Energy. Roseville, CA U.S.A. Argonne, IL USA. Technical Report. October 30, DOE Contract No. ANL Contract No. 2F-3110.

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1 Technical Report Energy Exemplar EE October 30, 2013 DOE Contract No. DE-FOA ANL Contract No. 2F-3110 Adjustable Speed Pumped-Storage Hydro- Generator ( PSH) Evaluation by PLEXOS Tao Guo, Guangjuan Liu, Lily Yu Energy Exemplar, LLC 3013 Douglas Blvd, Suite 120 Roseville, CA U.S.A. Vladimir Koritarov Decision and Information Sciences Division ARGONNE NATIONAL LABORATORY 9700 S. Cass Avenue, DIS/2211 Argonne, IL USA 1 P age

2 Acknowledgments ANL and EE gratefully acknowledge the support of DOE s Office of Energy Efficiency and Renewable Energy for funding this work. And many thanks go to the members of the Advisory Working Group for their insightful comments and assistance. The Advisory Working Group members include Alan Soneda Pacific Gas and Electric Company (PG&E) Ali Nourai DNV KEMA Brendan Kirby Kirby Consult Charlton Clark U.S. Department of Energy (DOE) Christophe Nicolet Power Vision Engineering Dave Harpman U.S. Department of the Interior, Bureau of Reclamation (USBR) Elliot Mainzer Bonneville Power Administration (BPA) Greg Brownell Sacramento Municipal Utility District (SMUD) J. Douglas Divine Eagle Crest Energy Company Jiri Koutnik Voith Kim Johnson RiverBank Power Klaus Engels E.On Kyle L. Jones US Army Corps of Engineers Landis Kannberg Pacific Northwest National Laboratory (PNNL) Le Tang ABB M. Jones Bonneville Power Administration (BPA) Matthew Hunsaker Western Electricity Coordinating Council (WECC) Maximilian Manderla Voith Michael Manwaring HDR Patrick O Connor U.S. Department of Energy (DOE) Paul Jacobson Electric Power Research Institute (EPRI) Rachna Handa U.S. Department of Energy (DOE)) Rahim Amerkhail U.S. Federal Energy Regulatory Commission (FERC) Rajesh Dham U.S. Department of Energy (DOE) Richard Gilker U.S. Department of Energy (DOE) Rick Jones HDR Rick Miller HDR Rob Hovsapian U.S. Department of Energy (DOE) Scott Flake Sacramento Municipal Utility District (SMUD) Stan Rosinski Electric Power Research Institute (EPRI) Steve Aubert ABB Tuan Bui California Dept. of Water Resources (CDWR) Xiaobo Wang California Independent System Operator (CAISO) Zheng Zhou Midwest Independent System Operator (MISO) 2 P age

3 List of Acronyms ADI Ace Diversity Interchange AGC Automatic generation control ANL Argonne National Laboratory AS PSH Adjustable Speed Pumped-storage Hydro Generator AS Ancillary Services BA Balancing Area BAA Balancing Area Authority BAU Business as Usual BPA Bonneville Power Administration CAISO California Independent System Operator CPS Control Performance Standards DA Day-ahead DCS Disturbance Control Standard DOE U.S. Department of Energy DSM Demand-side management DSS Dynamic Scheduling System ECC Enhanced Curtailment Calculator EDT Efficient Dispatch Toolkit EIM Energy Imbalance Market ERCOT Electric Reliability Council of Texas EWITS Eastern Wind Integration and Transmission Study FERC Federal Energy Regulatory Commission FS PSH Fixed Speed Pumped-storage Hydro Generator GW Gigawatts HA Hour-ahead ISO-NE ISO New England ITAP Intra-hour Transaction Accelerator Platform MISO Midwest Independent Transmission System Operator NERC North American Electric Reliability Corporation NREL National Renewable Energy Laboratory NTTG Northern Tier Transmission Group NWP numerical weather prediction NYISO New York Independent System Operator ORNL Oak Ridge National Laboratory PNNL Pacific Northwest National Laboratory RPS renewable portfolio standards RT Real Time RTO Regional Transmission Organization SCED Security Constrained Economic Dispatch SCUC Security Constrained Unit Commitment SMUD Sacramento Municipal Utility District SPP Southwest Power Pool TEPPC Transmission Expansion Planning and Policy Committee of the Western Electricity Coordinating Council VG Variable Generation 3 P age

4 WAPA Western Area Power Administration WI Western Interconnection WECC Western Electricity Coordinating Council WWSIS Western Wind and Solar Integration Study 4 P age

5 Executivee Summary Energy Exemplar is engaged in this project sponsored by the Department of Energy through Argonne National Laboratory to perform thee power system operation simulation to evaluate the Fixed-speed Pumped-stor age Hydro-generators (FS PSH) and the Adjustable-speed Pumped-storage Hydro-generators (AS PSH) in the areas of 1. Quantifying the value of the FS and AS PSHss under different market conditions and for different levels of variable renewable generation (wind and solar) in the system; 2. Providing informationn about the full range of benefits and value of PSH and CH plants and recommendations for appropriate business models for future PSH projects. As the renewablee generation penetration increases, the accommodation of the renewable generation variability and uncertainty presents the increasing challenges to the system operation. Especially, the challenges manifest as thee issues of over-generation and ramp capacity adequacy. For example, in CAISO, the double-peak daily load shape becomes the duck shape as the renewable generation penetration increases. The above diagram illustratess the possible over-generation in the high renewable generation hours. As the renewable generation decreases, the net load (load less the renewable generation) ramps up quickly that demands un-usually high ramp capacity. The PSHs is an effective sink to absorb the over-generation in the high renewable 5 P age

6 generation hours and provides the ramp capacity to accommodate the net load ramp demand. Energy Exemplar performed Western Interconnection (WI) system simulation for year 2022 to evaluate the impact of the proposed adjustable-speed pumped storage hydrogenerators (AS PSH) in the base renewable generation renewable (14% in WI) scenario and the high-wind renewable generation renewable (33% in WI) scenario. The proposed adjustable-speed PSHs include Swan Lake, Iowa Hill and Eagle Mountain. The existing FS PSHs and the proposed AS PSHs are listed in the following table. PSH Location Region Spinning Reserve Sharing Group Regulation Reserve Sharing Group Number of Units Total Capacity (MW) Generator Type Cabin Creek PSC RMPP Colorado Fixed speed Castaic LDWP CALIF_SOUTH LDWP Fixed speed Eastwood SCE CALIF_SOUTH SCE Fixed speed Elbert WACM RMPP Colorado Fixed speed Helms PG&E_VLY CALIF_NORTH PG&E Valley Fixed speed Horse Mesa SRP AZNMNV Arizona 3 96 Fixed speed Lake Hodge SDGE CALIF_SOUTH SDGE 2 40 Fixed speed Mormon Flat SRP AZNMNV Arizona 1 50 Fixed speed Eagle Mount SCE CALIF_SOUTH SCE Adjustable speed Iowa Hill SMUD CALIF_NORTH SMUD Adjustable speed Swan Lake BPA NWPP NWPP Adjustable speed Grand Total The simulations are performed for three focused areas of WI, California and the Balancing Authority of Northern California (BANC). The impacts of the PSHs to the entire WI, energy market (CAISO), and a portfolio (BANC) are examined. The value streams of the PSH and their impacts to the system operations are listed in the following table. PSH Value Stream Matrix and Item PLEXOS PSH Contribution Simulation Notes 1 Regulation reserve PSH revenue 2 Flexibility reserve PSH revenue 3 Contingency spinning reserve PSH revenue 4 Contingency non spinning reserve PSH revenue 5 Replacement / Supplemental reserve PSH revenue 6 Load following PSH revenue 7 Load leveling / Energy arbitrage PSH revenue 8 Integration of variable energy resources (VER) PSH revenue 9 Generating capacity Post process 6 P age

7 PSH Value Stream Matrix and Item PLEXOS PSH Contribution Simulation Notes 10 Portfolio effects PSH revenue 11 Reduced cycling of thermal units Societal Benefit 12 Reduced transmission congestion Societal Benefit 13 Reduced environmental emissions Societal Benefit 14 Transmission deferral Societal Benefit Also, the 3-stage sequential Day-ahead (DA), Hour-ahead (HA) and Real-time (RT) simulations are performed for the 4 typical weeks of year 2022 to examine the impacts of the PSHs to the sub-hourly system operation. The following summarizes the findings in this study. Energy arbitrage values The WI simulations for year 2022 show that, with the three proposed adjustable-speed PSHs, Swan Lake, Iowa Hill and Eagle Mountain, the production cost saving is 1% of the total WI production cost in the base renewable scenario, and 1.8% in the high-wind renewable scenario. The PSH values of these three AS PSHs are $45.3/kw-year (i.e., total system production cost saving divided by the PSH capacity) in the base renewable scenario and $72.04/kw-year in the high-wind renewable scenario. The California simulations for year 2022 show that, with the two proposed adjustablespeed PSH, Iowa Hill and Eagle Mountain, the production cost saving is 1.2% of the total production cost in California under the base renewable scenario, and 4.2% in the highwind renewable scenario. The PSH values of these two PSHs are $33.35/kw-year in the base renewable scenario and $105.61/kw-year in the high-wind renewable scenario. The BANC simulations for year 2022 show that, with the proposed adjustable-speed PSH, Iowa Hill, the production cost saving is 8.6% of the total BANC production cost in the base renewable scenario, and 16.45% in the high-wind renewable scenario. The PSH values of these two PSHs are $58.04/kw-year in the base renewable scenario and $126.83/kw-year in the high-wind renewable scenario. The 3-stage simulations for four typical weeks in year 2022 in the high-wind renewable scenario show that the average production cost over four typical weeks can be reduced by % from the WI RT simulations with the three proposed adjustable-speed PSHs, Swan Lake, Iowa Hill and Eagle Mountain; % from the CA RT simulations with the two proposed adjustable-speed PSHs, Iowa Hill and Eagle Mountain; % from the BANC RT simulations with the proposed adjustable-speed PSHs, Iowa Hill. Contributions to reserves: contingency, flexibility and regulation reserves 7 P age

8 The WI simulations for year 2022 show that the three proposed adjustable-speed PSHs, Swan Lake, Iowa Hill and Eagle Mountain, provide 1.7% ~ 8.19% of the total WI upward reserves and 12.0% ~ 12.9% of the total WI downward reserves in the base renewable scenario. The three adjustable-speed PSHs provide 0.6% ~ 4.2% of the total WI upward reserves and 10.6% ~ 12.3% of the total WI downward reserves for the highwind renewable scenario. The CA simulations for year 2022 show that the two proposed adjustable-speed PSHs, Iowa Hill and Eagle Mountain, provide 9.6% ~ 26.3% of the total CA upward reserves and 28.7% ~ 33.6% of the total CA downward reserves in the base renewable scenario. The two adjustable-speed PSHs provide 3.6% ~ 23.8% of the total CA upward reserves and 31.5% ~ 37.3% of the total CA downward reserves in the high-wind renewable scenario. The BANC simulations for year 2022 show that the proposed adjustable-speed PSH, Iowa Hill, provides 3.4% ~ 15.8% of the total BANC upward reserves and 23.5% ~ 29.5% of the total BANC downward reserves in the base renewable scenario. The adjustablespeed PSH provides 2.0% ~ 17.6% of the total BANC upward reserves and 14.3% ~ 20.5% of the total BANC downward reserves in the high-wind renewable scenario. The following table summarizes the reserve provisions from the PSHs in the base and high-wind renewable scenarios. Reserve Provisions from Adjustable speed PSH in % of Total Reserve Requirements WI Simulations CA Simulations BANC Simulations Base High wind Base High wind Base High wind Renewable Renewable Renewable Renewable Renewable Renewable Non Spinning 8.1% 4.2% 9.6% 17.6% 15.8% 17.6% Spinning 1.7% 0.6% 26.3% 2.4% 4.3% 2.4% Flexi Down 12.9% 12.3% 33.6% 14.3% 29.5% 14.3% Flexi Up 1.9% 0.4% 10.5% 2.0% 3.8% 2.0% Reg Down 12.0% 10.6% 28.7% 20.5% 23.5% 20.5% Reg Up 3.0% 1.3% 24.6% 1.9% 3.4% 1.9% Contribution to the renewable generation integration The contribution of the adjustable-speed PSHs to the renewable generation integration includes the following two areas. 1. Reserve provisions to cover the renewable generation variability and uncertainty, and 2. The renewable generation curtailment due to the over-generation. The reserve provisions from the adjustable-speed PSHs are listed in the above table. With the three adjustable-speed PSHs, Swan Lake, Iowa Hill and Eagle Mountain, the renewable generation curtailment from the WI simulations for year 2022 is reduced from 0.77% (1,356 GWh) to 0.55% (964 GWh) of the total renewable energy in the base renewable scenario; the renewable generation curtailment is reduced from 14% (48,403 8 P age

9 GWh) to 13% (44,211 GWh) of the total renewable energy in the high-wind renewable scenario. With the two adjustable-speed PSHs, Iowa Hill and Eagle Mountain, the renewable generation curtailment from the CA simulations for year 2022 is reduced from 46 GWh to 14 GWh in the base renewable scenario; the renewable generation curtailment is reduced from 380 GWh to 275 GWh in the high-wind renewable scenario. There is no renewable curtailment in the base renewable scenario in the BANC system. With the adjustable-speed PSH, Iowa Hill, the renewable generation curtailment from the BANC simulations for year 2022 is reduced from 19 GWh to 1.0 GWh in the high-wind renewable scenario; Contribution to the thermal generation cycling reductions The WI simulations for year 2022 show that, with the three proposed adjustable-speed PSHs, Swan Lake, Iowa Hill and Eagle Mountain, the total thermal startup cost is reduced by 15% (20 million $) in the base renewable scenario, and 10% (16 million $) in the high-wind renewable scenario. The ramp up and down in GW is reduced by 17% (1634 GW) and 16% (2257 GW) respectively in the base renewable scenario. The ramp up and down GW is reduced by 16% (1334 GW) and 15% (1904 GW) respectively in the high-wind renewable scenario. The CA simulations for year 2022 show that, with the two proposed adjustable-speed PSHs, Iowa Hill and Eagle Mountain, the total thermal startup cost is reduced by 22% (10 million $) in the base renewable scenario, and 20% (9 million $) in the high-wind renewable scenario. The ramp up and down in GW is reduced by 19% (699 GW) and 20% (1095 GW) respectively in the base renewable scenario. The ramp up and down in GW is reduced by 22% (683 GW) and 21% (998 GW) respectively in the high-wind renewable scenario. The BANC simulations for year 2022 show that, with the proposed adjustable-speed PSHs, Iowa Hill, the total thermal startup cost is reduced by 45% (2 million $) in the base renewable scenario, and 42% (2 million $) in the high-wind renewable scenario. The ramp up and down in GW is reduced by 37% (136 GW) and 39% (197 GW) respectively in the base renewable scenario. The ramp up and down in GW is reduced by 32% (119 GW) and 36% (174 GW) respectively in the high-wind renewable scenario. The 3-stage simulations for four typical weeks in year 2022 in the high-wind renewable scenario show that the average startup cost over four typical weeks can be reduced by 1. 7% from the WI RT simulations with the three proposed adjustable-speed PSHs, Swan Lake, Iowa Hill and Eagle Mountain, 2. 19% from the CA RT simulations with the two proposed adjustable-speed PSHs, Iowa Hill and Eagle Mountain, 3. 46% from the BANC RT simulations with the proposed adjustable-speed PSHs, Iowa Hill. The start-up cost difference between the RT simulation and the DA simulation could be over 60% in some week. The higher startup cost in the RT simulations is due to the CT 9 P age

10 commitment cost to accommodate the sub-hourly load and renewable generation variability and uncertainties. The 3-stage simulations for four typical weeks in year 2022 in the high-wind renewable scenario show that the average thermal generator ramp up and down in MW over four typical weeks can be reduced by 1. About 19% from the WI RT simulations with the three proposed adjustable-speed PSHs, Swan Lake, Iowa Hill and Eagle Mountain, 2. About 25% from the CA RT simulations with the two proposed adjustable-speed PSHs, Iowa Hill and Eagle Mountain, 3. About 25% from the BANC RT simulations with the proposed adjustable-speed PSHs, Iowa Hill. The ramp up and down difference between the RT simulation and the DA simulation could be over 170% in some week. The higher thermal generator ramp up and down in the RT simulations indicates that the thermal generators are ramp more to meet the subhourly load and renewable generation variability and uncertainties. Impact to the market generator participants The CA simulations show that the system generator profit (the generation and reserve revenue less the generation production cost) increases as more PSHs are introduced into the system in both the base and high-wind renewable scenarios. The profit increases are due to the LMP increases in the pumping hours, which yield higher generation revenues. The generator profit is smaller in the high-wind renewable scenario as opposed to the base renewable scenario because of lower LMPs in the high-wind renewable scenario. In the base renewable scenario, the reserve revenue is less than 10% of the total market revenue (energy revenue plus reserve revenue). The reserve revenue increases to 25% of the total market revenue in the high-wind renewable scenario due to higher flexibility and regulation reserve requirements. Contributions to the portfolio With the adjustable-speed PSHs, Iowa Hill, the BANC simulations show substantial reductions in the BANC production cost, emission, thermal generator cycling, and the renewable generation curtailment, as opposed to the case of without the PSHs. The significant reductions in the production cost, emission, thermal generation cycling and the renewable curtailment are due to the higher ratio of the PSH capacity and the portfolio peak demand. The reduction is even higher with the higher renewable generation level. Impact to the transmission congestions In the WI simulations, the WI average transmission congestion prices are reduced from $4/MWh in the case of no PSHs to $2/MWh in the cases of with FS and AS PSHs in the based renewable scenario. In both the base and high-wind renewable scenarios, the interface with the significant congestion price reduction is Intermountain Power Project DC-tie that is in the neighboring area of PSHs Castaic and Eagle Mountain. 10 P age

11 In the CA simulations, the CA average transmission congestion prices are reduced from $3.51/MWh in the case of no PSHs to $0.4/MWh in the case of with AS PSHs, and further to $0.24/MWh in the case of with FS and AS PSHs in the based renewable scenario. The CA average transmission congestion prices are reduced from $1.79/MWh in the case of no PSHs to $0.56/MWh in the case of with FS PSHs, and further to $0.37/MWh in the case of with FS and AS PSHs in the high-wind renewable scenario. Again, in both the base and high-wind renewable scenarios, the interface with the significant congestion price reduction is Intermountain Power Project DC-tie that is the neighboring area of PSHs Castaic and Eagle Mountain. The transmission congestion price is an indicator of transmission congestion in the transmission grid. The lower transmission congestion prices with PSHs indicate that PSHs helps mitigating the transmission congestion. 11 P age

12 Table of Contents 1 Introduction WI Database and Assumption Revisions Introduction of Western Interconnection Database Data readiness for the simulations Regional load representation Renewable Generation Profile Representations Contingency, Flexibility and Regulation Reserve Representations Adjustable Speed PSH Representation Data Assumption Revisions Modeling Approaches PLEXOS SCUC/ED algorithm Stage DA-HA-RT Sequential Simulations PSH Storage Modeling in 3-stage Sequential Simulations Scope of Simulations Simulation Results WI Simulation Results WI System Production Costs WI System Reserve Provisions by PSHs WI System Emission Production WI Thermal Generator Cycling WI Regional LMPs WI Transmission Congestions California Simulation Results Power Market Bidding Prices California System Production Costs California System Reserves and Provision by PSHs California System Emission Production California Thermal Generator Cycling California Regional LMPs California Generator Energy and Ancillary Services Revenue California Transmission Congestions SMUD Simulation Results SMUD System Production Costs SMUD System Reserves SMUD System Emission Production SMUD Thermal Generator Cycling SMUD Regional LMPs SMUD Transmission Congestions Three-Stage DA-HA-RT Sequential Simulations Intermittent Renewable Generation Variability and Uncertainty stage DA-HA-RT Simulation Results for California CA 3-stage Simulation Results for Four Typical Weeks in Year stage DA-HA-RT Simulation Results for WI WI 3-stage Simulation Results for Four Typical Weeks in Year P age

13 5.4 3-stage DA-HA-RT Simulation Results for SMUD SMUD 3-stage Simulation Results for Four Typical Weeks in Year Findings Energy arbitrage values Contributions to reserves: contingency, flexibility and regulation reserves Contributions to the emission reductions Contribution to the renewable generation integration Contributions to reserves: contingency, flexibility and regulation reserves Contribution to the thermal generation cycling reductions Impact to the market generator participants Contributions to the portfolio Impact to the transmission congestions Transmission Deferral Appendix Transmission Expansion Assumptions for High-wind Renewable Scenario References P age

14 List of Figures Figure 2-1 Diagram of the WI Load Regions Figure 2-2 The Average Heat Rates for Coal, CC, CT and Gas Steam Generators [4] Figure 3-1 PLEXOS Security Constrained Unit Commitment and Economic Dispatch Algorithm 33 Figure 3-2 DA-HA-RT 3-stage Sequential Simulations Figure 4-1 Comparison of WI Generation in Three Cases by Generator Type for the Base Renewable Scenario in Year Figure 4-2 Comparison of WI Generation in Three Cases by Generator Type for the High-wind Renewable Scenario in Year Figure 4-3 Comparison of WI Generation Cost in Three Cases by Generator Type for the Base Renewable Scenario in Year Figure 4-4 Comparison of WI Generation Cost in Three Cases by Generator Type for the Highwind Renewable Scenario in Year Figure 4-5 Comparison of Regional LMP in Three Cases for the Selected Regions in Year 2022 for the Base Renewable Scenario Figure 4-6 Comparison of Regional LMP in Three Cases for the Selected Regions in Year 2022 for the High-wind Renewable Scenario Figure 4-7 Logic flow for the Transmission Expansion Using Congestion Shadow Price Approach Figure 4-8 CAISO Energy Price-cost mark-up ( ) Figure 4-9 Comparison of CA Generation in Three Cases by Generator Type for the Base Renewable Scenario in Year Figure 4-10 Comparison of CA Generation in Three Cases by Generator type for the High-wind Renewable Scenario in Year Figure 4-11 Comparison of CA Generation Cost in Three Cases by Generator Type for the Base Renewable Scenario in Year Figure 4-12 Comparison of CA Generation Cost in Three Cases by Generator Type for the Highwind Renewable Scenario in Year Figure 4-13 Comparison of Regional LMP in Three Cases for the Selected Regions in CA in Year 2022 for the Base Renewable Scenario Figure 4-14 Comparison of Regional LMP in Three Cases for the Selected Regions in CA in Year 2022 for the High-wind Renewable Scenario Figure 4-15 SCE LMP in Week of July 17, 2022, in Three Cases for the High-wind Renewable Scenario Figure 4-16 Comparison of SMUD Generation of Two Cases by Generator Type for the Base Renewable Scenario in Year Figure 4-17 Comparison of SMUD Generation of Two Cases by Generator Type for the High-wind Renewable Scenario in Year Figure 4-18 Comparison of SMUD Generation Cost of Two Cases by Generator Type for the Base Renewable Scenario in Year Figure 4-19 Comparison of SMUD Generation Cost of Two Cases by Generator Type for the High-wind Renewable Scenario in Year Figure 4-20 Comparison of SMUD Regional LMP in Two Cases in Year 2022 for the Base Renewable Scenario Figure 4-21 Comparison of SMUD Regional LMP in Two Cases in Year 2022 for the High-wind Renewable Scenario Figure minute Actual Solar Generation and Hourly DA / HA Forecasts in Southern California in a Typical Winter Week of Year P age

15 Figure minute Actual Wind Generation and Hourly DA / HA Forecasts in Southern California in a Typical Winter Week of year Figure minute Actual Solar Generation and Hourly DA / HA Forecasts in Southern California in a Typical Summer Week of year Figure minute Actual Wind Generation and Hourly DA / HA Forecasts in Southern California in a Typical Summer Week of Year Figure 5-5 Wind and Solar generation forecasted error from DA to HA and HA to RT in Southern California in a typical winter week of year Figure 5-6 Wind and Solar generation forecasted error from DA to HA and HA to RT in Southern California in a typical winter week of year Figure 5-7 California Production Cost ($000) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance Outages in the RT Simulations) Figure 5-8 California Startup Cost ($000) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance Outages in the RT Simulations) Figure 5-9 California Thermal Generator Ramp Up (MW) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance Outages in the RT Simulations) Figure 5-10 California Thermal Generator Ramp Down (MW) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance Outages in the RT Simulations) Figure 5-11 California Production Cost ($000) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) Figure 5-12 California Startup Cost ($000) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) Figure 5-13 California Thermal Generator Ramp Up (MW) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) Figure 5-14 California Thermal Generator Ramp Down (MW) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) Figure 5-15 WI Production Cost ($000) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) Figure 5-16 WI Startup Cost ($000) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) Figure 5-17 WI Thermal Generator Ramp Up (MW) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) Figure 5-18 WI Thermal Generator Ramp Down (MW) from 3-stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) Figure 5-19 SMUD Production Cost ($000) from 3-stage Simulations for Two Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) P age

16 Figure 5-20 SMUD Startup Cost ($000) from 3-stage Simulations for Two Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) Figure 5-21 SMUD Thermal Generator Ramp Up (MW) from 3-stage Simulations for Two Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) Figure 5-22 SMUD Thermal Generator Ramp Down (MW) from 3-stage Simulations for Two Cases and Four Typical Weeks in Year 2022 in High-wind renewable scenario (Maintenance and Forced Outages in the RT Simulations) P age

17 List of Tables Table Renewable Generation Assumptions by BA in WI and the USA part of WI in year Table Comparison of the annual peaks of the load regions in years 2020 and Table Number of renewable generators modeled in the base and high-wind renewable sceneries Table Mapping of the load regions and the contingency reserve sharing groups Table Mapping of the load regions and the regulation / flexibility reserve sharing groups.. 27 Table Characteristics of three proposed adjustable speed PSHs Table Locations and Installed Capacity of the Existing FS PHS and Proposed AS PSHs in WI Table Assumptions revisions in the database Table Generator Characteristic Revisions and Eligibility for the Reserve Provisions Table Simulation Scenario Combinations Table Three Focused Simulation Areas: WI, California and SMUD Table Comparison of WI Production Cost in Three Cases for the Base Renewable Scenario in Year Table Comparison of WI Production Cost in Three Cases for the High-Wind Renewable Scenario in Year Table Comparison of WI Renewable Curtailment in the Base Renewable Scenario Table Comparison of WI Renewable Curtailment in the High-wind Renewable Scenario.. 43 Table Comparison of WI Reserve Requirements and Provisions by PSHs in Three Cases for the Base Renewable Scenario in Year Table Comparison of WI Reserve Requirements and Provisions by PSHs in Three Cases for the High-wind Renewable Scenario in Year Table Comparison of WI Emission Productions in Three Cases in Year 2022 for the Base Renewable Scenario Table Comparison of WI Emission Productions in Three Cases in Year 2022 for the High- Wind Renewable Scenario Table Comparison of Number of Starts and Startup Costs of the WI Thermal Generators in Year 2022 for the Base Renewable Scenario Table Comparison of Number of Starts and Startup Costs of the WI Thermal Generators in Year 2022 for the High-wind Renewable Scenario Table Comparison of Thermal Generator Ramp Up and Down of the WI Thermal Generators in Year 2022 for the Base Renewable Scenario Table Comparison of Thermal Generator Ramp Up and Down of the WI Thermal Generators in Year 2022 for the High-Wind Renewable Scenario Table Comparison of WI Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year Table Comparison of WI Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the High-wind Renewable Scenario in Year Table Statistics of CAISO Historical NP15 LMP and AS Clearing Prices in Year Table Correlation of CAISO Historical NP15 LMP and AS Clearing Prices in Year Table CA AS Bidding Price Scaling Factor by Generator Type Table Comparison of CA Production Cost in Three Cases for the Base Renewable Scenario in Year Table Comparison of CA Production Cost in Three Cases for the High-Wind Renewable Scenario in Year P age

18 Table Comparison of CA Renewable Curtailment in the Base Renewable Scenario Table Comparison of CA Renewable Curtailment in the High-wind Renewable Scenario.. 62 Table Comparison of CA Reserve Requirements and Provisions by PSHs in Three Cases for the Base Renewable Scenario in Year Table Comparison of CA Reserve Requirements and Provisions by PSHs in Three Cases for the High-wind Renewable Scenario in Year Table Comparison of CA Emission Productions in Three Cases in year 2022 for the Base Renewable Scenario Table Comparison of CA Emission Productions in Three Cases in Year 2022 for the High- Wind Renewable Scenario Table Comparison of Number of Starts and startup Costs of the CA Thermal Generators in Year 2022 for the Base Renewable Scenario Table Comparison of Number of Starts and startup Costs of the CA Thermal Generators in Year 2022 for the high-wind Renewable Scenario Table Comparison of Thermal Generator Ramp Up and Down of the CA Thermal Generators in Year 2022 for the Base Renewable Scenario Table Comparison of Thermal Generator Ramp Up and Down of the CA Thermal Generators in Year 2022 for the High-Wind Renewable Scenario Table California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the Base Renewable Scenario in Year Table California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the High-wind Renewable Scenario in Year Table California PSH Net Operating Revenue for the Base Renewable Scenarios in Year 2022 from the Simulations with FS PSHs Table California PSH Net Operating Revenue for the Base Renewable Scenarios in Year 2022 from the Simulations with FS & AS PSHs Table California PSH Net Operating Revenue for the High-Wind Renewable Scenarios in Year 2022 from the Simulation with FS PSHs Table California PSH Net Operating Revenue for the High-Wind Renewable Scenarios in Year 2022 from the Simulation with FS&AS PSHs Table Comparison of CA Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year Table Comparison of CA Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the High-wind Renewable Scenario in Year Table Comparison of SMUD Production Cost in Two Cases for the Base Renewable Scenario in Year Table Comparison of SMUD Production Cost in Two Cases for the High-Wind Renewable Scenario in Year Table Comparison of SMUD Renewable Curtailment in the High-wind Renewable Scenario Table Comparison of SMUD Reserve Requirements and Provisions by PSH in Two Cases for the Base Renewable Scenario in Year Table Comparison of SMUD Reserve Requirements and Provisions by PSH in Two Cases for the High-wind Renewable Scenario in Year Table Comparison of SMUD Emission Productions in Two Cases in Year 2022 for the Base Renewable Scenario Table Comparison of SMUD Emission Productions in Two Cases in Year 2022 for the High- Wind Renewable Scenario P age

19 Table Comparison of Number of Starts and Startup Costs of the SMUD Thermal Generators in Year 2022 for the Base Renewable Scenario Table Comparison of Number of Starts and Startup Costs of the SMUD Thermal Generators in Year 2022 for the High-wind Renewable Scenario Table Comparison of Thermal Generator Ramp Up and Down of the SMUD Thermal Generators in Year 2022 for the Base Renewable Scenario Table Comparison of Thermal Generator Ramp Up and Down of the SMUD Thermal Generators in Year 2022 for the High-wind Renewable Scenario Table Max and Min Wind and Solar Forecast Errors in Southern California in a Typical Winter Week of year Table Max and Min Wind and Solar Forecast Error in Southern California in a Typical Summer Week of Year Table Reserve Provisions from Adjustable Speed PSH in % of Total Reserve Requirements Table Transmission line expansion for high-wind renewable scenario Table Transmission interface expansion for high-wind renewable scenario P age

20 1 Introduction The work to be performed under this project is in response to the Funding Opportunity Announcement DE-FOA , which was issued by U.S. Department of Energy (DOE) on April 5, Argonne National Laboratory (Argonne) has teamed up with several project partners and submitted a proposal on June 6, In September 2011, DOE announced the selection of Argonne s team for an award for Subtopic 2.2: Detailed Analysis to Demonstrate the Value of Advanced Pumped Storage Hydropower in the U.S. Energy Exemplar is engaged in this project to perform the power system operation simulation to evaluate the Fixed Speed Pumped-storage Hydro-generators (FS PSH) and the Adjustable Speed Pumped-storage Hydro-generators (AS PSH) in the areas of 1. Quantifying the value of the FS and AS PSHs under different market conditions and for different levels of variable renewable generation (wind and solar) in the system; 2. Providing information about the full range of benefits and value of PSH and CH plants and recommendations for appropriate business models for future PSH projects. This report describes the database used for the power system operation simulation, the algorithm modeling the power system, the simulation results for the different renewable generation scenarios, and the findings. The report is organized in the following way: Section 2 describes WI Database and Assumption Revisions; Section 3 presents Modeling Approaches; Section 4 presents Simulation Results; Section 5 presents Three-Stage DA-HA-RT Sequential Simulations; Section 6 summarizes Findings. 20 P age

21 2 WI Database and Assumption Revisions 2.1 Introduction of Western Interconnection Database The Region used for the simulations in this study is the Western Interconnection (WI). The WECC TEPPC 2022 database is translated into PLEXOS. The database covers power systems in 39 load regions in the west coast off United States, plus provinces of British Columbia and Alberta in Canada, and Comision Federal de Electricidad (CFE) in northern Mexico as shown in Figure 2-1. Figure 2 1 Diagram of the WI Load Regions The Balancing Authority Areas (BAA) in the WI operates independently in term of unit commitment meeting their demands while performing the economic exchange with each other. The WI network is represented by Over 17,000 buses Over 22,000 transmission lines (1045 lines are enforced) 91 interfaces (enforced) and 33 Nomograms (enforced) The generation facilities consist of Over 3,700 generators (including renewables) ) 8 existing Pumped-Storage Hydro Plants (20 units) 21 P age

22 3 New Pumped-Storage Hydro Plants (11 units) The gas price is = $4.6/mmBTU. The forecasted energy and peak for the WI in year 2022 are Energy Demand for the WI = 985,457 GWh; Energy Demand for the USA part in the WI is 786,275 GWh; Coincident Peak for the WI = 168,972 MW; Coincident Peak for the USA part in the WI is 146,718 MW. The forecasted energy demand includes the transmission losses [1], [2]. Renewable Energy Mix Assumption in the USA part of the WI for year 2022 is Based Renewable Generation Scenario: Wind and solar generation energy is 108,993 (GWh) that is 14% of the energy demand in the USA part of the WI; High-wind Renewable Generation Scenario: Wind and solar generation energy is 273,842 (GWh) that is 34% of the energy demand in the USA part of the WI. The renewable generations by BA for the base and high-wind renewable generation scenarios are listed in the following table. Renewable Generation Assumptions by BA in WI and the USA part of WI in Year 2022 High wind Renewable Scenario Base Renewable Scenario BA Wind and Ratio of Wind and Ratio of Sum of Solar Renewable Solar Renewable Net Load (GWh) Energy (GWh) Energy and Load Energy (GWh) Energy and Load AESO 114, % 0.0% APS 43,062 11, % 5, % AVA 14,237 6, % 5, % BANC 16,442 6, % % BCTC 66, % 0.0% BPA 60,804 18, % 9, % CAISO 222,675 45, % 30, % CFE 19, % % CHPD 4, % 0.0% DOPD 2, % 0.0% EPE 11, % % GCPD 4,924 1, % 0.0% IID 4,541 4, % 3, % IPC 21,031 2, % 1, % LDWP 37,118 6, % 5, % NEVP 28,523 7, % 1, % NWMT 11,175 19, % 2, % 22 P age

23 Renewable Generation Assumptions by BA in WI and the USA part of WI in Year 2022 High wind Renewable Scenario Base Renewable Scenario BA Wind and Ratio of Wind and Ratio of Sum of Solar Renewable Solar Renewable Net Load (GWh) Energy (GWh) Energy and Load Energy (GWh) Energy and Load PACE 56,175 24, % 6, % PACW 21,128 9, % 8, % PGN 23, % 0.0% PNM 16,695 18, % 2, % PSC 39,347 11, % 6, % PSE 26,308 2, % % SCL 10, % 0.0% SPP 12,927 8, % % SRP 34,546 7, % 2, % TEP 15,087 3, % % TIDC 2, % 0.0% TPWR 5, % 0.0% WACM 31,332 45, % 8, % WALC 7,664 9, % 5, % WAUW 837 1, % % WI 985, , % 109, % WI USA 786, , % 108, % Table Renewable Generation Assumptions by BA in WI and the USA part of WI in year Data readiness for the simulations Regional load representation The day-ahead (DA) and hour-ahead (HA) load forecasts and 5-min actual loads in year 2020 are received from PNNL for the WECC VGS study [6]. The load forecasts and actual loads in year 2020 are translated to year 2022 with the weekly patterns synchronized in these two years. Then the DA and HA load forecasts and the RT 5- minutes actual loads in year 2022 are scaled by the peak ratios between year 2022 and year The peak ratios are calculated using the load regional peaks in the WECC TEPPC 2020 and 2022 database documents [1], [2]. The forecasted peak loads in year 2020 and year 2022 are listed in the following table. Load Region 2022 Peak (MW) 2020 Peak (MW) Peak Ratio of 2022/2020 AESO , APS , AVA P age

24 24 P age BCTC BPA CFE CHPD DOPD EPEC FAR EAST GCPD IID LADWP MAGIC NEVP NWMT PACE_ID PACE_UT PACE_WY PACW PG&E_BAY PG&E_VLY PGN PNM PSCO PSE SCE SCL SDGE SMUD SPPC SRP TEP TID TPWR TREAS WACM WALC WAUM Sum of Non coincident Peak 192, , Sum of Coincident Peak 168, , Table Comparison of the annual peaks of the load regions in years 2020 and Renewable Generation Profile Representations The wind and solar hourly day-ahead (DA) and 4-hour-ahead (4-HA) generation forecasts and the real-time (RT) 5-min actual generations in year 2020 are received for

25 the base renewable generation scenario and the high-wind renewable generation scenario from the NREL WWSIS phase 2 study [5]. The wind and solar generation forecasts and actual generation profiles in year 2020 are translated into year 2022 with the weekly patterns synchronized in these two years. The number of solar generators and wind generators for the base renewable scenario and the high-wind renewable scenario are listed in the following table. Scenario Number of Wind Generators Number of Solar Generators Base Renewable High wind Renewable Table Number of renewable generators modeled in the base and high wind renewable sceneries Contingency, Flexibility and Regulation Reserve Representations Contingency Reserves The requirements of contingency reserves, i.e. spinning and non-spinning reserves are defined for eight spinning reserve sharing groups. The mapping between the eight spinning reserve sharing groups and the thirty-nine load regions is specified in the following table. Spinning Reserve Sharing Group AESO AZNMNV BASIN BCH CALIF_NORTH CALIF_SOUTH Load Region AESO APS EPE NEVP PNM SRP TEP WALC FAR EAST MAGIC VLY PACE_ID PACE_UT PACE_WY SPP TREAS VLY BCH PG&E_BAY PG&E_VLY SMUD TIDC CFE IID LDWP 25 P age

26 SCE SDGE AVA BPA CHPD DOPD GCPD NWMT NWPP PACW PGN PSE SCL TPWR WAUW RMPP PSC WACM Table Mapping of the load regions and the contingency reserve sharing groups The spinning reserve requirement in a contingency reserve sharing group is 3% of the load in the group. The spinning reserve is provided by the eligible on-line generators in the group. The eligible generators to provide the spinning reserve are specified by generator type in Table Generator Characteristic Revisions. The non-spinning reserve requirement in a contingency reserve sharing group is 3% of the load in the group. The non-spinning reserve is provided by the eligible on-line generators and the off-line quick startup generators in the group. The eligible generators to provide the non-spinning reserve are specified by generator type in Table Generator Characteristic Revisions Flexibility and Regulation Reserves The hourly flexibility and regulation reserve requirements for the DA, 4-HA simulations and the 5-min regulation reserve requirements for the 5-min RT simulations in year 2020 are received for the base and high-wind renewable scenarios from the NREL WWSIS phase 2 study [5]. The reserve requirements in year 2020 are translated to year 2022 with the weekly patterns synchronized in these two years. The flexibility and regulation reserve requirements are defined for twenty flexibility / regulation reserve sharing groups. The mapping between the twenty flexibility / regulation reserve sharing groups and the thirty-nine load regions are specified in the following table. 26 P age Flex/regulation Reserve Sharing Group Alberta Arizona Load Region AESO APS SRP TEP

27 WALC British Columbia BCH PG&E_VLY California, North TIDC California, South SCE PSC Colorado WACM FAR EAST MAGIC VLY PACE_ID Idaho TREAS VLY IID IID LDWP LDWP Mexico (CFE) CFE NWMT Montana WAUW Nevada, North SPP Nevada, South NEVP EPE New Mexico PNM AVA BPA CHPD DOPD GCPD PACW PGN PSE SCL Northwest TPWR San Diego SDGE San Francisco PG&E_BAY SMUD SMUD Utah PACE_UT Wyoming PACE_WY Table Mapping of the load regions and the regulation / flexibility reserve sharing groups 2.3 Adjustable Speed PSH Representation There are eight existing Fixed Speed PSH (FS PSHs) plants in the WI. The existing PSHs can pump only at the full pumping capacity. Therefore, the existing FS PSHs cannot provide regulation reserve in the pumping mode. In the generating mode, the existing FS PSHs have the minimum generating capacity at 70% of their maximum generating capacity. Therefore the existing FS PSHs can provide reserves in the 27 P age

28 dispatchable generating capacity range of 30% of the maximum generating capacity in the generating mode. There are three proposed Adjustable Speed PSHs (AS PSHs) to be built in California and its adjacent areas. The table below provides key technical characteristics of the three PSH projects as they were specified in PLEXOS simulation runs. Please note that these projects are still in planning stage and final project characteristics may be different. Properties IOWA HILL EAGLE MOUNTAIN SWAN LAKE North Units Max Cap per Unit (MW) Min Cap per Unit (MW) Max Pump Load (MW) Min Pump Load (MW) Upper Storage (GWh) Lower Storage (GWh) Cycle Efficiency % % % Connected Bus 37001_CAMINO S ( 230KV) 28195_Red Bluff (500KV) 45035_CAPTJACK (500KV) Table Characteristics of three proposed adjustable speed PSHs The AS PSHs have the minimum pumping capacity at 70% of the maximum pumping capacity. Therefore the AS PSHs can provide reserves in the dispatchable pumping capacity range of 30% of the maximum pumping capacity in the pumping mode. The AS PSHs have the minimum generating capacity at 30% of the maximum generating capacity. Therefore, the AS PSHs can provide reserves in the dispatchable generating capacity range of 70% of the maximum generating capacity in the generating mode. The location and installed capacity of the existing FS and proposed AS PSHs are summarized in the following table. PSH Location Region Spinning Reserve Sharing Group Regulation Reserve Sharing Group Number of Units Total Capacity (MW) Generator Type Cabin Creek PSC RMPP Colorado 2 324Fixed Speed Castaic LDWP CALIF_SOUTH LDWP Fixed Speed Eastwood SCE CALIF_SOUTH SCE 1 199Fixed Speed Elbert WACM RMPP Colorado 2 200Fixed Speed Helms PG&E_VLY CALIF_NORTH PG&E Valley Fixed Speed Horse Mesa SRP AZNMNV Arizona 3 96Fixed Speed Lake Hodge SDGE CALIF_SOUTH SDGE 2 40Fixed Speed Mormon Flat SRP AZNMNV Arizona 1 50Fixed Speed Eagle Mount SCE CALIF_SOUTH SCE Adjustable Speed Iowa Hill SMUD CALIF_NORTH SMUD 3 399Adjustable Speed Swan Lake BPA NWPP NWPP Adjustable Speed 28 P age

29 Grand Total Table Locations and Installed Capacity of the Existing FS PHS and Proposed AS PSHs in WI 2.4 Data Assumption Revisions The WI database of year 2022 is translated from the WECC TEPPC Per stakeholder meetings, a few data revisions were performed to ensure that the assumptions in the database are close to the real world. The data revisions are listed in the following table. Items Revision Descriptions Notes 1 The existing FS PSHs are changed to be modeled by individual unit 2 The Min Pump Capacity is changed to be the Max Pump Capacity for the existing FS PSHs The Min Generating Capacity is changed to be 3 70% of the Max Generating Capacity for the existing FS PSHs The Min Generating Capacity is changed to 90% 4 of the Max Generating Capacity for the nuclear generators The Economic Demand Responses are modeled as 5 dispatchable with the dispatch prices in the range of $500/MWh and zero minimum capacity The Interruptible Demand Responses are modeled as dispatchable with the dispatch prices in the 6 range of $1,200~$1,872/MWh and zero minimum capacity Un served energy penalty price is changed to 7 $3,500/MWh. And the dump power price is changed to: $100/MWh Regulation reserve shortfall penalty price is set to 8 $1,100/MW Spinning reserve shortfall penalty price is set to 9 $900/MW Non spinning reserve shortfall penalty price is set 10 to $700/MW Flexibility reserve shortfall penalty price is set to 11 $600/MW Transmission line and interface limit penalty price 12 is changed to $6,000/MWh 13 All Co gen generators cannot provide reserves Fixed hydro generation profiles and renewable 14 generation profiles can be curtailed at the penalty price of: $22/MWh The existing PSHs cannot provide regulation reserves in the pumping mode. 29 P age

30 Three Block Heat Rate (HR) curves are created for generators of CC, Coal and CT, by escalating the HR curves from the NREL WWSIS Phase 2 study 15 with the ratio of HR at Max Capacity in the TEPPC database over the HR at Max Capacity from NREL WWSIS phase 2 study. The start cost of CCs and CTs is determined by only the start up fuel cost from the TEPPC 16 database. The start cost of other thermal generators is determined by the start cost from the TEPPC database. Table Assumptions revisions in the database See the rest of this subsection for details Further generator characteristic revisions are listed in the following table. Their eligibilities to provide different types of reserve are listed in the table as well. The yellow marked cells indicate the data revisions. Minimum Operating Capacity (% of Max Cap) Provide 5 minute Regulation Provide 10 minute Spinning and non Spinning Reserve Provide 60 min Flexibility Reserve Generator Type Biomass RPS 31 CC Cogen 51.7 CC Frame F 53.2 Yes Yes Yes CC Frame G 48.3 Yes Yes Yes CC G + H 55.0 Yes Yes Yes CC Old 57.1 CC Recent 53.2 Yes Yes Yes Coal Cogen 55 Coal Large Old 80 Yes Yes Yes Coal Large Recent 80 Yes Yes Yes Coal Small 70 Coal Small Old 70 Coal Small Recent 70 Yes Yes Yes Coal SuperC 80 Yes Yes Yes Conventional Hydro ~44 Yes Yes Yes Conventional Hydro_Fixeddispatch CT Cogen 43 CT Future Yes Yes CT Large Yes Yes CT LM 6000 Yes Yes CT Old Gas Yes Yes CT Old Oil 50 Yes Yes 30 P age

31 CT Small Yes Yes Demand CHP 99 Econ DR 0 Geothermal 50 IC 23 Yes Yes Interrupt. DR 0 Yes Yes Negative Bus Load Nuclear 90 Other Steam 34 Yes Yes PC Cogen 50 PC Steam 8 Yes Yes Fixed Speed Pumped Storage 70 Yes Yes Yes Pumping Load Small Hydro RPS Small Hydro RPS_Fixeddispatch Solar Steam Cogen 30 Steam Large Old 80 Yes Yes Steam Large Recent 80 Yes Yes Steam Small Old 70 Yes Yes Steam Small Recent 70 Yes Yes Wind Adjustable Speed Pumped Storage 30 Yes Yes Yes Table Generator Characteristic Revisions and Eligibility for the Reserve Provisions For the generators of Coal, CC and CT, the heat rates are defined at the 50%, 80% and 100% of the max capacities. In the simulation, the heat rates are linearly interpolated for the load points at 50%, 80% and 100% of the max capacities. In reference [4], the typical average heat rate curves derived from the Continuous Emission Monitoring System (CEMS) are shown in the following diagram. 31 P age

32 Figure 2 2 The Averagee Heat Rates for Coal, CC, CT and Gas Steam Generators [4]. Thesee generator heat rate curves are scaled by the average heat rate at the maximum capacity from the WECC TEPPC 2022 database before being applied to the generator heat rates in the database for this study. The non-spinning reserve requirements for eight contingency reserve sharing groups are changed to 3% of the loads in the contingency reserve sharing groups. The CT generators with the max capacity equal to or less thann 100 MW can provide the non- of Supply Adequacy module (PASA) to level the regional capacity reserve margin over the days in year 2022 by using the user-defined maintenance rates and durations. The forced outages are generated by using random draws on the user-defined annuall forced outage rates and durations. The maintenance outages willl be modeledd for the DA and 4-HA simulations. spinning off-line. The maintenance e outages are scheduled by the PLEXOS Projected Assessment And the maintenance and forced outages will be modeled in the 5-min RT simulations. 32 P age

33 3 Modeling Approaches 3.1 PLEXOS SCUC/ED algorithm PLEXOS Security Constrained Unit Commitment (SCUC) algorithm consists of two major logics: Unit Commitment using Mixed Integerr Programming and Network Applications. The SCUC / ED simulation algorithm is illustrated in the following figure. Figure 3 1 PLEXOS Security Constrained Unit Commitment and Economic Dispatch Algorithm The unit commitment and economic dispatch (UC/ED) logic performs the Energy-AS co- optimization using Mixed Integer Programming enforcing all resource and operation constraints. The UC/ED logic commits and dispatches resourcess to balance the system energy demand and meet the system reserve requirements. The resource schedules from the UC/ED are passed to the Network Applications logic. The Network Applications logic solves the DC-OPF to enforce the power flow limits and nomograms. The Network Applications logic also performs the contingency analysis if the contingencies are defined. If there are any transmission limitt violations, these transmission limits are passed to the UC/ /ED logic forr the re-run of UC/ED. The iteration continues until all transmission limit violations are resolved. Thus the co-optimization solution of Energy-AS-DC-OPF is reached. The same algorithm for the SCUC/ED is used by many ISO market scheduling software (some ISO market scheduling software may use AC-OPF is its transparency. Any cost component or in the Network Applications). One of the advantages of the MIP algorithm constraint in the MIP formula can be examined and explained. The MIP mathematical formulation for the Energy-AS-DCOPF-PSH co-optimization can be illustrated by the following formula. 33 P age

34 min, Subject to,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Where - Unit commitment status of generator at interval ; 1=on-line, 0=offline,,,, - Generation from generator at interval ; - Generation cost of generator at interval ;, (Energy Balance Constraint) PSH Storage Balance Constraint AS Requirement Constraints Generator AS capacity Constraints (Generation and AS Capacity Constraints Generation and AS Ramp Capacity Constraint Transmission line Limit Constraints Interface Limit Constraints Generator Chronological Constraints Resource Constraints User-Defined Constraints - Startup / shut down cost of generator at interval ; 34 P age

35 , - AS provision from generator to AS at interval ;, - AS provision cost of generator to AS at interval ; - PSH generating efficiency; - PSH pumping efficiency; - PSH generation at interval ; - PSH pump at interval ;,,,,,,,,, - Load at bus at interval ; - Transmission losses of line at interval ; - Min capacity of generator at interval ; - Max capacity of generation at interval ; - Max ramp up / down rate; - Min AS requirement for AS at interval ; - Min AS provision of generator for AS at interval ; - Max AS provision of generator for AS at interval ; - Power Transfer Distribution Factor of bus to transmission line for post-contingency network ( 0 is the pre-contingency network);, network ;,, network ;,, network ;,, network ;,, network ; - Line flow in transmission line at interval for post-contingency - Min line flow of transmission line at interval for post-contingency - Max line flow of transmission line at interval for post-contingency - Line coefficient of transmission line in interface ; - Min interface flow of interface at interval for post-contingency - Max interface flow of interface at interval for post- contingency The PSH pumping and generating are incorporated in Constraints (Energy Balance Constraint) and (PSH Storage Balance Constraint. By so doing, the PSH operation is co-optimized with other variables: energy, ancillary services, power flow, etc. This formula is different from other legacy PSH dispatch algorithm: generating a thermal cost curve, then dispatching PSH against the thermal cost curve, and finally re-dispatching 35 P age

36 thermal generators with the PSH operation frozen. This legacy PSH dispatch algorithm assumes that PSH is a price-taker facility and its operation does not impact the system prices. Actually, PSH can provide energy and ancillary service simultaneously and the market energy and AS prices will be impacted by thee PSH operation Stage DA-HA-RT Sequential Simulations PLEXOS is capable of simulating power markets at a sub-hourly interval. This feature is very useful when evaluating the ramp capacity adequacy for the renewable generation variability and uncertainty. Usually, the sub-hourly economic dispatch capability works in conjunction of the day-ahead (DA) and hour-aheadd (HA) unit commitment to mimic the real world market operation. The 3-stage DA-HA-RT sequential simulation approach is described as follows. Figure 3 2 DA HA RT 3 stage Sequential Simulations DA simulation mimics the DA SCUC/SCED o Day-ahead forecasted load/wind/solar generation time series are used; o The SCUC/ED optimization window is 244 hours at hourly interval; o The transmission network is modeled at the nodal level; o The contingency, flexibility up/down, regulation up/down reserves are modeled. HA simulation mimics the intra-day SCUC/SCED o The 4-hour-ahead forecasted wind / solar generation time series are used; o The hour-ahead forecasted load time series are used; 36 P age

37 o The SCUC/ED optimization window is 4-hour plus 20-hour look-ahead with 2-hour interval; o The unit commitment patterns from the DA simulation are frozen for generators with Min Up/Down Time greater than 4 hours; o The transmission network is modeled at the nodal level; o The contingency, flexibility up/down, regulation up/down reserves are modeled. RT simulation mimics the 5-min real-time SCED o The Actual 5-min load/wind/solar generation time series are used; o The SCED optimization window is twelve 5-min plus 23 look-ahead with hourly interval; o The unit commitment patterns from the HA simulation are frozen; o The transmission network is modeled at the nodal level; o The contingency, regulation up/down reserves are modeled. However, the flexibility up/down reserves are not modeled. The implication is that the capacity held in the HA simulation for the flexibility reserves is deployed to cover the load and renewable generation variability and uncertainty at the 5- min interval; o CT with max capacity less than 100MW could be committed or de-committed in the 5-min RT simulation. 3.3 PSH Storage Modeling in 3-stage Sequential Simulations In the DA simulation, the SCUC/ED is performed in a 24-hour window. The PSHs are dispatched by PLEXOS SCUC/ED according to the formulation in Section 3.1 PLEXOS SCUC/ED algorithm. The storage volume of a PSH at the end of the 24-hour optimization window is constrained to the storage volume at the beginning of the optimization window. A penalty price of $1,000/MWh is applied to the storage volume constraints. In the HA simulation, the SCUC/ED is performed in a 4-hour plus 20-hour look-ahead window. The simulation solution in the first 4 hours is saved; then the SCUC/ED is performed for the next 4-hours in a 4-hour plus 20-hour look-ahead window, and so on. The PSHs are re-dispatched in the HA simulation according to the formulation in Section 3.1 PLEXOS SCUC/ED algorithm. The storage volume of a PSH at the end of the optimization window is constrained to the storage volume from the DA simulation. A penalty price of $1,000/MWh is applied to the storage volume constraints. In the 5-min RT simulation, the SCUC/ED is performed in a twelve 5-minutes plus 23- hour look-ahead window. The simulation solution in the first twelve 5-minutes is saved; then the SCUC/ED is performed for the next twelve 5-minutes in a twelve 5-minutes plus 23-hour look-ahead window, and so on. The PSHs are re-dispatched in the RT simulation according to the formulation in Section 3.1 PLEXOS SCUC/ED algorithm. The storage volume of a PSH at the end of the optimization window is constrained to the storage volume from the HA simulation. A penalty price of $1,000/MWh is applied to the storage volume constraints. 37 P age

38 3.4 Scope of Simulations The simulation scope covers the base renewable generation scenario and the high-wind renewable generation scenario with and without fixed-speed PSHs or adjustable-speed PSHs modeled. The simulation scenario combinations are listed in the following table. Case Renewable Scenario FS PSHs Modeled AS PSHs Modeled Base 1 Base No No Base 2 Base Yes No Base 3 Base Yes Yes High wind 1 High wind No No High wind 2 High wind Yes No High wind 3 High wind Yes Yes Table Simulation Scenario Combinations The DA simulations are performed for the full year 2022 for all cases. However, the three-stage simulations are performed for four typical weeks for the each case: the third weeks of January, April, July and October in year 2022 starting on Sunday. This study focuses on three areas: WI, California and SMUD. In the WECC TEPPC database, the load region SMUD represents the Balancing Authority of Northern California (BANC) that includes Sacramento Municipal Utility District (SMUD), Modesto Irrigation District (MID), Roseville Electric, and Redding Electric Utility. For consistency, the name of SMUD is used in the remaining of this document for BANC. The California footprint and the SMUD footprint are carved out from the WI database. The simulations for the above mentioned cases are repeated for the carved-out California and SMUD footprints. The carved-out California footprint will be simulated as a bidbased market. The system information of the entire WI, the carved-out California footprint and the carved-out SMUD footprint is listed in Table Three Focused Simulation Areas: WI, California and SMUD Model System WI CA SMUD Load Regions Buses over 17,000 over 4000 over 250 Transmission Lines over 22,000 over 5952 over 300 Interfaces Generator over 3,700 0ver 700 over 60 Existing FS PSHs New AS PSHs Network Representation Nodal Nodal Zonal DA Simulation Step 24 hour 24 hour 24 hour HA Simulation Step 4 hours plus 20 4 hours plus 20 4 hours plus P age

39 RT Simulation Step hour look ahead hour look ahead hour look ahead 12 5 minutes plus 23 hour look ahead 12 5 minutes plus 23 hour look ahead 12 5 minutes plus 23 hour look ahead Simulation Base Cost base Bid base Cost base Table Three Focused Simulation Areas: WI, California and SMUD 39 P age

40 4 Simulation Results The simulation results for three focus areas, WI, California, and SMUD, are presented in this section for the cases of without PSHs, with FS PSHs and with additional AS PSHs, and the base and high-wind renewable scenarios. 4.1 WI Simulation Results The assumptions and settings for the WI simulations are reiterated as follows. 1. DA forecasted load/wind/solar: 24 to 48 hours ahead hours SCUC/ED with hourly interval 3. Nodal network representation 4. Contingency, flexibility up down, regulation up/down reserves modeled 5. Three cases, without PSHs, with the existing FS PSHs, with the existing FS and new AS PSHs, are simulated 6. The simulations are performed for the base and high-wind renewable scenarios 7. For the high-wind renewable scenario, the simplified transmission expansion is performed to deliver the renewable generations to the load buses 8. The WI simulations are cost-based WI System Production Costs The production cost of three cases for year 2022: without PSHs, with the existing FS PSHs, and with the additional AS PSHs, are listed in the following tables for both the base renewable scenario and the high-wind renewable scenario. Total PSH Annual Cost Generation Generation Production Annual Cost Savings due to Base Energy Energy Cost Reduction PSHs Renewable million Capacity $/kw GWh GWh million $ $ % MW year No PSH 997,546 14,737 With FS PSH 1,003,204 4,106 14, % 3, With FS&AS PSH 1,008,135 8,244 14, % 6, Table Comparison of WI Production Cost in Three Cases for the Base Renewable Scenario in Year 2022 Total Generation Energy PSH Generation Energy Production Annual Cost Annual Cost Savings High Wind Cost Reduction due to PSHs Renewable Capacity $/kwyear GWh GWh million $ million $ % (MW) No PSH 997,538 12,646 With FS PSH 1,007,140 6,925 12, % 3, With FS&AS PSH 1,015,512 13,811 12, % 6, Table Comparison of WI Production Cost in Three Cases for the High Wind Renewable Scenario in Year P age

41 With the existing PSHs, the WI total production cost is saved by 1.14% and 1.96% for the base renewable scenario and the high-wind renewable scenario respectively. With the additional AS PSHs are introduced in the system, the WI total production cost saving increases further to 2.11% and 3.77% for the base renewable scenario and the high-wind renewable scenario respectively. With the renewable generation penetration level increases to 33% of the WI demand, the production cost savings due to the PSHs operation increase. The PSHs are more valuable in the high renewable penetration level. The comparisons of the generation by generator type for the base and high-wind renewable scenarios are shown in the following two charts. In the base renewable scenario, The CC and CT generation is reduced as more PSHs are introduced into the system due to the fact that the PSHs generation replaces the CC and CT generation. However the Coal generation is increased to provide the PSHs pumping energy. Also the renewable generation is increased as more PSHs are introduced into the system due to less renewable generation being curtailed. 300, , ,000 Generation by Generator Type (GWh) Yearly for Base Renewable Scenario GWh 150, ,000 50,000 Base No PSH With FS PSH With FS&AS PSH Figure 4 1 Comparison of WI Generation in Three Cases by Generator Type for the Base Renewable Scenario in Year 2022 In the high-wind renewable scenario, both CC and Coal generations are reduced as more PSHs are introduced into the system due to the fact that the PSHs generation replaces the CC and Coal generation. Also the renewable generation is increased as more PSHs are introduced into the system due to less renewable generation being curtailed. 41 P age

42 300,000 Generation by Generator Type (GWh) Yearly for High wind Renewable Scenario GWh 250, , , ,000 50,000 Base No PSH With FS PSH With FS&AS PSH Figure 4 2 Comparison of WI Generation in Three Cases by Generator Type for the High wind Renewable Scenario in Year 2022 The comparisons of the production cost in the WI by generator type for the base and high-wind renewable scenarios are shown in the following two charts. In the base renewable scenario, the CC and CT production cost is reduced as more PSHs are introduced into the system. And the Coal production cost is increased slightly as more PSHs introduced into the system. Million $ 7,000 6,000 5,000 4,000 3,000 2,000 1,000 Total Generation Cost by Generator Type ($M) Yearly for Base Renewable Scenario Base No PSH With FS PSH With FS&AS PSH Figure 4 3 Comparison of WI Generation Cost in Three Cases by Generator Type for the Base Renewable Scenario in Year P age

43 In the high-wind renewable scenario, both CC and Coal production costs are reduced as more PSHs are introduced into the system. 6,000 5,000 4,000 Total Generation Cost by Gnerator Type ($M) Yearly for High wind Renewable Scenario Million $ 3,000 2,000 1,000 Base No PSH With FS PSH With FS&AS PSH Figure 4 4 Comparison of WI Generation Cost in Three Cases by Generator Type for the High wind Renewable Scenario in Year 2022 Due to the PSHs operations, the renewable curtailments in the WI system are reduced as shown in the following two tables for the base and high-wind renewable scenarios. WI Renewable Curtailment in the Base Renewable Scenario Renewable Curtailment Reduction Case GWh GWh % No PSH 1,921 0% With FS PSH 1, % With FS&AS PSH % Table Comparison of WI Renewable Curtailment in the Base Renewable Scenario WI Renewable Curtailment in the High wind Renewable Scenario Renewable Curtailment Reduction Case GWh GWh % No PSH 56,885 0% With FS PSH 48,403 8,482 15% With FS&AS PSH 44,211 12,675 22% Table Comparison of WI Renewable Curtailment in the High wind Renewable Scenario WI System Reserve Provisions by PSHs The system reserve requirements and provisions from the PSHs are compared with the three cases for the base renewable scenario and the high-wind renewable scenario in the following two tables. 43 P age

44 Base Renewable Base No PSH With FS PSH With FS&AS PSH PSH PSH Provision Total Req. Provision Total Req. (GWh) (GWh) (GWh) (GWh) Total Req. (GWh) PSH Provision (GWh) Non Spinning Reserve 29,564 29,564 1,364 29,564 3,757 Spinning Reserve 29,564 29, , Flexibility Down 10,732 10, ,732 1,463 Flexibility Up 10,732 10, , Regulation Down 12,423 12, ,423 1,652 Regulation Up 12,441 12, , Table Comparison of WI Reserve Requirements and Provisions by PSHs in Three Cases for the Base Renewable Scenario in Year 2022 High wind Renewable Base No PSH With FS PSH With FS&AS PSH PSH PSH Provision Total Req. Provision Total Req. (GWh) (GWh) (GWh) (GWh) Total Req. (GWh) PSH Provision (GWh) Non Spinning Reserve 29,564 29, ,564 2,017 Spinning Reserve 29,564 29, , Flexibility Down 23,062 23, ,062 3,072 Flexibility Up 23,062 23, , Regulation Down 17,487 17, ,487 2,333 Regulation Up 17,448 17, , Table Comparison of WI Reserve Requirements and Provisions by PSHs in Three Cases for the High wind Renewable Scenario in Year 2022 The reserve provisions from the adjustable speed PSHs increases substantially as opposed to the reserve provisions from the fixed speed PSHs. The reserve provision increase from the adjustable speed PSHs is due to 1. The larger dispatchable capacity in the generating mode. 2. The reserve provision in the pumping mode WI System Emission Production The system emission productions for the three cases in the base renewable scenario and the high-wind renewable scenario are listed in the following two tables. Base Renewable Emission Reduction (%) CO2 NOx SO2 Emission Reduction (ton) ton ton ton CO2 NOx SO2 CO2 NOx SO2 No PSH 388,463, , , % 0.0% 0.0% With FS PSH 391,262, , ,728 2,799,091 8,304 7, % 1.4% 1.8% With FS&AS PSH 393,954, , ,151 5,491,014 16,888 14, % 2.9% 3.6% Table Comparison of WI Emission Productions in Three Cases in Year 2022 for the Base Renewable Scenario 44 P age

45 High wind Renewable Emission Reduction (%) CO2 NOx SO2 Emission Reduction (ton) ton ton ton CO2 NOx SO2 CO2 NOx SO2 No PSH 318,768, , , % 0.0% 0.0% With FS PSH 312,657, , ,234 6,111,331 9,571 6, % 2.0% 1.9% With FS&AS PSH 311,549, , ,211 7,219,379 8,552 4, % 1.8% 1.3% Table Comparison of WI Emission Productions in Three Cases in Year 2022 for the High Wind Renewable Scenario In the base renewable scenario, the coal generation is increased to provide pumping energy so that the emission production is increased. In the high-wind renewable scenario, the coal generation is decreased so that the emission production is decreased. However, the allover emission production is reduced from the base renewable scenario to the highwind renewable scenario WI Thermal Generator Cycling The number of starts and startup cost of the thermal generators in the three cases for the base renewable scenario and the high-wind renewable scenario are listed in the following two tables. Base Renewable Total Number of Thermal Starts Total Thermal Start Cost Cost Reduction million $ million $ % No PSH 37, With FS PSH 31, % With FS&AS PSH 27, % Table Comparison of Number of Starts and Startup Costs of the WI Thermal Generators in Year 2022 for the Base Renewable Scenario Total Number of High Wind Renewable Thermal Starts Total Thermal Start Cost Cost Reduction million $ million $ % No PSH 40, With FS PSH 36, % With FS&AS PSH 31, % Table Comparison of Number of Starts and Startup Costs of the WI Thermal Generators in Year 2022 for the High wind Renewable Scenario In both the base and high-wind renewable scenarios, the number of starts and startup costs of the thermal generators are reduced substantially as more PSHs are introduced into the system. However, the allover number of starts and startup costs are increased from the base renewable scenario to the high-wind renewable scenario. The comparisons of the ramp up and down of thermal generators in the three cases for the base and high-wind renewable scenarios are listed in the following two tables. 45 P age

46 Base Renewable Total Thermal Generator Ramp Up Total Thermal Generator Ramp Down Ramp Up Reduction Ramp Down Reduction GW GW GW % GW % No PSH 11,501 16,508 With FS PSH 9,716 13,948 1, % 2, % With FS&AS PSH 8,081 11,691 3, % 4, % Table Comparison of Thermal Generator Ramp Up and Down of the WI Thermal Generators in Year 2022 for the Base Renewable Scenario High Wind Renewable Total Thermal Generator Ramp Up Total Thermal Generator Ramp Down Ramp Up Reduction Ramp Down Reduction GW GW GW % GW % No PSH 9,325 14,188 With FS PSH 8,394 12, % 1, % With FS&AS PSH 7,060 10,778 2, % 3, % Table Comparison of Thermal Generator Ramp Up and Down of the WI Thermal Generators in Year 2022 for the High Wind Renewable Scenario In both the base and high-wind renewable scenarios, the thermal generator ramp up and down are reduced substantially as more PSHs are introduced into the system WI Regional LMPs The comparisons of the average regional LMP for the selected regions for the base renewable scenario is shown in following chart. As more PSHs are introduced into the system, the average regional LMP is reduced uniformly for all selected regions Average Regional Price Year 2022 for Base Renewable Scenario $/MWh Base No PSH With FS PSH With FS&AS PSH APS BPA PG&E_VLY SCE SDGE 46 P age

47 Figure 4 5 Comparison of Regional LMP in Three Cases for the Selected Regions in Year 2022 for the Base Renewable Scenario The comparisons of the average regional LMP for the selected regions for the high-wind renewable scenario is shown in following chart. Some regional price increases and some regional price decreases as more PSHs are introduced into the system. However, overall, the regional LMP in the high-wind renewable scenario is reduced substantially as opposed to the base renewable scenario. For the analysis of the higher LMP with more PSHS introduced into the system for the high-wind renewable scenario, please refer to subsection California Regional LMPs Average Regional Price Year 2022 for High wind Renewable Scenario $/MWh Base No PSH With FS PSH With FS&AS PSH APS BPA PG&E_VLY SCE SDGE Figure 4 6 Comparison of Regional LMP in Three Cases for the Selected Regions in Year 2022 for the High wind Renewable Scenario WI Transmission Congestions WI Transmission Congestions in the Base Renewable Scenario The annual transmission interface congestion hours and average congestion prices for the base renewable scenario are listed in Table Comparison of WI Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year The average WI interface forward congestion shadow price is reduced from $4/MWh to $2/MWh as the FS and AS PSHs are introduced into the system. The average WI interface backward congestion shadow price is reduced from $2/MWh to $1/MWh as the FS and AS PSHs are introduced into the system. The most congested interfaces include Interstate WA-BC East, Intrastate AB DC2, P18 Montana-Idaho, P27 Intermountain Power Project DC Line, P45 SDG&E-CFE, 47 P age

48 P52 Silver Peak-Control 55 kv. Comparing the congestion prices of the three cases, No PSHs, with FS PSHs and with FS&AS PSHs, the most transmission congestion price reduction happens in Interfaces P27 Intermountain Power Project DC Line, P45 SDG&E-CFE, and P52 Silver Peak-Control 55 kv. These interfaces are in the neighboring areas where PSHs Castaic, Lake Hodge, and Eagle Mountain are located. 48 P age

49 Interfaces Hours Congested (hrs) Hours Congested Back (hrs) No PSH With FS PSH With FS&AS PSH Hours Shadow Shadow Shadow Hours Congeste Shadow Price Hours Hours Shadow Price Price Back Congested d Back Price Back Congested Congested Price ($/MW) ($/MW) (hrs) (hrs) ($/MW) ($/MW) (hrs) Back (hrs) ($/MW) Shadow Price Back ($/MW) Interstate WA BC East 1, , , Interstate WA BC West 0 1, , , Intrastate AB DC1 6,873 1, ,386 1, ,440 1, Intrastate AB DC2 8, , , Intrastate AZ Palo Verde East Intrastate WA North of Hanford P01 Alberta British Columbia 5 2, , , P03 Northwest British Columbia P08 Montana to Northwest P09 West of Broadview P14 Idaho to Northwest P18 Montana Idaho P23 Four Corners 345/500 Qualified Path P24 PG&E Sierra P25 PacifiCorp/PG&E 115 kv Interconnection P26 Northern Southern California P27 Intermountain Power Project DC Line 6, , , P30 TOT 1A P31 TOT 2A P33 Bonanza West P36 TOT P39 TOT P age

50 Interfaces Hours Congested (hrs) Hours Congested Back (hrs) No PSH With FS PSH With FS&AS PSH Hours Shadow Shadow Shadow Hours Congeste Shadow Price Hours Hours Shadow Price Price Back Congested d Back Price Back Congested Congested Price ($/MW) ($/MW) (hrs) (hrs) ($/MW) ($/MW) (hrs) Back (hrs) ($/MW) Shadow Price Back ($/MW) P40 TOT P41 Sylmar to SCE P42 IID SCE P45 SDG&E CFE 7, , , P47 Southern New Mexico (NM1) P52 Silver Peak Control 55 kv 0 1, , , P55 Brownlee East P59 WALC Blythe SCE Blythe 161 kv Sub P61 Lugo Victorville 500 kv Line P66 COI P73 North of John Day P75 Hemingway Summer Lake P80 Montana Southeast Grand Total 40,910 7, ,268 7, ,050 7, Table Comparison of WI Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year P age

51 Transmission Expansion for the High-wind Renewable Scenario The transmission in the existing TEPPC 2022 network was not adequate to accommodate the High-wind renewable Scenario, so some transmission expansion assumptions had to be made. The transmission expansion assumptions were added to allow the simulations to deliver the renewable energy at the high-wind renewable level. Without the transmission expansion assumptions, the simulation would not have been able to generate results for the High-wind renewable scenario. Given that this study is not a transmission expansion study, it is important to note that the transmission expansion methodology was simplistic. And the transmission expansion methodology did not include detailed economic or reliability analyses. Nor did it take into account issues such as rights of way, environmental concerns, policy constraints, or any other factor that might normally be considered in detailed transmission planning activities. The following steps were taken to generate the transmission expansion assumptions: 1. Perform PLEXOS nodal simulation with the renewable generation at the highwind renewable penetration level, 2. For any congested transmission line with the yearly average shadow price greater than $10/MWh, build a parallel transmission with the exact same characteristics of the congested transmission line, 3. For a congested transmission interface with the yearly average shadow price greater than $10/MWh, increase the transmission interface rating by 500 MW and build a parallel transmission line in the transmission interface if necessary, 4. Perform PLEXOS nodal simulation again and repeat the process until all monitored transmission lines and interfaces have the congestion prices less than $10/MWh. The transmission expansion steps can be illustrated in the following diagram. 51 P age

52 Figure 4 7 Logic flow for the Transmission Expansion Using Congestion Shadow Price Approach The transmission n expansion assumptions for the high-wind renewable scenario are listed in Appendix Transmission Expansion Assumptions s for High-wind Renewable Scenario. The solutions of the transmission expansion indicate that there iss more transfer capacity needed to deliver the renewable generation to the load centers under the High-wind renewable scenario WI Transmission Congestions in the High-wind Renewable Scenario The annual transmission interface congestion hours and average congestion prices for the high-wind renewable scenario are listed in Table Comparison of WI Transmissionn Interface Congestion Hours and Congestion Prices inn Three Cases for the High-wind Renewable Scenario in Year The most congested interfaces include P08 Montana to Northwest, P27 Intermountain Power Project DC Line,, P30 TOTT 1A, P33 Bonanza West, Comparing the congestion prices of the three cases, No PSHs, with FS PSHs and with FS&AS PSHs, the most transmission congestion price reduction happens in Interface P27 Intermountain Power Project DC Line. This interface is in the neighboring area where PSHs Castaic, Lake Hodge, and Eagle Mountain are located. 52 P age

53 Interface Hours Congeste d (hrs) Hours Congeste d Back (hrs) No PSH With FS PSH With FS&AS PSH Hours Hours Shadow Shadow Hours Shadow Shadow Hours Shadow Congeste Congeste Price Price Back Congeste Price Price Back Congeste Price d Back d Back ($/MW) ($/MW) d (hrs) ($/MW) ($/MW) d (hrs) ($/MW) (hrs) (hrs) Shadow Price Back ($/MW) Interstate WA BC East Interstate WA BC West Intrastate AB DC1 8, , , Intrastate AB DC2 8, , , Intrastate Aeolus South Intrastate AZ Palo Verde East Intrastate WA North of Hanford P01 Alberta British Columbia P03 Northwest British Columbia P08 Montana to Northwest 5, , , P09 West of Broadview P14 Idaho to Northwest P15 Midway LosBanos P16 Idaho Sierra P18 Montana Idaho P19 Bridger West P20 Path C P22 Southwest of Four Corners P23 Four Corners 345/500 Qualified Path P24 PG&E Sierra P25 PacifiCorp/PG&E 115 kv Interconnection P26 Northern Southern California P27 Intermountain Power Project 3, , , P age

54 DC Line Interface Hours Congeste d (hrs) Hours Congeste d Back (hrs) No PSH With FS PSH With FS&AS PSH Hours Hours Shadow Shadow Hours Shadow Shadow Hours Shadow Congeste Congeste Price Price Back Congeste Price Price Back Congeste Price d Back d Back ($/MW) ($/MW) d (hrs) ($/MW) ($/MW) d (hrs) ($/MW) (hrs) (hrs) Shadow Price Back ($/MW) P28 Intermountain Mona 345 kv P29 Intermountain Gonder 230 kv P30 TOT 1A 1, , , P31 TOT 2A P32 Pavant Gonder InterMtn Gonder 230 kv P33 Bonanza West 3, , , P35 TOT 2C P36 TOT 3 1, , , P37 TOT 4A P39 TOT P40 TOT P41 Sylmar to SCE P42 IID SCE P45 SDG&E CFE P47 Southern New Mexico (NM1) P48 Northern New Mexico (NM2) P52 Silver Peak Control 55 kv P61 Lugo Victorville 500 kv Line P65 Pacific DC Intertie (PDCI) 0.81 P66 COI 1, , , P73 North of John Day P75 Hemingway Summer Lake P age

55 Interface Hours Congeste d (hrs) Hours Congeste d Back (hrs) No PSH With FS PSH With FS&AS PSH Hours Hours Shadow Shadow Hours Shadow Shadow Hours Shadow Congeste Congeste Price Price Back Congeste Price Price Back Congeste Price d Back d Back ($/MW) ($/MW) d (hrs) ($/MW) ($/MW) d (hrs) ($/MW) (hrs) (hrs) Shadow Price Back ($/MW) P76 Alturas Project , P78 TOT 2B P79 TOT 2B P80 Montana Southeast Grand Total 43,889 4, ,220 4, ,538 5, Table Comparison of WI Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the High wind Renewable Scenario in Year P age

56 4.2 California Simulation Results In this simulation task, the California footprint is simulated. Before simulating the California footprint, the entire WI is simulated to produce the power flows for all transmission lines at the border of California with thee rest of the WI. The WI is simulated for the base renewable scenario and the high-wind renewable scenario. Then the California grid is carved out from the WI grid. The power flow exchanges between California and the rest of the WI is frozen inn the California simulations. The purpose of the Californiaa simulation is to examine the PSHs impact to the deregulated market. Though a few utilities, such as SMUD, LADWP, etc., are not part of CAISO, the entire California footprint is modeled since the grid of these utilities are closely intermingled with the CAISO grid Power Market Bidding Prices A critical factor in the power market simulation is to determine the generatorr bidding prices for the generator energy and ancillary services. The approach of the generator bidding price determination adopted in this study is to benchmark the regional prices from the simulations against the CAISO historical market prices Energy Market Bidding Prices The CAISO annual market report [3] is reviewed. The chart extracted from section 2.2 Overall Market Competitiveness of the report shows that the average energy market prices are close to the cost-based simulations by the CAISO department of market monitoring. The price-cost mark-up is the differencee of the market clearing price and the market marginal cost. The negative price-cost mark-up market marginal indicatess the averagee CAISO market clearing price is lower than the average cost. Figure 4 8 CAISO Energy Price cost mark up ( ) 56 P age

57 Therefore, for the energy market simulation, the generator marginal cost price is used as the energy bidding price Ancillary Service Market Bidding Prices The historical AS market clearing prices in year 2012 are analyzed. The analysis shows that the AS market clearing price is closely correlated with the energy market LMP that, in turn, is closely correlated with the regional load. The statistics and correlation of the CAISO NP15 LMP and AS clearing prices in year 2012 are shown in the following table. The following table shows strong correlations between the ancillary service prices and NP15 LMP. Statistics of CAISO Historical NP15 LMP and AS Clearing Price in Year 2012 AS Clearing Prices NP15 LMP Non Spinning Spinning Regulation Down Regulation Up Mean Max Min (10.45) STDEV STDEV % 29% 410% 118% 83% 94% Table Statistics of CAISO Historical NP15 LMP and AS Clearing Prices in Year 2012 Correlation of CAISO Historical NP15 LMP and AS Clearing Prices in Year 2012 AS Clearing Prices NP15 LMP Non Spinning Spinning Regulation Down Regulation Up NP15 LMP (0.43) 0.24 Non Spinning (0.05) 0.43 Spinning Regulation Down Regulation Up 0.67 Table Correlation of CAISO Historical NP15 LMP and AS Clearing Prices in Year 2012 AS Clearing Prices From the analysis, the following approach is adopted to mimic the generator AS bidding price in the simulations. 1. The hourly upward AS bidding prices follow the hourly California load profiles, and the hourly downward AS bidding prices follows the inverse of the hourly California load profiles; 2. The generators with a higher generation marginal cost will have lower AS bidding prices and the generators with a lower generation marginal cost will have higher AS bidding prices. The reason so doing is that the generators with higher generation marginal cost have lower energy profit margin, and the generators with lower generation marginal cost have higher energy profit margin. 3. The final hourly AS bidding price for a generator is the normalized hourly AS bidding price profiles times the AS bidding price scaling factor. The normalized hourly AS bidding price profiles is the normalized hourly California load profile 57 P age

58 for the upward AS, and the inverse of the normalized hourly California load profile for the downward AS. 4. The generator AS bidding price scaling factor has a higher value for higher quality reserves. 5. Hydro generators and PSHs have fast ramp capability, and are assumed to provide the AS before the thermal generators. The AS bidding price scaling factors, proportional to the generator energy profit margin, by generator type and by AS type are shown in the following table. AS Bidding Price Scaling Factor by Generator Type ($/MW) Generator Type Non Spin Spin Flex Dn Flex Up Reg Dn Reg Up CC Coal CT DR Hydro IC PSH STEAM Table CA AS Bidding Price Scaling Factor by Generator Type The assumptions and settings for the California simulations are reiterated as follows. 1. DA forecasted load/wind/solar: 24 to 48 hours ahead hours SCUC/ED with hourly interval 3. Nodal network representation 4. Contingency, flexibility up/down, regulation up/down reserves modeled 5. Three cases, no PSH, with the existing PSH, with existing and new PSH, are simulated 6. The simulations are performed for the base and high-wind renewable scenarios 7. For the high-wind renewable scenario, the simplified transmission expansion is performed to deliver the renewable generations to the load buses 8. The California simulations are bid-based. Since the exchange powers between California and the rest of WI are frozen in the simulations, the exchanges powers are not included in the following simulation results California System Production Costs The production cost of three cases for year 2022: No PSHs, with the existing FS PSHs, and with the additional AS PSHs, are listed in the following two tables for the base renewable scenario and the high-wind renewable scenario. Base Renewable 58 P age Total Generation Energy PSH Generation Energy Production Cost Annual Cost Reduction GWh GWh million $ million $ % Annual Cost Savings due to PSHs Capacity $/kwyear MW

59 No PSH 265,538 5,078 With FS PSH 267,001 2,725 4, % With FS&AS PSH 269,374 5,313 4, % Table Comparison of CA Production Cost in Three Cases for the Base Renewable Scenario in Year 2022 Total Generation Energy PSH Generation Energy Production Annual Cost Annual Cost Savings High Wind Cost Reduction due to PSHs Renewable Capacity $/kwyear GWh GWh million $ million $ % (MW) No PSH 253,872 4,120 With FS PSH 256,069 5,299 3, % With FS&AS PSH 257,018 9,456 3, % Table Comparison of CA Production Cost in Three Cases for the High Wind Renewable Scenario in Year 2022 With the FS PSHs, the California system production cost is reduced by 2.18% and 4.52% for the base renewable scenario and the high-wind renewable scenario respectively. With the additional AS PSHs, the California system production cost is reduced further by 3.36% and 9.12% for the base renewable scenario and the high-wind renewable scenario respectively. With the renewable generation increases to 33%, the production cost savings due to the PSHs operation increases. The PSHs are more valuable in the high renewable penetration level. The comparisons of the generation by generator type for the base and high-wind renewable scenarios are shown in the following two charts. In the base renewable scenario, both CC and CT generations are reduced as more PSHs are introduced into the system due to the fact that the PSHs generation replaces the CC and CT generation. However the Coal generation is slightly increased to provide the PSHs pumping energy. Also the renewable generation is increased as more PSHs are introduced into the system due to less renewable generation being curtailed. 59 P age

60 Generation by Generator Type (GWh) Yearly for Base Renewable Scenario GWh 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 Base No PSH With FS PSH With FS&AS PSH Figure 4 9 Comparison of CA Generation in Three Cases by Generator Type for the Base Renewable Scenario in Year 2022 In the high-wind renewable scenario, The CC, CT and Coal generations are reduced as more PSHs are introduced into the system due to the fact that the PSHs generation replaces the CC, CT and Coal generation. Also the renewable generation is increased as more PSHs are introduced into the system due to less renewable generation being curtailed. Generation by Generator Type (GWh) Yearly for High wind Renewable Scenario GWh 100,000 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 Base No PSH With FS PSH With FS&AS PSH Figure 4 10 Comparison of CA Generation in Three Cases by Generator type for the High wind Renewable Scenario in Year P age

61 The comparisons of the production cost in California by generator type for the base and high-wind renewable scenarios are shown in the following two charts. In the base renewable scenario, the CC and CT production cost is reduced as more PSHs are introduced into the system. And the Coal production cost is increased slightly as more PSHs introduced into the system. Million $ 3,500 3,000 2,500 2,000 1,500 1, Total Generation Cost by Generator Type ($M) Yearly for Base Renewable Scenario Base No PSH With FS PSH With FS&AS PSH Figure 4 11 Comparison of CA Generation Cost in Three Cases by Generator Type for the Base Renewable Scenario in Year 2022 In the high-wind renewable scenario, the CC, CT and Coal production cost is reduced as more PSHs are introduced into the system. 2,500 2,000 Total Generation Cost by Generator Type ($M) Yearly for High wind Renewable Scenario Million $ 1,500 1, Base No PSH With FS PSH With FS&AS PSH 61 P age

62 Figure 4 12 Comparison of CA Generation Cost in Three Cases by Generator Type for the High wind Renewable Scenario in Year 2022 Due to the PSHs operations, the California renewable curtailments are reduced as shown in the following two tables for the base and high-wind renewable scenarios. CA Renewable Curtailment in the Base Renewable Scenario Renewable Curtailment Reduction Case GWh GWh % No PSH 155 0% With FS PSH % With FS&AS PSH % Table Comparison of CA Renewable Curtailment in the Base Renewable Scenario CA Renewable Curtailment in the High wind Renewable Scenario Renewable Curtailment Reduction Case GWh GWh % No PSH 618 0% With FS PSH % With FS&AS PSH % Table Comparison of CA Renewable Curtailment in the High wind Renewable Scenario California System Reserves and Provision by PSHs The system reserve requirements and provisions from the PSHs are compared in the three cases for the base and high-wind renewable scenarios in the following two tables. Base Renewable 62 P age Base No PSH With FS PSH With FS&AS PSH PSH PSH Provision Total Req. Provision Total Req. (GWh) (GWh) (GWh) (GWh) Total Req. (GWh) PSH Provision (GWh) Non Spinning Reserve 8,505 8,505 7,090 8,505 7,905 Spinning Reserve 8,505 8, ,505 2,463 Flexibility Down 3,130 3, ,130 1,098 Flexibility Up 3,130 3, , Regulation Down 3,810 3, ,810 1,264 Regulation Up 3,839 3, ,839 1,109 Table Comparison of CA Reserve Requirements and Provisions by PSHs in Three Cases for the Base Renewable Scenario in Year 2022 High wind Renewable Base No PSH With FS PSH With FS&AS PSH PSH PSH Provision Total Req. Provision Total Req. (GWh) (GWh) (GWh) (GWh) Total Req. (GWh) PSH Provision (GWh) Non Spinning Reserve 8,505 8,505 4,774 8,505 5,492 Spinning Reserve 8,505 8, ,505 2,022

63 Flexibility Down 4,804 4, ,804 1,934 Flexibility Up 4,804 4, , Regulation Down 4,394 4, ,394 1,761 Regulation Up 4,442 4, ,442 1,201 Table Comparison of CA Reserve Requirements and Provisions by PSHs in Three Cases for the High wind Renewable Scenario in Year 2022 In both the base renewable scenario and the high-wind renewable scenario, the FS and AS PSHs provide around ¼ of the AS requirements for most AS. The reserve provisions from the adjustable speed PSHs increases substantially as opposed to the reserve provisions from the fixed speed PSHs. The reserve provision increase from the adjustable speed PSHs is due to 1. The larger dispatchable capacity in the generating mode. 2. The reserve provision in the pumping mode California System Emission Production The system emission productions in the three cases for the base and high-wind renewable scenarios are listed in the following two tables. Base Renewable CO2 NOx SO2 Emission Reduction (ton) Emission Reduction (%) Ton ton ton CO2 NOx SO2 CO2 NOx SO2 No PSH 65,429,529 53,681 6, % 0.0% 0.0% With FS PSH 64,741,362 53,512 6, , (87) 1.1% 0.3% 1.5% With FS&AS PSH 64,625,964 53,568 6, , (160) 1.2% 0.2% 2.7% Table Comparison of CA Emission Productions in Three Cases in year 2022 for the Base Renewable Scenario High wind Renewable CO2 NOx SO2 Emission Reduction (ton) Emission Reduction (%) Ton ton ton CO2 NOx SO2 CO2 NOx SO2 No PSH 51,515,736 44,936 5, % 0.0% 0.0% With FS PSH 49,692,105 44,010 5,350 1,823, (16) 3.5% 2.1% 0.3% With FS&AS PSH 47,904,187 43,177 5,427 3,611,549 1,759 (93) 7.0% 3.9% 1.7% Table Comparison of CA Emission Productions in Three Cases in Year 2022 for the High Wind Renewable Scenario In both the base renewable scenario and the high-wind renewable scenario, Emission CO2 and NOx are reduced and Emission SO2 is increased. However, the allover emission production is reduced from the base renewable scenario to the high-wind renewable scenario. 63 P age

64 4.2.5 California Thermal Generator Cycling The number of starts and startup cost of the thermal generators in the three cases for the base and high-wind renewable scenarios are listed in the following two tables. Base Renewable Total Number of Thermal Starts Total Thermal Start Cost Cost Reduction million $ million $ % No PSH 18, With FS PSH 14, % With FS&AS PSH 12, % Table Comparison of Number of Starts and startup Costs of the CA Thermal Generators in Year 2022 for the Base Renewable Scenario Total Number of High Wind Renewable Thermal Starts Total Thermal Start Cost Cost Reduction million $ million $ % No PSH 17, With FS PSH 14, % With FS&AS PSH 11, % Table Comparison of Number of Starts and startup Costs of the CA Thermal Generators in Year 2022 for the high wind Renewable Scenario In both the base and high-wind renewable scenarios, the number of starts and startup costs of the thermal generators are reduced substantially as more PSHs are introduced to the system. The comparisons of the ramp up and down of thermal generators in the three cases for the base and high-wind renewable scenarios are listed in the following two tables. Base Renewable Total Thermal Generator Ramp Up Total Thermal Generator Ramp Down Ramp Up Reduction Ramp Down Reduction GW GW GW % GW % No PSH 4,273 6,603 With FS PSH 3,623 5, % 1, % With FS&AS PSH 2,924 4,456 1, % 2, % Table Comparison of Thermal Generator Ramp Up and Down of the CA Thermal Generators in Year 2022 for the Base Renewable Scenario High Wind Renewable Total Thermal Generator Ramp Up Total Thermal Generator Ramp Down Ramp Down Ramp Up Reduction Reduction GW GW GW % GW % 64 P age

65 No PSH 3,609 5,681 With FS PSH 3,078 4, % % With FS&AS PSH 2,396 3,738 1, % 1, % Table Comparison of Thermal Generator Ramp Up and Down of the CA Thermal Generators in Year 2022 for the High Wind Renewable Scenario In both the base and high-wind renewable scenarios, the thermal generator ramp up and down are reduced substantially as more PSHs are introduced into the system California Regional LMPs The comparisons of the average regional LMP for the selected regions for the base renewable scenario is shown in following chart. As more PSHs are introduced into the system, the average regional LMP is increased for all selected regions Average Regional Price Year 2022 for Base Renewable Scenario $/MWh Base No PSH With FS PSH With FS&AS PSH PG&E_VLY SCE SDGE Figure 4 13 Comparison of Regional LMP in Three Cases for the Selected Regions in CA in Year 2022 for the Base Renewable Scenario The comparisons of the average regional LMP for the selected regions for the high-wind renewable scenario is shown in following chart. For all selected regions, the regional price increases as more PSHs introduced into the system. However, overall, the regional LMP in the high-wind renewable scenario is reduced substantially as opposed to the base renewable scenario. 65 P age

66 20.00 Average Regional Price Year 2022 for High wind Renewable Scenario $/MWh Base No PSH With FS PSH With FS&AS PSH PG&E_VLY SCE SDGE Figure 4 14 Comparison of Regional LMP in Three Cases for the Selected Regions in CA in Year 2022 for the Highwind Renewable Scenario By examining the hourly LMP in SCE in the week of July 17, 2022 from the simulation shown in the following diagram, it is observed that PSHs pump in the low LMP hours and drive the LMP up in these pumping hours. There are some price reductions in the high LMP hours due to the generation from PSHs. However the magnitude of the price increase in the low LMP hours is much higher than the magnitude of the price reduction in the high LMP hours. This observation explains the reason that the average regional LMP increases as more PSHs are introduced into the system. $/MWh Figure 4 15 SCE LMP in Week of July 17, 2022, in Three Cases for the High wind Renewable Scenario California Generator Energy and Ancillary Services Revenue The impacts of PSHs to the California generation and Ancillary Service Revenue for the base and high-wind renewable scenarios are shown in the following two tables. 66 P age SCE LMP in Week of July 17, 2022 in High wind Renewable Scenario No PSH With FS PSH With FS&AS PSH

67 The power exchanges between California and the rest of WI are not included in the following tables. The ancillary service revenue may be higher than in the real world due to the introduction of the flexibility up and down reserves. The simulations are performed for the entire California footprint. The revenues and profits include the non-caiso utilities in California footprint. The forecasted generation costs and revenues should be treated an indicator how PSHs can impact a bid-based market. It can be observed from the tables that 1. Overall, the CA system Net Operating Revenue (the energy and AS revenues less the generation cost) increases as more PSHs are introduced to the system in both the base and high-wind renewable scenarios. 2. The energy revenue increases as more PSHs are introduced into the system due to the fact that the LMP increase as more PSHs are introduced into the system as shown in the previous subsection. 3. The AS revenue does not show a pattern as more PSHS are introduced into the system. 4. The energy revenues are reduced in the high-wind renewable scenario as opposed to the base renewable scenario due to the fact that the LMPs are reduced in the high-wind renewable scenario. 5. The reserve revenues are increased in the high-wind renewable scenario as opposed to the base renewable scenario due to the fact the higher flexibility and regulation requirements in the high-wind renewable scenario yield higher AS prices. 6. In the base renewable scenario, the reserve revenue is less than 10% of the total market revenue (energy revenue plus reserve revenue). The reserve revenue increased to 25% of the total market revenue in the high-wind renewable scenario due to the fact of higher flexibility and regulation reserve requirements in the high-wind renewable scenario. 7. It should be pointed out that there are many generators that have negative profit, such as CCs, CTs, Steams, and even Nuclear in the high-wind renewable scenario cases. There are many hours that the over-generations and negative LMPs occur, especially in the high-wind renewable scenario. Also the LMPs do not reflect the generator startup cost and no-load cost. CAISO compensates these generators for the startup and no-load cost [3]. The profits in the following tables do not include this type of compensations. Please note that, in Table California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the Base Renewable Scenario in Year 2022and Table California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the High-wind Renewable Scenario in Year 2022, the pumping cost is not subtracted from the PSH profit. However, the pumping cost is subtracted from the PSH profit in Table to Table P age

68 California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the Base Renewable Scenario No PSH With FS PSH With FS&AS PSH Net Net Total Operatin Total Operatin Total Generati Generati Energy Reserves g Generati Generati Energy Reserves g Generati Generati Energy Reserves on on Cost Revenue Revenue Revenue on on Cost Revenue Revenue Revenue on on Cost Revenue Revenue (GWh) ($000) ($000) ($000) ($000) (GWh) ($000) ($000) ($000) ($000) (GWh) ($000) ($000) ($000) Generator Type Net Operatin g Revenue ($000) CC 76,184 2,889,437 2,398, ,040 (41,541) 74,151 2,785,726 2,384, ,270 60,458 73,279 2,725,054 2,388, ,989 74,969 Coal 15, , ,524 7, ,450 15, , ,118 7, ,289 15, , ,265 8, ,810 CoGen 8, , ,723 (8,822) 8, , , , , ,483 8,528 CT 6, , , ,170 (103,764) 6, , , ,255 (105,026) 5, , , ,484 (100,763) Hydro 38,682 1,048 1,077, ,187 1,179,026 38, ,125,789 98,138 1,222,963 38, ,184,057 53,831 1,236,988 Nuclear 37, ,535 1,015, ,559 37, ,262 1,074, ,836 37, ,702 1,134, ,441 Other 7,398 4, ,984 1, ,792 7,397 4, , ,447 7,378 3, , ,435 RPS 67, ,062 1,729,433 1,471,371 67, ,297 1,862,716 1,596,420 68, ,600 1,999,881 1,722,281 Steam 8, , ,647 49,994 (190,260) 8, , ,879 49,946 (177,389) 8, , ,563 44,300 (169,880) DR 2 1,054 1,054 17,412 17, ,873 13, ,890 1,890 FS PSH 2, ,302 18, ,507 1,551 53,826 14,831 68,657 AS PSH 3, ,728 37, ,802 Total 265,538 5,077,510 7,694, ,849 3,432, ,001 4,966,947 8,364, ,287 4,218, ,374 4,906,701 8,615, ,576 4,445,158 Table California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the Base Renewable Scenario in Year 2022 California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the High wind Renewable Scenario No PSH With FS PSH With FS&AS PSH Total Net Operatin Total Net Operatin Total Net Operatin Generati Generati Energy Reserves g Generati Generati Energy Reserves g Generati Generati Energy Reserves g Generator on on Cost Revenue Revenue Revenue on on Cost Revenue Revenue Revenue on on Cost Revenue Revenue Revenue Type (GWh) ($000) ($000) ($000) ($000) (GWh) ($000) ($000) ($000) ($000) (GWh) ($000) ($000) ($000) ($000) CC 52,802 2,053,833 1,081, ,103 (317,017) 48,666 1,873,071 1,070, ,108 (124,970) 44,339 1,692,346 1,055, ,556 (94,670) Coal 13, , ,827 26, ,594 13, , ,339 25,965 71,332 13, , ,909 26, , P age

69 California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the High wind Renewable Scenario No PSH With FS PSH With FS&AS PSH Total Net Operatin Total Net Operatin Total Net Operatin Generati Generati Energy Reserves g Generati Generati Energy Reserves g Generati Generati Energy Reserves g Generator on on Cost Revenue Revenue Revenue on on Cost Revenue Revenue Revenue on on Cost Revenue Revenue Revenue Type (GWh) ($000) ($000) ($000) ($000) (GWh) ($000) ($000) ($000) ($000) (GWh) ($000) ($000) ($000) ($000) CoGen 6, , ,144 (91,896) 6, , ,289 (82,468) 6, , ,397 (73,567) CT 6, ,018 84, ,700 (134,039) 6, ,018 85, ,850 (136,685) 6, ,730 91, ,313 (123,399) Hydro 37,805 2, , , ,839 37,983 2, , , ,210 38,228 3, ,609 86, ,294 Nuclear 36, , ,944 (69,015) 36, , ,219 (21,187) 36, , ,632 25,879 Other 7,242 4,730 89,704 2,524 87,498 7,257 4,895 99,163 2,117 96,386 7,258 3, ,737 1, ,468 RPS 84, , , ,659 85, , , ,572 86, ,095 1,061, ,865 Steam 8, , ,182 76,998 (294,698) 8, , ,460 77,732 (280,139) 8, , ,780 71,432 (269,759) DR 3 1,655 1,655 27,858 27, ,083 25, ,990 9,990 FS PSH 5, ,285 32, ,407 4,480 98,534 27, ,700 AS PSH 4, ,769 58, ,754 Total 253,872 4,120,437 3,692,120 1,247, , ,069 3,934,218 3,818,185 1,309,572 1,193, ,018 3,744,626 4,258,850 1,088,744 1,602,968 Table California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the High wind Renewable Scenario in Year P age

70 The Net Operating Revenue of the fixed speed and adjustable speed PSHs for the base and high-wind renewable scenarios are listed in the following tables. California PSH Net Operating Revenue for the Base Renewable Scenarios in Year 2022 from the Simulation With FS PSHs Product FS PSHs AS PSHs Grand Total Energy Generation (GWh) 2,725 2,725 Pump Energy (GWh) 3,840 3,840 Generation Cost ($000) Pump Cost ($000) 65,768 65,768 Energy Revenue ($000) 102, ,302 Subtotal Energy Net Profile ($000) 36,533 36,533 Non Spinning Reserve AS provision (GWh) 7,090 7,090 AS Revenue ($000) 7,557 7,557 Spinning Reserve AS provision (GWh) AS Revenue ($000) 1,218 1,218 Flexible Down AS provision (GWh) AS Revenue ($000) Flexible Up AS provision (GWh) AS Revenue ($000) Regulation Down AS provision (GWh) AS Revenue ($000) 4,562 4,562 Regulation Up AS provision (GWh) AS Revenue ($000) 4,436 4,436 Total AS Provision (GWh) 7,709 7,709 Subtotal AS Revenue ($000) 18,205 18,205 Total Profit ($000) 54,739 54,739 Capacity (MW) 2,626 2,626 Annual Profit Rate ($/kw Year) Table California PSH Net Operating Revenue for the Base Renewable Scenarios in Year 2022 from the Simulations with FS PSHs California PSH Net Operating Revenue for the Base Renewable Scenarios in Year 2022 from the Simulation With FS&AS PSHs Product FS PSHs AS PSHs Grand Total Energy 70 P age

71 California PSH Net Operating Revenue for the Base Renewable Scenarios in Year 2022 from the Simulation With FS&AS PSHs Product FS PSHs AS PSHs Grand Total Generation (GWh) 1,551 3,763 5,313 Pump Energy (GWh) 2,180 4,676 6,856 Generation Cost ($000) Pump Cost ($000) 43, , ,508 Energy Revenue ($000) 53, , ,554 Subtotal Energy Net Profile($000) 9,841 7,205 17,046 Non Spinning Reserve AS provision (GWh) 7, ,905 AS Revenue ($000) 8, ,563 Spinning Reserve AS provision (GWh) 126 2,337 2,463 AS Revenue ($000) 769 7,819 8,588 Flexible Down AS provision (GWh) 20 1,078 1,098 AS Revenue ($000) 165 5,564 5,728 Flexible Up AS provision (GWh) AS Revenue ($000) Regulation Down AS provision (GWh) 103 1,161 1,264 AS Revenue ($000) 2,661 17,698 20,360 Regulation Up AS provision (GWh) 104 1,005 1,109 AS Revenue ($000) 2,852 5,083 7,935 Total AS Provision (GWh) 7,841 6,339 14,180 Subtotal AS Revenue ($000) 14,831 37,074 51,905 Total Profit ($000) 24,671 44,279 68,951 Capacity (MW) 2,626 1,799 4,425 Annual Profit Rate ($/kw Year) Table California PSH Net Operating Revenue for the Base Renewable Scenarios in Year 2022 from the Simulations with FS & AS PSHs California PSH Net Operating Revenue for the High Wind Renewable Scenarios in Year 2022 from the Simulation With FS PSHs Product FS PSHs AS PSHs Grand Total Energy Generation (GWh) 5,299 5,299 Pump Energy (GWh) 7,501 7,501 Generation Cost ($000) 71 P age

72 California PSH Net Operating Revenue for the High Wind Renewable Scenarios in Year 2022 from the Simulation With FS PSHs Product FS PSHs AS PSHs Grand Total Pump Cost ($000) (13,229) (13,229) Energy Revenue ($000) 147, ,285 Subtotal Energy Net Profile ($000) 160, ,514 Non Spinning Reserve AS provision (GWh) AS Revenue ($000) 1,626 1,626 Spinning Reserve AS provision (GWh) AS Revenue ($000) Flexible Down AS provision (GWh) 4,774 4,774 AS Revenue ($000) 5,246 5,246 Flexible Up AS provision (GWh) AS Revenue ($000) 19,511 19,511 Regulation Down AS provision (GWh) AS Revenue ($000) 4,144 4,144 Regulation Up AS provision (GWh) AS Revenue ($000) 1,515 1,515 Total AS Provision (GWh) 5,709 5,709 Subtotal AS Revenue ($000) 32,122 32,122 Total Profit ($000) 192, ,636 Capacity (MW) 2,626 2,626 Annual Profit Rate ($/kw Year) Table California PSH Net Operating Revenue for the High Wind Renewable Scenarios in Year 2022 from the Simulation with FS PSHs California PSH Net Operating Revenue for the High Wind Renewable Scenarios in Year 2022 from the Simulation With FS&AS PSHs Product FS PSHs AS PSHs Grand Total Energy Generation (GWh) 4,480 4,976 9,456 Pump Energy (GWh) 6,338 6,183 12,521 Generation Cost ($000) Pump Cost ($000) (6,028) 31,074 25,045 Energy Revenue ($000) 98, , ,302 Subtotal Energy Net Profile ($000) 104,562 87, , P age

73 California PSH Net Operating Revenue for the High Wind Renewable Scenarios in Year 2022 from the Simulation With FS&AS PSHs Product FS PSHs AS PSHs Grand Total Non Spinning Reserve AS provision (GWh) 139 1,795 1,934 AS Revenue ($000) 1,695 13,239 14,934 Spinning Reserve AS provision (GWh) AS Revenue ($000) Flexible Down AS provision (GWh) 5, ,492 AS Revenue ($000) 6, ,184 Flexible Up AS provision (GWh) 272 1,489 1,761 AS Revenue ($000) 13,830 36,055 49,885 Regulation Down AS provision (GWh) 137 1,064 1,201 AS Revenue ($000) 3,868 4,660 8,528 Regulation Up AS provision (GWh) 254 1,768 2,022 AS Revenue ($000) 1,501 4,707 6,208 Total AS Provision (GWh) 6,206 6,405 12,611 Subtotal AS Revenue ($000) 27,166 58,985 86,151 Total Profit ($000) 131, , ,408 Capacity (MW) 2,626 1,799 4,425 Annual Profit Rate ($/kw Year) Table California PSH Net Operating Revenue for the High Wind Renewable Scenarios in Year 2022 from the Simulation with FS&AS PSHs From the above tables the followings can be observed. 1. In the high-wind renewable scenario, the pumping energy cost is priced at the negative. This indicates that PSHs pumping using the curtailed hydro and renewable energy so that the PSHs help the renewable generation integration. 2. The adjustable speed PSHs have much higher reserve revenue due to the fact that AS PSHs can provision reserves in the pumping mode and the generation dispatchable capacity has wider range than the FS PSHs California Transmission Congestions The annual transmission interface congestion hours and average congestion prices for the base renewable scenario are listed in Table Comparison of CA Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year P age

74 The power flows in the interties between California and the rest of WI are modeled as fixed exchanges, and the congestion of those interties are not reported. Comparing the case With FS PSHs and With FS&AS PSHs with the case No PSHs, the average CA transmission congestion price (in Green Columns) are reduced as more PSHs introduced into the system. Interface P27 Intermountain Power Project DC Line has the most congestion price reduction. 74 P age

75 Interfaces Hours Congested (hrs) No PSH With FS PSH With FS&AS PSH Shadow Shadow Shadow Price Hours Hours Shadow Price Hours Hours Shadow Price Back Congested Congested Price Back Congested Congested Price ($/MW) ($/MW) (hrs) Back (hrs) ($/MW) ($/MW) (hrs) Back (hrs) ($/MW) Hours Congested Back (hrs) Shadow Price Back ($/MW) P26 Northern Southern California P27 Intermountain Power Project DC Line 4, , , P42 IID SCE P61 Lugo Victorville 500 kv Line Grand Total 4, , , Table Comparison of CA Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year P age

76 Transmission Expansion for the High-wind Renewable Scenario The transmission in the existing TEPPC 2022 network was not adequate to accommodate the High-wind renewable Scenario, so some transmission expansion assumptions had to be made. The transmission expansion assumptions were added to allow the simulations to deliver the renewable energy at the high renewable penetration level. Without the transmission expansion assumptions, the simulation would not have been able to generate results for the High-wind renewable scenario. Given that this study is not a transmission expansion study, it is important to note that the transmission expansion methodology was simplistic. And the transmission expansion methodology did not include detailed economic or reliability analyses. Nor did it take into account issues such as rights of way, environmental concerns, policy constraints, or any other factor that might normally be considered in detailed transmission planning activities. The same transmission expansion assumptions in the WI simulations for the high-wind renewable scenario are used for the California simulations for the high-wind renewable scenario. The annual transmission interface congestion hours and average congestion prices for the high-wind renewable scenario are listed in the following table. Comparing the case With FS PSHs and With FS&AS PSHs with the case No PSHs, the average CA transmission congestion price (in Green Columns) is reduced as both FS and AS PSHs are introduced into the system. Again, Interface P27 Intermountain Power Project DC Line has the most congestion price reduction. 76 P age

77 Interface Hours Congested (hrs) Hours Congested Back (hrs) No PSH With FS PSH With FS&AS PSH Shadow Price ($/MW) Shadow Price Back ($/MW) Hours Congested (hrs) Hours Congested Back (hrs) Shadow Price ($/MW) Shadow Price Back ($/MW) Hours Congested (hrs) Hours Congested Back (hrs) Shadow Price ($/MW) Shadow Price Back ($/MW) P15 Midway LosBanos P26 Northern Southern California P27 Intermountain Power Project DC Line 3, , , P41 Sylmar to SCE P42 IID SCE P44 South of San Onofre P61 Lugo Victorville 500 kv Line Grand Total 4, , , Table Comparison of CA Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the High wind Renewable Scenario in Year P age

78 4.3 SMUD Simulation Results In this simulation task, the SUMD footprint is simulated. Before simulating the SMUD footprint, the entire WI is simulated to produce the power flows in all transmission lines at the border of SMUD and the rest of the WI grid. The WI is simulated for the base renewable scenario and the high-wind renewable scenario. Then the SMUD grid is carved out from the WI grid. The power exchanges between SMUD and the rest of the WI are frozen for the SMUD simulations. Since there is no serious transmission congestion in the SMUD footprint, the SMUD grid is modeled at a regional level, i.e., the SMUD grid is represented by a single node. The purpose of the SMUD simulation is to examine the PSHs impact to the utility portfolio. The assumptions and settings for the SMUD simulations are reiterated as follows. 1. DA forecasted load / wind / solar: 24 to 48 hours ahead hours SCUC / ED with hourly interval 3. Regional network representation 4. Contingency, Flexibility up / down, Regulation up / down reserves modeled 5. Since there is no existing PSHs in the SMUD footprint, two cases, no PSH and with new Adjustable Speed PSH, namely Iowa Hill, are simulated 6. The simulations are performed for the base and high-wind renewable scenarios 7. The SMUD simulations are cost-based. Since the exchange powers between SMUD and the rest of WI are frozen in the simulation, the exchanges powers are not included in the following simulation results SMUD System Production Costs The production cost of two cases for year 2022: No PSHs and with the new AS PSHs, are listed in the following tables for both the base renewable scenario and the high-wind renewable scenario. 78 P age Total Generation Energy PSH Generation Energy Total Generation Energy PSH Generation Energy Production Annual Cost Annual Cost Savings Base Cost Reduction due to PSHs Renewable Capacity $/kwyear GWh GWh million $ million $ % MW No PSH 16, With AS PSH 16, % Table Comparison of SMUD Production Cost in Two Cases for the Base Renewable Scenario in Year 2022 Production Annual Cost Annual Cost Savings High Wind Cost Reduction due to PSHs Renewable Capacity $/kwyear GWh GWh million $ million $ % (MW) No PSH 20, With AS PSH 19, % Table Comparison of SMUD Production Cost in Two Cases for the High Wind Renewable Scenario in Year 2022

79 The SMUD production cost is reduced by 8.62% and 16.45% for the base renewable scenario and the high-wind renewable scenario respectively. With the renewable generation increases to 33%, the production cost savings due to the PSHs operation increases. The PSHs is more valuable in the high renewable penetration level. The comparisons of the generation by generator type for the base and high-wind renewable scenarios are shown in the following two charts. In the base renewable scenario, The CC and CT generation is reduced as the new AS PSHs are introduced into the system due to the fact that the PSH generation replaces the CC and CT generation. Also the renewable generation is increased as the new AS PSHs are introduced into the system due to less renewable generation being curtailed. 8,000 7,000 6,000 5,000 Generation by Generator Type (GWh) Yearly for Base Renewable Scenario GWh 4,000 3,000 2,000 1,000 No PSH With AS PSH Figure 4 16 Comparison of SMUD Generation of Two Cases by Generator Type for the Base Renewable Scenario in Year 2022 In the high-wind renewable scenario, The CC and CT generation is reduced as the new AS PSHs are introduced into the system due to the fact that the PSH generation replaces the CC and CT generation. Also the renewable and hydro generation is increased as the new AS PSHs are introduced into the system due to less renewable and hydro generation being curtailed. 79 P age

80 8,000 Generation by Generator Type (GWh) Yearly for High wind Renewable Scenario GWh 7,000 6,000 5,000 4,000 3,000 2,000 1,000 No PSH With AS PSH CC CT Hydro Other RPS CoGen Pumped Storage Wind Solar Figure 4 17 Comparison of SMUD Generation of Two Cases by Generator Type for the High wind Renewable Scenario in Year 2022 The comparisons of the production cost in SMUD by generator type for the base and high-wind renewable scenarios are shown in the following two charts. In both the base and high-wind renewable scenarios, all thermal generator production cost is reduced as the new AS PSHs are introduced into the system. 250 Total Generation Cost by Generator Type ($M) Yearly for Base Renewable Scenario 200 Million $ No PSH With AS PSH Figure 4 18 Comparison of SMUD Generation Cost of Two Cases by Generator Type for the Base Renewable Scenario in Year P age

81 300 Total Generation Cost by Generator Type ($M) Yearly for High wind Renewable Scenario Million $ No PSH With AS PSH CC CT Hydro Other RPS CoGen Pumped Storage Wind Solar Figure 4 19 Comparison of SMUD Generation Cost of Two Cases by Generator Type for the High wind Renewable Scenario in Year 2022 There is no renewable curtailment in the base renewable scenario in the SMUD system. In the high-wind renewable scenario, the SMUD renewable curtailment is reduced from 19GWh in the case of PSHs to 1 GWh in the case with Iowa Hill as shown in the following table. SMUD Renewable Curtailment in the High wind Renewable Scenario Renewable Curtailment Reduction Case GWh GWh % No PSH 19 0% With Iowa Hill % Table Comparison of SMUD Renewable Curtailment in the High wind Renewable Scenario SMUD System Reserves The system reserve requirements and provisions from the PSH are compared in the two cases for the base and the high-wind renewable scenarios in the following two tables. Base No PSH With AS PSH Base Renewable Total Req. (GWh) PSH Provision (GWh) Total Req. (GWh) PSH Provision (GWh) Non Spinning Reserve Spinning Reserve Flexibility Down Flexibility Up Regulation Down Regulation Up Table Comparison of SMUD Reserve Requirements and Provisions by PSH in Two Cases for the Base Renewable Scenario in Year 2022 High wind Renewable Base No PSH With AS PSH 81 P age

82 Total Req. (GWh) PSH Provision (GWh) Total Req. (GWh) PSH Provision (GWh) Non Spinning Reserve Spinning Reserve Flexibility Down Flexibility Up Regulation Down Regulation Up Table Comparison of SMUD Reserve Requirements and Provisions by PSH in Two Cases for the High wind Renewable Scenario in Year SMUD System Emission Production The system emission productions in the two cases for the base and high-wind renewable scenarios are listed in the following two tables. Base Renewable Emission Reduction (%) CO2 NOx SO2 Emission Reduction (ton) Ton ton ton CO2 NOx SO2 CO2 NOx SO2 No PSH 2,856,489 1,880 3 With AS PSH 2,683,737 1, , % 5.5% 69.3% Table Comparison of SMUD Emission Productions in Two Cases in Year 2022 for the Base Renewable Scenario High wind Renewable CO2 NOx SO2 Emission Reduction (ton) Emission Reduction (%) Ton ton ton CO2 NOx SO2 CO2 NOx SO2 No PSH 3,299,928 2,168 3 With AS PSH 2,814,536 1, , % 13.7% 83.2% Table Comparison of SMUD Emission Productions in Two Cases in Year 2022 for the High Wind Renewable Scenario In both the base renewable scenario and the high-wind renewable scenario, all emission productions are reduced as the PSHs are introduced into the SMUD system SMUD Thermal Generator Cycling The number of starts and startup cost of the thermal generators in the two cases for the base and high-wind renewable scenarios are listed in the following two tables. Base Renewable Total Number of Thermal Starts Total Thermal Start Cost Cost Reduction million $ million $ % No PSH 1,812 5 With AS PSH % Table Comparison of Number of Starts and Startup Costs of the SMUD Thermal Generators in Year 2022 for the Base Renewable Scenario High Wind Renewable Total Number of Total Thermal Cost Reduction 82 P age

83 Thermal Starts Start Cost million $ million $ % No PSH 2,159 5 With AS PSH % Table Comparison of Number of Starts and Startup Costs of the SMUD Thermal Generators in Year 2022 for the High wind Renewable Scenario In both the base and high-wind renewable scenarios, the number of starts and startup costs of the thermal generators are reduced substantially as the PSHs are introduced into the SMUD system. The comparisons of the thermal generator ramp up and down in the two cases for the base and high-wind renewable scenarios are listed in the following two tables. Base Renewable Total Thermal Generator Ramp Up Total Thermal Generator Ramp Down Ramp Up Reduction Ramp Down Reduction GW GW GW % GW % No PSH With AS PSH % % Table Comparison of Thermal Generator Ramp Up and Down of the SMUD Thermal Generators in Year 2022 for the Base Renewable Scenario High Wind Renewable Total Thermal Generator Ramp Up Total Thermal Generator Ramp Down Ramp Up Reduction Ramp Down Reduction GW GW GW % GW % No PSH With AS PSH % % Table Comparison of Thermal Generator Ramp Up and Down of the SMUD Thermal Generators in Year 2022 for the High wind Renewable Scenario In both the base and high-wind renewable scenarios, the thermal generator ramp up and down are reduced substantially as the PSHs are introduced into the SMUD system SMUD Regional LMPs The comparison of the average SMUD LMP in the two cases for the base renewable scenario is shown in following chart. As the AS PSHs are introduced into the SMUD system, the average SMUD LMP is reduced. 83 P age

84 Figure 4 20 Comparison of SMUD Regional LMP in Two Cases in Yearr 2022 for the Base Renewable Scenario The comparisons s of the average SMUD LMP in the two cases for the high-wind renewable scenario is shown in following chart. Thee average SMUD LMP increases as the AS PSHs are introduced into the system. Figure 4 21 Comparison of SMUD Regional LMP in Two Cases in Yearr 2022 for the High wind Renewable Scenario SMUD Transmission Congestions Since the SMUD is modeled at the regional level, no transmission interfaces and lines are modeled. 84 P age