基于时空分布负荷预测的电动汽车充电优化

周凌锋; 王杰

基于时空分布负荷预测的电动汽车充电优化

周凌锋,
王杰

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)

详细信息

作者简介:
周凌锋(1993-),男,硕士研究生,研究方向为电动汽车负荷预测与优化控制、需求响应与分时电价制定机制,E-mail: zhoulingfeng1993@163.com;王杰(1960-),男,博士,教授,博士生导师,研究方向为复杂系统非线性控制、电力系统非线性控制与稳定分析,E-mail:jiewangxh@sjtu.edu.cn。

中图分类号: TM734

Electric Vehicles Charging Optimization Method Considering Spatial and Temporal Distribution Charging Demands Prediction

School of Electronic and Electric Engineering, Shanghai Jiaotong University, Shanghai 200240, China

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。
- 电动汽车 /
- 负荷预测 /
- 马尔可夫链 /
- 充电优化
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.
- electric vehicles /
- load prediction /
- Markov chain /
- charging optimization

[1]	刘宣,韩亮,高琛,等. 规模化插电式混合动力车提供调节服务能力评估[J].电力系统及其自动化学报,2016,28(4):85102. LIU Xuan,HAN Liang,GAO Chen,et al. Capability evaluation of the scaled plug-in hybrid electric vehicles for providing regulation service [J].Proceedings of the CSU-EPSA,2016,28(4):85102.
[2]	胡泽春,宋永华,徐智威,等.电动汽车接入电网的影响与利用 [J].中国电机工程学报,2012,32(4):110. HU Zechun, SONG Yonghua, XU Zhiwei, et al. Impacts and utilization of electric vehicles integration into power systems [J]. Proceedings of the CSEE,2012,32(4):110.
[3]	党杰,汤奕,宁佳,等. 基于用户意愿和出行规律的电动汽车充电负荷分配策略[J]. 电力系统保护与控制,2015,43(16):815. DANG Jie, TANG Yi, NING Jia, et al. A strategy for distribution of electric vehicles charging load based on user intention and trip rule [J]. Power System Protection and Control, 2015,43(16):815.
[4]	SABER A Y, VENAYAGAMOORTHY G K. One million plug-in electric vehicles on the road by 2015 [C] //12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA 04.Oct, 2009: 17.
[5]	ETEZADI-AMOLI M,CHOMA K,STEFANI J. Rapid-charge electric-vehicle stations [J].IEEE Trans. on Power Delivery, 2010,25(3):18831887.
[6]	赵俊华,文福拴,杨爱民,等.电动汽车对电力系统的影响及其调度与控制问题[J].电力系统自动化,2011,35(14):210. ZHAO Junhua, WEN Fushuan, YANG Aimin, et al. Impacts of electric vehicles on power systems as well as the associated dispatching and control problem [J]. Automation of Electric Power Systems,2011,35(14):210.
[7]	GAN L, TOPCU U, LOW S. Optimal decentralized protocol for electric vehicle charging [J]. IEEE Transactions on Power Systems, 2013, 28(2), 940951.
[8]	HE Y, VENKATESH B, GUAN L. Optimal scheduling for charging and discharging of electric vehicles[J]. IEEE Transactions on Smart Grid, 2012, 3(3), 10951105.
[9]	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, 22:4348. CHEN Jingpeng, AI Qian, XIAO Fei. Optimal charging scheduling for massive electric vehicles based on cluster response [J]. Automation of Electric Power Systems, 2016, 22:4348.
[10]	孙晓明,王玮,苏粟,等.基于分时电价的电动汽车有序充电控制设计[J].电力系统自动化,2013,37(1):191195. SUN Xiaoming, WANG Wei, SU Su, et al. Coordinated charging strategy for electric vehicles based on time-of-use price[J]. Automation of Electric Power Systems, 2013, 37(1):191195.
[11]	GALUS M D, WARAICH R A, NOEMBRINI F, et al. Integrating power systems, transport systems and vehicle technology for electric mobility impact assessment and efficient control [J]. IEEE Trans.on Smart Grid, 2012, 3(2):934949.
[12]	TAE-KYUNG L, BAREKET Z, GORDON T, et al. Stochastic modeling for studies of real-world PHEV usage: driving schedule and daily temporal distributions [J]. IEEE Trans. on Vehicular Technology, 2012, 61(4): 14931502.
[13]	张洪财,胡泽春,宋永华,等. 考虑时空分布的电动汽车充电负荷预测方法[J]. 电力系统自动化, 2014,38(1):1320. ZHANG Hongcai, HU Zechun, SONG Yonghua, et al. A prediction method for electric vehicle charging load considering spatial and temporal distribution[J]. Automation of Electric Power Systems, 2014, 38(1):1320.
[14]	陈丽丹,聂涌泉,钟庆. 基于出行链的电动汽车充电负荷预测模型[J]. 电工技术学报,2015,30(4):216225. CHEN Lidan, NIE Yongquan, ZHONG Qing. A model for electric vehicle charging load forecasting based on trip chains[J]. Transactions of China Electrotechnical Society, 2015, 30(4):216225.
[15]	GRAHN P,MUNKHAMMAR J,WIDEN J,et al. PHEV home charging model based on residential activity patterns [J]. IEEE Trans.on Power Systems,2013,28(3):25072515.
[16]	SOARES F J, LOPES J. A stochastic model to simulate electric vehicles motion and quantify the energy required from the grid[C]// Power Systems Computation Conference, 2011.

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基于时空分布负荷预测的电动汽车充电优化

Electric Vehicles Charging Optimization Method Considering Spatial and Temporal Distribution Charging Demands Prediction

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基于时空分布负荷预测的电动汽车充电优化

上海交通大学电子信息与电气工程学院, 上海 200240

English Abstract

Electric Vehicles Charging Optimization Method Considering Spatial and Temporal Distribution Charging Demands Prediction

School of Electronic and Electric Engineering, Shanghai Jiaotong University, Shanghai 200240, China

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留言板

基于时空分布负荷预测的电动汽车充电优化

Electric Vehicles Charging Optimization Method Considering Spatial and Temporal Distribution Charging Demands Prediction

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基于时空分布负荷预测的电动汽车充电优化

上海交通大学电子信息与电气工程学院, 上海 200240

English Abstract

Electric Vehicles Charging Optimization Method Considering Spatial and Temporal Distribution Charging Demands Prediction

School of Electronic and Electric Engineering, Shanghai Jiaotong University, Shanghai 200240, China

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目录