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Multi-objective energy management of multiple microgrids under random electric vehicle charging

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  • Tan, Bifei
  • Chen, Haoyong

Abstract

In view of the increasing development of decentralized power systems and electric vehicles, this paper seeks to improve the energy management performance of multiple microgrid systems under the uncertainty associated with electric vehicle charging. A multi-objective optimization model is established for minimizing the transmission losses, operating costs, and carbon emissions of multiple microgrid systems. Firstly, a novel method is proposed for forecasting electric vehicle charging loads based on a back propagation neural network improved by long short-term memory deep learning. Based on the forecast data, a double layer solution algorithm is proposed, which consists of an adaptive multi-objective evolutionary algorithm based on decomposition and differential evolution at the multiple microgrids layer and a modified consistency algorithm for fast economic scheduling at the single microgrid layer. Finally, a model system composed of four interconnected IEEE microgrids is simulated as a case study, and the performance of the proposed algorithm is compared with that of conventional multi-objective evolutionary algorithms based on decomposition. The simulation results demonstrate the superiority of the global search performance and the rapid convergence performance of the proposed improved algorithm.

Suggested Citation

  • Tan, Bifei & Chen, Haoyong, 2020. "Multi-objective energy management of multiple microgrids under random electric vehicle charging," Energy, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:energy:v:208:y:2020:i:c:s0360544220314675
    DOI: 10.1016/j.energy.2020.118360
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    5. Enriquez-Contreras, Luis Fernando & Barth, Matthew & Ula, Sadrul, 2024. "CO2 and Cost Impact Analysis of a Microgrid with Electric Vehicle Charging Infrastructure: A Case Study in Southern California," Institute of Transportation Studies, Working Paper Series qt8br5m587, Institute of Transportation Studies, UC Davis.
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    10. Mansouri, S.A. & Ahmarinejad, A. & Nematbakhsh, E. & Javadi, M.S. & Esmaeel Nezhad, A. & Catalão, J.P.S., 2022. "A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources," Energy, Elsevier, vol. 245(C).
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    13. Wang, Shubin & Li, Jiabao & Liu, Xinni & Zhao, Erlong & Eghbalian, Nasrin, 2022. "Multi-level charging stations for electric vehicles by considering ancillary generating and storage units," Energy, Elsevier, vol. 247(C).
    14. Seyed Hasan Mirbarati & Najme Heidari & Amirhossein Nikoofard & Mir Sayed Shah Danish & Mahdi Khosravy, 2022. "Techno-Economic-Environmental Energy Management of a Micro-Grid: A Mixed-Integer Linear Programming Approach," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    15. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
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