Enhancing renewable energy penetration: Multi-objective optimization of pumped-storage power station operations

The growing integration of renewable energy sources such as wind and solar power introduces significant intermittency, posing challenges to power grid stability. Pumped-storage power stations offer an effective solution by mitigating these fluctuations and enhancing system reliability. This study presents a multi-objective optimization framework for pumped-storage power operations, prioritizing renewable energy utilization while balancing technical and economic objectives. The model simultaneously minimizes residual load fluctuations and maximizes generation revenue, and is solved using an enhanced multi-objective particle swarm optimization algorithm that incorporates hybrid variable updates, adaptive parameter adjustment, constraint-dominance principles, and dynamic archive management. The framework is applied to the Heimifeng pumped-storage power station in Hunan, China, under typical residual load scenarios. Results show that the optimized operation consistently outperforms conventional dispatch: monthly residual load fluctuations are reduced by more than 10%, annual generation revenue increases to USD 3.29 million (22% above the actual USD 2.7 million), and annual carbon-equivalent mitigation improves by 39%. These findings provide a scientific basis for efficient peak-shaving operations, enabling stable renewable integration, improving the economic performance of pumped-storage power stations, and supporting the sustainable, low-carbon development of modern power grids.