Research on Cost Optimization Dispatching of Microgrids Based on Improved Particle Swarm Algorithm

The significant integration of renewable energy sources has brought many uncertainties to the power system, posing challenges to the construction of a new type of power system. Microgrids are a flexible and resilient improvement solution compared to traditional centralized power grids. They play a positive role in promoting the integration of new energy sources and have become an effective solution to enhance the reliability and operational efficiency of power systems. This paper constructs a microgrid model consisting of various types of generators, with the objective function of minimizing the total cost composed of operating costs and environmental costs. This study utilizes an enhanced particle swarm algorithm that improves upon the traditional method by fine-tuning the inertia factor and learning factor, as well as incorporating adaptive mutation operations. The case study findings reveal that the enhanced particle swarm algorithm demonstrates notable enhancements in performance over the conventional particle swarm algorithm, with an approximate 16.109% increase in computational speed and an average cost saving of about 4.886%.