基于马尔科夫链充电负荷预测的多区域充电桩优化配置研究
Optimal Allocation of Charging Piles in Multi-areas Considering Charging Load ForecastingBased on Markov Chain
作者：吕林(四川大学 电气信息学院, 四川 成都 610065)；许威(四川大学 电气信息学院, 四川 成都 610065)；向月(四川大学 电气信息学院, 四川 成都 610065)；张逸(国网福建省电力有限公司 电力科学研究院, 福建 福州 350007)；熊军(国网福建省电力有限公司 厦门供电公司, 福建 厦门 361000)
Author：LYU Lin(School of Electrical Eng. and Info., Sichuan Univ., Chengdu 610065, China)；XU Wei(School of Electrical Eng. and Info., Sichuan Univ., Chengdu 610065, China)；XIANG Yue(School of Electrical Eng. and Info., Sichuan Univ., Chengdu 610065, China)；ZHANG Yi(Electric Power Research Inst., State Grid Fujian Electric Power Co., Fuzhou 350007, China)；XIONG Jun(Xiamen Electric Power Supply Co., State Grid Fujian Electric Power Co., Xiamen 361000, China)
收稿日期：2016-04-13 年卷（期）页码：2017,49(3):170-178
期刊名称：工程科学与技术
Journal Name：Advanced Engineering Sciences
关键字：电动汽车;马尔科夫链;负荷需求;移动特性;充电桩
Key words：electric vehicle;Markov chain;charging demand;mobility characteristics;charging piles
基金项目：国家自然科学基金资助项目（51377111）；四川省科技厅应用基础项目资助（2015JY0128）；四川大学引进人才科研启动经费资助项目（20822041A4161）；国家电网总部科技项目资助
中文摘要
考虑用户出行习惯的复杂性、多样性，对区域充电桩进行合理的配置以满足充电需求。首先，通过马尔科夫链模型描述电动汽车用户一天出行过程中在行驶、充电、不充电也不行驶3种决策行为下，动力电池荷电状态的变化情况。以此确定该过程中电动汽车用户的实时充电行为，得出不同类型电动汽车的快充、慢充负荷需求。然后，考虑规划区内电动汽车的移动特性及其不同时段不同类型电动汽车辆数，预测各区域各时段充电负荷的需求情况。最后，以投资、运维成本最小为目标建立区域充电桩优化配置模型。该模型计及了电动汽车移动特性均衡等约束条件，并通过粒子群优化算法求解。对33节点4区域系统电动汽车充电负荷需求预测及其充电桩配置进行仿真，仿真结果验证了所提方法的有效性和可行性。
英文摘要
Considering the complexity and diversity of customers' travel habits,the charging piles need to be allocated appropriately to satisfy the charging demand.Firstly,Markov chain is used to describe the variation of battery state of charge on electric vehicle owners' trip in the whole day,according to three decision-making behavior including driving,charging,neither charging nor driving.Then the real-time charging behavior in the process could be determinated,which indicates the fast and slow charging demand of different vehicle types.Considering mobility characteristics of electric vehicles and the number of different types of electric vehicles in different time periods in some area,the total load demand could be forecasted.The optimal allocation model for charging piles is proposed and aims to minimize investment and operating costs for the charging piles.The mobility characteristics of electric vehicles are integrated into the constraints,and the model is solved by the particle swarm optimization algorithm.The effectiveness and feasiblility of the proposed method are verified by the 33-bus four-area case study on the charging load forecasting and optimal allocation of charing piles.
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