Double-layer optimal scheduling for wind-PV-hydro-hybrid energy storage system with multi-timescale coordination

A multi-scenario coordinated control method for wind–photovoltaic–hydro–hybrid energy storage system is proposed to address the challenges of intensified power fluctuations and underutilized energy storage resulting from high-penetration renewable integration. Typical wind and photovoltaic scenarios are generated using Latin Hypercube Sampling and reduced via an improved Load Curve–ISODATA clustering algorithm to characterize output uncertainty. A mixed-integer programming model is developed to minimize system output variability, with pumped storage utilized for inter-day energy shifting and battery energy storage for minute-level fluctuation suppression. Power allocation under hydropower–storage coupling constraints is optimized using the Gurobi solver. A capacity allocation model is formulated based on the Competition of Tribes and Cooperation of Members (CTCM) algorithm to enhance ancillary service revenues. In this framework, pumped storage prioritizes peak-valley arbitrage, while battery energy storage is allocated to fast-response frequency regulation. The effectiveness of the method proposed in this paper was verified through simulation of actual operating data from a northwest power grid in China. Calculation results indicate that the method proposed in this paper can reduce system volatility by 12.46 % while increasing revenue by $34,762. The system's return on investment reaches 15.3 %, with a payback period of 6.1 years, effectively enhancing both system stability and economic efficiency.