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

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

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.

Suggested Citation

  • Jiao, Feixiang & Zou, Yuan & Zhang, Xudong & Zhang, Bin, 2022. "Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222001232
    DOI: 10.1016/j.energy.2022.123220
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    References listed on IDEAS

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    5. Ullah, Zia & Wang, Shaorong & Wu, Guan & Hasanien, Hany M. & Rehman, Anis Ur & Turky, Rania A. & Elkadeem, Mohamed R., 2023. "Optimal scheduling and techno-economic analysis of electric vehicles by implementing solar-based grid-tied charging station," Energy, Elsevier, vol. 267(C).
    6. Juan Moreno-Castro & Victor Samuel Ocaña Guevara & Lesyani Teresa León Viltre & Yandi Gallego Landera & Oscar Cuaresma Zevallos & Miguel Aybar-Mejía, 2023. "Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review," Energies, MDPI, vol. 16(16), pages 1-24, August.
    7. Ahmadi Jirdehi, Mehdi & Sohrabi Tabar, Vahid, 2023. "Risk-aware energy management of a microgrid integrated with battery charging and swapping stations in the presence of renewable resources high penetration, crypto-currency miners and responsive loads," Energy, Elsevier, vol. 263(PA).
    8. Simolin, Toni & Rauma, Kalle & Rautiainen, Antti & Järventausta, Pertti, 2022. "Increasing charging energy at highly congested commercial charging sites through charging control with load balancing functionality," Applied Energy, Elsevier, vol. 326(C).
    9. Liu, Haoran & Yu, Jiaqi & Wang, Ruzhu, 2022. "Model predictive control of portable electronic devices under skin temperature constraints," Energy, Elsevier, vol. 260(C).
    10. Matej Tkac & Martina Kajanova & Peter Bracinik, 2023. "A Review of Advanced Control Strategies of Microgrids with Charging Stations," Energies, MDPI, vol. 16(18), pages 1-25, September.

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