Enhanced Representations of Probabilistic Resource Adequacy Risk in Power System Capacity Expansion Modeling

Abstract

Power system infrastructure investment optimizations traditionally rely on capacity-based reserve margin heuristics to produce least-cost solutions that also meet probabilistic resource adequacy criteria. While these conventional strategies have always had shortcomings, they are becoming increasingly untenable as the grid's supply-demand balance comes to depend more and more on variable renewable generation, energy storage technologies, and interregional transmission. This work develops several novel iterative mathematical formulations to better represent probabilistic risk and the potential contributions of these new resources inside optimization-based capacity expansion models, without relying on capacity accreditation or stochastic optimization. Strategies for capturing resource adequacy impacts from supply uncertainty, weather-driven variability, temporal energy shifting via storage, and spatial energy shifting via transmission are developed independently before being integrated into a unified mathematical framework. The final iterative framework is then applied to demonstrate the methods on multiple test systems representing diverse climate regions.