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

TL;DR

This paper introduces a decision-focused learning framework that significantly accelerates and improves the accuracy of day-ahead scheduling for underground pumped hydro energy storage, balancing profit and computational efficiency.

Contribution

It develops a neural network-based approach that guides recursive linearization in complex optimization, enabling faster and more accurate UPHES scheduling compared to traditional methods.

Findings

Achieves 1.1% profit improvement over baseline methods.

Provides a 300-fold speedup in real-time scheduling.

Maintains profitability within 3.6% of benchmarks.

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 refinement tool, the framework improves profit by 1.1% over piecewise MIQP baselines. Alternatively, as a real-time scheduler initialized with linear approximations, it achieves a 300-fold speedup (3.87s vs 1205.79s) while maintaining profitability within 3.6% of the piecewise MIQP benchmark. Thus, the presented DFL framework enables flexible prioritization between profit maximization and real-time responsiveness.