Research on optimizing autonomous operation of multi-objective clusters based on improved particle swarm algorithm

With the vigorous development of new power systems and the large-scale access of distributed energy, cluster control, as a new type of power system control scheme, can alleviate communication pressure during distributed energy control and achieve refined control of distributed energy. The cluster includes various elements such as distributed power sources, energy storage systems, and loads. Challenges in internal cluster control include difficulties in coordinated control and efficient energy utilization. Based on considerations of economic, environmental, and reliability factors, this paper establishes a cluster autonomous optimization scheduling model. By comprehensively considering operating costs, environmental impact, and system stability, the model aims to achieve efficient, economical, and environmentally friendly operation within clusters. An improved particle swarm algorithm is proposed to address the multi-objective optimization problem of cluster autonomous operation. By dynamically adjusting the inertia factor and introducing mutation operations, the algorithm's global search capabilities and local search accuracy are enhanced, effectively avoiding local optima and improving optimization results. The results indicate that compared to the autonomous solutions obtained by the original algorithm, the optimized operational plans obtained by the improved particle swarm algorithm can further enhance the cluster's wind power accommodation capacity, enable more precise control and coordination of controllable power within the group, and achieve superior economic and environmental benefits.