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

Feixiang Jiao, Yuan Zou*, Xudong Zhang*, Bin Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number123220
JournalEnergy
Volume247
DOIs
Publication statusPublished - 15 May 2022

Keywords

  • Electric vehicle
  • Microgrid
  • Model predictive control
  • Online dispatch
  • Uncertainty

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