Research on large scale EV charging optimization strategy

The charging behavior of large scale electric vehicle (EV) fleet has the time-spatial characteristics, in this paper the charging characteristics and driving behaviors of an EV was analyzed, and the EV charging load model has been developed. Based on the Monte Carlo method, the charging model of lar...

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Bibliographic Details
Published in2016 IEEE International Conference on Power System Technology (POWERCON) pp. 1 - 6
Main Authors Jianhua Zhang, Qin Zhou, Ming Li, Hang Long
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Batteries
Charging Infrastructure
EV Coordinated Charging
Load modeling
Mathematical model
Optimization
Pricing
Probability distribution
PSO Algorithm
State of charge
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Summary:The charging behavior of large scale electric vehicle (EV) fleet has the time-spatial characteristics, in this paper the charging characteristics and driving behaviors of an EV was analyzed, and the EV charging load model has been developed. Based on the Monte Carlo method, the charging model of large scale EVs has been built and the impacts of the charging behaviors of the large amount EVs has been simulated. The optimal EV orderly charging model has been developed with the objectives of system load shaving and charging cost minimization. The Particle Swarm Optimization (PSO) method was applied to solve the EV orderly charging problem in order to achieve the optimal charging strategy. An IEEE distribution network was used for the case simulations. The results show that the proposed EV orderly charging strategy could effectively reduce the system peak-valley load difference and minimize the impacts to the power grid caused by the large scale EV charging. When the real-time pricing or time-of-use pricing scheme applied, it could also significantly reduce the EV charging cost.
DOI:10.1109/POWERCON.2016.7754062