a)(1)(D)] Detailed information on RPU’s current five-year retail rate plan is presented in Chapter 15 (see also Chapter 14, section 14.4 for current customer class rate schedules). RPU uses a robust public rate setting process and a formal cost-of-service analysis when proposing or revising its retail rates. Chapter 15 includes information on rate comparisons with other utilities, public participation and outreach efforts, and enhanced low-income and fixed-income assistance programs. As a Publicly Owned Utility, RPU seeks to minimize rate increases to the greatest extent possible and only proposes the minimum amount of rate increases needed to cover its operating expenses and target reserve levels.

Ensure System and Local Reliability
[PUC code 9621(b)(3), 454.52(a)(1)(E)]
RPU’s forecasted monthly system energy and peak load levels are derived and described in Chapter 2. Based on these forecasts, detailed assessments of future Resource Adequacy capacity needs are presented and discussed in Chapter 10 (sections 10.1 and 10.2). Topics covered in Chapter 10 include RPU’s expected system, local, and flexible capacity requirements, maximum import capacity needs, and RA requirements through 2045. Additionally, sections 8.2 and 8.4 of Chapter 8 discuss RPU’s system energy needs and expected internal generation levels through 2028, while the incremental capacity impacts from longer-term future proposed resources are examined in detail in Chapter 12.

Finally, all the CEC requested capacity information for RPU has also been provided in our concurrently submitted CRAT workbook. It should be reiterated that RPU is part of the CAISO BAA and thus relies on the CAISO for operating reserves.

Strengthen the Diversity, Resiliency, and Sustainability of Transmission, Distribution, and Local Communities
[PUC code 9621(b)(3), 454.52(a)(1)(F)]
RPU does not manage or operate any bulk transmission assets. As discussed in Chapter 3 (sections 3.4 and 3.5) the City of Riverside is a PTO in the CAISO and has turned over the operational control of RPU’s transmission entitlements to the CAISO. RPU does manage, maintain, and control its own distribution system. This system is described in detail in Chapter 4. This chapter also includes an extended discussion on the Riverside Transmission Reliability Project (RTRP), which is designed to provide Riverside with a second point of interconnection to the SCE/CAISO bulk transmission system.

Additionally, an Integrated Capacity Analysis (ICA) study of RPU’s distribution system is presented in Chapter 13. This ICA study provided a high-level assessment of the ability of each RPU RPU 2023 Integrated Resource Plan
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distribution system feeder (circuit) to support the installation of additional DERs (specifically, new customer solar PV systems, electric vehicles, and fuel switching applications). This study represents the first step towards determining how to better optimize future distributed generation assets, and/or identify feeders that might develop future reliability concerns due to excessive DER saturation levels.

Distribution System and Demand-Side Energy Management
[PUC code 9621(b)(3), 454.52(a)(1)(G)]
As noted above, a preliminary but comprehensive ICA study of RPU’s distribution system is presented in Chapter 13. Furthermore, section 13.4 provides a narrative discussion on how this current and/or future ICA studies might be used to better integrate and accommodate future DER growth.

Likewise, section 13.5 describes RPU’s recent efforts to upgrade our distribution system communication and operational technology assets.

Localized Air Pollutants with Early Priority on DACs
[PUC code 9621(b)(3), 454.52(a)(1)(H)]
Chapter 17 discusses disadvantaged and low-income communities in Riverside and describes RPU’s efforts to minimize local air pollutants and greenhouse gas emissions; focusing specifically on disadvantaged communities as required by Senate Bill (SB) 350. The topics covered in this chapter include the identification of CalEnviroScreen and Justice40 disadvantaged communities within the City of Riverside (section 17.1), followed by a detailed review of RPU’s comprehensive citywide sustainability efforts (section 17.2). Additionally, Chapters 11 and 12 describe RPU’s planning efforts and strategies for decommissioning its GHG emitting internal generation assets by or before 2040.

Energy Efficiency and Demand Response Resources
[PUC code 9621(c)(1)(A)]
Chapter 6 presents an overview of RPU’s EE and DSM programs. This chapter reviews the methodologies for determining the overall cost effectiveness of DSM and EE programs, as well as the officially adopted EE targets reflected in RPU’s baseline load and peak demand forecasts. (See Chapter 2, section 2.2.5 for how historical EE savings information is factored into RPU’s load and peak demand forecasting models.) Following up on Chapter 6, Chapter 14 presents a review of RPU’s analysis of the costs to increase energy efficiency (EE) targets with respect to various EE measures and the value that these measures provide to the utility. More specifically, this chapter examines the costs associated with the three broad types of RPU EE measures and compares them to the avoided costs of energy. Avoided RPU 2023 Integrated Resource Plan
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cost analyses are differentiated between residential and commercial/industrial (CI) customer measures as well as whether the EE measure is for Baseload, Lighting, or Air Conditioning (HVAC).

Energy Storage
[PUC code 9621(c)(1)(B)]
The suitability and cost-effectiveness of multi-hour storage assets as a viable resource to address reliability is examined in detail in Chapters 11 and 12. More specifically, Chapters 11 and 12 describe RPU’s planning efforts and strategies for decommissioning its GHG emitting internal generation units by or before 2040 and replacing these natural gas assets with multi-hour storage assets.

Additionally, RPU intends to actively pursue one or more long-term RA contracts with 3rd-party multi- hour storage assets to meet some of its future RA needs, as discussed in Chapter 3, section 3.3.

Transportation Electrification
[PUC code 9621(c)(1)(C)]
Chapter 16 presents an overview of RPU’s and the City of Riverside’s efforts to support increasing levels of electric transportation. The discussion addresses the anticipated energy demand and reduced greenhouse gas (GHG) emissions that will result from the forecast transition of vehicles from using internal combustion engines (ICE) to electric motors supported by plug-in battery-electric vehicles (BEV or EV). Multiple forecasts for EVs and their associated annual loads within the RPU service territory are examined, along with the forecasted GHG emissions reductions corresponding to these various EV penetration scenarios. Additionally, the ICA study presented in Chapter 13 provides a high- level assessment of the ability of each RPU distribution system feeder to support additional electric vehicles. Finally, the relevant TE data information from these various studies has also been included in our concurrently submitted CRAT and EBT workbooks.

Address Procurement of a Diversified Procurement Portfolio [PUC code 9621(c)(1)(D)]
Chapter 3 provides a comprehensive overview of RPU’s diverse portfolio of thermal and renewable generation resources. As stated in Chapter 1, section 1.1, technology diversification has always represented one of the traditional guiding principles used by the utility when selecting new generation assets or contracts. Discussions concerning RPU’s future resource procurement strategies can be found in Chapters 1 (section 1.1), 3 (section 3.6), 11, 12, and 18 (section 18.1). Likewise, the expected energy and capacity supplied by RPU’s current and proposed future resources has also been included in our concurrently submitted CRAT, EBT, and RPT workbooks.

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Address Procurement of Resource Adequacy Requirements
[PUC code 9621(c)(1)(E)]
As previously mentioned, detailed assessments of future Resource Adequacy capacity needs are presented and discussed in Chapter 10 (sections 10.1 and 10.2). Topics covered in Chapter 10 include RPU’s expected system, local, and flexible capacity requirements, maximum import capacity needs, and RA requirements through 2045. Additionally, the incremental capacity impacts from longer-term future proposed resources are examined in detail in Chapter 12. Again, all the CEC requested capacity information for RPU has also been provided in our concurrently submitted CRAT workbook (including RPU’s planning reserve margin).

Historically, RPU has used utility-owned generation, power purchase agreements with assets that include CAISO certified RA attributes, and short-term resource adequacy contracts to meet its RA needs. RPU intends to continue using these types of assets and contracts in the future, in addition to new long-term RA (or full toll) contracts with battery energy storage assets.

SB 338: Net Peak Demand
[PUC code 9621(c)]
RPU’s net peak demand analysis is presented in Chapter 10, section 10.3. As previously stated, detailed assessments of future Resource Adequacy capacity needs are also presented and discussed in Chapter 10 (sections 10.1 and 10.2), while future preferred resources and their associated capacity additions are discussed in Chapters 11 and 12. It is worthwhile reiterating that RPU’s renewable resource mix includes a substantial amount of baseload geothermal generation and this generation contributes directly to meeting reliability needs during the hours of net peak demand.

Demand Forecast
[not specifically address in PUC code, but required to determine consistency with SB 350 requirements] Chapter 2 provides a complete overview of RPU’s long-term system load and peak demand forecasting methodology. This overview includes a discussion of the utility’s econometric forecasting approach, key input variables and assumptions, and pertinent model statistics, along with the utility’s 2023-2045 system load and peak demand forecasts that were produced in March 2022. In addition to being shown in Table 2.4.1, RPU annual system load and peak demand forecast values can be found in our concurrently submitted CRAT and EBT workbooks.

RPU uses regression based econometric models to forecast both its total expected GWh system loads and system MW peaks on a monthly basis. These models are calibrated to 15+ years of historical load data. The following input variables are used in these econometric models: (a) various monthly RPU 2023 Integrated Resource Plan
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weather summary statistics, (b) specific calendar effects, (c) unplanned for (but verified) expansion and contraction of industrial loads, (d) annual per capita personal income (PCPI) and monthly labor employment (Labor_Emp) econometric input variables for the Riverside – San Bernardino – Ontario metropolitan service area, (e) the cumulative load loss effects associated with retail customer solar PV installations and all of RPU’s measured energy efficiency (EE) programs, and (f) the expected net load gain due to increasing light-duty and medium/heavy-duty electric vehicle (EV) penetration levels and anticipated building electrification (fuel switching) within the RPU service territory. These models are used to project RPU wholesale gross monthly loads and peaks up to twenty-five years into the future.

Although RPU produces its own load and peak forecasts, staff do compare these forecasts for consistency with similar CEC IEPR demand forecasts for Riverside. Section 2.3.6 in Chapter 2 discusses such a comparison and shows that the RPU forecasts used throughout this IRP document track very closely with the February 2022 California Energy Demand 2022-2035 Managed Forecasts (Mid-Demand – AAEE Scenario 2 – AAFS Scenario 4 workbook, CEC publication TN241384).

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APPENDIX A

A.1 Ascend PowerSimm Simulation Framework
The Ascend solution values portfolios consisting of structured transactions, generation assets, load obligations, and hedges plus operating components of transmission, ancillary services, and conservation programs. The hierarchical portfolio structure of PowerSimm enables portfolio components to be valued individually or jointly as an element of the parent portfolio. The valuation of a utility portfolio or structured transaction follows from the application of analytic algorithms that optimize asset values and calculate hedge, load, and structured transaction values relative to underlying Monte Carlo simulations. Recognizing the importance of meaningful Monte Carlo simulations to valuations of portfolios and structured transactions, we present an overview of Ascend’s simulation methodology below.

The simulation framework of PowerSimm addresses uncertainty as viewed through today’s market expectations (forward prices) and the future realized delivery conditions for load, spot prices, and generation. PowerSimm supports the ability to modify inputs, model impacts, and evaluate key sources of uncertainty. The framework to simulate physical and financial uncertainty follows the process flow of Figure A.1.1. The simulation of volumetric and market prices further extends the correlated simulation of forward prices to model structural relationships during delivery. Examples of such relationships include weather on load, load on market prices, and gas and load on electric prices.

Additionally, relationships with very limited historical information can be modeled by specifying statistical distributions on values such as CO2 or REC prices. PowerSimm also performs fundamental modeling of demand and supply conditions to forecast market prices beyond the liquid portion of the forward curve.

Figure A.1.1. PowerSimm simulation framework.

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Simulation of electric system and customer loads follow from a common analytical structure that seeks to preserve the fundamental relationship between demand and price. The simulation process is divided into two separate components: 1) prior to delivery and 2) during delivery. The prior-to- delivery simulation of forward prices evolves current expectations through time from the start date to the end of the simulation horizon. The simulations during delivery capture the relationship of physical system conditions (i.e., weather, load, wind, run-of-river hydro, unit outages, and transmission) on market prices. The inter-relationship between ‘prior-to-delivery’ and ‘during-delivery’ simulations is central to linking expectations to realized observations that are either simulated or actual. Figure A.1.2 presents a graphical representation of this process.

For forward contracts representing prior-to-delivery simulations, monthly prices are evolved into the future from the current market prices to expiration for each contract. This process of evolving forward contracts into the future utilizes the current forward strip (market expectations of future prices) and the observed behavior of forward contract uncertainty and covariate relationships to create future price projections. For each simulation, the final evolved forward price becomes equal to market expectations. The average of the forward price simulations for each monthly contract will equal the final evolved spot price. During the prior-to-delivery simulations, monthly forward contracts are correlated with each other and across commodities. Seasonal hydro conditions are also correlated with the simulated forward prices.

Figure A.1.2. Simulation framework of forward and spot prices.

The during-delivery simulation process begins with simulation of weather. PowerSimm simulates up to approximately 30 different weather variables (e.g., daily min/max temperatures) for user-specified weather stations using a cascading Vector Auto-Regression (VAR) approach. This approach maintains both the temporal and spatial correlations of weather patterns for the region with a 3-step process. Ascend applies a cascading VAR approach to maintain inter-month temperature

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correlations consistent with the historical data. For example, if a hot July is likely to be followed by a hot August, the cascading VAR does a superior job of capturing this effect. The application of weather simulations supports the analysis of uncertainty through hundreds of weather scenarios without the limitation of the pure historical record where extreme weather events beyond observed conditions may occur (but obviously with a low probability). The second step of the process combines these weather simulations for input into the load simulation process. PowerSimm offers the capability to weight weather stations together. Typically this is done via energy or population weighting.

PowerSimm incorporates external demand factors, scaling and shaping the simulated loads to match forecasted monthly demand and peak demand values. The simulations of electric load use a state-space modeling framework to estimate seasonal patterns, daily and hourly time series patterns, and the impact of weather. The state-space framework of PowerSimm produces unparalleled benchmark results that reflect the explained effects of weather and time-series patterns and the unexplained components of uncertainty. State-space modeling uses the regression equations to explain the variability in price as it relates to demand.

The during-delivery simulation of prices addresses the more intuitive simulations of system conditions and spot prices. System conditions of unit outages, supply stack composition, system imports and exports, and transmission outages are modeled and simulated independently of weather, but also serve as determinants to the spot price of electricity.

PowerSimm dispatch models forced outages (off-line and derates). The stochastic component of forced outage modeling captures the uncertainty in outage duration. Users can specify the maintenance schedule or elect to have PowerSimm optimize the maintenance schedule with reserve requirements observed.

Finally, PowerSimm enables users to readily perform sensitivity runs by supplying percent scaling factors to the “base” level key components of uncertainty. These sensitivity runs can be input and run in batch mode.

A.2 Simulation Engine: Overview
The analytic processes to PowerSimm reside in the SimEngine. The heart of the Simulation Engine is a Monte Carlo simulation of physical elements and market prices. The SimEngine produces Monte Carlo simulations of weather, load, market prices, and wind and solar generation. This section discusses the analytic methodology of the SimEngine and the specific model structure to simulate the following elements:

1. Weather

2. Load

3. Forward Market Prices

4. Spot Electric Prices
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5. Spot Gas Prices

6. Wind and Solar Generation

A.2.1 State Space Modeling
State-space modeling in its simplest form is regression analysis with uncertainty. The uncertainty associated with regression analysis can be used to explain how weather relates to load or how yesterday’s forward price relates to today’s forward price. Simple regression analysis seeks to maximize the predictive capabilities of the explanatory variables on the dependent variable. An example of a simple linear regression equation is shown below and in Figure A.2.1.

𝑌𝑌 = 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 + β 𝑋𝑋 + ε
The regression line provides the best fit between the individual x values and maximizes the predictive value of each x observation and the dependent y variable. There exists several components of uncertainty in this equation including: i) uncertainty in the coefficient estimate β, ii) uncertainty in the residual error term ε, and iii) the covariate relationship between the uncertainty in β and the residual error. State-space modeling captures these elements of uncertainty.

Figure A.2.1. Example of a traditional regression analysis.

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For example, ten Monte Carlo simulations are shown in Figure A.2.2. The regression line is no longer completely straight because the state-space Monte Carlo simulations capture the uncertainty in the slope and add an element of random noise (i.e., residual error). The simulations also capture the covariate relationship between the uncertainty in the coefficient estimates and the residual error. By preserving the covariate relationships between the coefficients and the residual error we are able to maintain the relationship of the original data structure as we propagate results through time.

Figure A.2.2. Monte Carlo simulations.

Th
e simulation results shown above are for a single equation, which could correspond to a utility load or a nodal price. The simulation estimates capture the effect of uncertainty in the individual parameter estimates, as well as the residual error and the covariate relationship between the uncertainty distribution in the coefficient estimates and residual error. For a system of equations, correlation effects between equations are captured through the residual error term.

In this report, state-space modeling serves as the cornerstone of uncertainty analysis. The logic of the linked physical and market relationships needs to be supported with solid benchmark results, which demonstrate the statistical match of the input values to the simulated data.

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Parameter Estimation
The complexities of time series data can best be captured through the estimation of the state- space coefficients and conditional uncertainty estimates with full information maximum likelihood estimation (FIMLE). The FIMLE procedure allows for both the model estimation and the simulation of load based on perturbations of the parameter estimates that account for uncertainty of coefficient estimates and equation errors. FIMLE also accounts for the effects caused by temporal autocorrelation.

For example, to accurately reproduce the distribution of load, we need to have more than weather as a stochastic variable. By introducing additional variance through the coefficients and residuals of the regression, we can more effectively simulate the realized outcome and pattern of electricity demand.

The first step is to combine the historical parameters needed for the model estimation, which include all of the variables needed for the parameters described above. Then, based on the input data, the model equation is then constructed and fit in with the parameter estimates and equation errors being stored. These estimates are then fed into a series of statements that simulate load for the next two years based on both weather simulations that are previously generated, perturbations of the parameter estimations, and equation errors. Normally, this is done in a symmetric manner where i load simulations are run on i weather simulations for a total of 2i simulations.

Weighting of Data
PowerSimm applies a weighting system to the input data that enables end users to adjust the emphasis of different historic events or time periods in the parameter estimation process. Each simulation module comes with a default weighting system. The default weighting system can be replaced by user-defined weights enabled through the PowerSimm user interface.

A.2.2 Weather Simulation
Understanding variability in climate data is important for accurate characterization of electricity load and price volatility. Climate dynamics are too chaotic for individual daily events to be accurately forecast. Therefore, it is often best to quantify a climate data variable on a monthly time step. Since the specific daily weather events of the upcoming months cannot be accurately anticipated, they are relegated to random phenomena within monthly probability distributions based on historical and forecasted climate data.

Though regarded as ‘random’ phenomena, daily weather events are correlated both in time and space. In other words, weather events observed today can influence weather events tomorrow and weather events observed in one location can be correlated with weather events in other locations. A straightforward way to represent the statistics of daily weather variations is the class of spatial-temporal models for surface weather data known as weather generators.

The purpose of a weather simulation is to provide a set of outcomes for simulated daily and hourly weather variables (e.g., daily min and max dry bulb temp) across 20 or more weather stations in RPU 2023 Integrated Resource Plan

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the target region (e.g., Southern California). The simulation would maintain the appropriate correlation of observations among the weather stations.

In the modeling framework, weather forecasts are used as inputs to the short-term weather simulation model, but they can also be used as inputs to the long-term weather simulation model.

Seasonal weather forecasts adjust the simulated mean and variance from long-run expectations to coincide with the forecast expectations. The long-run expectations are developed from historic values realized over the last 20 years. These forecasts provide a consistent set of weather realizations through Monte Carlo simulations, and are then fed into the overall simulation engine.

Analytical Scope
Weather simulation focuses on providing all weather explanatory variables used in the simulation of load. The model automatically works with the historic time series data specific to each weather station and determines the relationship between neighboring weather stations. This allows for consistent simulation of weather.

Analytical Applicability
Both Customer and System load are driven from simulated weather. Therefore, the use of weather simulation as a primary driving factor would enable a PowerSimm routine to preserve the appropriate relationship between customer load and spot prices. PowerSimm utilizes a Monte Carlo simulation whereby a specified number of equally likely events (realizations) influence a set of outcomes. These outcomes are comprised of realized weather values to capture weather for each station and the relationship to other stations in California (or the Western US).

Input Data
The core of a weather simulation engine runs on a dataset containing the requested covariates to be simulated. The data is presented in columns and sorted by date on a daily time step. This allows the engine to estimate the simultaneous and lagged correlations between all of the covariates.

Historic weather data is input into WeatherSimm through the Oracle database. (National Climate Data Center (NCDC) has been Ascend’s preferred data source for historic data) Uploaded historic weather data should be consistent with the frequency of population of load data.

For long term (2+ years) simulations, trends in the historical data can be determined along with long-term weather forecast predictions made by groups such as the Climate Prediction Center (CPC) of the U.S. National Centers for Environmental Prediction, (NCEP) and the International Research Institute (IRI) for Climate Prediction.

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Output Data
As described above, the core engine runs on a dataset where the covariates are represented by columns in a single dataset. The core engine generates an identical simulation output dataset with an additional variable that identifies the simulation number. This dataset can be restructured into any format required.

A.2.3 Load Simulation
Developing accurate electricity load simulations is critical for determining the cost of service, risks, and hedging strategies. In addition, load simulation has significant bearing on electricity prices because of the strong non-linear relationship between electricity load and prices. Traditional mathematical statistics may not be able to represent full distributions of load. A simulation approach is advantageous where a specified number of likely events (realizations) can be used in conjunction with simulated weather parameters. The combination of weather and load simulations provides a unified simulation process that can be used to estimate the potential long-term load.

Input Data
All load simulations are based on historical actual hourly load values. Projected economic/load growth input variables can also be applied, when available. For utility or large customer load, a minimum of one year of historic data is required. External load forecasts can be applied to create the expected value of load forecasts. External forecasts can be in the form of either monthly demand or a specified 8760 load stream. These forecasted values become the expected value of the simulated load.

Output Data
The output data is identical to the hourly historic input load dataset except that it includes the requested number of load simulations for the requested simulation length. This dataset also includes the simulation date and time update along with a link table to describe the parameters used to run the simulation.

Model Specification
The simulation of electric load captures the uncertainty in electricity demand through the PowerSimm module LoadSimm. Variation in electricity load can be broken down into three structural components:
• Calendar aspects of ‘load shapes’ both on an hourly and daily basis, • Weather parameters that influence load,
• Temporal autocorrelation within load.

The structural components of load include hour of day (HOD) and day of week (DOW) load shapes, and interaction between HOD and DOW. Holidays, seasonal trends, and long-term growth RPU 2023 Integrated Resource Plan

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predictions are also important components, but the main explanatory factor for load is weather. An example of this simulated relationship is shown in Figure A.2.3. The current model structure simulates system and utility load.

Temporal autocorrelation within load allows for temporally correlated errors to be modeled with more detail. This takes into account the temporal correlation in the model estimation.

Figure A.2.3. Simulated and historical load and weather data.

A.2.4 Forward Prices
PowerSimm simultaneously simulates multiple strips of forward curves into the future where parameters for the stochastic processes and the covariate factors are estimated from historic data.

PowerSimm builds a system of simultaneous equations that captures the stochastic component of each individual forward contract and the covariate relationship between neighboring contract months, other commodities, and other factors (such as interest rates and exchange rates). The state-space modeling framework satisfies the criteria for developing a “Cash Flow at Risk” solution by producing simulations of prices that are realistic, benchmark well to historic data, and produce a payoff of cash flows consistent with market option quotes at multiple strike prices. The consistency of simulated prices with market expectations remains the principal benchmark criteria for forward market simulations.

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Input Data
PowerSimm requires a history of forward price quotes for each delivery month to simulate market prices into the future.

Output Data
PowerSimm outputs simulations of forward quotes to expiration for each contract. The simulations can be run on either a daily time step or a single time step until expiration. The simulation of forward prices produces a large number of simulated values. The reporting of these values is presented in terms of summary statistics that can be viewed in the standard output reports, which focus on the mean, 5th, and 95th percentile of simulation results.

Model Specification
The simulation of forward prices follows a state-space modeling framework. The correlation structure between each contract is preserved through a covariance matrix that maintains the covariate movements in uncertainty for different contracts and between different commodities. As a base simulation assumption, PowerSimm creates convergence between the initial forward price and the final forward price. PowerSimm also has the ability to weight the historic data used in the parameter estimation process to give more weight to more recent events and to reduce the leverage factor associated with outlier events.

A.2.5 Spot Electric Prices
Relationships between fundamental variables and electricity prices are measured from historic data. The simulated variables of load, hydro generation, imports/exports, reserve margins, supply stack, and gas prices are then used as explanatory variables for electricity prices through a structural state space model.

Within SimEngine, the process culminates in the simulation of spot electricity prices. Spot electricity prices preserve the weather, load, and price relationships that govern electric market price formation. The simulation inputs consist of the following modules:
• WeatherSimm
• LoadSimm
• HydroSimm
• TransSimm/Imports/Exports
• Gas Price Simulation Engine

These modules produce explanatory variables for electric spot market prices. Each simulation trajectory for heavy load (HL) and light load (LL) spot electric prices for each month are scaled to the final evolved forward price for electricity. The simulated daily HL and LL values are then further decomposed into hourly values with a state-space time series model.

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The hourly-simulated values of load, price, and congestion flows are then input into economic dispatch and hedge payoff processes. The final simulated values are then written to the Results Database.

Input Data
The input data consists of the following (with the optional explanatory variables notes in parentheses following the data element):
• Historic hourly load data
• Historic hourly or daily hydro generation
• Daily gas prices
• Transmission imports and exports (optional)
• Daily reserve margins (optional)
• Supply stack characteristics (optional)

Output Data
SimEngine produces simulation of daily HL and LL electric prices and hourly spot electric prices.

Summary statistics can be viewed in the standard output reports, which focus on the mean, 5th, and 95th percentile simulation results.

Methodology and Model Specification
The application of the fundamental drivers of electricity has influence on the daily and hourly formation of prices over both the intermediate and long-term prices. Over the intermediate term, daily HL and LL electricity prices are simulated so that the mean distribution of daily prices converges with the final evolved forward price.

Regional electricity prices are primarily a function of daily gas prices and daily reserve margins.

Each variable explains about 50% of the variability in prices and jointly they explain about 90% of the variability
The simulation of electricity prices follows the simulation of the exogenous variables that jointly explain electricity prices. These variables include gas prices and load and may also include unit outages, capacity, supply stack characteristics, hydro generation, imports, and exports. The variables load, unit outages, capacity, imports, and exports are factored directly into the calculation of daily reserve margins.

The simulated values for price are conditional upon the path-dependent weather and load simulations. The mean or median of the realized daily HL and LL spot prices are bucketed into monthly time steps and scaled to be centered around the monthly forward price.

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A.2.6 Spot Gas Prices
Developing accurate spot gas price simulations is critical for determining the cost of service, risks, and hedging strategies. A simulation approach is advantageous where a specified number of likely events (realizations) can be used in conjunction with exogenous system shocks such as extreme weather events. The combination of market electric prices and spot gas prices is critical to accurately capturing the cost of generation and driving dispatch of generation assets.

Input Data
Estimation of the parameters to simulated spot gas prices utilizes input of historical gas spot prices, weather, and daily HL and LL electric prices. The simulated weather is input into the model on a simulation basis.

Output Data
The output data is identical to the daily historic input dataset except that it includes the requested number of spot price simulations for the requested simulation length. This dataset also includes the simulation date and time update along with a link table to describe the parameters used to run the simulation.

SimEngine produces daily spot gas price simulations over the forecast horizon. The summary statistics can be viewed in the standard output reports, which focus on the mean, 5th, and 95th percentile simulation results.

A.2.7 Wind and Solar Generation
Developing accurate wind and/or solar generation simulations is critical for determining cost of service, risks, hedging strategies, and for estimating the relationship between the explanatory variables and price. Traditional mathematical statistics may not be able to represent full distributions of such generation. A simulation approach is advantageous where a specified number of likely events (realizations) can be used in conjunction with simulated weather parameters. The combination of weather and wind/solar generation simulations provides a unified simulation process that can be used to estimate the relationship between wind/solar production, electricity demand, and market prices.

Input Data
WindSimm requires input of historical hourly wind or solar generation. For new assets, the estimated hourly data is used for input values.

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Output Data
The output data is identical to the hourly historic input wind/solar generation dataset except that it includes the requested number of simulations for the requested simulation length. This dataset also includes the simulation date and time update along with a link table to describe the parameters used to run the simulation.

WindSimm produces simulations of hourly wind/solar generation over the forecast horizon.

WindSimm summary statistics can be viewed in the standard output reports, which focus on the mean, 5th, and 95th percentile simulation results.

Methodology and Model Specification
Variation in wind/solar generation can be broken down into three structural components:
• Calendar aspects of ‘generation shapes’ both on an hourly and daily basis • Weather parameters that influence generation
• Temporal autocorrelation within the generation data

The structural components of wind/solar generation include hour of day (HOD) and seasonal trends. The relationship between generation and electric load is maintained by using temperature as an explanatory factor.

Integration of these components into a modeling framework requires that the significant interactions among the components be taken into account. Weather parameters impact hourly and daily generation profiles depending on the HOD. There are also differences in the temporal autocorrelation contingent on seasonality. The combination of these main effects and their significant interactions can be used to accurately simulate generation.

WindSimm has three main components that influence changes in wind/solar generation. The first is the structural components that develop the ‘production shapes’ both on hourly and daily basis marked with bold fonts. The second is the weather variables that influence generation. The third is the temporal autocorrelation observed in the generation data. Beyond these main effects, there are significant interactions between these components that are incorporated for model accuracy.

A.3 Generation Dispatch
In PowerSimm, units are dispatched against multiple simulation sets of price, load and emissions, allowing for a distribution of outcomes. The core dispatch routine is based on a deterministic dynamic program-type model with backward and forward passes.

The setup configuration for Dispatch can be modified to maximize granularity and realism of unit operation or to maximize processing speed. Dispatch can also run autonomously from PowerSimm RPU 2023 Integrated Resource Plan

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for short-term and high-granularity dispatch simulations. Greater speed can be achieved through simplifying unit characteristics and/or increasing the size of the simulation time step (e.g., from hourly to 4-hourly time step size).

Generation units are economically dispatched by finding the sequence of states for the unit hour-by-hour that maximizes the Total Net Revenue (Total Gross Revenue – Total Production Costs).

Even when a collection of units is being dispatched to serve native load, it is treated as being dispatched economically, subject to a constraint condition: the overall portfolio of units should minimize the cost of production while maximizing revenue (if any) and subject to the condition that native load is serviced.

In addition to serving native load, units may also be constrained by a maximum number of starts in a month or how much of a specific emission they can generate. To enforce these constraints, penalties are added to the Net Revenue equation. These “economic” penalties and incentives do not show up in the final report on Costs and Net Revenue; they are simply used to satisfy the constraints.

This modified Net Revenue equation represents the new objective function. The mathematical problem of dispatch is to maximize the cumulative total value expressed by this function.

Peak-period and seasonal unit characteristic changes are handled by identifying a unit. When unit characteristics change radically between seasons, the dispatch may be split into separate blocks; effectively modeling the different blocks as separate units and then splicing their results.

Planned outages are represented by assigning large negative objective function values to all “ON” states for the outage period. Partial Planned Outages act in the same manner, but are restricted to generation levels beyond the specified threshold. Unplanned or Forced outages are deemed to take the unit operator “by surprise”. Unplanned outages are generated via random simulation.

Certain operational constraints (such as total generation limits, maximum starts, and emissions) involve iterative dispatch simulations using different adjustments to the objective function. The iterative dispatch loop seeks to obtain the minimum objective function adjustments that result in a dispatch result that obeys the conditions of the constraint. Startup/shutdown time, minimum run time, minimum down time and fuel switching constraints are all handled directly through the state-to-state mapping tables rather than through the objective function.

Finally, the PowerSimm dispatch engine can be configured to produce portfolio asset and dispatch simulations at the hourly granularity for one month to twenty-five (25) years into the future.

The end-user can specify the number of simulations, the time step granularity, the generation asset portfolio, multiple portfolio constraints and stress test scenarios, and the degree of detail in the output data tables. All output data is delivered via the OLAP cube into Excel pivot-tables; these tables can then be further customized and modified by the end-user, to meet specific reporting and/or computational applications.

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APPENDIX B
Ascend Analytics CAISO Market Report Release 4.1

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APPENDIX C
Chapter 14: Avoided Cost of Energy (ACOE) Worksheets

kW/h: 1.0
Calendar Year: 2024
EE Type: Baseload (Residential)

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Monthly kW Peak reduction Prob: 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Seasonal weighting of avoided kWh: 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 Assumed annual hours of operation: 5,694.0
Annual kWh Output: 5,694.0 CF: 65.0%
Energy Credit
SP15 Flat Power Price - 2024 ($/MWh) $87.22 $76.43 $49.41 $39.58 $32.41 $51.18 $82.98 $100.15 $79.34 $56.24 $63.13 $89.68 $/kWh-month credit: $41.37 $36.25 $23.44 $18.77 $15.37 $24.28 $39.36 $47.50 $37.63 $26.68 $29.94 $42.54 Annual kWh credit: $383.12
kWh value: $0.0673
System Capacity Credit
Monthly RA ratios: 0.150 0.150 0.200 0.300 0.500 1.200 2.600 2.800 2.600 1.100 0.250 0.150 $/kW-month value: $1.50 $1.50 $2.00 $3.00 $5.00 $12.00 $26.00 $28.00 $26.00 $11.00 $2.50 $1.50 $/kW-month credit: $1.50 $1.50 $2.00 $3.00 $5.00 $12.00 $26.00 $28.00 $26.00 $11.00 $2.50 $1.50 Annual kW credit: $120.00
kWh value: $0.0211 $120.00
Additional Local Capacity Credit
$/kW-year value: $30.00 (annual additional cost for Local RA benefits) kW reduction /MWh production factor: 0.0925
Annual kW credit: $15.80
kWh value: $0.0028
CY 2021 CY 2022
Ancillary Services Credit Total AS uplift costs ($): $1,701,734 $2,559,936 Annual kWh credit: $5.32 Wholesale System Load (MWh): 2,246,814 2,317,573 kWh value: $0.0009 Implied $/MWh credit: $0.93
Nov-22 Feb-23 May-23 Aug-23
Environmental (Carbon) Credit $26.80 $27.85 $30.33 $35.20 Annual kWh credit: $60.72 2021 EF: 0.3670 2022 EF: 0.3429 kWh value: $0.0107 $10.66
RPS Credit $20.00
Annual kWh credit: $50.11 44.00%
kWh value: 0.0088 $8.80
Transmission Credit (ISO TAC Rate)
Annual kWh credit: $82.08 2022 TAC Rate ($/MWh)
kWh value: 0.0144 $14.4157
Sum of Credits ($/kWh): $0.1259
Annual $ Value per installed kW: $717.15
Distribution Loss Factor Adjustment 5.40%
Loss Component (kWh value): $0.0072
Loss Adjusted kWh value: $0.1331
Loss Adjusted Annual $ Value: $758.09
$/kWh Value of EE: $0.1331 ← implied value to RPU for avoided load from EE Annual $ Value per installed kW EE: $758.09
$/MWh RPS Credit (PCC-1):
2024 Target RPS:

Implied $/MWh credit:

ACOE Worksheet (for avoided energy due to EE savings)

RA cost ($/kW-year):

Carbon Clearing Price (last 4 ARB Auctions):

RPU - PSD emission factors:

Implied $/MWh credit:

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kW/h: 1.0
Calendar Year: 2024
EE Type: Lighting (Residential)

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Monthly kW Peak reduction Prob: 1.000 0.961 0.885 0.808 0.770 0.731 0.731 0.770 0.808 0.885 0.961 1.000 Seasonal weighting of avoided kWh: 0.0970 0.0933 0.0858 0.0784 0.0746 0.0709 0.0709 0.0746 0.0784 0.0858 0.0933 0.0970 Assumed annual hours of operation: 3,066.0
Annual kWh Output: 3,066.0 CF: 35.0%
Energy Credit
SP15 LL Power Price - 2024 ($/MWh) $84.00 $77.00 $53.75 $45.85 $37.55 $47.35 $68.55 $82.35 $59.95 $55.40 $63.05 $88.15 $/kWh-month credit: $24.98 $22.03 $14.14 $11.02 $8.59 $10.29 $14.90 $18.84 $14.41 $14.57 $18.04 $26.22 Annual kWh credit: $198.02
kWh value: $0.0646
System Capacity Credit
Monthly RA ratios: 0.150 0.150 0.200 0.300 0.500 1.200 2.600 2.800 2.600 1.100 0.250 0.150 $/kW-month value: $1.50 $1.50 $2.00 $3.00 $5.00 $12.00 $26.00 $28.00 $26.00 $11.00 $2.50 $1.50 $/kW-month credit: $1.50 $1.44 $1.77 $2.42 $3.85 $8.77 $19.01 $21.56 $21.01 $9.74 $2.40 $1.50 Annual kW credit: $94.97
kWh value: $0.0310 $120.00
Additional Local Capacity Credit
$/kW-year value: $30.00 (annual additional cost for Local RA benefits) kW reduction /MWh production factor: 0.0925
Annual kW credit: $8.51
kWh value: $0.0028
CY 2021 CY 2022
Ancillary Services Credit Total AS uplift costs ($): $1,701,734 $2,559,936 Annual kWh credit: $2.86 Wholesale System Load (MWh): 2,246,814 2,317,573 kWh value: $0.0009 Implied $/MWh credit: $0.93
Nov-22 Feb-23 May-23 Aug-23
Environmental (Carbon) Credit $26.80 $27.85 $30.33 $35.20 Annual kWh credit: $32.70 2021 EF: 0.3670 2022 EF: 0.3429 kWh value: $0.0107 $10.66
RPS Credit $20.00
Annual kWh credit: $26.98 44.00%
kWh value: 0.0088 $8.80
Transmission Credit (ISO TAC Rate)
Annual kWh credit: $44.20 2022 TAC Rate ($/MWh)
kWh value: 0.0144 $14.4157
Sum of Credits ($/kWh): $0.1331
Annual $ Value per installed kW: $408.24
Distribution Loss Factor Adjustment 5.40%
Loss Component (kWh value): $0.0076
Loss Adjusted kWh value: $0.1408
Loss Adjusted Annual $ Value: $431.54
$/kWh Value of EE: $0.1408 ← implied value to RPU for avoided load from EE Annual $ Value per installed kW EE: $431.54
$/MWh RPS Credit (PCC-1):
2024 Target RPS:

Implied $/MWh credit:

ACOE Worksheet (for avoided energy due to EE savings)

RA cost ($/kW-year):

Carbon Clearing Price (last 4 ARB Auctions):

RPU - PSD emission factors:

Implied $/MWh credit:

RPU 2023 Integrated Resource Plan

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kW/h: 1.0
Calendar Year: 2024
EE Type: HVAC (Residential)

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Monthly kW Peak reduction Prob: 0.411 0.283 0.192 0.134 0.253 0.586 0.940 1.000 0.673 0.268 0.154 0.327 Seasonal weighting of avoided kWh: 0.0788 0.0541 0.0367 0.0256 0.0486 0.1122 0.1802 0.1916 0.1289 0.0513 0.0294 0.0626 Assumed annual hours of operation: 1,752.0
Annual kWh Output: 1,752.0 CF: 20.0%
Energy Credit
SP15 HL Power Price - 2024 ($/MWh) $89.75 $76.00 $46.00 $35.00 $28.35 $54.25 $94.35 $113.00 $96.30 $56.85 $63.20 $91.00 $/kWh-month credit: $12.39 $7.20 $2.96 $1.57 $2.41 $10.66 $29.79 $37.93 $21.75 $5.11 $3.26 $9.98 Annual kWh credit: $145.01
kWh value: $0.0828
System Capacity Credit
Monthly RA ratios: 0.150 0.150 0.200 0.300 0.500 1.200 2.600 2.800 2.600 1.100 0.250 0.150 $/kW-month value: $1.50 $1.50 $2.00 $3.00 $5.00 $12.00 $26.00 $28.00 $26.00 $11.00 $2.50 $1.50 $/kW-month credit: $0.62 $0.42 $0.38 $0.40 $1.27 $7.03 $24.44 $28.00 $17.50 $2.95 $0.39 $0.49 Annual kW credit: $83.89
kWh value: $0.0479 $120.00
Additional Local Capacity Credit
$/kW-year value: $30.00 (annual additional cost for Local RA benefits) kW reduction /MWh production factor: 0.0925
Annual kW credit: $4.86
kWh value: $0.0028
CY 2021 CY 2022
Ancillary Services Credit Total AS uplift costs ($): $1,701,734 $2,559,936 Annual kWh credit: $1.64 Wholesale System Load (MWh): 2,246,814 2,317,573 kWh value: $0.0009 Implied $/MWh credit: $0.93
Nov-22 Feb-23 May-23 Aug-23
Environmental (Carbon) Credit $26.80 $27.85 $30.33 $35.20 Annual kWh credit: $18.68 2021 EF: 0.3670 2022 EF: 0.3429 kWh value: $0.0107 $10.66
RPS Credit $20.00
Annual kWh credit: $15.42 44.00%
kWh value: 0.0088 $8.80
Transmission Credit (ISO TAC Rate)
Annual kWh credit: $25.26 2022 TAC Rate ($/MWh)
kWh value: 0.0144 $14.4157
Sum of Credits ($/kWh): $0.1682
Annual $ Value per installed kW: $294.75
Distribution Loss Factor Adjustment 5.40%
Loss Component (kWh value): $0.0096
Loss Adjusted kWh value: $0.1778
Loss Adjusted Annual $ Value: $311.58
$/kWh Value of EE: $0.1778 ← implied value to RPU for avoided load from EE Annual $ Value per installed kW EE: $311.58
$/MWh RPS Credit (PCC-1):
2024 Target RPS:

Implied $/MWh credit:

ACOE Worksheet (for avoided energy due to EE savings)

RA cost ($/kW-year):

Carbon Clearing Price (last 4 ARB Auctions):

RPU - PSD emission factors:

Implied $/MWh credit:

RPU 2023 Integrated Resource Plan

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kW/h: 1.0
Calendar Year: 2024
EE Type: Baseload (Comm/Indst)

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Monthly kW Peak reduction Prob: 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Seasonal weighting of avoided kWh: 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 0.0833 Assumed annual hours of operation: 6,570.0
Annual kWh Output: 6,570.0 CF: 75.0%
Energy Credit
SP15 Flat Power Price - 2024 ($/MWh) $87.22 $76.43 $49.41 $39.58 $32.41 $51.18 $82.98 $100.15 $79.34 $56.24 $63.13 $89.68 $/kWh-month credit: $47.73 $41.83 $27.04 $21.66 $17.74 $28.01 $45.41 $54.81 $43.42 $30.78 $34.55 $49.08 Annual kWh credit: $442.07
kWh value: $0.0673
System Capacity Credit
Monthly RA ratios: 0.150 0.150 0.200 0.300 0.500 1.200 2.600 2.800 2.600 1.100 0.250 0.150 $/kW-month value: $1.50 $1.50 $2.00 $3.00 $5.00 $12.00 $26.00 $28.00 $26.00 $11.00 $2.50 $1.50 $/kW-month credit: $1.50 $1.50 $2.00 $3.00 $5.00 $12.00 $26.00 $28.00 $26.00 $11.00 $2.50 $1.50 Annual kW credit: $120.00
kWh value: $0.0183 $120.00
Additional Local Capacity Credit
$/kW-year value: $30.00 (annual additional cost for Local RA benefits) kW reduction /MWh production factor: 0.0925
Annual kW credit: $18.23
kWh value: $0.0028
CY 2021 CY 2022
Ancillary Services Credit Total AS uplift costs ($): $1,701,734 $2,559,936 Annual kWh credit: $6.13 Wholesale System Load (MWh): 2,246,814 2,317,573 kWh value: $0.0009 Implied $/MWh credit: $0.93
Nov-22 Feb-23 May-23 Aug-23
Environmental (Carbon) Credit $26.80 $27.85 $30.33 $35.20 Annual kWh credit: $70.07 2021 EF: 0.3670 2022 EF: 0.3429 kWh value: $0.0107 $10.66
RPS Credit $20.00
Annual kWh credit: $57.82 44.00%
kWh value: 0.0088 $8.80
Transmission Credit (ISO TAC Rate)
Annual kWh credit: $94.71 2022 TAC Rate ($/MWh)
kWh value: 0.0144 $14.4157
Sum of Credits ($/kWh): $0.1231
Annual $ Value per installed kW: $809.02
Distribution Loss Factor Adjustment 5.40%
Loss Component (kWh value): $0.0070
Loss Adjusted kWh value: $0.1302
Loss Adjusted Annual $ Value: $855.20
$/kWh Value of EE: $0.1302 ← implied value to RPU for avoided load from EE Annual $ Value per installed kW EE: $855.20
$/MWh RPS Credit (PCC-1):
2024 Target RPS:

Implied $/MWh credit:

ACOE Worksheet (for avoided energy due to EE savings)

RA cost ($/kW-year):

Carbon Clearing Price (last 4 ARB Auctions):

RPU - PSD emission factors:

Implied $/MWh credit:

RPU 2023 Integrated Resource Plan

C-5

kW/h: 1.0
Calendar Year: 2024
EE Type: Lighting (Comm/Indst)

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Monthly kW Peak reduction Prob: 1.000 0.961 0.885 0.808 0.770 0.731 0.731 0.770 0.808 0.885 0.961 1.000 Seasonal weighting of avoided kWh: 0.0970 0.0933 0.0858 0.0784 0.0746 0.0709 0.0709 0.0746 0.0784 0.0858 0.0933 0.0970 Assumed annual hours of operation: 5,694.0
Annual kWh Output: 5,694.0 CF: 65.0%
Energy Credit
SP15 Flat Power Price - 2024 ($/MWh) $87.22 $76.43 $49.41 $39.58 $32.41 $51.18 $82.98 $100.15 $79.34 $56.24 $63.13 $89.68 $/kWh-month credit: $48.17 $40.60 $24.14 $17.67 $13.77 $20.66 $33.50 $42.54 $35.42 $27.48 $33.54 $49.53 Annual kWh credit: $387.02
kWh value: $0.0680
System Capacity Credit
Monthly RA ratios: 0.150 0.150 0.200 0.300 0.500 1.200 2.600 2.800 2.600 1.100 0.250 0.150 $/kW-month value: $1.50 $1.50 $2.00 $3.00 $5.00 $12.00 $26.00 $28.00 $26.00 $11.00 $2.50 $1.50 $/kW-month credit: $1.50 $1.44 $1.77 $2.42 $3.85 $8.77 $19.01 $21.56 $21.01 $9.74 $2.40 $1.50 Annual kW credit: $94.97
kWh value: $0.0167 $120.00
Additional Local Capacity Credit
$/kW-year value: $30.00 (annual additional cost for Local RA benefits) kW reduction /MWh production factor: 0.0925
Annual kW credit: $15.80
kWh value: $0.0028
CY 2021 CY 2022
Ancillary Services Credit Total AS uplift costs ($): $1,701,734 $2,559,936 Annual kWh credit: $5.32 Wholesale System Load (MWh): 2,246,814 2,317,573 kWh value: $0.0009 Implied $/MWh credit: $0.93
Nov-22 Feb-23 May-23 Aug-23
Environmental (Carbon) Credit $26.80 $27.85 $30.33 $35.20 Annual kWh credit: $60.72 2021 EF: 0.3670 2022 EF: 0.3429 kWh value: $0.0107 $10.66
RPS Credit $20.00
Annual kWh credit: $50.11 44.00%
kWh value: 0.0088 $8.80
Transmission Credit (ISO TAC Rate)
Annual kWh credit: $82.08 2022 TAC Rate ($/MWh)
kWh value: 0.0144 $14.4157
Sum of Credits ($/kWh): $0.1222
Annual $ Value per installed kW: $696.01
Distribution Loss Factor Adjustment 5.40%
Loss Component (kWh value): $0.0070
Loss Adjusted kWh value: $0.1292
Loss Adjusted Annual $ Value: $735.74
$/kWh Value of EE: $0.1292 ← implied value to RPU for avoided load from EE Annual $ Value per installed kW EE: $735.74
$/MWh RPS Credit (PCC-1):
2024 Target RPS:

Implied $/MWh credit:

ACOE Worksheet (for avoided energy due to EE savings)

RA cost ($/kW-year):

Carbon Clearing Price (last 4 ARB Auctions):

RPU - PSD emission factors:

Implied $/MWh credit:

RPU 2023 Integrated Resource Plan

C-6

kW/h: 1.0
Calendar Year: 2024
EE Type: HVAC (Comm/Indst)

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Monthly kW Peak reduction Prob: 0.411 0.283 0.192 0.134 0.253 0.586 0.940 1.000 0.673 0.268 0.154 0.327 Seasonal weighting of avoided kWh: 0.0788 0.0541 0.0367 0.0256 0.0486 0.1122 0.1802 0.1916 0.1289 0.0513 0.0294 0.0626 Assumed annual hours of operation: 2,628.0
Annual kWh Output: 2,628.0 CF: 30.0%
Energy Credit
SP15 HL Power Price - 2024 ($/MWh) $89.75 $76.00 $46.00 $35.00 $28.35 $54.25 $94.35 $113.00 $96.30 $56.85 $63.20 $91.00 $/kWh-month credit: $18.59 $10.81 $4.44 $2.35 $3.62 $16.00 $44.68 $56.90 $32.62 $7.66 $4.88 $14.97 Annual kWh credit: $217.52
kWh value: $0.0828
System Capacity Credit
Monthly RA ratios: 0.150 0.150 0.200 0.300 0.500 1.200 2.600 2.800 2.600 1.100 0.250 0.150 $/kW-month value: $1.50 $1.50 $2.00 $3.00 $5.00 $12.00 $26.00 $28.00 $26.00 $11.00 $2.50 $1.50 $/kW-month credit: $0.62 $0.42 $0.38 $0.40 $1.27 $7.03 $24.44 $28.00 $17.50 $2.95 $0.39 $0.49 Annual kW credit: $83.89
kWh value: $0.0319 $120.00
Additional Local Capacity Credit
$/kW-year value: $30.00 (annual additional cost for Local RA benefits) kW reduction /MWh production factor: 0.0925
Annual kW credit: $7.29
kWh value: $0.0028
CY 2021 CY 2022
Ancillary Services Credit Total AS uplift costs ($): $1,701,734 $2,559,936 Annual kWh credit: $2.45 Wholesale System Load (MWh): 2,246,814 2,317,573 kWh value: $0.0009 Implied $/MWh credit: $0.93
Nov-22 Feb-23 May-23 Aug-23
Environmental (Carbon) Credit $26.80 $27.85 $30.33 $35.20 Annual kWh credit: $28.03 2021 EF: 0.3670 2022 EF: 0.3429 kWh value: $0.0107 $10.66
RPS Credit $20.00
Annual kWh credit: $23.13 44.00%
kWh value: 0.0088 $8.80
Transmission Credit (ISO TAC Rate)
Annual kWh credit: $37.88 2022 TAC Rate ($/MWh)
kWh value: 0.0144 $14.4157
Sum of Credits ($/kWh): $0.1523
Annual $ Value per installed kW: $400.19
Distribution Loss Factor Adjustment 5.40%
Loss Component (kWh value): $0.0087
Loss Adjusted kWh value: $0.1610
Loss Adjusted Annual $ Value: $423.03
$/kWh Value of EE: $0.1610 ← implied value to RPU for avoided load from EE Annual $ Value per installed kW EE: $423.03
$/MWh RPS Credit (PCC-1):
2024 Target RPS:

Implied $/MWh credit:

ACOE Worksheet (for avoided energy due to EE savings)

RA cost ($/kW-year):

Carbon Clearing Price (last 4 ARB Auctions):

RPU - PSD emission factors:

Implied $/MWh credit: