Optimal Allocation of Charging Piles in Multi-areas Considering Charging Load Forecasting Based on Markov Chain

被引：0

作者：

Lyu L. ^{[1
]}

Xu W. ^{[1
]}

Xiang Y. ^{[1
]}

Zhang Y. ^{[2
]}

Xiong J. ^{[3
]}

机构：

[1] School of Electrical Eng. and Info., Sichuan Univ., Chengdu

[2] Electric Power Research Inst., State Grid Fujian Electric Power Co., Fuzhou

[3] Xiamen Electric Power Supply Co., State Grid Fujian Electric Power Co., Xiamen

来源：

Xiang, Yue (xiang@scu.edu.cn) | 1600年 / Sichuan University卷 / 49期

关键词：

Charging demand; Charging piles; Electric vehicle; Markov chain; Mobility characteristics;

D O I：

10.15961/j.jsuese.201600357

中图分类号：

学科分类号：

摘要：

Considering the complexity and diversity of customers' travel habits, the charging piles need to be allocated appropriately to satisfy the charging demand.Firstly, Markov chain is used to describe the variation of battery state of charge on electric vehicle owners' trip in the whole day, according to three decision-making behavior including driving, charging, neither charging nor driving.Then the real-time charging behavior in the process could be determinated, which indicates the fast and slow charging demand of different vehicle types.Considering mobility characteristics of electric vehicles and the number of different types of electric vehicles in different time periods in some area, the total load demand could be forecasted.The optimal allocation model for charging piles is proposed and aims to minimize investment and operating costs for the charging piles.The mobility characteristics of electric vehicles are integrated into the constraints, and the model is solved by the particle swarm optimization algorithm.The effectiveness and feasiblility of the proposed method are verified by the 33-bus four-area case study on the charging load forecasting and optimal allocation of charing piles. © 2017, Editorial Department of Advanced Engineering Sciences. All right reserved.

引用

页码：170 / 178

页数：8

共 18 条

[1]

Feng L., Zhang J., Li G., Et al., Cost reduction of a hybrid energy storage system considering correlation between wind and PV power, Protection and Control of Modern Power Systems, 1, pp. 1-9, (2016)

[2]

Liu J., Xiang Y., Yang W., Et al., Key technology analysis and study for charging and swapping service network and its coordinated planning with distribution network, Electric Power Construction, 36, 7, pp. 47-55, (2015)

[3]

Li R., Su H., Optimal allocation of charging facilities for electric vehicles based on queuing theory, Automation of Electric Power Systems, 35, 14, pp. 58-61, (2011)

[4]

Tang X., Liu J., Liu Y., Et al., Electric vehicle charging station planning based on computational geometry method, Automation of Electric Power Systems, 36, 8, pp. 24-30, (2012)

[5]

Qian K., Zhou C., Allan M., Et al., Modeling of load demand due to EV battery charging in distribution systems, IEEE Transactions on Power Systems, 26, 2, pp. 802-810, (2011)

[6]

Tian L., Shi S., Jia Z., A statistical model or charging power demand of electric vehicles, Power System Technology, 34, 11, pp. 126-130, (2010)

[7]

Luo Z., Hu Z., Song Y., Et al., Study on plug-in electric vehicles charging load calculating, Automation of Electric Power Systems, 35, 14, pp. 36-42, (2011)

[8]

Zhang H., Hu Z., Song Y., Et al., A prediction method for electric vehicle charging load considering spatial and temporal distribution, Automation of Electric Power Systems, 38, 1, pp. 13-20, (2014)

[9]

Wang X., Liu J., Tang X., Et al., Study on coordinated charging of electric based on spatial load forecasting, Zhejiang Electric Power, 2, pp. 7-13, (2014)

[10]

Xiong H., Xiang T., Zhu Y., Et al., Electric vehicle public charging station location optimal planning, Automation of Electric Power Systems, 36, 20, pp. 1-6, (2012)

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