基于邻域自适应粒子群优化算法的地表水源热泵机组优化调度  

Optimal Dispatch of Surface Water Source Heat Pump Based on Neighborhood Adaptive Particle Swarm Optimization Algorithm

作　　者：王维[1] 吴亮红[1] 刘振族 李坚 贾睿 张红强[1] WANG Wei;WU Lianghong;LIU Zhenzu;LI Jian;JIA Rui;ZHANG Hongqiang(School of Information and Electrical Engineering,Hunan University of Science and Technology,Xiangtan 411201)

机构地区：[1]湖南科技大学信息与电气工程学院,湘潭411201

出　　处：《系统科学与数学》2021年第6期1520-1532,共13页Journal of Systems Science and Mathematical Sciences

基　　金：国家自然科学基金项目(61672226);湖南省自然科学基金项目(2018JJ2137);湖南省教育厅优秀青年项目(19B200)资助课题。

摘　　要：为提高湘潭城市中心区某地表水源热泵区域能源系统的运行效率,建立了系统优化调度模型,并提出了一种邻域自适应粒子群优化算法(NAPSO)求解所建模型.针对运行过程中可能出现不可行解的情况,提出了一种将不可行解调整为可行解的方法.实验结果表明,在夏季和冬季时,NAPSO算法求解结果比该系统目前所使用的调度方法每日分别可节省5.14%和4.50%的运行成本,与其他5种算法相比每日分别最高可节省0.57%和0.50%的运行成本,是一种求解地表水源热泵机组优化调度的有效方法.In order to improve the operating efficiency of the surface water source heat pump regional energy system in the central area of Xiangtan city,a system optimization scheduling model is established,and a neighborhood adaptive particle swarm optimization algorithm(NAPSO)is proposed to solve the constructed model.Aiming at the case where infeasible solutions may appear during the operation,a method is proposed to adjust the infeasible solutions to feasible solutions.The experimental results show that during the summer and winter,in comparison to the current scheduling method,the NAPSO algorithm can save 5.14%and 4.50%of the operating cost per day,respectively.Compared with the other five algorithms,the NAPSO algorithm can save up to 0.57%and 0.50%of the operating cost per day,respectively.The simulation experiment results show that the proposed algorithm is an effective method to solve the optimal scheduling of surface water source heat pump units.

关 键 词：地源热泵 优化调度 邻域自适应 粒子群优化 

分 类 号：TU83[建筑科学—供热、供燃气、通风及空调工程]