Coordinated Planning Strategy for Electric Vehicle Charging Stations and Coupled Traffic-Electric Networks
Coordinated Planning Strategy for Electric Vehicle Charging Stations and Coupled Traffic-Electric Networks
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电动汽车充电站与交通电力耦合网络协调规划策略
DOI：
10.1109/tpwrs.2018.2867176
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发表时间：
2019
影响因子：
6.6
通讯作者：
Zhiyi Li
中科院分区：
文献类型：
--
作者：
Xu Wang;Mohammad Shahidehpour;Chuanwen Jiang;Zhiyi Li
随着电动汽车的迅速发展，需要将交通网络与配电网络耦合起来，对充电站进行有效的规划。本文对交通-电力耦合网络中电动汽车充电站的协调规划策略进行了跨学科的研究。提出了一个综合规划模型，该模型确定了TN和PDN的最优扩展策略，包括新建充电站、充电点、TN车道和PDN线路的站点和规模。在TN中，我们使用无约束交通分配模型（UTAM）来明确捕获交通流的稳态分布。在PDN中，操作条件由线性化的DistFlow描述。此外，利用电动汽车充电站位置模型表征了电动汽车充电需求以及TN和PDN的相互依赖性。为了检索全局最优解，对协调规划模型建立了具有UTAM约束的等效混合整数线性规划模型。通过交通-电力耦合网络的数值实验，验证了模型和求解方法的有效性。
The proliferation of electric vehicles (EVs) calls for an effective planning of charging stations by coupling the transportation network (TN) with the power distribution network (PDN). This paper conducts an interdisciplinary study on the coordinated planning strategy of EV charging stations in a coupled traffic-electric network. A comprehensive planning model is proposed, which determines the optimal expansion strategies for both TN and PDN, including sites and sizes of new charging stations, charging spots, TN lanes, and PDN lines. In TN, we use an unconstrained traffic assignment model (UTAM) to explicitly capture the steady-state distribution of traffic flows. In PDN, operating conditions are described by a linearized DistFlow. Moreover, EV charging demands and interdependency of TN and PDN are characterized by EV charging station location model. To retrieve a global optimal solution, an equivalent mixed-integer linear programming model with UTAM constraints is formulated for the coordinated planning model. Numerical experiments on coupled traffic-electric networks are conducted to validate the effectiveness of the proposed model and the solution method.
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影响因子：
7
作者：
Hongcai Zhang;S. Moura;Zechun Hu;Wei Qi;Yonghua Song
通讯作者：
Hongcai Zhang;S. Moura;Zechun Hu;Wei Qi;Yonghua Song
DOI：
10.1287/opre.19.7.1691
发表时间：
1971-12
期刊：
Oper. Res.
影响因子：
--
作者：
L. Wolsey
通讯作者：
L. Wolsey
影响因子：
6.6
作者：
Wei Wei-Wei;S. Mei;Lei Wu;Jianhui Wang;Yujuan Fang
通讯作者：
Wei Wei-Wei;S. Mei;Lei Wu;Jianhui Wang;Yujuan Fang
影响因子：
6.8
作者：
Fang He;Di Wu;Yafeng Yin;Yongpei Guan
通讯作者：
Fang He;Di Wu;Yafeng Yin;Yongpei Guan
影响因子：
9.6
作者：
Zhiyi Li;M. Shahidehpour;S. Bahramirad;A. Khodaei
通讯作者：
Zhiyi Li;M. Shahidehpour;S. Bahramirad;A. Khodaei