| 引用本文: | 李晓辉,李磊,刘伟东,赵新,谢秦.基于动态交通信息的电动汽车充电负荷时空分布预测[J].电力系统保护与控制,2020,48(1):117-125.[点击复制] |
| LI Xiaohui,LI Lei,LIU Weidong,ZHAO Xin,XIE Qin.Spatial-temporal distribution prediction of charging load for electric vehicles based on dynamic traffic information[J].Power System Protection and Control,2020,48(1):117-125[点击复制] |
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| 摘要: |
| 电动汽车充电负荷预测是研究电动汽车与电网互动的重要前提。针对交通路网信息对电动汽车行驶规律的影响，考虑电动汽车的交通工具特性和移动负荷特性，提出了一种基于动态交通信息的电动汽车充电负荷时空分布预测方法。该方法首先针对城市路网多交叉口特征，提出建立考虑路段阻抗和节点阻抗的动态路网模型。并根据路网规模确定了相应的交通网-配电网的交互模型。其次引入OD矩阵分析方法和实时Dijkstra动态路径搜索算法为电动汽车分配起止节点和规划行驶路径，模拟其动态行驶过程和充电行为。最后设计了电动汽车路径规划实验和典型区域实际路网充电负荷预测实验。结果表明，电动汽车充电负荷在不同功能区域分布存在差异且时间分布上不均匀，验证所提方法的有效性和可行性。 |
| 关键词: 电动汽车 动态交通信息 配电网 充电负荷 路径规划 |
| DOI：10.19783/j.cnki.pspc.181616 |
| 投稿时间：2018-12-29修订日期：2019-04-19 |
| 基金项目:国家重点研发计划项目资助(2016YFB0101800);国网天津市电力公司科技项目资助(KJ18-1-31) |
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| Spatial-temporal distribution prediction of charging load for electric vehicles based on dynamic traffic information |
| LI Xiaohui,LI Lei,LIU Weidong,ZHAO Xin,XIE Qin |
| (State Grid Tianjin Electric Power Company Electric Power Science Research Institute, Tianjin 300021, China;State Grid Tianjin Electric Power Company, Tianjin 300010, China) |
| Abstract: |
| Charging load prediction of electric vehicles is an important prerequisite for studying the interaction between electric vehicles and power grid. Aiming at the influence of traffic road network information on the driving rule of electric vehicles, the characteristics of both transportation and mobile load are taken into consideration and a spatial-temporal distribution prediction method of charging load for electric vehicles based on dynamic traffic information is presented. In this methodology, given the characteristic of multiple intersections in the urban road network, a dynamic road network model with the impedance of the road segment and the impedance of the node is firstly established. And also, the corresponding interactive model of transportation network-distribution network is determined according to the scale of road network. And then, the OD matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced to assign start-stop nodes and plan driving paths for electric vehicles and simulate their dynamic driving process and charging behavior. At last, the electric vehicle path planning experiment and the actual road network charging load prediction experiment in typical regions are designed. The results show that the charging load of electric vehicles varies in different functional regions and their temporal distribution is also uneven, verifying the effectiveness and feasibility of the proposed strategy. This work is supported by National Key Research and Development Program of China (No. 2016YFB0101800) and Science and Technology Project of State Grid Tianjin Electric Power Company (No. KJ18-1-31). |
| Key words: electric vehicle dynamic traffic information distribution network charging load route planning |
