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Multi-energy management with hierarchical distributed multi-scale strategy for pelagic islanded microgrid clusters

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  • Hu, Mian
  • Wang, Yan-Wu
  • Xiao, Jiang-Wen
  • Lin, Xiangning

Abstract

The pelagic islands usually include resource islands and load islands with electricity and natural gas transactions among them. Since they are apart from the main land, the finiteness of energy resources results in crucial need of developing efficient energy management framework for the pelagic islanded microgrid clusters (PIMGCs). In this paper, we introduce a novel multi-energy management framework for a PIMGC, where the operators on resource islands sell energy resources, while the aggregators and users on load islands dispatch and consume energy resources, respectively. The multi-scale energy management strategy is proposed, where the operators determine their daily optimal energy supply in a distributed collaborative way by adopting the primal-dual subgradient method, each aggregator determines its daily optimal energy demand and hourly optimal energy usage, and each user determines its hourly optimal energy consumption. A hierarchical day-ahead distributed algorithm is proposed to obtain the Nash equilibrium strategy, where the operators minimize their aggregate operational cost, each aggregator maximizes its revenue and each user maximizes its payoff. Simulation results are provided to show the effectiveness and benefits of the proposed multi-energy management framework for the PIMGCs.

Suggested Citation

  • Hu, Mian & Wang, Yan-Wu & Xiao, Jiang-Wen & Lin, Xiangning, 2019. "Multi-energy management with hierarchical distributed multi-scale strategy for pelagic islanded microgrid clusters," Energy, Elsevier, vol. 185(C), pages 910-921.
    • Handle: RePEc:eee:energy:v:185:y:2019:i:c:p:910-921
    DOI: 10.1016/j.energy.2019.07.087
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    Cited by:

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    3. Wang, Yijian & Cui, Yang & Li, Yang & Xu, Yang, 2023. "Collaborative optimization of multi-microgrids system with shared energy storage based on multi-agent stochastic game and reinforcement learning," Energy, Elsevier, vol. 280(C).
    4. Obara, Shin’ya & Fujimoto, Shoki & Sato, Katsuaki & Utsugi, Yuta, 2021. "Planning renewable energy introduction for a microgrid without battery storage," Energy, Elsevier, vol. 215(PB).
    5. Wu, Chuantao & Wang, Tao & Zhou, Dezhi & Cao, Shankang & Sui, Quan & Lin, Xiangning & Li, Zhengtian & Wei, Fanrong, 2023. "A distributed restoration framework for distribution systems incorporating electric buses," Applied Energy, Elsevier, vol. 331(C).
    6. Wu, Chuantao & Zhou, Dezhi & Lin, Xiangning & Sui, Quan & Wei, Fanrong & Li, Zhengtian, 2022. "A novel energy cooperation framework for multi-island microgrids based on marine mobile energy storage systems," Energy, Elsevier, vol. 252(C).
    7. Shi, Mengshu & Huang, Yuansheng & Lin, Hongyu, 2023. "Research on power to hydrogen optimization and profit distribution of microgrid cluster considering shared hydrogen storage," Energy, Elsevier, vol. 264(C).
    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. Chen, Tengpeng & Cao, Yuhao & Qing, Xinlin & Zhang, Jingrui & Sun, Yuhao & Amaratunga, Gehan A.J., 2022. "Multi-energy microgrid robust energy management with a novel decision-making strategy," Energy, Elsevier, vol. 239(PA).
    10. Chen, Weidong & Wang, Junnan & Yu, Guanyi & Chen, Jiajia & Hu, Yumeng, 2022. "Research on day-ahead transactions between multi-microgrid based on cooperative game model," Applied Energy, Elsevier, vol. 316(C).
    11. 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).

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