Integrated energy cycle operation control strategy based on particle swarm optimization

In the context of the rapid development of the Integrated Energy System (IES), an hybrid multi objective particle swarm optimization algorithm and fuzzy membership function solving multi-objective operation control method for the agricultural comprehensive energy cycle system are proposed to address the issues of high production costs and low energy utilization efficiency in agricultural parks. On the basis of establishing a multi-objective optimization operation control model that takes into account economic and environmental aspects, the optimization objectives are to minimize the system operating cost and maximize the absorption rate. The constraints include power balance, equipment operation, photovoltaic scheduling output, etc., and an improved comprehensive multi-objective optimization algorithm is used to solve the problem. The simulation results show that compared to traditional particle swarm optimization algorithms, the proposed algorithm has faster convergence speed and more accurate optimization results. Compared to the single objective model, the proposed multi-objective operation control model can balance environmental and economic aspects during the operation control process. It not only reduces the operating cost of the agricultural comprehensive energy cycle system but also improves the photovoltaic consumption rate.