Probabilistic day-ahead vs real-time price forecasts for PV and storage assets in ERCOT.
A physics-informed AI system that learns the causal structure of the electricity grid to forecast wholesale prices and optimize trading decisions.
Currently live in ERCOT, the most volatile electricity market in the United States, proving significant financial value for power producers and asset owners.
Successes in one of the world's top proving grounds for energy transition innovation demonstrate what's possible as we scale globally.
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Foresight Grid generates probabilistic forecasts of Day-Ahead vs Real-Time price spreads (DART), enabling better bidding, hedging, and arbitrage decisions for power producers, storage operators, and energy traders across ERCOT.
Designed to reduce imbalance exposure and improve decision quality in volatile power markets. Early pilots indicate measurable improvements in decision outcomes under high-volatility conditions.
Wind, solar, and thermal generators optimizing market revenue
Qualified Scheduling Entities and virtual traders seeking better DART signals
Battery and solar portfolio operators maximizing dispatch and arbitrage value
Across CAISO, ERCOT, and PJM, wholesale electricity is a $100B+ market, with $370-530B in derivatives layered on top. Yet every grid participant makes decisions using tools designed for a dispatchable grid that no longer exists. The result: billions in missed opportunities, mismatched hedges, and mispriced risk.
"Forecast differences between day-ahead and real-time markets have grown so significant that RTOs across the country are being forced to create entirely new ancillary service products just to manage the uncertainty created by variable energy resources."
Source: FERC, 2023 State of the Markets Report, pp. 10-12 (March 2024)
https://www.ferc.gov/reports-analysis
Forecast errors don't just cost traders money. They inflate the risk premium on every solar project, every storage asset, and every renewable investment decision made on the grid.
ERCOT runs 288 five-minute dispatch cycles daily. Battery operators must decide when to charge, discharge, or hold, often without knowing what the next hour looks like. The winners have better foresight.
ERCOT Day-Ahead to Real-Time spreads can swing up to $28,000/MWh in a single day. While prices themselves are capped, the DART spread is not. Legacy forecasting tools predict averages. Markets reward those who predict the spreads.
ERCOT restructured its market on December 5, 2025. Most forecasting systems are still catching up. Foresight Grid's physics-informed AI system understands the causal structure of the grid, not just its history. When market rules change, the physics don't. Neither does our edge.
Foresight Grid is building a purpose-built AI system designed from the ground up for wholesale electricity markets. Unlike general-purpose AI tools or legacy statistical methods, our model is trained directly on grid physics, market structure, and real-time settlement outcomes.
We own and operate every layer of the system: the model, the retraining pipeline, and the inference deployment. No third-party model dependency. No black-box APIs. Our customers get full transparency. We maintain complete control over performance.
Our initial focus is ERCOT, the Texas wholesale market. It is one of the most dynamic, fastest-growing electricity markets in the world, and the most volatile in the United States. From there, we plan to expand across US deregulated markets and eventually into volatile wholesale markets globally.
Foresight Grid serves power plant operators, including wind and solar, battery storage owners, virtual traders, and Qualified Scheduling Entities who need AI-grade decision intelligence to compete in volatile wholesale markets.
33 years in the solar industry. Miguel has worked for SunPower, First Solar, and Solar World, and led projects across 32 countries. He previously founded 8760 Consulting (acquired) and brings deep expertise in energy project development, M&A, and commercial strategy.
PhD in Electrical Power Engineering with doctoral research focused on AI-based solar power prediction. Published in Nature Scientific Reports, IEEE, and AIP Publishing. Her work sits at the intersection of deep learning and power systems, a rare combination that is central to our technical approach.
Over 20 years in wholesale power markets and trading. Arun has served as Senior Director of Derivatives Trading at Vistra, the largest power generator in ERCOT. Prior leadership roles include six years as President of Rivercrest Power Trading at BioUrja Group and over seven years as SVP and Head of Nodal Power Trading for North America at EDF Trading. He advises Foresight Grid on ERCOT derivatives, trading workflow design, and marketplace structure.
We are working with solar, storage, and virtual trading operators across ERCOT. If you're curious about how AI-native forecasting could work for your assets, we'd like to hear from you.
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