ERCOT LMP Merchant Curve: Texas Power Price Forecasts

The core structural difference is market design. ERCOT is an energy-only market: generators recover all fixed and variable costs through real-time and day-ahead energy prices plus ancillary service revenues. There is no forward capacity obligation and no long-run capacity auction. PJM, by contrast, runs a Base Residual Auction that commits capacity three years forward at a cleared price, providing a predictable revenue floor for dispatchable generators and softening the energy price signal during scarcity. In practice, this means ERCOT prices are more volatile but carry higher upside in tight conditions, while PJM prices are more predictable but subject to capacity market design risk. The two markets also differ in renewable penetration (ERCOT is materially higher), grid interconnection (ERCOT is electrically isolated), and congestion complexity (PJM has 900+ delivery nodes versus ERCOT’s four load zones).

West Hub is the primary generation zone for ERCOT’s wind and solar fleet. The Competitive Renewable Energy Zones (CREZ) transmission lines were built to move West Texas generation to load centers in the Dallas-Fort Worth and Houston areas, but they operate at or near capacity during high-generation periods. When West Texas wind output is high and load in North and Houston zones is moderate, congestion causes West Hub prices to trade at a discount to load-zone hubs, sometimes deeply negative during curtailment events. For a project delivering at West Hub, the expected LMP may be 10-30% below what a Houston Hub or North Hub curve would show. Noreva models this West-to-load-zone basis as a forward distribution based on historical congestion patterns and planned transmission additions, providing a project-specific delivery price rather than relying on system hub curves.

Scarcity event modeling in ERCOT requires separating two distinct price regimes: the structural price level that reflects gas costs and renewable output across the majority of hours, and the tail-risk events where reserves fall and ORDC adders push prices toward the $5,000/MWh system cap. Noreva models both components separately. The structural forward curve is built from fundamental dispatch assumptions. The scarcity overlay uses a statistical framework calibrated to historical event frequency and magnitude, adjusted for post-Uri infrastructure changes, summer demand growth, and the marginal dispatchable capacity position. The framework produces a full price distribution rather than a single expected value, allowing project finance cases to be built at P50, P75, and P90 revenue levels. For context on storm-driven scarcity events: lessons from storm Fern.

Without a capacity market, ERCOT generators carry the full revenue risk of their fixed cost recovery. A gas peaker that runs 200 hours per year must recover its capital and fixed O&M costs from those 200 hours of energy and ancillary service revenue alone, whereas a PJM peaker might earn 20-30% of its total revenue from the capacity auction regardless of how many hours it runs. The practical effect is that ERCOT requires a steeper energy price curve to support investment in new dispatchable capacity. The ORDC scarcity adder is the primary mechanism ERCOT uses to allow prices to rise high enough during reserve shortfalls to signal new investment. This design concentrates revenue risk in a small number of high-value hours, which makes financial modeling and project finance structuring in ERCOT more sensitive to scarcity assumptions than in any other U.S. ISO. For analysis of private capital’s response to these dynamics: private markets and the power build-out.