基于需求侧管理的电动汽车有序充放电策略

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Abstract In this study, Monte Carlo random sampling statistics reflecting the family vehicles is established by using results of user behavior, charging regularity and charging power demand expectations. On this basis, the peak-valley gap charging model of electricity price strategies is established to respectively study the natural and coordinated strategy with a large-scale electric vehicle charging penetration. The genetic algorithm is adopted to solve this optimization model of time-span based scheme. By means of the strategy of demand side management for effective guidance, peak-valley gap of the load curve is reduced due to the oriented charging load. In this sense, coordinated electric vehicle charging optimization control is achieved.

Key words： Charging and discharging demand side management time of use tariff Monte Carlo simulation genetic algorithm peak-valley difference

Received: 26 September 2013 Published: 05 November 2014

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TM71

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	Sun Jinwen
	Wan Yunfei
	Zheng Peiwen
	Lin Xiangning

Cite this article:

Sun Jinwen,Wan Yunfei,Zheng Peiwen等. Coordinated Charging and Discharging Strategy for Electric Vehicles Based on Demand Side Management[J]. Transactions of China Electrotechnical Society, 2014, 29(8): 64-69.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I8/64

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