Economic Optimization of a Community-scale Integrated Energy Microgrid Based on PSO Algorithm

With the share of distributed energy increasing in modern power system, the construction of integrated energy systems and integrated energy services have been promoted on a large scale. Among them, as a development direction of distributed energy, CCHP (Combined Cooling, Heating and Power) system draws great attentions. For such a system, many studies have shown that its economic benefits depend largely on the scheduling strategy. To discuss the economic dispatching strategy of the integrated energy micro-grid and achieve rapid and sustainable development of urban power supply. This paper studies the day-ahead scheduling strategy of a typical CCHP integrated energy microgrid in a northern Chinese city in summer and uses an artificial intelligence algorithm (Particle Swarm Optimization, PSO) to find the economically optimal solution for operating costs, while considering the environmental costs. This research has a certain reference value for the optimal operation of the community-scale integrated energy system.