| 基于集群响应的规模化电动汽车充电优化调度 |
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| 引用本文： | 陈静鹏,艾芊,肖斐.基于集群响应的规模化电动汽车充电优化调度[J].电力系统自动化,2016,40(22):43-48. |
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| 作者姓名： | 陈静鹏 艾芊 肖斐 |
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| 作者单位： | 上海交通大学电子信息与电气工程学院, 上海市 200240,上海交通大学电子信息与电气工程学院, 上海市 200240,上海交通大学电子信息与电气工程学院, 上海市 200240 |
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| 基金项目： | 国家自然科学基金资助项目(51577115);国家重点研发计划资助项目(2016YFB0901304) |
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| 摘 要： | 大规模电动汽车(EV)的充电需求和充电负荷分布将呈现出规律性,从群体的角度对EV进行优化调度,可以降低问题的维度,提高优化计算的效率。基于区域EV的集群响应特性,建立了以负荷峰谷差最小化为目标的EV群体充电概率分布模型。在此基础上,根据EV群体对充电电价的灵敏度,建立EV集群响应的实时电价模型,通过电价对EV的充电行为进行有序引导,从而实现电网的"移峰填谷"策略。以典型的区域配电网负荷数据为例,验证了文中EV充电优化调度方法的有效性。最后,对EV群体响应实时电价的灵敏度,以及不同灵敏度下EV群体和代理商的节省成本进行讨论。
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| 关 键 词： | 电动汽车 集群响应 协调充电 需求侧管理 充电电价 |
| 收稿时间： | 2015/12/22 0:00:00 |
| 修稿时间： | 2016/9/27 0:00:00 |
Optimal Charging Scheduling for Massive Electric Vehicles Based on Cluster Response |
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| CHEN Jingpeng,AI Qian and XIAO Fei.Optimal Charging Scheduling for Massive Electric Vehicles Based on Cluster Response[J].Automation of Electric Power Systems,2016,40(22):43-48. |
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| Authors: | CHEN Jingpeng AI Qian and XIAO Fei |
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| Affiliation: | School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China,School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China and School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China |
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| Abstract: | The charging demand and load distributing of massive electric vehicles(EVs)should obey some regularity. From the perspective of cluster response, the dimensions of the EV scheduling problem will be greatly reduced and the efficiency of optimization calculations will be improved. An optimization model of local agents for minimizing peak-valley difference is established based on the cluster response characteristics of EVs. Moreover, a real-time pricing model for EVs cluster response is built according to EVs'' sensitivity to charging price. The charging behavior of EVs is guided through charging price so as to realize the strategy of power grid peak shaving. By taking typical distribution network load data of a certain area as a test case, the effectiveness of the charging load regulation method is verified. Finally, the EVs'' sensitivity to real-time pricing response and cost savings of EVs and local agents of different sensitivity are discussed. This work is supported by National Natural Science Foundation of China(No. 51577115)and National Key Research and Development Program of China(No. 2016YFB0901304). |
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| Keywords: | electric vehicle(EV) cluster response charging coordination demand side management charging price |
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