This page lists system reliability modeling datasets used by Energy Division to model the CAISO balancing area electric system, typically in support of the Resource Adequacy (RA) and Integrated Resource Plan (IRP) proceedings.  Prior versions of this webpage were titled “Unified RA and IRP Modeling Datasets.”  CPUC staff have changed the name to more directly describe the content and avoid naming specific CPUC proceedings since the modeling datasets have several use cases across multiple CPUC proceedings.

All data were exported from the “Strategic Energy & Risk Valuation Model” (SERVM), a probabilistic system reliability and production cost model developed by Astrapé Consulting.  In 2023, Energy Division updated inputs to support modeling primarily for the IRP 2023 Preferred System Plan development work.  Key inputs to SERVM in support of this work are provided on this page.

Electricity Consumption Profiles

Hourly MW consumption profiles for study years 2024, 2026, 2030, and 2035, for all regions modeled, based on 1998-2020 weather.  Median annual energy and peak demand for all California regions are based on the CEC’s 2022 IEPR Update Demand Forecast.  SERVM’s modeled consumption is developed from the CEC’s managed forecast by backing out the effects of BTM PV, BTM storage, AAEE, AAFS, TOU rates, and electric vehicle (light/medium/heavy duty) charging load.  Each of these demand modifier components are separately modeled in SERVM.  Energy and peak demand for non-California regions are derived from the WECC Anchor Dataset 2032.

Hourly Consumption Demand, all regions, all weather years

These profiles are aligned to start on the day of week corresponding to the calendar year, e.g. 2026 will start on a Thursday.  Leap years drop Dec 31 such that all years are 8760 hours long.


Generating Units

The following list of generating units modeled in SERVM includes “baseline” and “LSE Plan” units.  “Baseline” means online or in development, consistent with the definition in the IRP proceeding.  “LSE Plan” means planned/new resources aggregated from LSE IRP filings in November 2022.  The “baseline” was derived from the CAISO Master Generating Capability Lists through January 2023, the January 2023 NQC list, the 2032 WECC Anchor Data Set, and the November 2022 LSE IRP Compliance Filings.  "LSE Plan" units were derived from aggregating all planned/new contracts in the November 2022 LSE Compliance Filings.  Both 25 MMT by 2035 and 30 MMT by 2035 LSE Plan portfolio units are included in the list.  Notes in the column headings explain each column where necessary.

SERVM Generator List (updated 10/5/2023)


Hydro Generation Profiles

Hourly MW profiles for the indicated study year.  The profiles are provided covering 23 weather years (1998-2020) for a relatively low hydro availability year (based on 2015) and a relatively high hydro availability year (based on 2005).  SERVM models hydro data from 1998-2020 but only 2015 and 2005 are posted here for brevity.

2024 low hydro (2015 hydro)

2024 high hydro (2005 hydro)

2026 low hydro (2015 hydro)

2026 high hydro (2005 hydro)

2030 low hydro (2015 hydro)

2030 high hydro (2005 hydro)

2035 low hydro (2015 hydro)

2035 high hydro (2005 hydro)


Hydro SERVM-specific input variables

Hydro-Variables 2023PSP post

This file contains SERVM-specific input variables defining hydro unit inputs.  The inputs were developed from 23 years (1998-2020) of monthly data from the EIA and 4 years of hourly data from the CAISO, BPAT, and EIA.  The source data was translated into monthly generation, daily minimum, average, and maximum generation, and monthly maximum output parameters.  SERVM schedules the hydro according to net load conditions and these constraints resulting in the output profiles listed above.


Renewables Generation Profiles

Hourly MW profiles for the indicated study year, for all unit categories in the three CAISO regions modeled, based on 1998-2020 weather patterns.  These profiles correspond to the 25 MMT portfolio results presented on slides 35-40 of this slide deck: 2023 Proposed PSP Reliability & Emissions Slide Deck

2024 Renewables Profiles

2026 Renewables Profiles

2030 Renewables Profiles

2035 Renewables Profiles

Hourly MW profiles for study years 2024, 2026, 2030, and 2035 for non-CAISO California regions, based on 1998-2020 weather patterns.

NON-CAISO Renewables Profiles

The (large) file below contains the NORMALIZED hourly profiles that are the basis for the actual profiles posted above, based on 1998-2020 weather patterns.  The actual profiles above are scaled to match the installed capacities in the portfolio for each year.  The file below includes a README describing each file briefly.

Normalized solar, wind, and load modifiers profiles, 1998-2020 weather updated Nov 2023 

The following file maps weather year profiles to weather stations to latitude and longitude as well as resource zones as defined in the RESOLVE model.

ProfileToWeatherStationMap 20230920

Besides utility-scale solar and wind categories, these files include the following categories of demand modifiers:

  • AAEE – Additional Achievable Energy Efficiency
  • AAFS – Additional Achievable Fuel Switching
  • BTMPV – Behind-The-Meter solar PV
  • BTMStorageShapeCharge – Charging profile of BTM storage
  • BTMStorageShapeDischarge – Discharging profile of BTM storage
  • EV – Light and med-heavy-duty Electric Vehicles including AATE
  • TOU_LoadIncrease – Time-Of-Use rate impacts that increase hourly load
  • TOU_LoadDecrease  – Time-Of-Use rate impacts that decrease hourly load

Day of week dependent demand modifier profiles such as AAEE and EV are aligned to start on the day of week corresponding to the calendar year, e.g. 2026 will start on a Thursday.  Leap years drop Dec 31 such that all years are 8760 hours long.  This is important to maintain day of week alignment with the consumption demand profiles listed above.

Representation of the CAISO Transmission System and Neighbors

Transmission capability in MW

Region Transfer

Hurdle Rates

Burner-Tip Fuel and Carbon Prices

Monthly Fuel Price Projection

GHG Price Projection