Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station

被引:35
|
作者
Jiao, Feixiang
Zou, Yuan [1 ]
Zhang, Xudong [1 ]
Zhang, Bin
机构
[1] Beijing Inst Technol, Beijing Collaborat & Innovat Ctr Elect Vehicles, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Microgrid; Electric vehicle; Online dispatch; Model predictive control; Uncertainty; IN ELECTRIC VEHICLES; ENERGY MANAGEMENT; OPTIMIZATION; COORDINATION; UNCERTAINTY;
D O I
10.1016/j.energy.2022.123220
中图分类号
O414.1 [热力学];
学科分类号
摘要
To achieve carbon neutrality and meet the increased charging demand of electric vehicles, microgrids incorporating renewable energy and charging stations are considered one of the potential solutions. However, the inevitable uncertainties of renewable energy and load demand become a challenge for the online charging dispatch of the microgrid. Considering these uncertainties, this paper proposes a two stage optimal framework for the online dispatch of a grid-connected DC microgrid. The first stage presents a power coordination model to obtain the schedule plans of the main grid, the energy storage unit and the charging station, where the combined robust and stochastic model predictive control approach with different granular models is developed to solve this problem and effectively deal with these uncertainties. In the second stage, the charging station allocation model is designed to determine the charging power for every EV, which takes into account the max-min fairness of the charging power. Numerical cases in the presence of uncertainties are studied to evaluate the proposed dispatch framework and the solving approaches. The simulation results show its superiority in both computational efficiency and operating cost.(c) 2022 Published by Elsevier Ltd.
引用
收藏
页数:13
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