Economic Operation Oriented of Day-Ahead Optimal Scheduling for Photovoltaic-Storage-Hydrogen Systems

Photovoltaic (PV) generation is characterized by intermittency and volatility, making it difficult to match load demand in real time. To improve the quality and stability of PV generation, this paper proposes a day-ahead optimal scheduling and economic operation strategy based on a PV–storage–hydrogen system, aiming to achieve rational coordination and planning among PV generation, energy storage, and grid output. Firstly, a cyclic energy storage system topology consisting of PV panels, electrolyzers, hydrogen storage tanks, and fuel cells is constructed. Considering both economic and environmental factors, a multi-objective day-ahead optimal scheduling model is developed. Subsequently, the proposed model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Based on real operational data, the NSGA-II algorithm outperforms the Particle Swarm Optimization (PSO) algorithm in reducing total costs and enhancing system scheduling flexibility, demonstrating superior economic efficiency and environmental performance. In addition, a centralized control system based on the B/S (Browser/Server) architecture is designed and implemented, enabling the server to compute scheduling schemes in real time and dispatch control commands to each subsystem to achieve automated scheduling and coordinated control.