基于粒子群算法的冷热电联供系统优化调度  

Dispatch optimization of CCHP system based on particle swarm optimization

作　　者：余倩 黄亮[1,2] YU Qian;HUANG Liang(Wuhan University of Technology,Wuhan Hubei 430070,China;Wuhan Complex Dimension Data Technology Co.,Ltd.,Wuhan Hubei 430070,China)

机构地区：[1]武汉理工大学,湖北武汉430070 [2]复变时空(武汉)数据科技有限公司,湖北武汉430070

出　　处：《宁夏电力》2021年第4期15-21,共7页Ningxia Electric Power

摘　　要：近年来随着用户对供能形式的需求不断丰富,传统的以单一电能为形式的供能系统已经无法满足能源用户的需求。考虑了冷热电联供系统(combined cooling heating and power,CCHP)对不同能源协同供应的特点,对以风能、太阳能、天然气和储能协同供能的冷热电联供优化问题进行研究。综合考虑不同费率结构以及可再生能源带来的功率波动,以经济成本和环境成本为目标,构建了含燃气发电机、燃气锅炉、电制冷机、蓄电池组等机组的冷热联供能源协同优化模型。采用粒子群算法对多目标进行求解优化,结果表明该算法能够同时满足系统的经济性和环保性,对促进各种能源的综合利用具有实际意义。With the increasing demand of users for energy supply forms in recent years,the traditional energy supply system in the form of single electric energy can no longer meet the demand of energy users.Taking account of the characteristics of the combined cooling,heating and power(CCHP)system for the coordinated supply of different energy sources,The optimization of CCHP supply with wind energy,solar energy,natural gas and energy storage were studied.Considering the power fluctuation caused by different rate structures and renewables,aiming for economic cost and environmental cost,a collaborative optimization model of cooling and heating supply for units including gas generator,gas-fired boiler,electric refrigerator and battery banks was established.The particle swarm optimization(PSO)was used to solve the multi-objective optimization.The results show that the PSO can fulfill both the economic performance and environmental performance of the system,and is of significance to facilitate comprehensive utilization of various energy sources.

关 键 词：冷热电联供系统 费率结构 可再生能源 粒子群 

分 类 号：TM74[电气工程—电力系统及自动化]