基于粒子群算法的多热源供热系统调节优化

Regulation Optimization of Multi-heat Source Heating System Based on Particle Swarm Optimization Algorithm

采用混合均值中心反向学习粒子群优化算法(PSO-HMC算法),考虑供热管道热损失,以供热成本(主要为热源热费、电费)最小作为目标,对多热源供热系统调节方式(质调节、量调节、质量调节)进行优化。结合3个热源的算例,在用户热负荷一定条件下,对3种调节方式的优化结果进行比较分析。PSO-HMC算法的优化模型仿真计算结果可信。质量调节方式的供热成本最低。由优化结果可知:对于多热源供热量比例,量调节方式与质量调节方式接近。量调节方式下,采用不同热源供水温度时,热源A~C供热量比例的变化范围不大。

The hybrid mean center reverse learning particle swarm optimization algorithm(PSO-HMC algorithm)is used to optimize the regulation modes(constant flow regulation,variable flow regulation and integrative flow regulation)of the multi-heat source heating system,taking the heat loss of the heating pipeline into account,and taking the minimum heating cost(mainly the heat charge of heat source and the electricity charge)as the goal.Combined with the calculation example of three heat sources,the optimization results of the three regulation modes are compared and analyzed under a certain userheat load.The simulation results of optimization model of PSO-HMC algorithm are reliable.The heating cost of integrative flow regulation mode is the lowest.From the optimization results,it can be seen that for the proportion of multi-heat source heating,the variable flow regulation mode is close to the integrative flow regulation mode.Under the variable flow regulation mode,when the water supply temperature of different heat sources is adopted,the change range of heating proportion of heat sources A-C is small.