| 基于时空分布负荷预测的电动汽车充电优化 |
| |
| 引用本文： | 周凌锋,王杰.基于时空分布负荷预测的电动汽车充电优化[J].现代电力,2018,35(5):10-16. |
| |
| 作者姓名： | 周凌锋 王杰 |
| |
| 作者单位： | 上海交通大学电子信息与电气工程学院, 上海 200240 |
| |
| 基金项目： | 国家自然科学基金项目(61374155);高等学校博士学科点专项科研基金(20130073110030) |
| |
| 摘 要： | 本文提出了一种综合考虑电动汽车出行特点,充电地域差别及用户充电习惯的电动汽车时空分布负荷预测模型。考虑多次充电场景,模拟实时充电行为,利用马尔可夫链确定各出行目的地的转移概率并提出了一种基于蒙特卡洛模拟的双层充电负荷预测模型对充电负荷的时空分布进行模拟预测。根据时空预测初步结果,以夜间充电为例,对在夜间入网充电车辆的无序充电行为进行了充电优化。近一步,考虑不同荷电状态(SOC) 阈值对电网优化充电的影响。结果表明,本文提出的预测模型对电动汽车负荷的时空分布预测具有一定的参考价值,夜间充电负荷的优化方法实现了充电负荷的实时优化,对电动汽车入网的负荷优化具有一定的指导意义。
|
| 关 键 词： | 电动汽车 负荷预测 马尔可夫链 充电优化 |
| 收稿时间： | 2017-09-26 |
Electric Vehicles Charging Optimization Method Considering Spatial and Temporal Distribution Charging Demands Prediction |
| |
| Affiliation: | School of Electronic and Electric Engineering, Shanghai Jiaotong University, Shanghai 200240, China |
| |
| Abstract: | A spatial and temporal distribution prediction model is built considering electric vehicles (EVs) trip characteristics, charging area difference and users' charging habits in this paper. Charging demands forecasting for EVs based on Monte Carlo simulation is proposed by simulating real-time charging behavior under multiple charging scenarios. Based on the preliminary prediction results, optimal charging strategy for EVs based on evolutionary algorithm is established with the night charging scenario as an example. Furthermore, the influence of different charging state (SOC) thresholds on the optimal charging is conducted. The results show that the proposed prediction model provides preferable charging load distribution and the optimal charging strategy achieves the real-time valley filling. This paper has significant guidance for load optimization of electric vehicles. |
| |
| Keywords: | |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《现代电力》浏览原始摘要信息 |
|
点击此处可从《现代电力》下载全文 |
|
