Optimization Of Hybrid Heat Source Heating System Based On Particle Swarm Algorithm

Energy and environment system is closely related to human production,and nowadays,energy consume increasingly and environmental pollution becomes serious gradually.It's time to transfer energy consumption from fossil energy to clean energy.Mixed heat source heating system can complement each other's advantages in various forms of energy,and improve the overall performance of the energy utilization system in terms of energy consumption,economic costs,and environmental benefits.Therefore,After analyzing the basic topology and the working principle of several kinds of the heating system of the mixed heat source,a multi-objective optimization model is built for mixed heat source heating system,and the economic benefit and the pollution emission were two objective function of the model.The model is not only takes into account the heat and power balance,but also the capacity of the transport pipe was took as a constraint.Then,in order to solve the multi-objective model,several commonly-used multi-objective algorithms are compared,and a suitable solution algorithm is selected,and the algorithm is optimized and improved,so as to perform the optimal scheduling solution between the mixed heat sources.And the dynamic-objective method is introduced to solve the complex constraint of heat network.Finally the scheduling results between heat sources is represented as a Pareto solution,so that the decision maker can choose the optimum solution according to their different preferences on economic and environment.The simulation results show that the target model of Mixed heat source heating system is consistent with the actual operation mode of the heating system.Application results of the load optimization assignment indicate that the proposed optimization approach provides a more flexible scheduling scheme for decision makers,and that the combined benefits of the optimized load distribution schemes are higher than those of conventional practices.