Enhancing hydro energy synergy: Optimizing short-term pumped-storage power operations under high renewable energy penetration

With the rapid expansion of renewable energy, pumped-storage power (PSP) stations play an increasingly vital role in stabilizing power systems and absorbing variable energy outputs. However, optimizing short-term PSP operations remains highly challenging due to the complexity of discrete decisions, nonlinear system dynamics, and strict physical constraints. While dynamic programming (DP) offers global search capabilities, its application is severely limited by state-space explosion and computational inefficiency. This study proposes a unified DP–Discrete Differential Dynamic Programming (DP-DDDP) framework with three strategies to address these challenges in a unified framework. By integrating DP's global search capability with DDDP's local refinement strengths, the algorithm bridges near-optimal performance and computational tractability. Three specifically adapted strategies, decision range restriction, dynamic corridor width adjustment, and iterative state discretization refinement, are integrated to enhance constraint handling and efficiency. The method is applied to the Heimifeng PSP station in Hunan Province, using real-world operational data and seasonally varying residual load scenarios. Results demonstrate that the hybrid algorithm consistently generates high-quality near-optimal solutions while ensuring deterministic operational stability. Compared to practical schemes, it reduces residual load variance by up to 58.8% and increases daily power generation benefits by up to 55.7%. It also reduces computation time from 1300 to 120 s and improves discrete constraint precision from 3% to 0.1%. This research makes a novel methodological contribution to power system optimization and demonstrates its real-world value as a scalable, high-precision tool for PSP scheduling. It provides a practical pathway for enhancing hydro energy synergy under high renewable energy penetration.