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作 者:马文彪 程亮 陈红坤[2] MA Wenbiao;CHENG Liang;CHEN Hongkun(Shuozhou Power Supply Company,Stato Grid Shanxi Electric Power Co.,Ltd.,Shuozhou 036000,China;School of Electrical Engineering,Wuhan University,Wuhan 430072,China)
机构地区:[1]国网山西省电力有限公司朔州市供电公司,山西朔州036000 [2]武汉大学电气工程学院,湖北武汉430072
出 处:《电力科学与技术学报》2020年第3期83-91,共9页Journal of Electric Power Science And Technology
基 金:国家电网有限公司科技项目(SGSXSZ00FCJS1700013)。
摘 要:在电动汽车规模发展的背景下,合理地优化控制电动汽车充电负荷,进行主动配电网网架规划具有重要的现实意义。该文在对电动汽车充电负荷进行优化调度的基础上,建立主动配电网网架规划双层模型。首先,该模型计及分布式电源和负荷的不确定性,研究电动汽车充电负荷预测的主要流程框架并阐述充电负荷调度方法,以此构建主动配电网网架规划的数学模型,模型中规划层以单位用电量的年综合经济代价最小为目标,运行层以负荷曲线方差最小为目标。其次,在模型求解过程中,利用Prim算法改进粒子群算法的种群生成过程,通过罚函数形式处理约束条件,提高求解效率与精度。最后利用配电网实际算例验证模型的合理性和有效性。Under the circumstances of development for scaled electric vehicles(EVs),it is of great significance to r ationally control and optimize the EV charging load and carry out the structure planning of the active distribution network.This paper establishes a bi-level model for structure planning of active distribution networks based on the regulation of the EV charging load.Firstly,taking the uncertainties of distributed generation and load into consideration,the main process framework for EV charging load forecasting is constructed,and then the dispatching method of the charging load is described and the mathematical model of structure planning for active distribution networks is build up.In such a mathematical model,the planning layer aims at the lowest comprehensive economic cost for kilowatt-hour of electricity and the operational layer is targeted at minimizing the load curve variance.Secondly,in the process of model solving,the Prim algorithm is utilized to improve the population generation process of particle swarm optimization(PSO)and constraints are processed by a penalty function so as to improve the efficiency and precision of a lgorithm.Finally,the rationality and validity of the model are verified by an actual distribution network.
分 类 号:TM715[电气工程—电力系统及自动化]
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