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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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    6. 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.
    7. Md Shafiullah & Akib Mostabe Refat & Md Ershadul Haque & Dewan Mabrur Hasan Chowdhury & Md Sanower Hossain & Abdullah G. Alharbi & Md Shafiul Alam & Amjad Ali & Shorab Hossain, 2022. "Review of Recent Developments in Microgrid Energy Management Strategies," Sustainability, MDPI, vol. 14(22), pages 1-30, November.
    8. Wu, Kunming & Li, Qiang & Chen, Ziyu & Lin, Jiayang & Yi, Yongli & Chen, Minyou, 2021. "Distributed optimization method with weighted gradients for economic dispatch problem of multi-microgrid systems," Energy, Elsevier, vol. 222(C).
    9. Tan, Bifei & Chen, Simin & Liang, Zipeng & Zheng, Xiaodong & Zhu, Yanjin & Chen, Haoyong, 2024. "An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles," Applied Energy, Elsevier, vol. 356(C).
    10. Luo, Lizi & He, Pinquan & Gu, Wei & Sheng, Wanxing & Liu, Keyan & Bai, Muke, 2022. "Temporal-spatial scheduling of electric vehicles in AC/DC distribution networks," Energy, Elsevier, vol. 255(C).
    11. Liu, Xinrui & Zhang, Mingchao & Xie, Xiangpeng & Zhao, Liang & Sun, Qiuye, 2022. "Consensus-based energy management of multi-microgrid: An improved SoC-based power coordinated control method," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    12. 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).
    13. 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.
    14. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    15. 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).
    16. Zhou, Xu & Ma, Zhongjing & Zou, Suli & Zhang, Jinhui, 2022. "Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions," Applied Energy, Elsevier, vol. 324(C).
    17. Rezaei, Navid & Pezhmani, Yasin & Khazali, Amirhossein, 2022. "Economic-environmental risk-averse optimal heat and power energy management of a grid-connected multi microgrid system considering demand response and bidding strategy," Energy, Elsevier, vol. 240(C).
    18. Villanueva-Rosario, Junior Alexis & Santos-García, Félix & Aybar-Mejía, Miguel Euclides & Mendoza-Araya, Patricio & Molina-García, Angel, 2022. "Coordinated ancillary services, market participation and communication of multi-microgrids: A review," Applied Energy, Elsevier, vol. 308(C).

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