Short-term optimal scheduling of hydro–wind–PV and multi-storage complementary systems based on opposition-based learning PSO algorithm

The introduction of energy storage systems in multi-energy complementary systems ensures efficient energy use and distribution, enhancing the system’s economic benefits. However, current research not only lacks the application of energy storage in large-scale hydro–wind–PV hybrid systems, but also uses only one type of energy storage system in the complementary system, neglecting the synergistic effect between various energy storage systems. To address this research gap, this study proposes a hydro–wind–PV joint scheduling model that considers the coordinated optimization of pumped storage and battery storage. Through this synergy, the energy storage systems can further optimize the exploitation of energy storage potential and improve energy utilization. Additionally, a particle swarm optimization algorithm based on opposite-based learning (PSO-OBL) is proposed, tailored for short-term optimization. The model and algorithm are validated through their application to a power grid in the southwest region of China. The results demonstrate that the integration of pumped storage and battery storage significantly enhances the system’s economic efficiency, and the PSO-OBL algorithm outperforms traditional algorithms in both convergence and solution quality. By analyzing 4 typical days, the findings show that multiple energy storage systems can effectively cooperate under varying environmental conditions, further improving energy self-sufficiency and maximizing the benefits of energy storage. Compared with the traditional model, the system’s economic efficiency can be improved by a maximum of 3.01 %, and the load self-sufficiency rate is increased by 2.32 %. This study provides practical reference for optimal scheduling of multiple energy storage systems.