Residential Area Electric Vehicle Charging Pricing Strategy Based on Uncertainty Measure

被引：0
|
作者：
Yang J. [1 ]
Gou F. [1 ]
Huang Y. [2 ]
He Z. [1 ]
机构：
[1] School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, Sichuan Province
[2] State Grid Jiangxi Ganxi Power Supply Company, Xinyu, 338000, Jiangxi Province
来源：
Dianwang Jishu/Power System Technology | 2018年 / 42卷 / 01期
关键词：
Bilayer particle swarm optimization; Electric vehicle aggregator; Pricing strategy; Uncertainty;
D O I：
10.13335/j.1000-3673.pst.2017.1099
中图分类号：
学科分类号：
摘要：
Considering impact of increased residential load fluctuation caused by uncertainty of electric vehicle owners' response to time-of-use price in electricity market, this paper presents a pricing strategy of electric vehicle charging in residential area based on uncertainty measure. Firstly, a price elasticity coefficient is introduced to quantitatively analyze the response probability of electric vehicles to time-of-use price, aiming to establishing a dynamic probabilistic model of electric vehicle charging load. Secondly, entropy and overload probability are introduced to measure dynamic probability fluctuation of total load. On the premise that overload time of transformer is acceptable, a pricing strategy is formulated to maximize expected profit of electric vehicle aggregator. Then, bilayer particle swarm optimization algorithm is applied to solve the proposed pricing strategy. Finally, based on a residential area, the effect of pricing strategy is assessed. Numerical results show that the proposed pricing strategy can not only ensures income increase of electric vehicle aggregator, but also effectively reduce risk of transformer overload in each period in the residential area, and reduce uncertainty of total load fluctuation. © 2018, Power System Technology Press. All right reserved.
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页码：96 / 102
页数：6
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