Our system-wide ERCOT simulation runs day-ahead market and real-time market at hourly and 15-min granularity respectively across the entire modeling horizon. Key outputs of the model include prices for energy and ancillary services, generation output, and transmission flows, among others. The modeling horizon extends to 2049, with varying levels of locational granularity:
- Nodal modeling at the substation level from 2025 - 2034.
- Zonal modeling at load zone level (North, South, Houston, and West) for 2034 onwards.
This split reflects a trade-off between modeling detail and input data quality for distant future. In the nearer term, there is more detailed, locational data on system topology for load, generation and transmission. As such, we can appropriately capture critical locational signals — congestion, basis risk, and site-specific value drivers. Beyond 2034, there is less locational detail available and a higher level uncertainty around system topology. It is our belief, that speculating on the specifics of a 2049 topology introduces ‘false precision’.
To avoid this, we feel it is most appropriate to move to a zonal representation of the system for the later years in the horizon. This enables us to maintain some location-awareness in the model whilst avoiding the pitfalls of false precision.
Inputs
The table below summarizes the key input types to our model, and provides a high-level description of how each input is constructed alongside key data sources used to inform our assumptions. Further details for each of these inputs can be found at the links provided.
| Input Type | Input | Summary | Key Data Sets |
|---|---|---|---|
| Generation | Capacity buildout | Capacity values are determined by our Capacity Expansion Model and buildout constraints are informed from historical Interconnection Queue data | ERCOT GIS Queue |
| Generation | Renewable load factors and outages | Renewable resource availability and outage profiles are empirically modeled using historical SCED High Sustained Limit (HSL) data to capture seasonal and hourly variability. | ERCOT SCED High Sustained Limit (HSL) data |
| Generation | Start costs | Start costs for thermal technologies are determined empirically based on historical SCED data and forecast fuel prices. | ERCOT SCED Data, EIA AEO commodity price forecasts |
| Generation | Short-run marginal costs | Short-run marginal costs (SRMCs) are empirically estimated for each generation site using historical SCED pricing data combined with forward-looking fuel commodity price forecasts. | ERCOT SCED Data, EIA AEO commodity price forecasts |
| Demand | Nodal Demand | Nodal demand is modeled hourly at the substation level through 2034, combining weather zone load shapes and nodal distribution factors. Beyond 2034, stylized zonal growth assumptions extend demand projections to 2050. | ERCOT RTP Load Profiles, ERCOT LTSA, Permian Basin Study |
| Transmission | Nodal Transmission Network | Up to 2034, the nodal transmission network is built from individual lines with detailed ratings and reactance values, updated to reflect planned topologies from ERCOT’s Regional Transmission Plan and key Permian Basin upgrades. | ERCOT RTP Economic Start Cases, CIM Ratings Report, Permian Basin Reliability Planning Study |
Modeling
Alongside the core modeling functionality described here, there are additional market features modeled in our representation of the ERCOT system.
Outputs
Macro databook
The Macro Databook summarizes key system-level outputs with yearly granularity, capturing installed generation capacity by technology type, generation volumes, commodity prices, zonal electricity prices, and top-bottom two-hour spreads (TB2s). Provided in CSV format, it represents Modo Energy’s proprietary view on capacity expansion pathways, future commodity prices, and market dynamics, clearly reflecting the core assumptions underpinning our ERCOT modeling approach and enabling high-level market analysis.
Site specific databook
The site specific databook provides detailed monthly and annual performance metrics for individual battery assets, including cycling behavior, merchant, and ancillary service revenues. Cycling counts and prices are averaged over each period, while total revenues are also presented on a per MW basis, to facilitate asset benchmarking and investment evaluation. These revenue outputs can directly support financial modeling, due diligence processes, and investment decisions.