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A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles

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  • Wu, Chuanshen
  • Gao, Shan
  • Liu, Yu
  • Song, Tiancheng E.
  • Han, Haiteng

Abstract

Managing uncertainty is key to enhancing robustness in microgrids. This study focuses on the uncertainties in aggregated electric vehicles (EVs) and establishes a two-layer model predictive control (MPC) strategy for charging EVs with a microgrid. The uncertainty in sampling results of EV connection times to the microgrid and the uncertainty in sampling results of state of charge (SOC) of each EV at the time of the initial connection are both taken into account through multi-uncertainty sampling. In order of consider the impact of extreme scenarios, as feedback from arriving EVs are combined with multi-uncertainty sampling for higher accuracy of sampling results during rolling optimization. Simulation results show that, compared to conventional strategies, the proposed strategy has better performance in reducing forecasting error and regulating the charging and discharging of aggregated EVs with uncertainties.

Suggested Citation

  • Wu, Chuanshen & Gao, Shan & Liu, Yu & Song, Tiancheng E. & Han, Haiteng, 2021. "A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles," Renewable Energy, Elsevier, vol. 163(C), pages 1385-1396.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:1385-1396
    DOI: 10.1016/j.renene.2020.08.137
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    References listed on IDEAS

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    4. Chang, Weiguang & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Two-stage coordinated operation framework for virtual power plant with aggregated multi-stakeholder microgrids in a deregulated electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 943-956.
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    7. Bastida-Molina, Paula & Hurtado-Pérez, Elías & Moros Gómez, María Cristina & Vargas-Salgado, Carlos, 2021. "Multicriteria power generation planning and experimental verification of hybrid renewable energy systems for fast electric vehicle charging stations," Renewable Energy, Elsevier, vol. 179(C), pages 737-755.
    8. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    9. Fatin Ishraque, Md. & Shezan, Sk. A. & Ali, M.M. & Rashid, M.M., 2021. "Optimization of load dispatch strategies for an islanded microgrid connected with renewable energy sources," Applied Energy, Elsevier, vol. 292(C).
    10. Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    11. Miguel Matos & João Almeida & Pedro Gonçalves & Fabiano Baldo & Fernando José Braz & Paulo C. Bartolomeu, 2024. "A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities," Energies, MDPI, vol. 17(3), pages 1-25, January.
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    13. Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
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