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An efficient and economical storage and energy sharing model for multiple multi-energy microgrids

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  • Cao, Wenzhi
  • Xiao, Jiang-Wen
  • Cui, Shi-Chang
  • Liu, Xiao-Kang

Abstract

Multi-energy microgrids are facing a dilemma that realizing high local energy efficiency requires large-capacity ESS with hefty investment costs. To address the dilemma, an efficient and economic hybrid storage and energy sharing model for multiple microgrids is proposed. Specificly, a hybrid energy storage system (HESS) is introduced, which contains an electrical battery and a heat storage tank and is able to realize energy conversion. The multiple microgrids can share energy through the HESS in a collaborative way. An energy optimization problem is formulated to minimize the overall energy costs including energy purchase cost and HESS operating cost. ADMM algorithm is used to solve the problem in a distributed manner to avoid privacy concerns. The storage and energy sharing benefits of the microgrids and the HESS are determined by Nash bargaining solution. Simulation results show that the model can effectively improve the utilization of the renewable energy, and lead to considerable economic benefits for both the microgrids and the HESS.

Suggested Citation

  • Cao, Wenzhi & Xiao, Jiang-Wen & Cui, Shi-Chang & Liu, Xiao-Kang, 2022. "An efficient and economical storage and energy sharing model for multiple multi-energy microgrids," Energy, Elsevier, vol. 244(PB).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pb:s0360544222000275
    DOI: 10.1016/j.energy.2022.123124
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    9. Qiu, Rui & Zhang, Haoran & Wang, Guotao & Liang, Yongtu & Yan, Jinyue, 2023. "Green hydrogen-based energy storage service via power-to-gas technologies integrated with multi-energy microgrid," Applied Energy, Elsevier, vol. 350(C).
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    14. Xianan Jiao & Jiekang Wu & Yunshou Mao & Weiming Luo & Mengxuan Yan, 2023. "An Optimal Method of Energy Management for Regional Energy System with a Shared Energy Storage," Energies, MDPI, vol. 16(2), pages 1-21, January.
    15. Zhang, Dongdong & Li, Chunjiao & Goh, Hui Hwang & Ahmad, Tanveer & Zhu, Hongyu & Liu, Hui & Wu, Thomas, 2022. "A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems," Renewable Energy, Elsevier, vol. 189(C), pages 1383-1406.
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    18. Marcelino, C.G. & Leite, G.M.C. & Wanner, E.F. & Jiménez-Fernández, S. & Salcedo-Sanz, S., 2023. "Evaluating the use of a Net-Metering mechanism in microgrids to reduce power generation costs with a swarm-intelligent algorithm," Energy, Elsevier, vol. 266(C).
    19. Zhang, Kaoshe & Gao, Congchong & Zhang, Gang & Xie, Tuo & Li, Hua, 2024. "Electricity and heat sharing strategy of regional comprehensive energy multi-microgrid based on double-layer game," Energy, Elsevier, vol. 293(C).
    20. Chen, Yujia & Pei, Wei & Ma, Tengfei & Xiao, Hao, 2023. "Asymmetric Nash bargaining model for peer-to-peer energy transactions combined with shared energy storage," Energy, Elsevier, vol. 278(PB).
    21. 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).
    22. Gang Liang & Yu Wang & Bing Sun & Zheng Zhang, 2024. "An Optimization Method for the Distributed Collaborative Operation of Multilateral Entities Considering Dynamic Time-of-Use Electricity Price in Active Distribution Network," Energies, MDPI, vol. 17(2), pages 1-19, January.
    23. Zhong, Xiaoqing & Zhong, Weifeng & Liu, Yi & Yang, Chao & Xie, Shengli, 2023. "A communication-efficient coalition graph game-based framework for electricity and carbon trading in networked energy hubs," Applied Energy, Elsevier, vol. 329(C).
    24. Chenhui Xu & Yunkai Huang, 2023. "Integrated Demand Response in Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach," Energies, MDPI, vol. 16(12), pages 1-19, June.

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