基于二层规划并计及负荷预测的电动汽车充电桩选址定容方法

刘晓天; 傅军; 赵思翔; 钟侃; 杨新宇; 李雪梅

刘晓天,傅军,赵思翔,钟侃,杨新宇,李雪梅.基于二层规划并计及负荷预测的电动汽车充电桩选址定容方法[J].电测与仪表,2021,58(5):144-150.
Liu Xiaotian,FU Jun,ZHAO Sixiang,ZHONG Kan,YANG Xinyu,LIXuemei.Location and Volume Determination Method of Electric Vehicle Charging Pile Based on Bi-level Programming and Considering Load Forecasting[J].Electrical Measurement & Instrumentation,2021,58(5):144-150.

基于二层规划并计及负荷预测的电动汽车充电桩选址定容方法

Location and Volume Determination Method of Electric Vehicle Charging Pile Based on Bi-level Programming and Considering Load Forecasting

DOI：10.19753/j.issn1001-1390.2021.05.021

中文关键词: 电动汽车充电站负荷预测选址定容二层规划萤火虫算法

英文关键词:electric vehicle charging station load forecasting location and capacity planning bi-level programming firefly algorithms

基金项目:

作者中文名	作者英文名	单位
刘晓天	Liu Xiaotian	国网冀北电力有限公司北京市西城区
傅军	FU Jun	国网冀北电力有限公司电力科学研究院北京市西城区
赵思翔	ZHAO Sixiang	国网冀北电力有限公司电力科学研究院北京市西城区
钟侃	ZHONG Kan	国网冀北电力有限公司电力科学研究院北京市西城区
杨新宇	YANG Xinyu	国网冀北电力有限公司北京市西城区
李雪梅	LIXuemei	国网冀北电力有限公司电力科学研究院北京市西城区

摘要点击次数: 2041

中文摘要:

针对城区内电动汽车充电桩选址定容问题,计及充电负荷需求预测并采用二层规划理论建立选址定容模型。首先针对充电负荷需求预测,采用自回归模型建立其负荷预测模型。以充电桩规划节点与容量为控制变量,以系统综合规划成本最小为目标函数,基于网络拓扑结构和充电负荷预测,计及设备容量配置约束等必要约束条件建立上层模型；以电动汽车用户充电方案为控制变量,最小化用户综合充电成本建立下层模型。下层模型基于上层模型的选址定容方案制定充电计划,同时向上层模型反馈综合充电成本。上层模型制定方案需要计及下层模型的策略。采用萤火虫算法设计模型的求解流程,最后通过一个算例针对河北某地城区内某新区内电动汽车充电站选址定容方案进行了优化,验证了本文模型的有效性和正确性。

英文摘要:

Aiming at the location and capacity determination of charging piles for electric vehicles in urban areas, considering the demand forecasting of charging load, a location and capacity determination model was established by bi-level programming. Firstly, according to the demand forecasting of charging load, the load forecasting model was established by using autoregressive model. Taking the node and capacity of charging pile planning as control variables and the minimum cost of system comprehensive planning as objective function, the upper model was established based on network topology and charging load forecasting, taking into account the necessary constraints such as equipment capacity allocation constraints. Taking the charging scheme of electric vehicle users as the control variable, the lower model was established to minimize the comprehensive charging cost of users. The lower model makes charging plan based on the location and capacity scheme of the upper model, and feeds back the comprehensive charging cost to the upper model. The upper model needs to take the strategy of the lower model into account. The model solving process was designed by using firefly algorithm. Finally, an example was given to optimize the location and capacity of the charging station for electric vehicles in a new area of Xi"an city. The validity and correctness of the model are verified.

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