This talk presents game-theoretic equilibrium frameworks alongside optimization and machine learning approaches for enabling efficient energy storage participation in electricity markets. I will demonstrate that strategic storage participation converges to efficient market outcomes under the existing two-settlement U.S. market design. Building on this foundation, I will examine the energy storage arbitrage problem in detail, framing it within a dynamic programming perspective, and present solution strategies ranging from classical stochastic optimization to decision-focused learning pipelines.
School of Electrical Engineering and Computer Science
October 2025
Fridays in October—October 3, October 10, October 17, October 24
The David G. Pollart Center for Arts and Humanities is organizing its First-Generation Graduate Students Seminar Series for Fall…
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