Probabilistic Resource Adequacy Assessment of ERCOT Under High Data Center Load Growth
Authors: Chen Chen, Joseph Nyangon, Tarek Ibrahim and Talha Ali | Journal: Environmental Research: Energy, Volume 3, Number 3
Abstract
The Electric Reliability Council of Texas (ERCOT) faces mounting reliability challenges as electrification, large flexible loads (e.g. data centers, hydrogen, crypto, and industrial demand), and high renewable penetration reshape system dynamics. Traditional reserve margin–based planning is increasingly inadequate, as periods of highest risk are shifting to net peak hours and extended low-renewable events. To address these challenges, we developed a probabilistic resource adequacy (RA) framework using PLEXOS® to evaluate ERCOT’s long-term RA outlook through 2050.
We modeled baseline growth, high load growth from data centers and cryptocurrency, enhanced ancillary service requirements, and targeted transmission expansion, with sensitivities to technology costs, fuel prices, and policy uncertainty (e.g. Inflation Reduction Act repeal). We quantified reliability outcomes through probabilistic metrics, including loss of load expectation, loss of load probability, and expected unserved energy, to capture the marginal contribution of conventional, renewable, and storage resources.
Our results show that rapid load growth, particularly from data centers, intensifies RA risks absent complementary transmission and resource expansion; however, transmission upgrades and enhanced ancillary services improve adequacy by mitigating congestion and stabilizing net load variability. We benchmarked our framework against the February 2021 Winter Storm Uri through hourly operational simulations, which validate the framework’s capability to replicate observed stress conditions and unserved energy outcomes.
These findings collectively underscore that ERCOT’s future reliability hinges on coordinated planning across generation, transmission, and demand-side flexibility. This study provides a novel integration of long-term capacity expansion and probabilistic RA modeling, offering actionable insights for system operators, policymakers, and market stakeholders navigating ERCOT’s transition to a high-renewable, high-load future.