Ad Pu nerator (PSH) EXOS...10 Portfolio effects PSH revenue 11 Reduced cycling of thermal units Societal Benefit 12 Reduced transmission congestion Societal Benefit 13 Reduced environmental

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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)

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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

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

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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 hydro-generators (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 2 324 Fixed‐speed

Castaic LDWP CALIF_SOUTH LDWP 6 1175 Fixed‐speed

Eastwood SCE CALIF_SOUTH SCE 1 199 Fixed‐speed

Elbert WACM RMPP Colorado 2 200 Fixed‐speed

Helms PG&E_VLY CALIF_NORTH PG&E Valley 3 1212 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 4 1400 Adjustable‐speed

Iowa Hill SMUD CALIF_NORTH SMUD 3 399 Adjustable‐speed

Swan Lake BPA NWPP NWPP 4 1380 Adjustable‐speed

Grand Total 31 6475

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 PSH Contribution

PLEXOS 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

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PSH Value Stream Matrix and

Item PSH Contribution

PLEXOS 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.

EnergyarbitragevaluesThe 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 adjustable-speed 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 high-wind 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

1. 1.6% from the WI RT simulations with the three proposed adjustable-speed PSHs, Swan Lake, Iowa Hill and Eagle Mountain;

2. 2.4% from the CA RT simulations with the two proposed adjustable-speed PSHs, Iowa Hill and Eagle Mountain;

3. 14.9% from the BANC RT simulations with the proposed adjustable-speed PSHs, Iowa Hill.

Contributionstoreserves:contingency,flexibilityandregulationreserves

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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 high-wind 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 adjustable-speed 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 Renewable

High‐wind Renewable

Base Renewable


Base 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%

ContributiontotherenewablegenerationintegrationThe 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

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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;

ContributiontothethermalgenerationcyclingreductionsThe 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

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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 sub-hourly load and renewable generation variability and uncertainties.

ImpacttothemarketgeneratorparticipantsThe 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.

ContributionstotheportfolioWith 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.

ImpacttothetransmissioncongestionsIn 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”.

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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.

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Table of Contents 1 Introduction ........................................................................................................................... 20 2 WI Database and Assumption Revisions .............................................................................. 21

2.1 Introduction of Western Interconnection Database ............................................ 21 2.2 Data readiness for the simulations ..................................................................... 23

2.2.1 Regional load representation ....................................................................... 23 2.2.2 Renewable Generation Profile Representations .......................................... 24 2.2.3 Contingency, Flexibility and Regulation Reserve Representations ............ 25

2.3 Adjustable Speed PSH Representation .............................................................. 27 2.4 Data Assumption Revisions ............................................................................... 29

3 Modeling Approaches ........................................................................................................... 33 3.1 PLEXOS SCUC/ED algorithm .......................................................................... 33 3.2 3-Stage DA-HA-RT Sequential Simulations ..................................................... 36 3.3 PSH Storage Modeling in 3-stage Sequential Simulations ................................ 37 3.4 Scope of Simulations .......................................................................................... 38

4 Simulation Results ................................................................................................................ 40 4.1 WI Simulation Results ....................................................................................... 40

4.1.1 WI System Production Costs ...................................................................... 40 4.1.2 WI System Reserve Provisions by PSHs .................................................... 43 4.1.3 WI System Emission Production ................................................................ 44 4.1.4 WI Thermal Generator Cycling .................................................................. 45 4.1.5 WI Regional LMPs ..................................................................................... 46 4.1.6 WI Transmission Congestions .................................................................... 47

4.2 California Simulation Results ............................................................................ 56 4.2.1 Power Market Bidding Prices ..................................................................... 56 4.2.2 California System Production Costs ........................................................... 58 4.2.3 California System Reserves and Provision by PSHs .................................. 62 4.2.4 California System Emission Production ..................................................... 63 4.2.5 California Thermal Generator Cycling ....................................................... 64 4.2.6 California Regional LMPs .......................................................................... 65 4.2.7 California Generator Energy and Ancillary Services Revenue .................. 66 4.2.8 California Transmission Congestions ......................................................... 73

4.3 SMUD Simulation Results ................................................................................. 78 4.3.1 SMUD System Production Costs ................................................................ 78 4.3.2 SMUD System Reserves ............................................................................. 81 4.3.3 SMUD System Emission Production .......................................................... 82 4.3.4 SMUD Thermal Generator Cycling ............................................................ 82 4.3.5 SMUD Regional LMPs ............................................................................... 83 4.3.6 SMUD Transmission Congestions .............................................................. 84

5 Three-Stage DA-HA-RT Sequential Simulations ................................................................. 85 5.1 Intermittent Renewable Generation Variability and Uncertainty ...................... 85 5.2 3-stage DA-HA-RT Simulation Results for California ...................................... 89

5.2.1 CA 3-stage Simulation Results for Four Typical Weeks in Year 2022 ...... 90 5.3 3-stage DA-HA-RT Simulation Results for WI ............................................... 102

5.3.1 WI 3-stage Simulation Results for Four Typical Weeks in Year 2022 .... 102

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5.4 3-stage DA-HA-RT Simulation Results for SMUD ........................................ 110 5.4.1 SMUD 3-stage Simulation Results for Four Typical Weeks in Year 2022 110

6 Findings ............................................................................................................................... 118 6.1 Energy arbitrage values .................................................................................... 118 6.2 Contributions to reserves: contingency, flexibility and regulation reserves. ... 119 6.3 Contributions to the emission reductions ......................................................... 119 6.4 Contribution to the renewable generation integration ...................................... 120 6.5 Contributions to reserves: contingency, flexibility and regulation reserves .... 120 6.6 Contribution to the thermal generation cycling reductions .............................. 120 6.7 Impact to the market generator participants ..................................................... 122 6.8 Contributions to the portfolio ........................................................................... 122 6.9 Impact to the transmission congestions ............................................................ 122 6.10 Transmission Deferral ...................................................................................... 123

7 Appendix – Transmission Expansion Assumptions for High-wind Renewable Scenario....................................................................................................................................... 124 8 References ........................................................................................................................... 127

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List of Figures Figure 2-1 Diagram of the WI Load Regions ................................................................................. 21 Figure 2-2 The Average Heat Rates for Coal, CC, CT and Gas Steam Generators [4]. .............. 32 Figure 3-1 PLEXOS Security Constrained Unit Commitment and Economic Dispatch Algorithm 33 Figure 3-2 DA-HA-RT 3-stage Sequential Simulations ................................................................. 36 Figure 4-1 Comparison of WI Generation in Three Cases by Generator Type for the Base Renewable Scenario in Year 2022 ................................................................................................ 41 Figure 4-2 Comparison of WI Generation in Three Cases by Generator Type for the High-wind Renewable Scenario in Year 2022 ................................................................................................ 42 Figure 4-3 Comparison of WI Generation Cost in Three Cases by Generator Type for the Base Renewable Scenario in Year 2022 ................................................................................................ 42 Figure 4-4 Comparison of WI Generation Cost in Three Cases by Generator Type for the High-wind Renewable Scenario in Year 2022 ....................................................................................... 43 Figure 4-5 Comparison of Regional LMP in Three Cases for the Selected Regions in Year 2022 for the Base Renewable Scenario ................................................................................................. 47 Figure 4-6 Comparison of Regional LMP in Three Cases for the Selected Regions in Year 2022 for the High-wind Renewable Scenario ......................................................................................... 47 Figure 4-7 Logic flow for the Transmission Expansion Using Congestion Shadow Price Approach ....................................................................................................................................................... 52 Figure 4-8 CAISO Energy Price-cost mark-up (2009-2012) ......................................................... 56 Figure 4-9 Comparison of CA Generation in Three Cases by Generator Type for the Base Renewable Scenario in Year 2022 ................................................................................................ 60 Figure 4-10 Comparison of CA Generation in Three Cases by Generator type for the High-wind Renewable Scenario in Year 2022 ................................................................................................ 60 Figure 4-11 Comparison of CA Generation Cost in Three Cases by Generator Type for the Base Renewable Scenario in Year 2022 ................................................................................................ 61 Figure 4-12 Comparison of CA Generation Cost in Three Cases by Generator Type for the High-wind Renewable Scenario in Year 2022 ....................................................................................... 62 Figure 4-13 Comparison of Regional LMP in Three Cases for the Selected Regions in CA in Year 2022 for the Base Renewable Scenario ........................................................................................ 65 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 ................................................................................ 66 Figure 4-15 SCE LMP in Week of July 17, 2022, in Three Cases for the High-wind Renewable Scenario ......................................................................................................................................... 66 Figure 4-16 Comparison of SMUD Generation of Two Cases by Generator Type for the Base Renewable Scenario in Year 2022 ................................................................................................ 79 Figure 4-17 Comparison of SMUD Generation of Two Cases by Generator Type for the High-wind Renewable Scenario in Year 2022 ................................................................................................ 80 Figure 4-18 Comparison of SMUD Generation Cost of Two Cases by Generator Type for the Base Renewable Scenario in Year 2022 ....................................................................................... 80 Figure 4-19 Comparison of SMUD Generation Cost of Two Cases by Generator Type for the High-wind Renewable Scenario in Year 2022 ............................................................................... 81 Figure 4-20 Comparison of SMUD Regional LMP in Two Cases in Year 2022 for the Base Renewable Scenario ..................................................................................................................... 84 Figure 4-21 Comparison of SMUD Regional LMP in Two Cases in Year 2022 for the High-wind Renewable Scenario ..................................................................................................................... 84 Figure 5-1 5-minute Actual Solar Generation and Hourly DA / HA Forecasts in Southern California in a Typical Winter Week of Year 2022 ......................................................................................... 85

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Figure 5-2 5-minute Actual Wind Generation and Hourly DA / HA Forecasts in Southern California in a Typical Winter Week of year 2022 .......................................................................................... 86 Figure 5-3 5-minute Actual Solar Generation and Hourly DA / HA Forecasts in Southern California in a Typical Summer Week of year 2022 ...................................................................................... 87 Figure 5-4 5-minute Actual Wind Generation and Hourly DA / HA Forecasts in Southern California in a Typical Summer Week of Year 2022 ...................................................................................... 87 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 2022. ........................................................................... 88 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 2022. ........................................................................... 89 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) ................................................................................................................................... 91 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) ................................................................................................................................... 93 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) ..................................................................................................... 95 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) ..................................................................................................... 96 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) ..................................................................................................... 98 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) ................................................................................................................... 99 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) ...................................................................................... 100 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) ...................................................................................... 101 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) ................................................................................................................. 104 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) ........................................................................................................................... 106 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) ................................................................................................... 108 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) ................................................................................................... 109 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) ................................................................................................................. 112

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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) ........................................................................................................................... 114 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) ................................................................................................... 116 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) ...................................................................................... 117

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List of Tables Table 2.1-1 Renewable Generation Assumptions by BA in WI and the USA part of WI in year 2022 ............................................................................................................................................... 23 Table 2.2-1 Comparison of the annual peaks of the load regions in years 2020 and 2022 .......... 24 Table 2.2-2 Number of renewable generators modeled in the base and high-wind renewable sceneries ....................................................................................................................................... 25 Table 2.2-3 Mapping of the load regions and the contingency reserve sharing groups ............... 26 Table 2.2-4 Mapping of the load regions and the regulation / flexibility reserve sharing groups .. 27 Table 2.3-1 Characteristics of three proposed adjustable speed PSHs ........................................ 28 Table 2.3-2 Locations and Installed Capacity of the Existing FS PHS and Proposed AS PSHs in WI .................................................................................................................................................. 29 Table 2.4-1 Assumptions revisions in the database ...................................................................... 30 Table 2.4-2 Generator Characteristic Revisions and Eligibility for the Reserve Provisions .......... 31 Table 3.4-1 Simulation Scenario Combinations ............................................................................ 38 Table 3.4-2 Three Focused Simulation Areas: WI, California and SMUD .................................... 39 Table 4.1-1 Comparison of WI Production Cost in Three Cases for the Base Renewable Scenario in Year 2022 .................................................................................................................................. 40 Table 4.1-2 Comparison of WI Production Cost in Three Cases for the High-Wind Renewable Scenario in Year 2022 ................................................................................................................... 40 Table 4.1-3 Comparison of WI Renewable Curtailment in the Base Renewable Scenario .......... 43 Table 4.1-4 Comparison of WI Renewable Curtailment in the High-wind Renewable Scenario .. 43 Table 4.1-5 Comparison of WI Reserve Requirements and Provisions by PSHs in Three Cases for the Base Renewable Scenario in Year 2022 ........................................................................... 44 Table 4.1-6 Comparison of WI Reserve Requirements and Provisions by PSHs in Three Cases for the High-wind Renewable Scenario in Year 2022.................................................................... 44 Table 4.1-7 Comparison of WI Emission Productions in Three Cases in Year 2022 for the Base Renewable Scenario ..................................................................................................................... 44 Table 4.1-8 Comparison of WI Emission Productions in Three Cases in Year 2022 for the High-Wind Renewable Scenario ............................................................................................................ 45 Table 4.1-9 Comparison of Number of Starts and Startup Costs of the WI Thermal Generators in Year 2022 for the Base Renewable Scenario ............................................................................... 45 Table 4.1-10 Comparison of Number of Starts and Startup Costs of the WI Thermal Generators in Year 2022 for the High-wind Renewable Scenario ....................................................................... 45 Table 4.1-11 Comparison of Thermal Generator Ramp Up and Down of the WI Thermal Generators in Year 2022 for the Base Renewable Scenario ........................................................ 46 Table 4.1-12 Comparison of Thermal Generator Ramp Up and Down of the WI Thermal Generators in Year 2022 for the High-Wind Renewable Scenario ................................................ 46 Table 4.1-13 Comparison of WI Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year 2022 .................................................. 50 Table 4.1-14 Comparison of WI Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the High-wind Renewable Scenario in Year 2022 .......................................... 55 Table 4.2-1 Statistics of CAISO Historical NP15 LMP and AS Clearing Prices in Year 2012 ...... 57 Table 4.2-2 Correlation of CAISO Historical NP15 LMP and AS Clearing Prices in Year 2012 ... 57 Table 4.2-3 CA AS Bidding Price Scaling Factor by Generator Type ........................................... 58 Table 4.2-4 Comparison of CA Production Cost in Three Cases for the Base Renewable Scenario in Year 2022 .................................................................................................................................. 59 Table 4.2-5 Comparison of CA Production Cost in Three Cases for the High-Wind Renewable Scenario in Year 2022 ................................................................................................................... 59

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Table 4.2-6 Comparison of CA Renewable Curtailment in the Base Renewable Scenario .......... 62 Table 4.2-7 Comparison of CA Renewable Curtailment in the High-wind Renewable Scenario .. 62 Table 4.2-8 Comparison of CA Reserve Requirements and Provisions by PSHs in Three Cases for the Base Renewable Scenario in Year 2022 ........................................................................... 62 Table 4.2-9 Comparison of CA Reserve Requirements and Provisions by PSHs in Three Cases for the High-wind Renewable Scenario in Year 2022.................................................................... 63 Table 4.2-10 Comparison of CA Emission Productions in Three Cases in year 2022 for the Base Renewable Scenario ..................................................................................................................... 63 Table 4.2-11 Comparison of CA Emission Productions in Three Cases in Year 2022 for the High-Wind Renewable Scenario ............................................................................................................ 63 Table 4.2-12 Comparison of Number of Starts and startup Costs of the CA Thermal Generators in Year 2022 for the Base Renewable Scenario ............................................................................... 64 Table 4.2-13 Comparison of Number of Starts and startup Costs of the CA Thermal Generators in Year 2022 for the high-wind Renewable Scenario ........................................................................ 64 Table 4.2-14 Comparison of Thermal Generator Ramp Up and Down of the CA Thermal Generators in Year 2022 for the Base Renewable Scenario ........................................................ 64 Table 4.2-15 Comparison of Thermal Generator Ramp Up and Down of the CA Thermal Generators in Year 2022 for the High-Wind Renewable Scenario ................................................ 65 Table 4.2-16 California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the Base Renewable Scenario in Year 2022 ............................................... 68 Table 4.2-17 California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the High-wind Renewable Scenario in Year 2022 ....................................... 69 Table 4.2-18 California PSH Net Operating Revenue for the Base Renewable Scenarios in Year 2022 from the Simulations with FS PSHs ..................................................................................... 70 Table 4.2-19 California PSH Net Operating Revenue for the Base Renewable Scenarios in Year 2022 from the Simulations with FS & AS PSHs ............................................................................ 71 Table 4.2-20 California PSH Net Operating Revenue for the High-Wind Renewable Scenarios in Year 2022 from the Simulation with FS PSHs ............................................................................... 72 Table 4.2-21 California PSH Net Operating Revenue for the High-Wind Renewable Scenarios in Year 2022 from the Simulation with FS&AS PSHs ....................................................................... 73 Table 4.2-22 Comparison of CA Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year 2022 .................................................. 75 Table 4.2-23 Comparison of CA Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the High-wind Renewable Scenario in Year 2022 .......................................... 77 Table 4.3-1 Comparison of SMUD Production Cost in Two Cases for the Base Renewable Scenario in Year 2022 ................................................................................................................... 78 Table 4.3-2 Comparison of SMUD Production Cost in Two Cases for the High-Wind Renewable Scenario in Year 2022 ................................................................................................................... 78 Table 4.3-3 Comparison of SMUD Renewable Curtailment in the High-wind Renewable Scenario ....................................................................................................................................................... 81 Table 4.3-4 Comparison of SMUD Reserve Requirements and Provisions by PSH in Two Cases for the Base Renewable Scenario in Year 2022 ........................................................................... 81 Table 4.3-5 Comparison of SMUD Reserve Requirements and Provisions by PSH in Two Cases for the High-wind Renewable Scenario in Year 2022.................................................................... 82 Table 4.3-6 Comparison of SMUD Emission Productions in Two Cases in Year 2022 for the Base Renewable Scenario ..................................................................................................................... 82 Table 4.3-7 Comparison of SMUD Emission Productions in Two Cases in Year 2022 for the High-Wind Renewable Scenario ............................................................................................................ 82

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Table 4.3-8 Comparison of Number of Starts and Startup Costs of the SMUD Thermal Generators in Year 2022 for the Base Renewable Scenario ........................................................................... 82 Table 4.3-9 Comparison of Number of Starts and Startup Costs of the SMUD Thermal Generators in Year 2022 for the High-wind Renewable Scenario .................................................................... 83 Table 4.3-10 Comparison of Thermal Generator Ramp Up and Down of the SMUD Thermal Generators in Year 2022 for the Base Renewable Scenario ........................................................ 83 Table 4.3-11 Comparison of Thermal Generator Ramp Up and Down of the SMUD Thermal Generators in Year 2022 for the High-wind Renewable Scenario ................................................ 83 Table 5.1-1 Max and Min Wind and Solar Forecast Errors in Southern California in a Typical Winter Week of year 2022 ............................................................................................................. 86 Table 5.1-2 Max and Min Wind and Solar Forecast Error in Southern California in a Typical Summer Week of Year 2022 ......................................................................................................... 88 Table 6.2-1 Reserve Provisions from Adjustable Speed PSH in % of Total Reserve Requirements ..................................................................................................................................................... 119 Table 6.9-1 Transmission line expansion for high-wind renewable scenario .............................. 126 Table 6.9-2 Transmission interface expansion for high-wind renewable scenario ..................... 126

20 | P a g e

1 Introduction

The work to be performed under this project is in response to the Funding Opportunity Announcement DE-FOA-0000486, which was issued by U.S. Department of Energy (DOE) on April 5, 2011. Argonne National Laboratory (Argonne) has teamed up with several project partners and submitted a proposal on June 6, 2011. 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.

21 | P

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22 | P a g e

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

BA Sum of Net Load (GWh)

High‐wind Renewable Scenario Base Renewable Scenario

Wind and Solar Energy (GWh)

Ratio of Renewable Energy and

Load



Load

AESO 114,066 ‐ 0.0% ‐ 0.0%

APS 43,062 11,582 26.9% 5,355 12.4%

AVA 14,237 6,007 42.2% 5,566 39.1%

BANC 16,442 6,512 39.6% 536 3.3%

BCTC 66,095 ‐ 0.0% ‐ 0.0%

BPA 60,804 18,153 29.9% 9,848 16.2%

CAISO 222,675 45,771 20.6% 30,482 13.7%

CFE 19,021 709 3.7% 686 3.6%

CHPD 4,077 ‐ 0.0% ‐ 0.0%

DOPD 2,047 ‐ 0.0% ‐ 0.0%

EPE 11,161 583 5.2% 150 1.3%

GCPD 4,924 1,035 21.0% ‐ 0.0%

IID 4,541 4,835 106.5% 3,772 83.1%

IPC 21,031 2,528 12.0% 1,160 5.5%

LDWP 37,118 6,629 17.9% 5,461 14.7%

NEVP 28,523 7,118 25.0% 1,903 6.7%

NWMT 11,175 19,994 178.9% 2,338 20.9%

23 | P a g e

Renewable Generation Assumptions by BA in WI and the USA part of WI in Year 2022

BA Sum of Net Load (GWh)

High‐wind Renewable Scenario Base Renewable Scenario



Load



Load

PACE 56,175 24,830 44.2% 6,288 11.2%

PACW 21,128 9,607 45.5% 8,643 40.9%

PGN 23,163 55 0.2% ‐ 0.0%

PNM 16,695 18,066 108.2% 2,149 12.9%

PSC 39,347 11,330 28.8% 6,036 15.3%

PSE 26,308 2,813 10.7% 704 2.7%

SCL 10,926 118 1.1% ‐ 0.0%

SPP 12,927 8,575 66.3% 921 7.1%

SRP 34,546 7,795 22.6% 2,413 7.0%

TEP 15,087 3,244 21.5% 696 4.6%

TIDC 2,718 14 0.5% ‐ 0.0%

TPWR 5,605 28 0.5% ‐ 0.0%

WACM 31,332 45,541 145.3% 8,321 26.6%

WALC 7,664 9,696 126.5% 5,890 76.9%

WAUW 837 1,386 165.6% 361 43.1%

WI 985,457 274,551 27.9% 109,679 11.1%

WI‐USA 786,275 273,842 34.8% 108,993 13.9% Table 2.1‐1 Renewable Generation Assumptions by BA in WI and the USA part of WI in year 2022

2.2 Data readiness for the simulations

2.2.1 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 2020. 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 15867 15,049 1.05

APS 9787 8,407 1.16

AVA 2720 2882 0.94

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BCTC 11996 11393 1.05

BPA 10463 10377 1.01

CFE 3461 3250 1.06

CHPD 722 719 1.00

DOPD 424 458 0.93

EPEC 2244 2135 1.05

FAR EAST 725 555 1.31

GCPD 858 865 0.99

IID 1201 1242 0.97

LADWP 8200 6778 1.21

MAGIC 1382 1170 1.18

NEVP 6734 6583 1.02

NWMT 1833 1866 0.98

PACE_ID 862 834 1.03

PACE_UT 8487 8180 1.04

PACE_WY 1858 1871 0.99

PACW 4266 3904 1.09

PG&E_BAY 8940 9309 0.96

PG&E_VLY 12126 14593 0.83

PGN 4220 4294 0.98

PNM 2976 2852 1.04

PSCO 7954 9320 0.85

PSE 5322 5355 0.99

SCE 22311 26232 0.85

SCL 1909 1924 0.99

SDGE 4817 5033 0.96

SMUD 4303 4886 0.88

SPPC 2158 2137 1.01

SRP 7521 8800 0.85

TEP 3128 3660 0.85

TID 674 787 0.86

TPWR 1040 1031 1.01

TREAS 2777 2504 1.11

WACM 4724 4651 1.02

WALC 1600 1591 1.01

WAUM 153 118 1.30

Sum of Non‐coincident Peak 192,743 197,595 0.98

Sum of Coincident Peak 168,972 174,134 0.97 Table 2.2‐1 Comparison of the annual peaks of the load regions in years 2020 and 2022

2.2.2 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

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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 79 60

High‐wind Renewable 151 405Table 2.2‐2 Number of renewable generators modeled in the base and high‐wind renewable sceneries

2.2.3 Contingency, Flexibility and Regulation Reserve Representations

2.2.3.1 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.


Load Region

AESO AESO

AZNMNV

APS

EPE

NEVP

PNM

SRP

TEP

WALC

BASIN

FAR EAST

MAGIC VLY

PACE_ID

PACE_UT

PACE_WY

SPP

TREAS VLY

BCH BCH

CALIF_NORTH

PG&E_BAY

PG&E_VLY

SMUD

TIDC

CALIF_SOUTH

CFE

IID

LDWP

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SCE

SDGE

NWPP

AVA

BPA

CHPD

DOPD

GCPD

NWMT

PACW

PGN

PSE

SCL

TPWR

WAUW

RMPPPSC

WACM Table 2.2‐3 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 2.4-2 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 2.4-2 Generator Characteristic Revisions.

2.2.3.2 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.

Flex/regulation Reserve Sharing Group Load Region

Alberta AESO

Arizona

APS

SRP

TEP

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WALC

British Columbia BCH

California, North

PG&E_VLY

TIDC

California, South SCE

Colorado

PSC

WACM

Idaho

FAR EAST

MAGIC VLY

PACE_ID

TREAS VLY

IID IID

LDWP LDWP

Mexico (CFE) CFE

Montana

NWMT

WAUW

Nevada, North SPP

Nevada, South NEVP

New Mexico

EPE

PNM

Northwest

AVA

BPA

CHPD

DOPD

GCPD

PACW

PGN

PSE

SCL

TPWR

San Diego SDGE

San Francisco PG&E_BAY

SMUD SMUD

Utah PACE_UT

Wyoming PACE_WY Table 2.2‐4 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

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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 3 4 4

Max Cap per Unit (MW) 133 350 345

Min Cap per Unit (MW) 39.9 105 103.5

Max Pump Load (MW) 133 350 345

Min Pump Load (MW) 79.8 210 207

Upper Storage (GWh) 5 25.5 10

Lower Storage (GWh) 5 25.5 10

Cycle Efficiency 80.472% 80.472% 80.472%

Connected Bus 37001_CAMINO S

( 230KV) 28195_Red Bluff

(500KV) 45035_CAPTJACK

(500KV) Table 2.3‐1 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


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 6 1175Fixed 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 3 1212Fixed 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 4 1400Adjustable Speed

Iowa Hill SMUD CALIF_NORTH SMUD 3 399Adjustable Speed

Swan Lake BPA NWPP NWPP 4 1380Adjustable Speed

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Grand Total 31 6475 Table 2.3‐2 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 2022. 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 existing PSHs cannot provide regulation reserves in the pumping mode.

3 The Min Generating Capacity is changed to be 70% of the Max Generating Capacity for the existing FS PSHs

4 The Min Generating Capacity is changed to 90% of the Max Generating Capacity for the nuclear generators

5 The Economic Demand Responses are modeled as dispatchable with the dispatch prices in the range of $500/MWh and zero minimum capacity

6

The Interruptible Demand Responses are modeled as dispatchable with the dispatch prices in the range of $1,200~$1,872/MWh and zero minimum capacity

7 Un‐served energy penalty price is changed to $3,500/MWh. And the dump power price is changed to: ‐ $100/MWh

8 Regulation reserve shortfall penalty price is set to $1,100/MW

9 Spinning reserve shortfall penalty price is set to $900/MW

10 Non‐spinning reserve shortfall penalty price is set to $700/MW

11 Flexibility reserve shortfall penalty price is set to $600/MW

12 Transmission line and interface limit penalty price is changed to $6,000/MWh

13 All Co‐gen generators cannot provide reserves

14 Fixed hydro generation profiles and renewable generation profiles can be curtailed at the penalty price of: ‐$22/MWh

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15

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 with the ratio of HR at Max Capacity in the TEPPC database over the HR at Max Capacity from NREL WWSIS phase 2 study.

See the rest of this subsection for details

16

The start cost of CCs and CTs is determined by only the start‐up fuel cost from the TEPPC database. The start cost of other thermal generators is determined by the start cost from the TEPPC database.

Table 2.4‐1 Assumptions revisions in the database

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.

Generator Type

Minimum Operating Capacity (% of Max Cap)

Provide 5‐minute

Regulation

Provide 10‐minute Spinning and non‐Spinning Reserve

Provide 60‐min

Flexibility Reserve

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

50

Yes Yes

CT Large Yes Yes

CT LM 6000 Yes Yes

CT Old Gas Yes Yes

CT Old Oil Yes Yes

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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 2.4‐2 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.

32 | P

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34 | P a g e

min ∙ ∙ , ∙ ,

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∙ ∀ ,

(Energy Balance Constraint) ∙ ∀ ,

PSHStorageBalanceConstraint

,, ∀ , ,

AS RequirementConstraints

,,

, ,, ∀ , , ,

Generator AScapacityConstraints

, ∙ , , ∙ ∀ , ,

(GenerationandASCapacityConstraints

,, ∙ ∀ , ,

GenerationandASRampCapacityConstraint

, , ∙ , ∀ , ,

Transmissionline LimitConstraints

, , ∙ ,

∈

, , ∀ , ,

Interface LimitConstraints Generator Chronological Constraints

Resource Constraints User-Defined Constraints

Where

- Generation from generator at interval ;

- Generation cost of generator at interval ;

- Unit commitment status of generator at interval ; 1=on-line, 0=off-line

- Startup / shut down cost of generator at interval ;

35 | P a g e

, - 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);

, - Line flow in transmission line at interval for post-contingency network ;

, , - Min line flow of transmission line at interval for post-contingency network ;

, , - Max line flow of transmission line at interval for post-contingency network ;

- Line coefficient of transmission line in interface ;

, , - Min interface flow of interface at interval for post-contingency network ; , , - Max interface flow of interface at interval for post- contingency

network ;

The PSH pumping and generating are incorporated in Constraints “(Energy Balance Constraint)” and “(PSHStorageBalanceConstraint ”. 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

36 | P

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37 | P a g e

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.

38 | P a g e

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 3.4‐1 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 bid-based market. The system information of the entire WI, the carved-out California footprint and the carved-out SMUD footprint is listed in Table 3.4-2 Three Focused Simulation Areas: WI, California and SMUD

Model System WI CA SMUD

Load Regions 39 9 1

Buses over 17,000 over 4000 over 250

Transmission Lines over 22,000 over 5952 over 300

Interfaces 91 31 0

Generator over 3,700 0ver 700 over 60

Existing FS PSHs 8 4 0

New AS PSHs 3 2 1

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 20‐

39 | P a g e

hour look‐ahead hour look‐ahead hour look‐ahead

RT Simulation Step 12 5‐minutes plus

23‐hour look‐ahead12 5‐minutes plus

23‐hour look‐ahead12 5‐minutes plus

23‐hour look‐ahead

Simulation Base Cost‐base Bid‐base Cost‐baseTable 3.4‐2 Three Focused Simulation Areas: WI, California and SMUD

40 | P a g e

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 2. 24 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.

4.1.1 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.

Base Renewable

Total Generation Energy

PSH Generation Energy

Production Cost

Annual Cost Reduction

Annual Cost Savings due to

PSHs

GWh GWh million $ million

$ % Capacity MW

$/kW‐year

No PSH 997,546 ‐ 14,737 ‐ ‐ ‐ ‐

With FS PSH 1,003,204 4,106 14,569 167 1.14% 3,296 50.82

With FS&AS PSH 1,008,135 8,244 14,426 311 2.11% 6,475 48.06

Table 4.1‐1 Comparison of WI Production Cost in Three Cases for the Base Renewable Scenario in Year 2022

High‐Wind Renewable



Production Cost


Annual Cost Savings due to PSHs

GWh GWh million $ million $ % Capacity (MW)

$/kw‐year

No PSH 997,538 ‐ 12,646 ‐ ‐ ‐ ‐

With FS PSH 1,007,140 6,925 12,398 248 1.96% 3,296 75.29

With FS&AS PSH 1,015,512 13,811 12,169 477 3.77% 6,475 73.67

Table 4.1‐2 Comparison of WI Production Cost in Three Cases for the High‐Wind Renewable Scenario in Year 2022

41 | P a g e

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.

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.

‐

50,000

100,000

150,000

200,000

250,000

300,000

GWh

Generation by Generator Type (GWh)‐Yearly for Base Renewable Scenario

Base ‐ No PSH

With FS PSH

With FS&AS PSH

42 | P a g e

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.

Figure 4‐3 Comparison of WI Generation Cost in Three Cases by Generator Type for the Base Renewable Scenario in Year 2022

‐

50,000

100,000

150,000

200,000

250,000

300,000GWh

Generation by Generator Type (GWh)‐Yearly for High‐wind Renewable Scenario

Base ‐ No PSH

With FS PSH

With FS&AS PSH

‐

1,000

2,000

3,000

4,000

5,000

6,000

7,000

Million $

Total Generation Cost by Generator Type ($M)‐Yearly for Base Renewable Scenario

Base ‐ No PSH

With FS PSH

With FS&AS PSH

43 | P a g e

In the high-wind renewable scenario, both CC and Coal production costs are reduced as more PSHs are introduced into the system.

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 1,356 565 29%

With FS&AS PSH 964 958 50%Table 4.1‐3 Comparison of WI Renewable Curtailment in the Base Renewable Scenario

WI Renewable Curtailment in the High‐wind Renewable Scenario


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 4.1‐4 Comparison of WI Renewable Curtailment in the High‐wind Renewable Scenario

4.1.2 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.

‐

1,000

2,000

3,000

4,000

5,000

6,000

Million $

Total Generation Cost by Gnerator Type ($M)‐Yearly for High‐wind Renewable Scenario

Base ‐ No PSH

With FS PSH

With FS&AS PSH

44 | P a g e

Base Renewable

Base ‐ No PSH With FS PSH With FS&AS PSH

Total Req. (GWh)

PSH Provision (GWh)

Total Req. (GWh)

PSH Provision (GWh)

Total Req. (GWh)

PSH Provision (GWh)

Non‐Spinning Reserve 29,564 ‐ 29,564 1,364 29,564 3,757

Spinning Reserve 29,564 ‐ 29,564 182 29,564 679

Flexibility Down 10,732 ‐ 10,732 74 10,732 1,463

Flexibility Up 10,732 ‐ 10,732 100 10,732 299

Regulation Down 12,423 ‐ 12,423 163 12,423 1,652

Regulation Up 12,441 ‐ 12,441 205 12,441 580Table 4.1‐5 Comparison of WI Reserve Requirements and Provisions by PSHs in Three Cases for the Base Renewable Scenario in Year 2022



Total Req. (GWh)

PSH Provision (GWh)

Total Req. (GWh)

PSH Provision (GWh)

Total Req. (GWh)

PSH Provision (GWh)

Non‐Spinning Reserve 29,564 ‐ 29,564 766 29,564 2,017

Spinning Reserve 29,564 ‐ 29,564 22 29,564 187


Flexibility Up 23,062 ‐ 23,062 35 23,062 119

Regulation Down 17,487 ‐ 17,487 485 17,487 2,333

Regulation Up 17,448 ‐ 17,448 95 17,448 319 Table 4.1‐6 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.

4.1.3 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

CO2 NOx SO2 Emission Reduction (ton)Emission

Reduction (%)

ton ton ton CO2 NOx SO2 CO2 NOx SO2

No PSH 388,463,385 573,025 410,404 ‐ ‐ ‐ 0.0% 0.0% 0.0%

With FS PSH 391,262,476 581,329 417,728 ‐2,799,091 ‐8,304 ‐7,324 ‐0.7% ‐1.4% ‐1.8%

With FS&AS PSH 393,954,399 589,914 425,151 ‐5,491,014 ‐16,888 ‐14,747 ‐1.4% ‐2.9% ‐3.6%Table 4.1‐7 Comparison of WI Emission Productions in Three Cases in Year 2022 for the Base Renewable Scenario

45 | P a g e


CO2 NOx SO2 Emission Reduction (ton)Emission

Reduction (%)

ton ton ton CO2 NOx SO2 CO2 NOx SO2

No PSH 318,768,466 467,931 326,318 ‐ ‐ ‐ 0.0% 0.0% 0.0%

With FS PSH 312,657,135 458,360 320,234 6,111,331 9,571 6,084 1.9% 2.0% 1.9%

With FS&AS PSH 311,549,087 459,379 322,211 7,219,379 8,552 4,107 2.3% 1.8% 1.3%Table 4.1‐8 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 high-wind renewable scenario.

4.1.4 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 37,804 153 ‐ ‐

With FS PSH 31,797 130 24 15.46%

With FS&AS PSH 27,548 109 44 28.57%Table 4.1‐9 Comparison of Number of Starts and Startup Costs of the WI Thermal Generators in Year 2022 for the Base Renewable Scenario





No PSH 40,852 176 ‐ ‐

With FS PSH 36,024 161 15 8.48%

With FS&AS PSH 31,925 145 31 17.70%Table 4.1‐10 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.

46 | P a g e

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 1,786 15.53% 2,560 15.51%

With FS&AS PSH 8,081 11,691 3,420 29.74% 4,817 29.18%Table 4.1‐11 Comparison of Thermal Generator Ramp Up and Down of the WI Thermal Generators in Year 2022 for the Base Renewable Scenario




Ramp Down Reduction

GW GW GW % GW %

No PSH 9,325 14,188 ‐ ‐ ‐ ‐

With FS PSH 8,394 12,682 931 9.98% 1,506 10.62%

With FS&AS PSH 7,060 10,778 2,265 24.29% 3,410 24.04%Table 4.1‐12 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.

4.1.5 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.

‐

10.00

20.00

30.00

40.00

50.00

APS BPA PG&E_VLY SCE SDGE

$/M

Wh

Average Regional Price‐Year 2022 for Base Renewable Scenario

Base ‐ No PSH

With FS PSH

With FS&AS PSH

47 | P a g e

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 4.2.6 California Regional LMPs.

Figure 4‐6 Comparison of Regional LMP in Three Cases for the Selected Regions in Year 2022 for the High‐wind Renewable Scenario

4.1.6 WI Transmission Congestions

4.1.6.1 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 4.1-13 Comparison of WI Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year 2022.

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”,

‐

10.00

20.00

30.00

40.00

50.00

APS BPA PG&E_VLY SCE SDGE

$/M

Wh

Average Regional Price‐Year 2022 for High‐wind Renewable Scenario

Base ‐ No PSH

With FS PSH

With FS&AS PSH

48 | P a g e

“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.

49 | P a g e

No PSH With FS PSH With FS&AS PSH

Interfaces

Hours Congested

(hrs)

Hours Congested Back (hrs)

Shadow Price

($/MW)

Shadow Price Back ($/MW)

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back

($/MW)

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back

($/MW)

Interstate WA‐BC East 1,395 0 15 0 1,413 0 12 0 1,489 0 15 0

Interstate WA‐BC West 0 1,171 0 5 0 1,201 0 4 0 1,238 0 3

Intrastate AB DC1 6,873 1,887 0 0 7,386 1,374 0 0 7,440 1,320 0 0

Intrastate AB DC2 8,760 0 17 0 7,862 898 0 0 8,083 677 0 0

Intrastate AZ Palo Verde East 51 0 0 0 49 0 0 0 79 0 1 0

Intrastate WA North of Hanford 0 2 0 0 1 1 0 0 2 2 0 0

P01 Alberta‐British Columbia 5 2,094 0 32 7 2,089 0 32 8 2,098 0 32

P03 Northwest‐British Columbia 0 87 0 0 0 76 0 0 0 63 0 0

P08 Montana to Northwest 132 0 0 0 187 0 0 0 272 0 0 0

P09 West of Broadview 3 0 0 0 5 0 0 0 7 0 0 0

P14 Idaho to Northwest 0 18 0 0 0 23 0 0 0 13 0 0

P18 Montana‐Idaho 428 0 12 0 435 0 11 0 389 0 9 0P23 Four Corners 345/500 Qualified Path 0 0 0 0 0 1 0 0 0 0 0 0

P24 PG&E‐Sierra 84 0 2 0 115 0 3 0 132 0 2 0P25 PacifiCorp/PG&E 115 kV Interconnection 0 111 0 19 0 117 0 26 0 128 0 15

P26 Northern‐Southern California 42 321 0 1 49 318 0 1 39 362 0 0P27 Intermountain Power Project DC Line 6,096 0 36 0 5,617 0 3 0 6,096 0 3 0

P30 TOT 1A 142 0 1 0 142 0 1 0 164 0 0 0

P31 TOT 2A 8 10 0 0 2 7 0 0 2 4 0 0

P33 Bonanza West 59 0 0 0 77 0 0 0 74 0 0 0

P36 TOT 3 74 0 2 0 73 0 1 0 50 0 1 0

P39 TOT 5 32 0 0 0 27 0 0 0 16 0 0 0

50 | P a g e


Interfaces

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back ($/MW)

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back

($/MW)

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back

($/MW)

P40 TOT 7 15 0 0 0 5 0 0 0 3 0 0 0

P41 Sylmar to SCE 0 0 0 0 0 0 0 0 0 1 0 0

P42 IID‐SCE 39 0 0 0 46 0 0 0 81 0 0 0

P45 SDG&E‐CFE 7,274 0 166 0 7,279 0 159 0 7,304 0 139 0

P47 Southern New Mexico (NM1) 247 0 9 0 201 0 7 0 196 0 5 0

P52 Silver Peak‐Control 55 kV 0 1,341 0 86 0 1,112 0 83 0 1,017 0 68

P55 Brownlee East 2 0 0 0 0 0 0 0 1 0 0 0P59 WALC Blythe ‐ SCE Blythe 161 kV Sub 146 0 1 0 151 0 0 0 107 0 0 0

P61 Lugo‐Victorville 500 kV Line 2 0 0 0 7 0 0 0 6 0 0 0

P66 COI 232 0 1 0 366 0 1 0 245 0 0 0

P73 North of John Day 0 0 0 0 0 0 0 0 1 0 0 0

P75 Hemingway‐Summer Lake 0 223 0 9 0 222 0 5 0 286 0 5

P80 Montana Southeast 9 0 0 0 6 0 0 0 4 0 0 0

Grand Total 40,910 7,265 4 2 40,268 7,439 2 2 41,050 7,209 2 1Table 4.1‐13 Comparison of WI Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year 2022

51 | P a g e

4.1.6.2 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 high-wind 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.

52 | P

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53 | P a g e


Interface

Hours Congested (hrs)

HoursCongested Back (hrs)

Shadow Price

($/MW)

Shadow Price Back($/MW)



Shadow Price

($/MW)




Shadow Price

($/MW)


Interstate WA‐BC East ‐ 556 ‐ 4.64 ‐ 539 ‐ 4.51 ‐ 540 ‐ 3.96

Interstate WA‐BC West ‐ 91 ‐ 0.16 ‐ 90 ‐ 0.17 ‐ 102 ‐ 0.13

Intrastate AB DC1 8,284 476 ‐ ‐ 8,248 512 0.00 ‐ 7,766 994 ‐ ‐

Intrastate AB DC2 8,358 402 0.00 ‐ 8,637 123 20.13 ‐ 8,760 ‐ 0.01 ‐

Intrastate Aeolus South ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ 1 ‐ 0.00 ‐

Intrastate AZ Palo Verde East 11 ‐ 0.65 ‐ 8 ‐ 0.07 ‐ 14 ‐ 0.06 ‐

Intrastate WA North of Hanford ‐ 39 ‐ 0.02 ‐ 30 ‐ 0.02 ‐ 34 ‐ 0.03

P01 Alberta‐British Columbia 3 382 0.00 0.14 2 334 0.00 0.13 ‐ 224 ‐ 0.06

P03 Northwest‐British Columbia 74 213 0.02 0.60 58 197 0.02 0.31 24 161 0.01 0.33

P08 Montana to Northwest 5,102 ‐ 21.82 ‐ 5,249 ‐ 23.65 ‐ 5,443 ‐ 25.83 ‐

P09 West of Broadview 238 ‐ 0.29 ‐ 306 ‐ 0.36 ‐ 247 ‐ 0.27 ‐

P14 Idaho to Northwest 1 66 0.00 1.60 ‐ 65 ‐ 1.14 2 63 0.00 0.71

P15 Midway‐LosBanos 334 13 0.93 0.02 302 20 0.79 0.03 288 23 0.76 0.03

P16 Idaho‐Sierra 18 105 0.39 1.09 38 111 0.80 1.00 35 121 0.31 0.91

P18 Montana‐Idaho ‐ ‐ ‐ ‐ ‐ 1 ‐ 0.00 ‐ 2 ‐ 0.02

P19 Bridger West 76 ‐ 0.14 ‐ 119 ‐ 0.21 ‐ 150 ‐ 0.26 ‐

P20 Path C ‐ 46 ‐ 0.70 ‐ 51 ‐ 0.37 ‐ 47 ‐ 0.30

P22 Southwest of Four Corners 27 ‐ 0.07 ‐ 83 ‐ 0.23 ‐ 115 ‐ 0.36 ‐P23 Four Corners 345/500 Qualified Path 248 10 1.77 0.01 321 17 1.94 0.04 358 17 1.79 0.10

P24 PG&E‐Sierra 2 56 0.01 0.37 3 97 0.14 0.65 1 190 0.00 1.56P25 PacifiCorp/PG&E 115 kV Interconnection ‐ 147 ‐ 16.40 ‐ 104 ‐ 11.03 ‐ 65 ‐ 6.54

P26 Northern‐Southern California 594 197 1.45 0.26 600 181 1.77 0.22 576 176 0.90 0.24

P27 Intermountain Power Project 3,216 ‐ 42.50 ‐ 3,216 ‐ 35.08 ‐ 6,072 ‐ 26.24 ‐

54 | P a g e


Interface



Shadow Price

($/MW)




Shadow Price

($/MW)




Shadow Price

($/MW)


DC Line

P28 Intermountain‐Mona 345 kV ‐ 54 ‐ 0.07 ‐ 38 ‐ 0.69 ‐ 33 ‐ 0.24

P29 Intermountain‐Gonder 230 kV 52 417 1.42 2.14 33 432 0.79 2.63 40 423 0.80 2.60

P30 TOT 1A 1,524 ‐ 10.19 ‐ 1,516 9 9.82 2.00 1,565 ‐ 9.60 ‐

P31 TOT 2A ‐ 37 ‐ 1.18 ‐ 62 ‐ 3.77 ‐ 38 ‐ 1.23P32 Pavant‐Gonder InterMtn‐Gonder 230 kV 19 ‐ 0.10 ‐ 12 ‐ 0.06 ‐ 21 ‐ 0.12 ‐

P33 Bonanza West 3,275 ‐ 27.05 ‐ 3,506 ‐ 26.97 ‐ 3,840 ‐ 28.88 ‐

P35 TOT 2C ‐ 2 ‐ 0.01 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐

P36 TOT 3 1,025 ‐ 1.18 ‐ 1,284 ‐ 1.91 ‐ 1,329 ‐ 1.48 ‐

P37 TOT 4A 292 ‐ 0.87 ‐ 288 ‐ 0.77 ‐ 315 ‐ 0.78 ‐

P39 TOT 5 35 224 0.01 0.11 36 311 0.62 0.11 25 355 0.09 0.14

P40 TOT 7 2 ‐ 0.00 ‐ 1 2 0.00 0.00 7 ‐ 0.00 ‐

P41 Sylmar to SCE ‐ 4 ‐ 0.04 ‐ 7 ‐ 0.04 ‐ 1 ‐ 0.01

P42 IID‐SCE 138 ‐ 0.27 ‐ 150 ‐ 0.24 ‐ 168 ‐ 0.21 ‐

P45 SDG&E‐CFE 52 ‐ 0.59 ‐ 37 ‐ 0.68 ‐ 50 ‐ 0.59 ‐

P47 Southern New Mexico (NM1) 701 ‐ 6.07 ‐ 632 ‐ 7.34 ‐ 605 ‐ 5.72 ‐

P48 Northern New Mexico (NM2) ‐ 121 ‐ 0.12 ‐ 214 ‐ 0.18 ‐ 221 ‐ 0.22

P52 Silver Peak‐Control 55 kV 5 ‐ 0.15 ‐ 2 ‐ 0.06 ‐ 4 ‐ 0.09 ‐

P61 Lugo‐Victorville 500 kV Line 37 ‐ 1.18 ‐ 80 ‐ 0.45 ‐ 58 ‐ 0.24 ‐

P65 Pacific DC Intertie (PDCI) ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ 0.81 ‐

P66 COI 1,148 ‐ 4.17 ‐ 1,446 ‐ 5.01 ‐ 1,675 ‐ 5.06 ‐

P73 North of John Day 8 ‐ 0.76 ‐ 4 ‐ 0.83 ‐ 16 ‐ 0.90 ‐

P75 Hemingway‐Summer Lake ‐ 196 0.19 8.02 2 212 0.25 8.30 1 227 0.29 4.34

55 | P a g e


Interface



Shadow Price

($/MW)




Shadow Price

($/MW)




Shadow Price

($/MW)


P76 Alturas Project ‐ 826 0.15 4.03 ‐ 997 0.31 4.69 ‐ 1,211 0.40 5.58

P78 TOT 2B1 ‐ 5 ‐ 0.05 ‐ 3 ‐ 0.02 ‐ ‐ ‐ ‐

P79 TOT 2B2 17 2 1.13 0.01 14 ‐ 0.69 ‐ 20 ‐ 0.85 ‐

P80 Montana Southeast 213 5 4.01 0.01 227 4 3.75 0.01 187 4 1.56 0.01

Grand Total 43,889 4,692 2.22 0.46 45,220 4,763 2.11 0.46 48,538 5,272 2.27 0.32Table 4.1‐14 Comparison of WI Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the High‐wind Renewable Scenario in Year 2022

56 | P

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57 | P a g e

Therefore, for the energy market simulation, the generator marginal cost price is used as the energy bidding price.

4.2.1.2 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.

Statistics of CAISO Historical NP15 LMP and AS Clearing Price in Year 2012

NP15 LMP

AS Clearing Prices

Non‐Spinning Spinning

Regulation Down Regulation Up

Mean 36.77 0.60 4.07 4.98 5.62

Max 113.15 66.36 66.36 74.75 66.36

Min (10.45) ‐ ‐ ‐ ‐

STDEV 10.49 2.45 4.80 4.13 5.28

STDEV % 29% 410% 118% 83% 94%Table 4.2‐1 Statistics of CAISO Historical NP15 LMP and AS Clearing Prices in Year 2012

The following table shows strong correlations between the ancillary service prices and NP15 LMP.

Correlation of CAISO Historical NP15 LMP and AS Clearing Prices in Year 2012

NP15 LMP

AS Clearing Prices

Non‐Spinning Spinning

Regulation Down

Regulation Up

NP15 LMP 0.93 0.34 0.28 (0.43) 0.24

AS Clearing

Prices

Non‐Spinning 0.71 0.49 (0.05) 0.43

Spinning 0.71 0.13 0.91

Regulation Down 0.69 0.17

Regulation Up 0.67Table 4.2‐2 Correlation of CAISO Historical NP15 LMP and AS Clearing Prices in Year 2012

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

58 | P a g e

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 3 9 15 15 30 30

Coal 5 15 35 35 60 60

CT 2 6 10 10

DR 2 6 10 10

Hydro 1 3 5 5 10 10

IC 2 6 10 10

PSH 1 3 5 5 10 10

STEAM 2 6 10 10 Table 4.2‐3 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 2. 24 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.

4.2.2 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



Production Cost



GWh GWh million $ million $ % Capacity MW

$/kW‐year

59 | P a g e

No PSH 265,538 ‐ 5,078 ‐ ‐ ‐ ‐

With FS PSH 267,001 2,725 4,967 111 2.18% 2626 42.10

With FS&AS PSH 269,374 5,313 4,907 171 3.36% 4425 38.60

Table 4.2‐4 Comparison of CA Production Cost in Three Cases for the Base Renewable Scenario in Year 2022




Production Cost




$/kw‐year

No PSH 253,872 ‐ 4,120 ‐ ‐ ‐ ‐

With FS PSH 256,069 5,299 3,934 186 4.52% 2626 70.91

With FS&AS PSH 257,018 9,456 3,745 376 9.12% 4425 84.97

Table 4.2‐5 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.


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.

60 | P a g e

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.

Figure 4‐10 Comparison of CA Generation in Three Cases by Generator type for the High‐wind Renewable Scenario in Year 2022

‐

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000GWh


Base ‐ No PSH

With FS PSH

With FS&AS PSH

‐ 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000

100,000

GWh


Base ‐ No PSH

With FS PSH

With FS&AS PSH

61 | P a g e

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.

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.

‐

500

1,000

1,500

2,000

2,500

3,000

3,500

Million $


Base ‐ No PSH

With FS PSH

With FS&AS PSH

‐

500

1,000

1,500

2,000

2,500

Million $

Total Generation Cost by Generator Type ($M)‐Yearly for High‐wind Renewable Scenario

Base ‐ No PSH

With FS PSH

With FS&AS PSH

62 | P a g e

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


Case GWh GWh %

No PSH 155 ‐ 0%

With FS PSH 46 108 70%

With FS&AS PSH 14 141 91%Table 4.2‐6 Comparison of CA Renewable Curtailment in the Base Renewable Scenario

CA Renewable Curtailment in the High‐wind Renewable Scenario


Case GWh GWh %

No PSH 618 ‐ 0%

With FS PSH 380 238 39%

With FS&AS PSH 275 343 55%Table 4.2‐7 Comparison of CA Renewable Curtailment in the High‐wind Renewable Scenario

4.2.3 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


Total Req. (GWh)

PSH Provision (GWh)

Total Req. (GWh)

PSH Provision (GWh)

Total Req. (GWh)

PSH Provision (GWh)


Spinning Reserve 8,505 ‐ 8,505 224 8,505 2,463


Flexibility Up 3,130 ‐ 3,130 13 3,130 341

Regulation Down 3,810 ‐ 3,810 171 3,810 1,264

Regulation Up 3,839 ‐ 3,839 164 3,839 1,109Table 4.2‐8 Comparison of CA Reserve Requirements and Provisions by PSHs in Three Cases for the Base Renewable Scenario in Year 2022



Total Req. (GWh)

PSH Provision (GWh)

Total Req. (GWh)

PSH Provision (GWh)

Total Req. (GWh)

PSH Provision (GWh)


Spinning Reserve 8,505 ‐ 8,505 247 8,505 2,022

63 | P a g e


Flexibility Up 4,804 ‐ 4,804 26 4,804 200

Regulation Down 4,394 ‐ 4,394 377 4,394 1,761

Regulation Up 4,442 ‐ 4,442 144 4,442 1,201Table 4.2‐9 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.

4.2.4 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 6,006 ‐ ‐ ‐ 0.0% 0.0% 0.0%

With FS PSH 64,741,362 53,512 6,093 688,166 170 (87) 1.1% 0.3% ‐1.5%

With FS&AS PSH 64,625,964 53,568 6,165 803,565 113 (160) 1.2% 0.2% ‐2.7%Table 4.2‐10 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)



No PSH 51,515,736 44,936 5,334 ‐ ‐ ‐ 0.0% 0.0% 0.0%

With FS PSH 49,692,105 44,010 5,350 1,823,631 925 (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 4.2‐11 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.

64 | P a g e

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



No PSH 18,514 56 ‐ ‐

With FS PSH 14,646 46 10 17.35%

With FS&AS PSH 12,134 36 20 35.40%Table 4.2‐12 Comparison of Number of Starts and startup Costs of the CA Thermal Generators in Year 2022 for the Base Renewable Scenario

High‐Wind Renewable Total Number of Thermal Starts



No PSH 17,862 54 ‐ ‐

With FS PSH 14,351 44 11 19.56%

With FS&AS PSH 11,864 35 20 36.42%Table 4.2‐13 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



Ramp Down Reduction

GW GW GW % GW %

No PSH 4,273 6,603 ‐ ‐ ‐ ‐

With FS PSH 3,623 5,552 650 15.20% 1,052 15.93%

With FS&AS PSH 2,924 4,456 1,349 31.56% 2,147 32.51%Table 4.2‐14 Comparison of Thermal Generator Ramp Up and Down of the CA Thermal Generators in Year 2022 for the Base Renewable Scenario




Ramp Down Reduction

GW GW GW % GW %

65 | P a g e

No PSH 3,609 5,681 ‐ ‐ ‐ ‐

With FS PSH 3,078 4,737 531 14.71% 945 16.63%

With FS&AS PSH 2,396 3,738 1,214 33.63% 1,943 34.20%Table 4.2‐15 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.

4.2.6 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.

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.

‐

5.00

10.00

15.00

20.00

25.00

30.00

35.00

PG&E_VLY SCE SDGE

$/M

Wh

Average Regional Price‐Year 2022 for Base Renewable Scenario

Base ‐ No PSH

With FS PSH

With FS&AS PSH

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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

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.

Figure 4‐15 SCE LMP in Week of July 17, 2022, in Three Cases for the High‐wind Renewable Scenario

4.2.7 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.

‐

5.00

10.00

15.00

20.00

PG&E_VLY SCE SDGE

$/M

Wh

Average Regional Price‐Year 2022 for High‐wind Renewable Scenario

Base ‐ No PSH

With FS PSH

With FS&AS PSH

‐30

‐20

‐10

0

10

20

30

40

50

1

10

19

28

37

46

55

64

73

82

91

100

109

118

127

136

145

154

163

$/M

Wh

SCE LMP in Week of July 17, 2022 in High‐wind Renewable Scenario

No PSH

With FS PSH

With FS&AS PSH

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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 4.2-16 California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the Base Renewable Scenario in Year 2022and Table 4.2-17 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 4.2-18 to Table 4.2-21.

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California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the Base Renewable Scenario


Generator Type

Generation

(GWh)

Total Generation Cost ($000)

Energy Revenue ($000)

Reserves Revenue ($000)

Net Operatin

g Revenue($000)

Generation

(GWh)




Net Operatin

g Revenue($000)

Generation

(GWh)




Net Operatin

g Revenue($000)

CC 76,184 2,889,437 2,398,856 449,040 (41,541) 74,151 2,785,726 2,384,914 461,270 60,458 73,279 2,725,054 2,388,034 411,989 74,969

Coal 15,459 192,999 605,524 7,925 420,450 15,588 194,690 918,118 7,860 731,289 15,632 195,320 800,265 8,865 613,810

CoGen 8,134 246,545 237,723 ‐ (8,822) 8,185 247,069 247,283 ‐ 214 8,376 251,955 260,483 ‐ 8,528

CT 6,535 480,617 189,683 187,170 (103,764) 6,158 461,234 184,953 171,255 (105,026) 5,804 445,010 180,763 163,484 (100,763)

Hydro 38,682 1,048 1,077,887 102,187 1,179,026 38,702 964 1,125,789 98,138 1,222,963 38,710 901 1,184,057 53,831 1,236,988

Nuclear 37,271 524,535 1,015,093 ‐ 490,559 37,465 527,262 1,074,099 ‐ 546,836 37,638 529,702 1,134,143 ‐ 604,441

Other 7,398 4,314 200,984 1,123 197,792 7,397 4,163 211,870 740 208,447 7,378 3,240 222,362 313 219,435

RPS 67,161 258,062 1,729,433 ‐ 1,471,371 67,908 266,297 1,862,716 ‐ 1,596,420 68,537 277,600 1,999,881 ‐ 1,722,281

Steam 8,712 478,901 238,647 49,994 (190,260) 8,722 479,213 251,879 49,946 (177,389) 8,705 477,742 263,563 44,300 (169,880)

DR 2 1,054 1,054 17,412 17,412 1 330 330 13,873 13,873 0 178 178 1,890 1,890

FS PSH ‐ 2,725 ‐ 102,302 18,205 120,507 1,551 ‐ 53,826 14,831 68,657

AS PSH ‐ ‐ 3,763 ‐ 127,728 37,074 164,802

Total 265,538 5,077,510 7,694,883 814,849 3,432,222 267,001 4,966,947 8,364,252 821,287 4,218,593 269,374 4,906,701 8,615,283 736,576 4,445,158

Table 4.2‐16 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


Generator Type

Generation

(GWh)




Net Operatin

g Revenue($000)

Generation

(GWh)




Net Operatin

g Revenue($000)

Generation

(GWh)




Net Operatin

g Revenue($000)

CC 52,802 2,053,833 1,081,713 655,103 (317,017) 48,666 1,873,071 1,070,992 677,108 (124,970) 44,339 1,692,346 1,055,120 542,556 (94,670)

Coal 13,518 170,464 455,827 26,231 311,594 13,496 169,973 215,339 25,965 71,332 13,508 170,130 290,909 26,634 147,413

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California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the High‐wind Renewable Scenario


Generator Type

Generation

(GWh)




Net Operatin

g Revenue($000)

Generation

(GWh)




Net Operatin

g Revenue($000)

Generation

(GWh)




Net Operatin

g Revenue($000)

CoGen 6,715 207,040 115,144 ‐ (91,896) 6,636 203,757 121,289 ‐ (82,468) 6,599 200,964 127,397 ‐ (73,567)

CT 6,641 497,018 84,280 278,700 (134,039) 6,519 491,018 85,483 268,850 (136,685) 6,279 478,730 91,018 264,313 (123,399)

Hydro 37,805 2,755 517,907 179,687 694,839 37,983 2,799 555,414 200,595 753,210 38,228 3,359 626,609 86,044 709,294

Nuclear 36,164 508,959 439,944 ‐ (69,015) 36,338 511,405 490,219 ‐ (21,187) 36,718 516,753 542,632 ‐ 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 3,893 109,737 1,624 107,468

RPS 84,324 193,104 796,763 ‐ 603,659 85,218 195,940 909,511 ‐ 713,572 86,058 201,095 1,061,961 ‐ 860,865

Steam 8,659 480,878 109,182 76,998 (294,698) 8,656 481,331 123,460 77,732 (280,139) 8,574 476,971 135,780 71,432 (269,759)

DR 3 1,655 1,655 27,858 27,858 0 30 30 25,083 25,083 1 384 384 9,990 9,990

FS PSH 5,299 ‐ 147,285 32,122 179,407 4,480 ‐ 98,534 27,166 125,700

AS PSH 4,976 ‐ 118,769 58,985 177,754

Total 253,872 4,120,437 3,692,120 1,247,100 818,783 256,069 3,934,218 3,818,185 1,309,572 1,193,539 257,018 3,744,626 4,258,850 1,088,744 1,602,968

Table 4.2‐17 California Generator Generation, Generation Cost, Energy Revenue and Ancillary Service Revenue for the High‐wind Renewable Scenario in Year 2022

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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) 102,302 102,302

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) 224 224

AS Revenue ($000) 1,218 1,218

Flexible Down


AS Revenue ($000) 389 389

Flexible Up


AS Revenue ($000) 43 43

Regulation Down


AS Revenue ($000) 4,562 4,562

Regulation Up


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) 20.84 20.84 Table 4.2‐18 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


Energy

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California PSH Net Operating Revenue for the Base Renewable Scenarios in Year 2022 from the Simulation With FS&AS PSHs


Generation (GWh) 1,551 3,763 5,313

Pump Energy (GWh) 2,180 4,676 6,856

Generation Cost ($000) ‐ ‐ ‐

Pump Cost ($000) 43,985 120,523 164,508

Energy Revenue ($000) 53,826 127,728 181,554

Subtotal Energy Net Profile($000) 9,841 7,205 17,046


AS provision (GWh) 7,469 436 7,905

AS Revenue ($000) 8,310 253 8,563

Spinning Reserve

AS provision (GWh) 126 2,337 2,463

AS Revenue ($000) 769 7,819 8,588

Flexible Down


AS Revenue ($000) 165 5,564 5,728

Flexible Up

AS provision (GWh) 19 322 341

AS Revenue ($000) 74 657 731

Regulation Down


AS Revenue ($000) 2,661 17,698 20,360

Regulation Up


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) 9.40 24.61 15.58 Table 4.2‐19 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


Energy

Generation (GWh) 5,299 5,299

Pump Energy (GWh) 7,501 7,501

Generation Cost ($000) ‐ ‐

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California PSH Net Operating Revenue for the High‐Wind Renewable Scenarios in Year 2022 from the Simulation With FS PSHs


Pump Cost ($000) (13,229) (13,229)

Energy Revenue ($000) 147,285 147,285

Subtotal Energy Net Profile ($000) 160,514 160,514



AS Revenue ($000) 1,626 1,626

Spinning Reserve


AS Revenue ($000) 80 80

Flexible Down

AS provision (GWh) 4,774 4,774

AS Revenue ($000) 5,246 5,246

Flexible Up


AS Revenue ($000) 19,511 19,511

Regulation Down


AS Revenue ($000) 4,144 4,144

Regulation Up


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) 192,636 192,636

Capacity (MW) 2,626 2,626

Annual Profit Rate ($/kW‐Year) 73.36 73.36 Table 4.2‐20 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


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) 98,534 118,769 217,302

Subtotal Energy Net Profile ($000) 104,562 87,695 192,257

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California PSH Net Operating Revenue for the High‐Wind Renewable Scenarios in Year 2022 from the Simulation With FS&AS PSHs




AS Revenue ($000) 1,695 13,239 14,934

Spinning Reserve

AS provision (GWh) 45 155 200

AS Revenue ($000) 148 265 412

Flexible Down

AS provision (GWh) 5,359 133 5,492

AS Revenue ($000) 6,125 59 6,184

Flexible Up


AS Revenue ($000) 13,830 36,055 49,885

Regulation Down


AS Revenue ($000) 3,868 4,660 8,528

Regulation Up


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) 131,728 146,680 278,408

Capacity (MW) 2,626 1,799 4,425

Annual Profit Rate ($/kW‐Year) 50.16 81.53 62.92 Table 4.2‐21 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.

4.2.8 California Transmission Congestions

The annual transmission interface congestion hours and average congestion prices for the base renewable scenario are listed in Table 4.2-22 Comparison of CA Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year 2022.

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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.

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Interfaces

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back

($/MW)

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back

($/MW)

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back

($/MW)

P26 Northern‐Southern California 297 355 0.13 0.68 345 376 0.27 0.63 370 430 0.31 0.50P27 Intermountain Power Project

DC Line 4,632 ‐ 87.40 ‐ 2,413 ‐ 9.57 ‐ 1,782 ‐ 5.35 ‐

P42 IID‐SCE 44 ‐ 0.09 ‐ 64 ‐ 0.13 ‐ 105 ‐ 0.21 ‐

P61 Lugo‐Victorville 500 kV Line 11 ‐ 0.02 ‐ 6 ‐ 0.01 ‐ 8 ‐ 0.02 ‐

Grand Total 4,984 355 3.51 0.03 2,828 376 0.40 0.03 2,265 430 0.24 0.02Table 4.2‐22 Comparison of CA Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the Base Renewable Scenario in Year 2022

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4.2.8.1 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.

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Interface

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back

($/MW)

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back

($/MW)

Hours Congested

(hrs)


Shadow Price

($/MW)

Shadow Price Back

($/MW)

P15 Midway‐LosBanos 343 15 0.81 0.03 343 20 0.79 0.02 386 18 0.89 0.02

P26 Northern‐Southern California 656 133 0.93 0.22 644 133 1.46 0.20 587 134 1.38 0.23P27 Intermountain Power Project

DC Line 3,216 ‐ 40.55 ‐ 1,752 ‐ 8.26 ‐ 3,864 ‐ 5.85 ‐

P41 Sylmar to SCE ‐ ‐ ‐ ‐ ‐ 2 ‐ 0.01 ‐ 2 ‐ 0.02

P42 IID‐SCE 187 ‐ 0.43 ‐ 176 ‐ 0.32 ‐ 204 ‐ 0.35 ‐

P44 South of San Onofre ‐ ‐ ‐ ‐ 1 ‐ 0.00 ‐ 2 ‐ 0.00 ‐

P61 Lugo‐Victorville 500 kV Line 83 1 1.95 ‐ 104 ‐ 3.07 ‐ 68 ‐ 0.82 ‐

Grand Total 4,485 149 1.79 0.01 3,020 155 0.56 0.01 5,111 154 0.37 0.01 Table 4.2‐23 Comparison of CA Transmission Interface Congestion Hours and Congestion Prices in Three Cases for the High‐wind Renewable Scenario in Year 2022

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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 2. 24 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.

4.3.1 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.

Base Renewable



Production Cost



GWh GWh million $ million $ % Capacity MW

$/kW‐year

No PSH 16,100 ‐ 269 ‐ ‐ ‐ ‐

With AS PSH 16,273 467 246 23 8.62% 400 58.04Table 4.3‐1 Comparison of SMUD Production Cost in Two Cases for the Base Renewable Scenario in Year 2022




Production Cost




$/kw‐year

No PSH 20,318 ‐ 308 ‐ ‐ ‐ ‐

With AS PSH 19,952 440 258 51 16.45% 400 126.83Table 4.3‐2 Comparison of SMUD Production Cost in Two Cases for the High‐Wind Renewable Scenario in Year 2022

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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.


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.

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.

‐

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

GWh


No PSH

With AS PSH

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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.

Figure 4‐18 Comparison of SMUD Generation Cost of Two Cases by Generator Type for the Base Renewable Scenario in Year 2022

‐

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

CC CT Hydro Other RPS CoGen PumpedStorage

Wind Solar

GWh


No PSH

With AS PSH

‐

50

100

150

200

250

Million $


No PSH

With AS PSH

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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


Case GWh GWh %

No PSH 19 ‐ 0%

With Iowa Hill 1 18 95%Table 4.3‐3 Comparison of SMUD Renewable Curtailment in the High‐wind Renewable Scenario

4.3.2 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 Renewable

Base ‐ No PSH With AS PSH

Total Req. (GWh)

PSH Provision (GWh)

Total Req. (GWh)

PSH Provision (GWh)

Non‐Spinning Reserve 493 ‐ 493 78

Spinning Reserve 493 ‐ 493 21

Flexibility Down 156 ‐ 156 46

Flexibility Up 156 ‐ 156 6

Regulation Down 238 ‐ 238 56

Regulation Up 237 ‐ 237 8 Table 4.3‐4 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

‐

50

100

150

200

250

300

CC CT Hydro Other RPS CoGen PumpedStorage

Wind Solar

Million $

Total Generation Cost by Generator Type ($M)‐Yearly for High‐wind Renewable Scenario

No PSH

With AS PSH

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Total Req. (GWh)

PSH Provision (GWh)

Total Req. (GWh)

PSH Provision (GWh)

Non‐Spinning Reserve 493 ‐ 493 87

Spinning Reserve 493 ‐ 493 12

Flexibility Down 601 ‐ 601 86

Flexibility Up 601 ‐ 601 12

Regulation Down 439 ‐ 439 90

Regulation Up 467 ‐ 467 9 Table 4.3‐5 Comparison of SMUD Reserve Requirements and Provisions by PSH in Two Cases for the High‐wind Renewable Scenario in Year 2022

4.3.3 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

CO2 NOx SO2 Emission Reduction (ton)Emission Reduction

(%)


No PSH 2,856,489 1,880 3 ‐ ‐ ‐ ‐ ‐ ‐

With AS PSH 2,683,737 1,777 1 172,752 103 2 6.0% 5.5% 69.3%Table 4.3‐6 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)



No PSH 3,299,928 2,168 3 ‐ ‐ ‐ ‐ ‐ ‐

With AS PSH 2,814,536 1,872 1 485,392 296 3 14.7% 13.7% 83.2%Table 4.3‐7 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.

4.3.4 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




No PSH 1,812 5 ‐ ‐

With AS PSH 828 3 2 44.83%Table 4.3‐8 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

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Thermal Starts Start Cost


No PSH 2,159 5 ‐ ‐

With AS PSH 773 3 2 41.87%Table 4.3‐9 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



Ramp Down Reduction

GW GW GW % GW %

No PSH 367 502 ‐ ‐ ‐ ‐

With AS PSH 231 305 136 37.03% 197 39.29%Table 4.3‐10 Comparison of Thermal Generator Ramp Up and Down of the SMUD Thermal Generators in Year 2022 for the Base Renewable Scenario




Ramp Down Reduction

GW GW GW % GW %

No PSH 369 489 ‐ ‐ ‐ ‐

With AS PSH 250 315 119 32.16% 174 35.59%Table 4.3‐11 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.

4.3.5 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.

84 | P

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85 | P a g e

5 Three-Stage DA-HA-RT Sequential Simulations

5.1 Intermittent Renewable Generation Variability and Uncertainty

The intermittent renewable generation variability and uncertainty places challenges to the power system planning and operation. One of the questions that the power industry needs to answer is: what is the impact of the sub-hourly renewable generation variable and uncertainty to the system operation?

The following four charts show the 5-minute solar and wind generation variability and uncertainty in the Southern California in the high-wind renewable generation scenario in a typical winter week and a typical summer week of year 2022. The source of the data is the WWSIS Phase 2 study by NREL.

Figure 5‐1 5‐minute Actual Solar Generation and Hourly DA / HA Forecasts in Southern California in a Typical Winter Week of Year 2022

0

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82

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1945

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Southern California Solar RT Generation and DA/HA Forecasts in Week of January 16 of 2022

RT

HA

DA

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Figure 5‐2 5‐minute Actual Wind Generation and Hourly DA / HA Forecasts in Southern California in a Typical Winter Week of year 2022

The maximum and minimum forecast errors in this winter week are listed in the following table.

Forecast Error(MW) in a Typical Winter Week

Solar Generation Wind Generation

RT‐HA HA‐DA RT‐HA HA‐DA

Max 3002 53 1524 3167

Min ‐3220 ‐995 ‐2142 ‐1353

Table 5.1‐1 Max and Min Wind and Solar Forecast Errors in Southern California in a Typical Winter Week of year 2022

0

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8000

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1597

1681

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1849

1933

MW

Southern California Wind RT Generation and DA/HA Forecasts in Week of January 16 of 2022

RT

HA

DA

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Figure 5‐3 5‐minute Actual Solar Generation and Hourly DA / HA Forecasts in Southern California in a Typical Summer Week of year 2022

Figure 5‐4 5‐minute Actual Wind Generation and Hourly DA / HA Forecasts in Southern California in a Typical Summer Week of Year 2022

The maximum and minimum forecast errors in a typical summer week are listed in the following table.

Forecast Error(MW) in a Typical Summer Week

Solar Generation Wind Generation

0

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6000

7000

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9000

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337

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1177

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1681

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1849

1933

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Southern California Solar RT Generation and DA/HA Forecasts in Week of July 17 of 2022

RT

HA

DA

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Southern California Wind RT Generation and DA/HA Forecasts in Week of July 17 of 2022

RT

HA

DA

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RT‐HA HA‐DA RT‐HA HA‐DA

Max 2863 727 3969 991

Min ‐2163 ‐1697 ‐2969 ‐3558

Table 5.1‐2 Max and Min Wind and Solar Forecast Error in Southern California in a Typical Summer Week of Year 2022

The following two figures show the wind and solar forecast errors between RT and HA, and between HA and DA in the typical weeks of winter and summer of 2022. It is obvious that the wind and solar generation forecast error from HA to RT has higher frequency and magnitude (blue curves).

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 2022.

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Southern California Wind and Solar Generation RT‐HA and HA‐DA Forecast Errors in Week of January 16 of 2022

RT‐HA

HA‐DA

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Southern California Wind and Solar Generation RT‐HA and HA‐DA Forecast Errors in Week of July 17 of 2022

RT‐HA

HA‐DA

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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 2022.

To meet the sub-hourly renewable generation variability and uncertainty, the system needs to ramp generators more and/or to cycle the quick-startup generators more. This project task examines the hourly security constrained unit commitment and the sub-hourly security constrained dispatch to quantify the PSH impact to the system production cost and the sub-hourly generator ramping and cycling. The simulation approach adopted is the 3-stage DA-HA-RT sequential simulation as described in Section 3.2 3-Stage DA-HA-RT Sequential Simulations. The 3-stage simulations are performed for three cases: No PSH, With FS PSH and With FS&AS PSH in the base and high-wind renewable scenarios. The 3-stage simulations will cover the three study footprint areas: WI, California and SMUD.

In the following three subsections, the system production cost and the generator ramp and cycle from the DA, HA and RT simulations are presented. First the simulation solutions in the California footprint are presented.

5.2 3-stage DA-HA-RT Simulation Results for California

Before simulating the California system, the WI simulations are performed to produce the power flows for the interties between California and the rest of WI for both the base and high-wind renewable scenarios. Then the power flows for these intertie lines are frozen in the California DA, HA and RT simulations.

The assumptions and settings for the California 3-stage simulations are reiterated as follows.

1. Four typical weeks are simulated for the California footprint: the 3rd week of January, April, July and October;

2. DA simulations: a. DA forecasted load / wind / solar: 24 to 48 hours ahead b. 24-hours SCUC / ED with hourly interval c. Nodal network representation d. Contingency, Flexibility up / down, Regulation up / down reserves

modeled e. Generator maintenance outages are modeled

3. HA simulations: a. 4-HA forecasted load / wind / solar: 4 hours ahead b. 4-hours plus 20-hours look-ahead SCUC / ED with hourly interval c. Nodal network representation d. Contingency, Flexibility up / down, Regulation up / down reserves

modeled e. Unit Commitment patterns from the DA simulation are frozen for the

generators with the min down time greater than 4 hours f. Generator maintenance outages are modeled

4. RT 5-min simulations: a. 5-min actual load / wind / solar generation b. 12 5-minutes plus 23-hours look-ahead SCUC / ED with 5-minutes

interval

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c. Nodal network representation d. Contingency, regulation up / down reserves modeled. Flexibility up /

down reserves are deployed e. Unit Commitment patterns from the HA simulation are frozen for the

generators with the min down time greater than 1 hour. The CT generator with the min down time of 1 hour or less than 1 hour can be committed or de-committed

f. Generator maintenance outages (and forced outages) are modeled 5. Three cases, no PSH, with the existing FS PSHs, with existing FS PSHs and two

new adjustable speed PSHs (Iowa Hill and Eagle Mountain), are simulated; 6. The simulations are performed for the high-wind renewable scenario; 7. For the high-wind renewable scenario, the simplified transmission expansion is

performed to deliver renewable generation to load buses; 8. The California simulations are bid-based (For the energy and AS bidding price

determinations, please refer to subsection 4.2.1 Power Market Bidding Prices).

5.2.1 CA 3-stage Simulation Results for Four Typical Weeks in Year 2022

To focus on the PSH impact to the system operation and the difference between the 5-min SCUC/ED and the hourly SCUC/ED, the results of the 3-stage DA-HA-RT simulations with only the generator maintenance outages are examined first. These simulations are performed for the four typical weeks and the high-wind renewable scenario.

The California system cost is presented in the following chart. In this chart, the production costs are labeled by “case name” and “simulation stage”. For example, the production cost for the case of no PSH from the DA simulation is labeled as “No PSH DA”.

From the following chart we can have the following observations.

1. In general, the production cost from the HA simulations (the red columns) is higher than that from the DA simulations (the blue columns), and the production cost from the RT simulations (the green columns) is higher than that from the DA and HA simulations. The average production cost over four weeks from the RT simulations (the green columns) is 6% higher than that from the DA simulations (the blue columns). The higher production cost in the RT simulations is due to more thermal generator ramping and more quick-startup CT commitment to meet the sub-hourly load and renewable generation variability and uncertainty.

2. The production costs from the DA-HA-RT simulations are reduced as PSHs are introduction to the system.

a. With the fixed speed PSHs, the average production cost from the RT simulations over four weeks (the solid green columns) is reduced by 4% as opposed to that from the RT simulations without PSHs (the dotted green columns).

b. With the additional adjustable speed PSHs, the average production cost from the RT simulations over four weeks (the tilted strip green columns) is reduced by 6% as opposed to that from the RT simulation without PSHs (the dotted green columns).

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 70,345 62,365 113,498 74,921

No PSH HA 73,183 66,020 121,994 76,456

No PSH RT 72,255 67,653 124,247 76,832

FS PSH DA 65,730 61,555 112,887 70,960

FS PSH HA 68,099 65,319 121,098 71,728

FS PSH RT 67,910 66,104 122,881 71,920

FS&AS PSH DA 64,901 56,624 112,063 69,672

FS&AS PSH HA 66,715 60,374 120,478 71,041

FS&AS PSH RT 66,300 62,263 122,150 70,953

40,000

50,000

60,000

70,000

80,000

90,000

100,000

110,000

120,000

130,000

$000

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)

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The startup costs from the 3-stage simulations for the three cases and four typical weeks with only maintenance outages in the DA, HA and RT simulations are presented in the following chart.

The followings can be observed from the chart.

1. The start-up cost from the RT simulations (the green columns) is higher than that from the DA and HA simulations (the blue and red columns) due to the additional CT commitment in the RT simulations to meet the sub-hourly load and renewable generation variability and uncertainty.

2. Comparing the startup costs between the RT simulations and the DA simulations, the followings are observed.

a. Without PSHs, the average startup cost from the RT simulations over four weeks (the dotted green columns) is 36% higher than that from the DA simulations (the dotted blue columns).

b. With the fixed speed PSHs, the average startup cost from the RT simulations over four weeks (the solid green columns) is 18.9% higher than that from the DA simulations (the solid blue columns).

c. With the fixed speed and adjustable speed PSHs, the average startup cost from the RT simulations over four weeks (the tilted strip green columns) is 21.2% higher than that from the DA simulations (the titled strip blue columns).

3. Comparing the startup costs from the RT simulations without and with PSHs, the followings are observed.

a. With the fixed speed PSHs, the average startup cost from the RT simulation over four weeks (the solid green columns) is reduced by 31% as opposed to the RT simulations without PSHs (the dotted green columns).

b. With the additional adjustable speed PSHs, the average startup cost from the RT simulation over four weeks (the tilted strip green columns) is reduced by 47% as opposed to the RT simulations without PSHs (the dotted green columns).

These observations indicate that the PSHs reduce the additional CT commitment and the associated startup cost.

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 1,032 821 1,595 990

No PSH HA 1,138 954 1,750 1,024

No PSH RT 1,468 1,267 2,103 1,206

FS PSH DA 595 753 1,344 799

FS PSH HA 667 781 1,468 686

FS PSH RT 876 969 1,536 771

FS&AS PSH DA 439 489 1,157 559

FS&AS PSH HA 464 578 1,313 541

FS&AS PSH RT 544 737 1,341 582

‐

500

1,000

1,500

2,000

2,500

$000

California Start & Shutdown 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)

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The thermal generator ramp up and down from the 3-stage simulations for the three cases and four typical weeks with only maintenance outages in the DA, HA and RT simulation are presented in the following two charts.

The followings can be observed from the two charts.

1. The thermal generator ramp up and down (MW) from the RT simulations (the green columns) is substantially higher than the DA and HA simulations (the blue and red columns) due to the thermal generator ramp to meet the sub-hourly load and renewable generation variability and uncertainty.

2. Comparing the ramp up and down between the RT simulations and DA simulations, the followings are observed.

a. Without PSHs, the average thermal generator ramp up and down from the RT simulations over four weeks (the dotted green columns) is 597,120 (MW) and 663,359 (MW) higher than that from the DA simulations (the dotted blue columns) respectively.

b. With the fixed speed PSHs, the average thermal generator ramp up and down from the RT simulations over four weeks (the solid green columns) is 508,451 (MW) and 532,650 (MW) higher than that from the DA simulations (the solid blue columns) respectively.

c. With the additional adjustable speed PSHs, the average thermal generator ramp up and down (MW) from the RT simulations over four weeks (the tilted strip green columns) is 391,860 (MW) and 412,259 (MW) higher than that from the DA simulations (the tilted blue columns) respectively.

3. Comparing the thermal generator ramp up and down from the RT simulations without and with PSHs, the followings are observed.

a. With the fixed speed PSHs, the average thermal generator ramp up and down in MW from the RT simulations over four weeks (the solid green columns) is reduced by 17% and 20% as opposed to the RT simulations without PSHs (the dotted green columns) respectively.

b. With the additional adjustable speed PSHs, the average thermal generator ramp up and down in MW from the RT simulations over four weeks (the titled strip green columns) is reduced by 36% and 38% as opposed to the RT simulations without PSHs (the dotted green columns) respectively.

These observations indicate that the PSHs reduce the thermal generator ramp substantially.

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 90,243 73,114 103,422 98,910

No PSH HA 97,857 84,188 108,668 98,186

No PSH RT 231,484 212,432 288,633 230,260

FS PSH DA 56,852 62,980 92,600 79,716

FS PSH HA 74,210 75,666 86,781 84,791

FS PSH RT 190,215 178,966 221,568 209,850

FS&AS PSH DA 44,186 47,275 78,141 58,612

FS&AS PSH HA 53,161 60,795 81,030 72,222

FS&AS PSH RT 144,449 135,476 177,903 162,247

‐

50,000

100,000

150,000

200,000

250,000

300,000

350,000

MW

California Thermal Ramp Up (MW) from 3‐stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High‐wind Renewable Scenario (Maintenance


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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 126,251 105,086 162,019 139,545

No PSH HA 136,165 123,375 170,400 148,735

No PSH RT 278,692 262,414 365,607 289,547

FS PSH DA 78,908 92,754 137,761 112,699

FS PSH HA 98,170 108,153 136,473 117,426

FS PSH RT 219,470 215,182 274,207 245,913

FS&AS PSH DA 62,994 65,165 119,140 82,934

FS&AS PSH HA 71,026 82,397 125,830 99,613

FS&AS PSH RT 164,784 161,190 224,567 191,951

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100,000

150,000

200,000

250,000

300,000

350,000

400,000

MW

California Thermal 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)

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In the real world system operations, the unexpected generator forced outages occur at real time. To closely mimic the system operations, the generator maintenance outages are modeled in the DA and HA simulations; the generator maintenance and forced outage are modeled in the RT simulations. The production costs, startup cost, and thermal generator ramp up and down from the 3-stage DA-HA-RT simulations with the forced outages modeled in the RT simulations for the four typical weeks and the high-wind renewable scenario are presented in following four charts.

When the forced outages are modeled in the RT simulations, the followings can be observed.

1. The average production cost from the RT simulations (the green columns) over four typical weeks is increased further by additional 7.3%, 5.6% and 5.4% as opposed to the DA simulations in the three cases: No PSH, with fixed speed PSHs, and with fixed and adjustable speed PSHs (the blue columns) respectively.

2. The average startup cost from the RT simulations (the green columns) over four typical weeks is increased further by additional 25.2%, 29.7% and 36.9% as opposed to the DA simulations in the three cases: No PSH, with fixed speed PSHs, and with fixed and adjustable speed PSHs (the blue columns) respectively. The additional startup costs include the startup costs from the generators whose unit commitment frozen in the RT simulation due to the forced outages.

3. However, the thermal generator ramp up and down in MW does not change substantially as opposed to that without the forced outages modeled in the RT simulations.

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 70,345 62,365 113,498 74,921

No PSH HA 73,183 66,020 121,994 76,456

No PSH RT 73,025 66,910 146,355 78,183

FS PSH DA 65,730 61,555 112,887 70,960

FS PSH HA 68,099 65,319 121,098 71,728

FS PSH RT 69,166 65,690 136,580 74,787

FS&AS PSH DA 64,901 56,624 112,063 69,672

FS&AS PSH HA 66,715 60,374 120,478 71,041

FS&AS PSH RT 67,950 61,806 134,365 73,865

40,000

60,000

80,000

100,000

120,000

140,000

160,000

$000

California Production Cost ($000) from 3‐stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High‐wind Renewable Scenario (Maintenance & Forced


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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 1,032 821 1,595 990

No PSH HA 1,138 954 1,750 1,024

No PSH RT 1,690 1,320 2,580 1,575

FS PSH DA 595 753 1,344 799

FS PSH HA 667 781 1,468 686

FS PSH RT 1,104 1,027 1,973 1,085

FS&AS PSH DA 439 489 1,157 559

FS&AS PSH HA 464 578 1,313 541

FS&AS PSH RT 695 819 1,796 870

‐

500

1,000

1,500

2,000

2,500

3,000

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California Start & Shutdown Cost ($000) from 3‐stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High‐wind Renewable Scenario (Maintenance &

Forced Outages in the RT Simulations)

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 90,243 73,114 103,422 98,910

No PSH HA 97,857 84,188 108,668 98,186

No PSH RT 190,172 183,622 279,121 223,804

FS PSH DA 56,852 62,980 92,600 79,716

FS PSH HA 74,210 75,666 86,781 84,791

FS PSH RT 180,138 173,451 238,585 198,983

FS&AS PSH DA 44,186 47,275 78,141 58,612

FS&AS PSH HA 53,161 60,795 81,030 72,222

FS&AS PSH RT 141,633 123,405 182,166 139,780

‐

50,000

100,000

150,000

200,000

250,000

300,000

MW

California Thermal Ramp Up (MW) from 3‐stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High‐wind Renewable Scenario (Maintenance Forced


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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 126,251 105,086 162,019 139,545

No PSH HA 136,165 123,375 170,400 148,735

No PSH RT 242,776 235,879 374,331 294,266

FS PSH DA 78,908 92,754 137,761 112,699

FS PSH HA 98,170 108,153 136,473 117,426

FS PSH RT 212,500 212,259 306,593 242,682

FS&AS PSH DA 62,994 65,165 119,140 82,934

FS&AS PSH HA 71,026 82,397 125,830 99,613

FS&AS PSH RT 163,926 152,739 243,329 176,124

‐

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

MW

California Thermal Ramp Down (MW) from 3‐stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High‐wind Renewable Scenario (Maintenance &


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5.3 3-stage DA-HA-RT Simulation Results for WI

The assumptions and settings for the WI 3-stage simulations are reiterated as follows.

1. Four typical weeks are simulated: the 3rd week of January, April, July and October in year 2022;

2. DA simulations: a. DA forecasted load / wind / solar: 24 to 48 hours ahead b. 24-hours SCUC / ED with hourly interval c. Nodal network representation d. Contingency, Flexibility up / down, Regulation up / down reserves


3. HA simulations: a. 4-HA forecasted load / wind / solar: 4 hours ahead b. 4-hours plus 20-hours look-ahead SCUC / ED with hourly interval c. Nodal network representation d. Contingency, Flexibility up / down, Regulation up / down reserves




interval c. Nodal network representation d. Contingency, Regulation up / down reserves modeled. Flexibility up /

down reserve are deployed e. Unit Commitment patterns from the HA simulation are frozen for the


f. Generator maintenance and forced outages are modeled 5. Three cases, no PSH, with the existing FS PSHs, with the existing FS PSHS and

three proposed adjustable speed PSHs (Swan Lake, Iowa Hill and Eagle Mountain), are simulated;

6. The simulations are performed for the 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.

5.3.1 WI 3-stage Simulation Results for Four Typical Weeks in Year 2022

In the following simulations, the generator maintenance outages are modeled in the DA and HA simulations, and the generator maintenance and forced outages are modeled in the RT simulations.

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The WI production cost ($000) from 3-stage simulations for three cases and four typical weeks in year 2022 in the high-wind renewable scenario is listed in the following chart.


1. In general, the production cost from the HA simulations (the red columns) is higher than that from the DA simulations (the blue columns), and the production cost from the RT simulations (the green columns) is higher than that from the DA and HA simulations. The average production cost over four weeks from the RT simulations (the green columns) is about 5% higher than that from the DA simulations (the blue columns). The higher production cost from the RT simulations indicates that the generators ramp more and the quick-startup CT generators are committed more to meet the sub-hourly load and renewable generation variability and uncertainty.


a. With the fixed speed PSHs, the average production cost from the RT simulations over four weeks (the solid green columns) is reduced by 2% as opposed to that from the RT simulations without PSHs (the dotted green columns).

b. With the additional adjustable speed PSHs, the average production cost from the RT simulations over four weeks (the tilted strip green columns) is reduced by 4% as opposed to that from the RT simulation without PSHs (the dotted green columns).

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 228,695 214,661 313,533 229,191

No PSH HA 233,654 216,952 331,003 230,707

No PSH RT 225,001 215,732 368,063 228,507

FS PSH DA 222,854 209,927 311,589 225,023

FS PSH HA 226,743 212,886 329,238 225,451

FS PSH RT 219,813 212,271 360,280 224,063

FS&AS PSH DA 219,878 202,987 307,278 222,105

FS&AS PSH HA 218,699 207,623 325,511 222,560

FS&AS PSH RT 213,990 208,154 355,829 222,013

20,000

70,000

120,000

170,000

220,000

270,000

320,000

370,000

420,000

$000

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


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The WI startup costs from the 3-stage simulations for the three cases and four typical weeks are presented in the following chart.


1. The start-up cost from the RT simulations (the green columns) is higher than that from the DA and HA simulations (the blue and red columns) due to

a. The additional CT commitment in the RT simulations to meet the load and renewable generation variability and uncertainty;

b. The additional start up because of the forced outages in the RT simulations from the generators whose unit commitments are frozen.

2. Comparing the startup costs between the RT simulations and DA simulations, the followings are observed.

a. Without PSHs, the average startup cost from the RT simulations over four weeks (the dotted green columns) is 50% higher than that from the DA simulations (the dotted blue columns).

b. With the fixed speed PSHs, the average startup cost from the RT simulations over four weeks (the solid green columns) is 41.9% higher than that from the DA simulations (the solid blue columns).

c. With the additional adjustable speed PSHs, the average startup cost from the RT simulations over four weeks (the tilted strip green columns) is 43.8% higher than that from the DA simulations (the tilted strip blue columns).

3. Comparing the startup costs between the RT simulations without and with PSHs, the followings are observed.

a. With the fixed speed PSHs, the average startup cost from the RT simulation over four weeks (the solid green columns) is reduced by 11% as opposed to the RT simulations without PSHs (the dotted green columns).

b. With the additional adjustable speed PSHs, the average startup cost from the RT simulation over four weeks (the tilted strip green columns) is reduced by 18% as opposed to the RT simulations without PSHs (the dotted green columns).

These observations indicate that the PSHs reduce the additional CT commitment and the associated startup cost.

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 2,309 3,096 4,904 3,546

No PSH HA 2,429 3,049 4,711 3,438

No PSH RT 3,938 4,516 6,949 5,387

FS PSH DA 2,198 2,999 4,440 3,373

FS PSH HA 2,399 3,108 4,308 3,207

FS PSH RT 3,558 4,263 5,715 4,924

FS&AS PSH DA 2,221 2,716 3,836 3,122

FS&AS PSH HA 2,362 2,731 3,851 3,124

FS&AS PSH RT 3,429 3,815 5,262 4,603

‐

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

$000

WI Start & Shutdown Cost ($000) from 3‐stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High‐wind Renewable Scenario (Maintenance and


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The WI thermal generator ramp up and down in MW from the 3-stage simulations for the three cases and four typical weeks are presented in the following two charts.

The followings can be observed form the two charts.

1. The thermal generator ramp up and down (MW) from the RT simulations (the green columns) is substantially higher than the DA and HA simulations (the blue and red columns) due to the thermal generator ramp to meet the load and renewable generation variability and uncertainty.

2. Comparing the thermal generator ramp up and down in MW between the RT simulations and DA simulations, the followings are observed.

a. Without PSHs, the average thermal generator ramp up and down from the RT simulations over four weeks (the dotted green columns) is 979,290 (MW) and 1,129,793 (MW) higher than that from the DA simulations (the dotted blue columns) respectively.

b. With the fixed speed PSHs, the average thermal generator ramp up and down (MW) from the RT simulations over four weeks (the solid green columns) is 946,998 (MW) and 1,051,968 (MW) higher than that from the DA simulations (the solid blue columns) respectively.

c. With the additional adjustable speed PSHs, the average thermal generator ramp up and down (MW) from the RT simulations over four weeks (the tilted green columns) is 767,880 (MW) and 850,045 (MW) higher than that from the DA simulations (the titled blue columns) respectively.

3. Comparing the thermal generator ramp up and down in MW between the RT simulations without and with PSHs, the followings are observed.

a. With the fixed speed PSHs, the average thermal generator ramp up and down in MW from the RT simulations over four weeks (the solid green columns) is reduced by 5% and 8% as opposed to the RT simulations without PSHs (the dotted green columns) respectively.

b. With the additional adjustable speed PSHs, the average thermal generator ramp up and down in MW from the RT simulations over four weeks (the tilted strip green columns) is reduced by 23% and 25% as opposed to the RT simulations without PSHs (the dotted green columns) respectively.

These observations indicate that the PSHs, specially the adjustable speed PSHs, reduce the thermal generator ramp substantially.

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 165,526 185,239 278,785 216,093

No PSH HA 182,007 177,998 237,843 212,766

No PSH RT 331,426 390,611 647,133 455,762

FS PSH DA 145,704 168,637 266,402 197,938

FS PSH HA 157,300 162,739 221,009 202,610

FS PSH RT 332,571 382,242 548,575 462,291

FS&AS PSH DA 107,822 136,537 215,443 172,903

FS&AS PSH HA 128,817 137,314 196,595 185,905

FS&AS PSH RT 290,773 285,918 445,140 378,754

‐

100,000

200,000

300,000

400,000

500,000

600,000

700,000

MW

WI Thermal 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


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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 247,543 266,399 413,835 308,090

No PSH HA 265,926 260,447 358,439 305,085

No PSH RT 438,213 504,762 834,943 587,742

FS PSH DA 219,455 238,935 382,624 277,453

FS PSH HA 236,387 232,198 329,386 284,025

FS PSH RT 433,287 474,351 691,397 571,401

FS&AS PSH DA 171,946 197,419 315,295 242,889

FS&AS PSH HA 188,335 198,574 291,804 254,916

FS&AS PSH RT 371,701 365,393 573,253 467,245

‐

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

MW

WI Thermal Ramp Down (MW) from 3‐stage Simulations for Three Cases and Four Typical Weeks in Year 2022 in High‐wind Renewable Scenario (Maintenance adn Forced


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5.4 3-stage DA-HA-RT Simulation Results for SMUD

Before simulating SMUD, the WI simulations are performed to produce the power exchanges between the SMUD footprint and the rest of WI for both the base and high-wind renewable scenarios. Then the power exchanges are frozen in the SMUD DA, HA and RT simulations.

The assumptions and settings for the SMUD 3-stage simulations are reiterated as follows.

1. Four typical weeks are simulated for the SMUD footprint: the 3rd week of January, April, July and October in year 2022.

2. DA simulations: a. DA forecasted load / wind / solar: 24 to 48 hours ahead b. 24-hours SCUC / ED with hourly interval c. Regional network representation d. Contingency, Flexibility up / down, Regulation up / down reserves


3. HA simulations: a. 4-HA forecasted load / wind / solar: 4 hours ahead b. 4-hours plus 20-hours look-ahead SCUC / ED with hourly interval c. Regional network representation d. Contingency, Flexibility up / down, Regulation up / down reserves




interval c. Regional network representation d. Contingency, Regulation up / down reserves modeled. Flexibility up /

down reserve are deployed e. Unit Commitment patterns from the HA simulation are frozen for the


f. Generator maintenance and forced outages are modeled 5. Two cases, no PSH, and with the new Adjustable Speed PSHs (Iowa Hill), are

simulated; 6. The simulations are performed for the high-wind renewable scenarios; 7. The SMUD simulations are cost-based.

5.4.1 SMUD 3-stage Simulation Results for Four Typical Weeks in Year 2022

The SMUD production cost ($000) from 3-stage simulations for two cases and four typical weeks in year 2022 in the high-wind renewable scenario is listed in the following chart.

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1. In general, the production cost from the HA simulations (the red columns) is higher than that from the DA simulations (the blue columns), and the production cost from the RT simulations (the green columns) is higher than that from the DA and HA simulations.

a. Without the PSHs, the average production cost over four weeks from the RT simulations (the dotted green columns) is about 10.5% higher than that from the DA simulations (the dotted blue columns).

b. With the adjustable speed PSHs (Iowa Hill), the average production cost over four weeks from the RT simulations (the tilted strip green columns) is about 40% higher than that from the DA simulations (the tilted strip blue columns).


3. With the adjustable speed PSHs (Iowa Hill), the average production cost from the RT simulations over four weeks (the tilted strip green columns) is reduced by 14.3% as opposed to that from the RT simulation without PSHs (the dotted green columns).

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 4,389 2,617 4,802 2,253

No PSH HA 4,343 2,716 6,673 2,390

No PSH RT 4,245 2,562 6,334 2,390

FS&AS PSH DA 3,055 1,481 3,448 1,523

FS&AS PSH HA 3,022 1,190 5,277 1,644

FS&AS PSH RT 3,748 2,369 5,258 1,934

‐

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

$000

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


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The SMUD startup costs from the 3-stage simulations for the two cases and four typical weeks are presented in the following chart.


1. In general, the start-up cost from the RT simulations (the green columns) is higher than that from the DA and HA simulations (the blue and red columns) due to

a. The additional CT commitment in the RT simulations to meet the load and renewable generation variability and uncertainty;

b. The additional start up because of the forced outages in the RT simulation from the generators whose unit commitments are frozen.

2. Comparing the startup costs between the RT simulations and DA simulations, the followings are observed.

a. Without PSHs, the average startup cost from the RT simulations over four weeks (the dotted green columns) is increased to $499,000 from $421,000 from the DA simulations (the dotted blue columns).

b. With the adjustable speed PSHs (Iowa Hill), the average startup cost from the RT simulations over four weeks (the tilted strip green columns) is increased to $446,000 from $193,000 from the DA simulations (the tilted strip blue columns).

3. With the adjustable speed PSHs (Iowa Hill), the average startup cost from the RT simulations over four weeks (the titled strip green columns) is reduced by 10.6% as opposed to the RT simulations without PSHs (the dotted green columns).

This observation indicates that the PSHs reduce the additional CT commitment and the associated startup cost.

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 124 114 68 115

No PSH HA 120 135 101 146

No PSH RT 127 138 94 140

FS&AS PSH DA 20 60 77 36

FS&AS PSH HA 64 31 70 59

FS&AS PSH RT 121 131 79 115

‐

20

40

60

80

100

120

140

160

$000

SMUD Start & Shutdown Cost ($000) from 3‐stage Simulations for Two Cases and Four Typical Weeks in Year 2022 in High‐wind Renewable Scenario (Maintenance and Forced


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The SMUD thermal generator ramp up and down in MW from the 3-stage simulations for the two cases and four typical weeks are presented in the following two charts.

The followings can be observed from these two charts.

1. The thermal generator ramp up and down (MW) from the RT simulations (the green columns) is substantially higher than that from the DA and HA simulations (the blue and red columns) due to the thermal generator ramp to meet the sub-hourly load and renewable generation variability and uncertainty.

2. Comparing the Ramp Up and Down in MW between the RT simulations and DA simulations, the followings are observed.

a. Without PSHs, the average thermal generator ramp up and down from the RT simulations over four weeks (the dotted green columns) is 12,225 (MW) and 14,290 (MW) higher than that from the DA simulations (the dotted blue columns) respectively.

b. With the adjustable speed PSHs, the average thermal generator ramp up and down (MW) from the RT simulations over four weeks (the tilted green columns) is 14,037 (MW) and 18,613 (MW) higher than that from the DA simulations (the titled blue columns) respectively.

3. With the adjustable speed PSHs, the average thermal generator ramp up and down from the RT simulations over four weeks (the tilted strip green columns) is reduced by 22.1% and 22.9% as opposed to the RT simulations without PSHs (the dotted green columns) respectively.

This observation indicates that the adjustable speed PSHs reduce the thermal generator ramp substantially.

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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 4,710 3,224 4,627 3,128

No PSH HA 4,790 3,283 6,479 3,872

No PSH RT 7,772 3,181 9,916 7,046

FS&AS PSH DA 3,242 741 2,882 854

FS&AS PSH HA 3,644 648 3,607 2,068

FS&AS PSH RT 7,092 258 9,041 5,365

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4,000

6,000

8,000

10,000

12,000

MW

SMUD Thermal Ramp Up (MW) from 3‐stage Simulations for Two Cases and Four Typical Weeks in Year 2022 in High‐wind Renewable Scenario (Maintenance and


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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)

1/22/2022 4/23/2022 7/23/2022 10/22/2022

No PSH DA 7,886 6,051 5,995 5,565

No PSH HA 7,613 6,430 8,638 6,712

No PSH RT 10,797 6,520 12,155 10,315

FS&AS PSH DA 3,957 2,287 4,604 1,228

FS&AS PSH HA 5,636 1,463 4,513 3,231

FS&AS PSH RT 9,913 3,318 10,148 7,310

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4,000

6,000

8,000

10,000

12,000

14,000

MW

SMUD Thermal Ramp Down (MW) from 3‐stage Simulations for Two Cases and Four Typical Weeks in Year 2022 in High‐wind Renewable Scenario (Maintenance and


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6 Findings

The findings from the simulation result analyses are listed as follows.

6.1 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 PSHs are $45.3/kw-year 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 adjustable speed 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 high-wind 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 SMUD simulations for year 2022 show that, with the proposed adjustable speed PSH, Iowa Hill, the production cost saving is 8.6% of the total SMUD 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

1. 1.6% from the WI RT simulations with the three proposed adjustable speed PSHs, Swan Lake, Iowa Hill and Eagle Mountain (the cost difference between “FS PSH RT” and “FS&AS PSH RT” in 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));

2. 2.4% from the CA RT simulations with the two proposed adjustable speed PSHs, Iowa Hill and Eagle Mountain (the cost difference between “FS PSH RT” and “FS&AS PSH RT” in 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));

3. 14.9% from the SMUD RT simulations with the proposed adjustable speed PSHs, Iowa Hill (the cost difference between “No PSH RT” and “FS&AS PSH RT” in 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)).

Though the production cost savings in percentage from the RT simulations are comparable to the production cost savings from the DA simulations, the production cost from the RT simulations are higher than that from the DA simulations. The production cost difference between the RT simulation and the DA simulation could be over 50% in some week. The higher production cost in the RT simulations is due to the sub-hourly

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thermal dispatch at less economic loading points and the CT commitment cost to accommodate the sub-hourly load and renewable generation variability and uncertainties.

6.2 Contributions to reserves: contingency, flexibility and regulation reserves.

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 high-wind 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 SMUD simulations for year 2022 show that the proposed adjustable speed PSH, Iowa Hill, provides 3.4% ~ 15.8% of the total SMUD upward reserves and 23.5% ~ 29.5% of the total SMUD downward reserves in the base renewable scenario. The adjustable speed PSH provides 2.0% ~ 17.6% of the total SMUD upward reserves and 14.3% ~ 20.5% of the total SMUD 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 SMUD Simulations

Base Renewable


Base Renewable


Base 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%Table 6.2‐1 Reserve Provisions from Adjustable Speed PSH in % of Total Reserve Requirements

6.3 Contributions to the emission reductions

The regional simulations, WI and California, do not show significant emission reduction with the Adjustable Speed PSHs introduced in the system in both the base and high-wind renewable scenarios. However, the emission productions are reduced from the base renewable scenario to the high-wind renewable scenario.

The SMUD portfolio simulations show a significant emission reductions when the adjustable speed PSHs, Iowa Hill, is introduced to the system in both the base and high-wind renewable scenarios.

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6.4 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 6.2-1 Reserve Provisions from Adjustable Speed PSH in % of Total Reserve Requirements.

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 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;

6.5 Contributions to reserves: contingency, flexibility and regulation reserves

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 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 SMUD system. With the adjustable speed PSH, Iowa Hill, the renewable generation curtailment from the SMUD simulations for year 2022 is reduced from 19 GWh to 1.0 GWh in the high-wind renewable scenario.

6.6 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

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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 SMUD 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 SMUD RT simulations with the proposed adjustable speed PSHs, Iowa Hill.

Though the startup cost savings in percentage from the RT simulations are comparable to the startup cost savings from the DA simulations, the startup cost from the RT simulations are higher than that from the DA simulations. The startup 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 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 SMUD RT simulations with the proposed adjustable speed PSHs, Iowa Hill.

Though the thermal generator ramp up and down reduction in percentage from the RT simulations are comparable to the ramp up and down reduction from the DA simulations, the ramp up and down from the RT simulations are higher than that from the DA

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simulations. 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 sub-hourly load and renewable generation variability and uncertainties.

6.7 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.

6.8 Contributions to the portfolio

With the adjustable speed PSHs, Iowa Hill, the SMUD simulations show substantial reductions in the SMUD 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.

6.9 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. Since the preliminary transmission expansion was performed for the high-wind renewable scenario, there is no significant WI average transmission congestion price reduction. However, in both the base and high-wind renewable scenarios, the interface with the significant congestion price reduction is “P27 Intermountain Power Project DC Line” that is in the neighboring area of PSHs “Castaic” and “Eagle Mountain”.

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 FS PSHs, and further to $0.24/MWh in the case of with FS&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&AS PSHs in the high-wind renewable scenario. The lower transmission congestion price in the high-wind renewable scenario is due to the transmission expansion assumptions for the high-wind renewable scenario. Again, in both the base and high-wind renewable scenarios, the interface with the significant congestion price reduction is “P27 Intermountain Power Project DC Line” that is the neighboring area of PSHs “Castaic” and “Eagle Mountain”.

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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.

6.10 Transmission Deferral

In the base renewable scenario, PSHs help reducing the transmission congestion for some interfaces in the Southern California. The interface with the most congestion price reduction is “P27 Intermountain Power Project DC Line” after the PSHs are introduced to the system.

In the high-wind renewable scenario, the interface with the most congestion price reduction is “P27 Intermountain Power Project DC Line” after the PSHs are introduced to the system, though the preliminary transmission expansion is performed to deliver the renewable generation to the load centers.

This study shows that PSHs can help reduce the transmission congestion or defer the transmission build-out in the neighboring areas where the PSHs are located.

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7 Appendix – Transmission Expansion Assumptions for High-wind Renewable Scenario

Line From Bus From Bus Region To Bus

To Bus Region

Capacity (MW)

ARR___PS_11014 to ARROYO_11017 1 1 11014_ARR___PS EPE 11017_ARROYO EPE 275

ARR___PS_11014 to ARROYO_11017 1 2 11014_ARR___PS EPE 11017_ARROYO EPE 275

B‐A_10025 to GUADLUPE_10116 1 1 10025_B‐A PNM 10116_GUADLUPE PNM 1076

B‐A_10025 to GUADLUPE_10116 1 2 10025_B‐A PNM 10116_GUADLUPE PNM 1076

BILINGS_62082 to BLGS PHA_62045 1 1 62082_BILINGS NWMT 62045_BLGS PHA NWMT 300

BILINGS_62082 to BLGS PHA_62045 1 2 62082_BILINGS NWMT 62045_BLGS PHA NWMT 300

BONANZA_65193 to MONA_65995 1 1 65193_BONANZA PACE_UT 65995_MONA PACE_UT 725

CBK 500_50791 to CR_NEST1_54458 1 1 50791_CBK 500 BCH 54458_CR_NEST1 AESO 940

CBK 500_50791 to CR_NEST1_54458 1 2 50791_CBK 500 BCH 54458_CR_NEST1 AESO 940

FLAGSTAF_79024 to PINPKBRB_79053 1 1 79024_FLAGSTAF WALC 79053_PINPKBRB WALC 747

GATES_30055 to MIDWAY_30060 1 1 30055_GATES PG&E_VLY 30060_MIDWAY PG&E_VLY 1931.2

GLENCANY_79032 to GLENCANY_79031 1 1 79032_GLENCANY WALC 79031_GLENCANY WALC 300



H ALLEN_18001 to H ALLEN_18019 1 1 18001_H ALLEN NEVP 18019_H ALLEN NEVP 300




HA PS_18002 to H ALLEN_18001 1 1 18002_HA PS NEVP 18001_H ALLEN NEVP 300



LANGDON2_54158 to CR_NEST1_54458 01 1 54158_LANGDON2 AESO 54458_CR_NEST1 AESO 940


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To Bus Region

Capacity (MW)


LANGDON2_54158 to LANGDOB9_58158 T1 1 54158_LANGDON2 AESO 58158_LANGDOB9 AESO 1200



LAR.RIVR_73107 to LAR.RIVR_73108 1 1 73107_LAR.RIVR WACM 73108_LAR.RIVR WACM 600

MATLB1_54451 to MATL AB_56451 T1 1 54451_MATLB1 AESO 56451_MATL AB AESO 330

NEWMAN_11111 to NEWMAN_B_11204 1 1 11111_NEWMAN EPE 11204_NEWMAN_B EPE 184



NLY 230_50784 to NLY 2PS2_50822 2 1 50784_NLY 230 BCH 50822_NLY 2PS2 BCH 400



OJO_10232 to TAOS_12082 1 1 10232_OJO PNM 12082_TAOS PNM 299

OJO_10232 to TAOS_12082 1 2 10232_OJO PNM 12082_TAOS PNM 299

PINPKBRB_79053 to PINPK_19062 1 1 79053_PINPKBRB WALC 19062_PINPK WALC 600

REDBUTTE_66280 to UTAH‐NEV_67657 1 1 66280_REDBUTTE PACE_UT 67657_UTAH‐NEV PACE_UT 300




RIOPUERC_10390 to B‐A_10025 2 1 10390_RIOPUERC PNM 10025_B‐A PNM 1195.1

RIOPUERC_10390 to WESTMESA_10369 1 1 10390_RIOPUERC PNM 10369_WESTMESA PNM 1195.1

SANJN PS_79060 to SAN_JUAN_10292 1 1 79060_SANJN PS WACM 10292_SAN_JUAN PNM 600

UTAH‐NEV_67657 to HA PS_18002 1 1 67657_UTAH‐NEV PACE_UT 18002_HA PS NEVP 300




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To Bus Region

Capacity (MW)

WABAMUN9_54134 to CARVEL02_55364 96 1 54134_WABAMUN9 AESO 55364_CARVEL02 AESO 121

WABAMUN9_54134 to CARVEL02_55364 96 2 54134_WABAMUN9 AESO 55364_CARVEL02 AESO 121Table 6.10‐1 Transmission line expansion for high‐wind renewable scenario

Before Expansion After Expansion

Row Labels Max Flow Min Flow Max Flow Min Flow

Interstate AB‐MT 325 ‐300 325 ‐600

Interstate WA‐BC East 400 ‐400 2400 ‐400

Interstate WA‐BC West 3000 ‐2850 3000 ‐3850

Intrastate CA PDCI South 2780 ‐3100 3780 ‐3100

P01 Alberta‐British Columbia 700 ‐720 700 ‐2160

P03 Northwest‐British Columbia 3000 ‐3150 3000 ‐4150

P18 Montana‐Idaho 337 ‐256 674 ‐256

P24 PG&E‐Sierra 160 ‐150 160 ‐300

P26 Northern‐Southern California 4000 ‐3000 4000 ‐4000

P31 TOT 2A 690 ‐690 1380 ‐690

P35 TOT 2C 600 ‐580 2400 ‐1160

P36 TOT 3 1680 ‐1680 2680 ‐1680

P38 TOT 4B 829 ‐829 1658 ‐829

P40 TOT 7 890 ‐890 1335 ‐890

P45 SDG&E‐CFE 408 ‐800 2448 ‐800

P48 Northern New Mexico (NM2) 1970 ‐1970 1970 ‐2970

P52 Silver Peak‐Control 55 kV 17 ‐17 34 ‐170

P59 WALC Blythe ‐ SCE Blythe 161 kV Sub 218 ‐218 436 ‐218

P80 Montana Southeast 600 ‐600 600 ‐1200Table 6.10‐2 Transmission interface expansion for high‐wind renewable scenario

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8 References

[1] WECC TEPPC, “Assumptions Matrix for the 2020 TEPPC Dataset.pdf”, 2020

[2] WECC TEPPC, “2022_CommonCase_InputAssumptions.doc”, 2022

[3] Department of Market Monitoring, CAISO, “2012 Annual Report on Market Issues and Performance”, April 2013, http://www.caiso.com/Documents/2012AnnualReport-MarketIssues-Performance.htm

[4] D. Lew and G. Brinkman, National Renewable Energy Laboratory, N. Kumar, P. Besuner, D. Agan, and S. Lefton, Intertek APTECH, “Impacts of Wind and Solar on Fossil-Fueled Generators”, Presented at IEEE Power and Energy Society General Meeting, San Diego, California July 22–26, 2012

[5] Lew, D., Brinkman, G., Ibanez, E., Florita, A., Heaney, M., Hodge, B.-M., Hummon, M., Stark, G., King, J., Lefton, S., Kumar, N., Agan, D., Jordan, G., Venkataraman, S. (2013). “The Western Wind and Solar Integration Study Phase 2”, NREL/TP-5500-55588. Golden, CO: National Renewable Energy Laboratory.

[6] Matt Hunsaker, Nader Samaan, Michael Milligan, Tao Guo, Guangjuan Liu, Jake Toolson, “Balancing Authority Cooperation Concepts to Reduce Variable Generation Integration Costs in the Western Interconnection: Intra-Hour Scheduling”, WECC Variable Generation Subcommittee project report, March 29, 2013