Accelerating Underground Pumped Hydro Energy Storage Scheduling with Decision-Focused Learning

Abstract

Underground pumped hydro energy storage (UPHES) systems play a critical role in grid-scale energy storage for renewable integration, yet optimal day-ahead scheduling remains computationally prohibitive due to nonlinear turbine performance characteristics and discrete operational modes. This paper presents a decision-focused learning (DFL) framework that addresses the computational-accuracy trade-off in UPHES day-ahead scheduling. The proposed methodology employs neural networks to predict penalty weights that guide recursive linearization, transforming the intractable MINLP into a sequence of convex quadratic programs trained end-to-end via differentiable optimization layers. Case studies across 19 representative Belgian electricity market scenarios demonstrate that the DFL framework effectively navigates the trade-off between solution quality and computation time. As a refiner, DFL adds approximately 1.2 s of post-processing to improve profit by 1.1% over piecewise MIQP baselines. As a real-time scheduler initialized with a fast global linear MIQP, the end-to-end time is 3.87 s (a 300-fold speedup) while maintaining profitability within 3.6% of the piecewise MIQP benchmark. An MIP-free deployment mode, which warm-starts from a historical schedule database and thus needs no mixed-integer solve, retains 92% of benchmark profit on held-out price scenarios.

13 pages, 7 figures. Accepted for publication in IEEE Transactions on Sustainable Energy