Multi-objective optimization of capacity configuration in a wind–PV–compressed air energy storage hybrid system

Compressed air energy storage (CAES) technology plays a crucial role in mitigating the volatility and intermittency of wind and photovoltaic (PV) power generation, thereby enhancing energy efficiency and system stability. This study proposes a novel load-oriented hybrid system integrating wind, PV and CAES, while investigating its capacity optimization and scheduling strategies. A multi-objective optimization model is developed to balance power curtailment, load power deficiency, and system investment costs, ensuring economic efficiency and operational reliability. The model incorporates wind and PV generation variability, the charging and discharging characteristics, power constraints and storage capacity of the CAES system. The weight coefficients for power curtailment rates, load power deficiency rates, and system investment costs are set to 0.25, 0.40, and 0.35, respectively. Using seasonal data from a region in China, the optimization results show different capacity needs for each season. Analyze these seasonal capacities to support the final configuration plan. The installed wind power capacity in winter (1853 MW) slightly exceeds that in summer (1834 MW), while PV capacity in winter (761 MW) is significantly lower than in summer (933 MW). The CAES power capacity in winter (305 MW) exceeds that in summer (218 MW), while the storage duration is 2.4 h in winter and 2.6 h in summer. The optimized system effectively utilizes the complementary characteristics of wind and solar power generation, reducing power curtailment and shortages, and lowering investment costs. This study provides an effective solution for integrating high wind and PV power shares into the grid.