| 基于历史行驶路线的电动汽车充电站布局优化 |
| |
| 引用本文： | 付凤杰,方雅秀,董红召,陈炜烽.基于历史行驶路线的电动汽车充电站布局优化[J].电力系统自动化,2018,42(12):72-80. |
| |
| 作者姓名： | 付凤杰 方雅秀 董红召 陈炜烽 |
| |
| 作者单位： | 浙江工业大学机械工程学院;杭州数元信息科技有限公司 |
| |
| 基金项目： | 国家自然科学基金资助项目(61773347)；浙江省自然科学基金资助项目(LY17F030017) |
| |
| 摘 要： | 由于电动汽车面临着续航里程不足的问题,充电设施的合理布设显得至关重要。利用基于自动车辆识别数据的历史行驶路线来模拟电动汽车出行,给定一种充电站布局方案,即可利用基于出行特征的典型充电选择模式来模拟电动汽车的充电行为,继而准确地计算电动汽车的充电需求、充电成本和运营商利润。然后,以电动汽车的充电—出行成本比与运营商的成本—收益比之和最小为优化目标,以用户心理和电网负荷等因素为约束条件,确定充电站的最优布局方案。最后,以一个区域(151个信号交叉口)为例,验证了该方法的有效性,同时分析了工作日/非工作日以及交通需求增长和电池发展对布局方案的影响。
|
| 关 键 词： | 历史行驶路线 充电需求 电网负荷 遗传算法 充电站布局 |
| 收稿时间： | 2017/10/26 0:00:00 |
| 修稿时间： | 2018/5/10 0:00:00 |
Optimized Allocation of Charging Stations for Electric Vehicles Based on Historical Trajectories |
| |
| FU Fengjie,FANG Yaxiu,DONG Hongzhao and CHEN Weifeng.Optimized Allocation of Charging Stations for Electric Vehicles Based on Historical Trajectories[J].Automation of Electric Power Systems,2018,42(12):72-80. |
| |
| Authors: | FU Fengjie FANG Yaxiu DONG Hongzhao and CHEN Weifeng |
| |
| Affiliation: | College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China,College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China,College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China and Hangzhou Shuyuan Information Technology Co. Ltd., Hangzhou 310012, China |
| |
| Abstract: | A reasonable layout of charging facilities is crucial to electric vehicles(EVs)due to the endurance mileage deficiency. Travel behaviors of EVs are simulated based on the historical trajectories from the automatic number plate recognition(ANPR)data. Thus, by giving an allocation of charging stations, charging behaviors of EVs are simulated using the typical pattern of charging choices based on their trip characteristics. The charging demand and cost of EVs and the profit of operators can be obtained. Consequently, reasonable locations and capacities of charging stations for an optimized layout are determined by minimizing the sum of the charging-trip ratio(CTR)and the cost-benefit ratio(CBR). Furthermore, they should be restricted by user psychology and the capacity of power grid. A case study(including 151 signal intersections)is given to demonstrate the effectiveness of optimization method. Moreover, the influence of workdays/weekends, the increase of traffic demand and the development of battery on the allocation is analyzed. |
| |
| Keywords: | historical trajectories charging demand load of power grid genetic algorithm allocation of charging stations |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《电力系统自动化》浏览原始摘要信息 |
|
点击此处可从《电力系统自动化》下载全文 |
|
