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考虑多目标约束的充电桩数量规划研究 被引量:7

Study on Quantity Planning of Charging Piles Considering Multi-objective Constraints
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摘要 当前多数对充电桩规划方法的研究缺乏对电动汽车(electric vehicle,EV)优化调度能力的考虑,大量可调度容量利用率极低。为降低充电桩闲置率并促进系统分布式光伏消纳能力,提出合理应用EV优化调度能力的充电桩数量规划模型。该模型结合设备成本、充放电损耗提出充电桩规划方法,以协助区域电网配置合适的充电桩数量。首先构建了详细的EV及储能设备充放电模型,然后以最小化系统供需不平衡及综合经济成本作为目标函数。最后通过粒子群算法依次求解出EV集群和储能设备的充放电功率,在EV集群调节能力不足或受限时,通过储能设备进行补充调节。仿真算例验证了所提规划模型的有效性。 Most current studies on charging pile planning methods lack consideration of electric vehicle(EV)optimal scheduling capability,and the utilization rate of a large schedulable capacity is very low.In order to reduce the idle rate of charging piles and promote the distributed photovoltaic absorption capacity of the system,this paper proposes a quantity planning model of charging piles with reasonable application of EV optimal dispatching capacity.This model combines equipment cost and charge-discharge loss to propose a charging pile planning method to assist regional power network to configure an appropriate number of charging piles.Firstly,a detailed EV and charging-discharging model of energy storage equipment is constructed,with the objective function of minimizing the imbalance between supply and demand of the system and comprehensive economic cost.Finally,particle swarm optimization algorithm is used to solve the charge and discharge power of EV cluster and energy storage equipment successively.When EV cluster adjustment capacity is insufficient or limited,energy storage equipment is used for supplementary adjustment.A simulation example verifies effectiveness of the proposed programming model.
作者 方舟 张俊 孙元章 陈思远 FANG Zhou;ZHANG Jun;SUN Yuanzhang;CHEN Siyuan(School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,Hubei Province,China;Qingdao Academy of Intelligent Industries,Qingdao 266109,Shandong Province,China)
出处 《电网技术》 EI CSCD 北大核心 2020年第2期704-712,共9页 Power System Technology
关键词 EV 充电桩 分布式光伏 规划模型 供需不平衡 electric vehicle charging pile distributed photovoltaic programming model unbalanced supply and demand
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