Capacity buildout
Description
Capacity additions are determined by our proprietary Capacity Expansion Model based on techno-economic criteria (see more here). In the front end of our forecast, we apply a minimum build limit based on the quantity of generation with high likelihood to come online based on interconnection progress. For gas generation, we set a maximum build due to supply chain constraints around the construction of new gas turbines.
After the buildout values are decided for each of ERCOT’s load zones, projects from the interconnection queue are systematically selected based on a likelihood order that’s determined by factors such as their progress in securing interconnection agreements and financing.
We exclude Texas Energy Fund (TEF) projects from the Capacity Expansion Model, as their build decisions do not follow a standard financial framework. Instead, they are incorporated separately based on our proprietary assessment of build decisions and expected commercial operation timelines. Given the policy-driven nature of these assets, alternative commissioning timelines for TEF-backed projects are explored through dedicated sensitivity analysis cases.
Plant retirements are incorporated based on publicly announced decommissioning (from EIA) where available. In the absence of such announcements, retirements are projected using technology-specific lifespan assumptions, drawing from the EIA Generator Inventory dataset. For coal-fired generation in particular, alternative decommissioning schedules and retirement volumes are examined through sensitivity analysis cases to reflect uncertainty around policy, market conditions, and plant economics.
The chart below shows installed capacity by technology for each modeled scenario. Use the selector to switch between Modo Energy’s Central Scenario and four alternative future scenarios. Read more about the input assumptions behind these scenarios, and the wholesale price and battery revenue outcomes they produce, on the Scenarios page.
Data sources
| Source | Description | Link |
|---|---|---|
| ERCOT Generator Interconnection Status (GIS) queue | Informs short-term upper and lower limit buildout assumptions based on project development milestones. The October 2026 release uses the June 2026 report | ERCOT GIS Reports |
| ERCOT Monthly Outlook for Resource Adequacy (MORA) | Existing generator inventory and announced commercial operation dates. The October 2026 release uses the September 2026 report | ERCOT MORA |
| EIA Generator Inventory | Asset-level data on generation technology, operational status, and age; used for retirement assumptions | EIA Generator Inventory |
| EIA Annual Energy Outlook (AEO) | Directional guide for long-term capacity growth, referencing the High Renewables scenario | AEO Scenarios |
Unit Commitment
Unit commitment refers to the modeling of generator on/off decisions through binary variables that determine whether a generating unit is online or offline at any given hour.
In the model, a binary commitment variable is assigned to each thermal generating unit. Operational limits — specifically minimum and maximum generation levels — are conditional on this commitment status. When a unit is online, it must operate within its defined minimum stable output and maximum output levels. When offline, a unit’s output is constrained to zero. This structure allows the model to capture key operational behaviors such as startup costs, minimum load requirements, and restricted flexibility, reflecting real-world generator limitations.
For running thermal units, a capability floor is applied. A unit that was online on the replayed day is not limited to the output it happened to produce that day. Instead, its available capacity is lifted toward its demonstrated capability, adjusted by season. Outages are left untouched, so a unit that was offline on the replayed day stays offline.
Short-run marginal costs
Description
Short-run marginal costs (SRMCs) are estimated for all generation technologies in the system, including thermal and renewable assets. These values are derived primarily from fuel prices and heat rate assumptions. Fuel prices are sourced from the EIA’s Annual Energy Outlook commodity input data for coal and natural gas.
Where available, historical operating data is used to capture time-varying heat rates, allowing for seasonal differences and more realistic dispatch behavior. Specifically, we use ERCOT’s 60-day SCED disclosure data to estimate historical generator performance and derive implied unit-level heat rates. For new or prospective resources without operational data, technology-average heat rates are used (again derived from SCED data). We use multiple bid steps for thermal generation, including new generation, which again are informed by historical SCED data. For renewable technologies such as wind and solar, SRMCs are adjusted to reflect tax credits, which can result in negative marginal costs.
Natural gas prices start from the Henry Hub trajectory in the EIA’s Annual Energy Outlook (AEO 2026) and are blended with the CME Henry Hub futures strip. Futures carry full weight in the first quoted month and fade linearly to zero by the end of the strip in 2038, after which the curve follows the AEO alone. Monthly seasonality is taken from the futures strip. No Texas basis is added, because the AEO prices Texas gas within a few cents of Henry Hub. Coal prices follow the AEO electric power price for the Texas Reliability Entity region. Recognizing the inherent uncertainty in fuel markets, alternative commodity price paths are evaluated through sensitivity analysis cases to assess their impact on dispatch, revenues, and capacity outcomes.
The chart below compares natural gas and coal prices across the three scenarios that vary fuel costs: the Central Scenario, High Gas Buildout (10% lower), and Low Gas Buildout (15% higher). Use the selector to switch between scenarios.
Policy drivers
- Production tax credits (PTC) and investment tax credits (ITC) are incorporated into SRMC and CAPEX assumptions for each eligible technology. Under the OBBB Act, these credits phase out for renewable projects commissioned after 2028, and phase down in a step-wise schedule for storage projects commissioned after 2034.
Assumptions and caveats
- Technology-average heat rates are used for future or unbuilt units.
- We use a two-step bid step curve, while plants may bid with dozens of steps in SCED if desired.
- For existing generators, historical bids from the synthetic weather year are used, adjusted for inflation and commodity price changes. Only the fuel component of each offer curve is scaled by the ratio of the forecast gas price to the historical gas price on the replayed day.
Data sources
| Source | Description | Link |
|---|---|---|
| EIA Annual Energy Outlook (AEO) | Provides long-term commodity price trajectories for fuels. The October 2026 release uses AEO 2026 | EIA AEO Data |
| CME Henry Hub natural gas futures | Near-term gas price curve and monthly seasonality, blended with the AEO | CME Henry Hub |
| ERCOT 60-day SCED disclosure data | Used to estimate time-varying, unit-specific heat rates from historical generator dispatch | ERCOT Market Data Portal |
| PTC/ITC guidelines | Informs tax credit adjustments to renewable SRMCs | IRS Energy Credits |
Start costs
Description
Startup costs are modeled for thermal technologies that require discrete commitment decisions. Nuclear, solar, wind, and storage are assumed to be continuously available and do not incur startup costs in the model.
Start cost values are derived from historical market data, imputed using forecast commodity prices to estimate time-varying startup costs. These values are expressed as a fixed cost in $/MW per start and are applied only in the unit commitment step of the model to influence plant scheduling decisions.
Assumptions and caveats
- Startup costs are only applied to technologies that require explicit unit commitment.
- No modeling of hot vs. cold start cost differences.
- Startup costs are excluded from economic dispatch; they influence commitment only.
Data sources
| Source | Description | Link |
|---|---|---|
| Historical startup cost data | Used to estimate $/MW/start values by tech, adjusted using forecast fuel prices | ERCOT Market Data Portal |
| Fuel price forecasts (EIA AEO) | Informs variation in startup costs over time | EIA AEO Data |
Synthetic weather year
Weather-dependent inputs are replayed from historical ERCOT data. Rather than replaying a single historical year, the forecast uses a synthetic weather year stitched together from months of different years between 2020 and 2025. The model was run under each weather year in turn, and a historical year was then selected to represent each calendar month. The aim is to normalize for weather as far as possible, so that no single year is responsible for replayed scarcity in the same few days each forecast year. The same mapping is applied to every forecast year and every scenario.
| Month | Weather year | Month | Weather year |
|---|---|---|---|
| January | 2022 | July | 2022 |
| February | 2020 | August | 2023 |
| March | 2023 | September | 2020 |
| April | 2023 | October | 2023 |
| May | 2025 | November | 2025 |
| June | 2023 | December | 2024 |
The synthetic weather year drives zonal load shapes, wind and solar load factors, thermal availability and outages, the bids of existing generators, and transmission line loadings. Each forecast date is mapped to the same calendar date in the selected historical year. Winter Storm Uri (February 2021) is not part of the synthetic year.
Renewable load factors and outages
Description
Both load factors and outages profiles are modeled similarly and use historical available capacity data from ERCOT’s SCED disclosures; specifically, the High Sustained Limit (HSL) of individual units. Each generator’s HSL is expressed as a percentage of its nameplate capacity (sourced from the MORA dataset) to calculate an empirical load factor.
For existing wind and solar generators, this observed load factor is held constant throughout the forecast horizon. New build generators are assigned load factors based on the regional (zonal) average for the same technology class, reflecting geographic diversity and typical resource performance in that area.
Outages are modeled using the same HSL-derived method. Reductions in available capacity, reflecting planned or unplanned outages, are captured in the empirical load factor calculation.
Assumptions and caveats
- Load factors and outage rates are replayed from the synthetic weather year described above and do not evolve over time.
- New builds inherit regional average load factors for their technology class.
- Outages are captured via reduced HSL values, without explicit modeling of whether an outage is planned or unplanned.
- No adjustment is made to load factors or outages over time to reflect technology improvements or generator wear and tear.
Data sources
| Source | Description | Link |
|---|---|---|
| ERCOT 60-day SCED disclosure data | Provides High Sustained Limits (HSL) for calculating load factors and outages | ERCOT Market Data |
| ERCOT MORA dataset | Used to match generators to nameplate capacities | ERCOT MORA |