作者:\n\t王清，李琮琮，王平欣，吴青青，蔡小雨\n

Authors:\n\tWANG Qing, LI Congcong, WANG Pingxin, WU Qingqing, CAI Xiaoyu \n
摘要:\n\t为提高电采暖系统的用电能效，提出了一种基于粒子群算法（PSO，particle swarm optimization）的蓄直组合型电采暖系统运行优化策略。建立了电采暖建筑墙体内外影响因素数学模型，在确定电暖气数量前提下利用Matlab中的Simulink工具箱搭建整体系统。结合需求响应思想建立以用户采暖电费最小为目标函数，选取不同子模块构成控制模块实现仿真验证，并采用反余弦方法对学习因子进行更新的改进粒子群算法，对设定目标函数进行求解。最后通过山东济南某企业用电数据算例，对比能耗和经济性两个方面可得：全天总能耗低于实际的能耗、测得两个连续工作日电费相较未优化时分别节约了17.16%和16.48%。\n

Abstract:\n\tIn order to improve the energy efficiency of the electric heating system, a particle swarm optimization (PSO, Particle Swarm Optimization)-based operation optimization strategy for the direct storage combined electric heating system is proposed.A mathematical model of influencing factors inside and outside the walls of electric heating buildings is established, and the simulink toolbox in matlab is used to build the overall system under the premise of determining the quantity of electric heating.Combining demand response ideas, the objective function is to establish the minimum heating and electricity cost of the user, and different sub-modules are selected to form the control module to achieve simulation verification, and the inverse cosine method is used to update the improved particle swarm algorithm to update the learning factor to solve the set objective function.Finally, through a calculation example of electricity consumption data of an enterprise in Jinan, Shandong, comparing energy consumption and economy can be obtained: the total energy consumption throughout the day is lower than the actual energy consumption, and the electricity bill is reduced by 17.16% compared with the unoptimized time.\n


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