Multi-Energy Collaborative Optimal Scheduling of Virtual Power Plants Considering Demand Response

Virtual Power Plant (VPP), by integrating distributed energy resources, energy storage systems, and demand response resources, has become a critical carrier for enhancing grid flexibility. This paper focuses on a VPP incorporating photovoltaic generation, wind power, gas turbines, and adjustable loads. A multi-objective optimal scheduling model is developed, considering economic costs, carbon emission constraints, and load fluctuation mitigation. A solution strategy based on the Modified Sparrow Search Algorithm (MSSA) is proposed, which introduces dynamic inertia weights and crossover-mutation operators to improve global search capability. Simulation tests are conducted on the IEEE 14-bus system. The results demonstrate that the proposed method reduces system operating costs and carbon emissions while narrowing the peak-valley load difference, thereby validating the effectiveness of the model. Additionally, the implemented algorithm exhibits fast convergence speed and high computational accuracy.