摘要
综合能源系统包含多种分布式能源,各能源相互补充,能有效提高能源的利用率,在经济、环保等方面具有显著优势。冷热电联供系统作为综合能源系统的重要补充,具有灵活可靠、高效清洁等优点,现已得到广泛的发展和重视。综合考虑各微源的发电特性和冷热电负荷需求,建立了包含燃料电池、微燃机、余热锅炉、吸收式制冷机和蓄能装置的多目标冷热电联供系统模型。该模型考虑分时电价对冷热电联供系统的影响,以经济成本和环境成本作为目标函数,提出基于Pareto理论的多目标蜂群算法作为模型的求解算法。以实际冷热电联供系统为算例进行仿真,验证了所提模型的有效性,并与多目标粒子群算法进行对比,结果表明,采用基于Pareto理论的多目标蜂群算法能更有效地降低经济成本和环境成本。
The integrated energy system contains a variety of distributedenergysources,eachofwhichcomplementseachother,ef. fectively improves energy utilization, and has significant advantages in terms of economy and environmental protection. As an important supplement to the integrated energy system, the combined cooling, heating and power system has the advantages of flexibility, reliability andhighefficiency,andisnowwidelydevelopedandvalued.Consid. ering the power generation characteristics of each micro.source and the demand of cold and heat load, a multi.objective cogeneration sys. tem model including fuel cell, micro.combustion engine, waste heat boiler, absorption chiller and energy storage device is established. Themodelconsiderstheimpactoftime.of.useelectricitypriceonmi. cro.grid system. Taking economic cost and environmental cost as the objectivefunction,amulti.targetbeecolonyalgorithmbasedonPare. to theory is proposed as the model solving algorithm. The actual cool. ingandpowersupplysystemisusedasanexampletoverifytheeffec. tiveness of the proposed model, and compared with the multi.objec. tive particle swarm optimization algorithm. The results show that the multi.targetbeecolonyalgorithmbasedonParetotheorycanbemore effective,andreduceeconomicandenvironmentalcosts.
作者
王文静
于立涛
撖奥洋
张智晟
WANG Wenjing;YU Litao;HAN Aoyang;ZHANG Zhisheng(Electrical Engineering College, Qingdao University, Qingdao 266071, China;State Grid Qingdao Power Supply Company, Qingdao 266002, China)
出处
《电力需求侧管理》
2019年第4期48-53,共6页
Power Demand Side Management
基金
国家自然科学基金项目(51477078)~~
关键词
综合能源系统
冷热电联供
Pareto理论
多目标蜂群算法
优化调度
integrated energy system
combined cooling, heating and power
Pareto theory
multiobjective artificial bee colony
optimized scheduling
