Multi-objective Optimal Scheduling of Small Integrated Energy System Based on Improved Particle Swarm Optimization Algorithm

In order to explore a low-carbon and economical way of supplying energy for buildings and promote the transformation of the energy system, a small integrated energy system including energy supply equipment, energy conversion equipment and energy storage equipment is designed. After comprehensively considering operating cost and carbon emission, a multi-objective optimal scheduling model of the system is constructed. A genetic particle swarm optimization algorithm based on random dynamic inertia weight is proposed to solve the model and analyze the output of each piece of equipment in the system under the optimal operating strategy. An example of a building in a region with hot summer and cold winter is introduced for verification, and the simulation results show that the proposed small integrated energy system model is economical and environmentally-friendly, and the improved particle swarm optimization algorithm has a better optimization effect.