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SMU Data Science Review

Abstract

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, 2025). This paper proposes a framework to comply with the new legislation by including an Availability Model to quantify how behind-the-meter resources (battery storage, fast-ramping generation, and flexible IT workloads) can contribute to grid stability.

The model combines short-horizon forecasting with reliability modeling and an optimization layer to produce probabilistic contribution estimates (response depth, speed, and duration) under Monte Carlo scenarios. Outputs include reserve-coverage metrics such as reduction in blackout probability (LOLP) and magnitude (EENS).

The results demonstrate individual facilities offer marginal benefits; a coordinated fleet of modern hyperscale data centers can contribute to grid-scale stability. This supports a policy of aggregated partnership to minimize disruption while enabling Texas to meet the nation’s AI infrastructure challenge.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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