How the model builds future supply stacks for the WECC region
Future build of the generation stack across WECC is determined by the WECC Anchor Data Set (ADS) generator inventory in the short term, and a techno-economic capacity expansion model in the longer term.
Through December 31, 2034 (inclusive), the generation stack is based on the WECC ADS, which provides a detailed view of existing and planned assets including committed additions and expected retirements.
From 2035 onward, the in-house capacity expansion model takes over, reflecting the project economics of various power generators in a future modeled world across the WECC interconnection.
In every year modeled, investment decisions for new power generation combine with an hourly dispatch to reflect new and existing assets’ operation. If a generator’s existence will reduce the overall cost of running the system, it will be built. Build decisions are made given the same information as the full market dispatch (generator technicals, fuel & carbon, renewable profiles, outages, transmission limits are common to both). Investment decisions are therefore made for the same world as the market dispatch model actually sees, rather than a simplified proxy.
More information on the underlying capacity expansion model methodology is here.
At a glance
| Parameter | Description |
|---|---|
| Geography | WECC: CAISO and neighboring balancing areas |
| Horizon | Annual decisions to 2050 |
| Temporal resolution | Hourly dispatch on representative days per model year |
| Windowing | Rolling one‑year optimization (co-optimised investment + dispatch → state carry‑over) |
| Reliability | Slice-of-day resource adequacy with technology-specific de-rating factors |
| Outputs | Annual capacity additions/retirements by zone & technology, reliability metrics, and dispatch time series for sampled days |
Building a future generation stack
Technologies governed by economics are included as build candidates in the model:
- Solar PV
- Onshore wind
- Offshore wind
- Gas CCGT (combined cycle gas turbine)
- Gas SCGT (simple cycle gas turbine)
- Battery energy storage (BESS): multiple duration configurations
Note: Storage is modeled with power/energy limits, round-trip efficiency, and state-of-charge tracking.
Where the expansion model does not include a technology as a build option (e.g. nuclear, large hydro, hydrogen), official datasets and development plans are used to guide installed capacity levels. These are included as predetermined buildout rather than optimization variables.
Retirements
Gas plant retirements follow a proposed schedule informed by:
- CAISO 2024 20-Year Transmission Outlook: 15,000 MW of gas retirement by 2045, with regional targets (CAISO 20-Year Outlook)
- CPUC Draft 2025 IRP Inputs & Assumptions: linear phaseout of combined heat and power (CHP) plants between 2031 and 2040 (CPUC Draft 2025 I&A)
Diablo Canyon is assumed to receive a third operating life extension (administered by the NRC), consistent with the broader US trend toward extended nuclear lifetimes. As a result, no nuclear retirements are modeled within the forecast horizon. This and other asset-level assumptions are informed by Modo Energy’s in-house research team.
Policy and market features
The WECC capacity expansion model captures several policy mechanisms that are critical drivers of investment in the Western US.
Renewable Portfolio Standards (RPS)
State-level RPS mandates require load-serving entities (LSEs) to procure a minimum share of electricity from qualifying renewable sources. The model enforces RPS targets as constraints on the optimization, ensuring the build plan delivers sufficient renewable generation to meet compliance obligations across the forecast horizon.
California’s RPS — established under SB 100 (2018) — requires 60% of retail electricity sales to come from RPS-eligible renewable resources by 2030, rising to 100% from renewable and zero-carbon resources by 2045. SB 1020 (2022) added interim clean energy targets of 90% by 2035 and 95% by 2040.
RPS-eligible technologies
Under California’s RPS Eligibility Guidebook, the following generation technologies qualify as eligible renewable resources:
- Solar: Photovoltaic (PV) and solar thermal electric
- Wind: Onshore and offshore wind generation
- Geothermal: Geothermal power plants
- Biomass and biogas: Biomass, biogas (including pipeline biomethane), biodiesel, digester gas, landfill gas, and municipal solid waste conversion
- Small hydroelectric: Facilities of 30 MW or less, and conduit hydroelectric
- Fuel cells: Fuel cells using an RPS-eligible renewable fuel
- Ocean energy: Ocean wave, ocean thermal, and tidal current technologies
Large hydroelectric facilities and nuclear generation are not RPS-eligible, although they can count toward the broader SB 100 zero-carbon electricity target from 2030 onward.
Greenhouse Gas Emissions
California’s cap-and-trade program places an economy-wide limit on greenhouse gas (GHG) emissions and creates a carbon price signal through tradeable allowances. The program, administered by the California Air Resources Board (CARB), sets an annual emissions cap that declines over time, consistent with the state’s target of carbon neutrality by 2045 under AB 1279.
The model incorporates CPUC GHG allowance price projections — drawn from the CPUC’s Integrated Resource Planning (IRP) inputs and assumptions — into the dispatch economics of fossil-fueled generation. This raises the effective operating cost of thermal plants and improves the relative competitiveness of clean energy investments. As allowance prices rise over the forecast horizon, this mechanism increasingly favors low-carbon technologies in capacity expansion decisions.
The table below shows the GHG emissions constraints used in the model, expressed in million metric tons (MMT) within the CAISO footprint. These are drawn from the CPUC’s Draft 2025 IRP Inputs & Assumptions (Table 100).
| Scenario | 2025 | 2026 | 2028 | 2030 | 2035 | 2040 | 2045 |
|---|---|---|---|---|---|---|---|
| 30 MMT by 2035 & 8 MMT by 2045 | 39.9 | 38.2 | 34.6 | 31.1 | 24.8 | 16.0 | 7.1 |
Renewable Energy Credits
Renewable energy credits (RECs) provide an additional revenue stream for qualifying renewable generators. The model accounts for REC values when evaluating the economics of new renewable investments, reflecting the incremental income that developers can expect from credit sales. This mechanism can make otherwise marginal renewable projects economically viable and accelerates buildout beyond what energy market revenues alone would support.
Slice-of-Day Resource Adequacy
CAISO’s resource adequacy (RA) framework requires load-serving entities to procure sufficient capacity to meet system reliability standards. Starting in 2025, the CPUC replaced the legacy monthly peak-based RA program with a slice-of-day framework. The model implements this as a constraint on the capacity expansion optimization.
Under the previous system, LSEs had to secure enough capacity to meet a single forecasted peak demand each month. Under slice-of-day, capacity targets are set for each hour of the day, based on the “worst day” of each month. This changes how different technologies contribute to reliability:
- Hourly capacity targets: Every resource receives a capacity value for each hour. Dispatchable technologies receive a flat credit across the day, while solar and wind receive hourly profiles reflecting their expected generation pattern.
- Exceedance-based accreditation: Solar and wind are now credited based on historic generation profiles rather than the previous ELCC methodology, which derated California’s solar fleet to as low as 11% of nameplate. Under exceedance, solar can receive up to 90% credit during midday hours.
- Storage: Batteries must demonstrate sufficient excess capacity in other hours to cover their charging needs, but can count their capacity across multiple daily cycles. This shifts their value toward the critical evening net load ramp.
This structure ensures the model builds a portfolio that is reliable across all hours — not just at system peak — which is particularly important in WECC where the “net peak” occurs in the evening as solar output declines.
Note: Because resource adequacy is enforced as a constraint within the optimization, the model can extract an implied RA price for each hour — representing the cost to the system of needing one additional MW of capacity to meet the reliability requirement in that hour. These prices reflect how tight or relaxed the system is in different periods.
For more detail on how slice-of-day reforms affect resource revenues, see Modo Energy’s analysis of CAISO resource adequacy under slice-of-day.
Build constraints
The model incorporates practical constraints that shape investment patterns:
- Interconnection queue dynamics: Near-term buildout is anchored to projects with active interconnection applications, with queue attrition rates applied to reflect real-world project completion rates.
- Minimum project sizes: Technologies have minimum viable scales that reflect real-world development economics.
Technology cost projections
The capacity expansion model uses capital and operational cost projections from NREL’s Annual Technology Baseline (ATB). These projections cover learning curves and cost declines for solar, wind, gas, and storage technologies across the forecast horizon.
Example capacity buildout
The model produces annual capacity expansion pathways for each technology across all modeled zones.
Data sources
Policy and emissions
| Source | Description | Link |
|---|---|---|
| CEC RPS Eligibility Guidebook | Eligible renewable technology definitions and certification requirements | RPS Guidebook, 10th Edition |
| CPUC IRP Inputs & Assumptions | GHG allowance price projections and emissions constraints | CPUC IRP |
| CPUC Resource Adequacy | Slice-of-day RA framework and filing requirements | CPUC RA Homepage |
| CPUC SERVM | GHG allowance price projections used in capacity planning | CPUC SERVM |
Technology costs and fleet data
| Source | Description | Link |
|---|---|---|
| EIA-860 | Asset-level data on generation technology and operational status | EIA-860 |
| NREL ATB | CAPEX/OPEX projections for all candidate technologies | NREL ATB |
| WECC ADS | Generator inventory for existing fleet characterization | WECC ADS |
Financial assumptions
| Category | Source |
|---|---|
| CapEx | NREL Annual Technology Baseline (ATB) |
| GHG allowance prices | CPUC IRP Inputs & Assumptions |
| OpEx | NREL Annual Technology Baseline (ATB) |