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Distributed MPC-based energy scheduling for islanded multi-microgrid considering battery degradation and cyclic life deterioration

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  • Nawaz, Arshad
  • Wu, Jing
  • Ye, Jun
  • Dong, Yidi
  • Long, Chengnian

Abstract

In this paper, distributed model predictive control (MPC) based energy scheduling problem is presented for islanded multi-microgrids. The objective is to achieve supply–demand balance in an individual microgrid through energy coordination and reduce the battery degradation for its extended cycle life. In order to achieve desired objective, the battery state of charge is taken as zone control optimization problem and new slack variable is introduced as optimized variable to restrict the battery state of charge in optimal range with considering battery degradation. Furthermore, accelerated distributed augmented Lagrangian (ADAL) based distributed coordination strategy is presented, which can improve power supply reliability through coordinated cooperation among MGs for energy exchanges. The system framework is modeled as mixed-integer dynamic model which switches between different operating conditions. A mixed-integer quadratic programming approach is addressed to solve the MPC optimization problem. The effectiveness of the proposed scheme is validated through comparative performance with existing model. Finally, convergence analysis and comparative fast convergence performance of the proposed scheme is provided.

Suggested Citation

  • Nawaz, Arshad & Wu, Jing & Ye, Jun & Dong, Yidi & Long, Chengnian, 2023. "Distributed MPC-based energy scheduling for islanded multi-microgrid considering battery degradation and cyclic life deterioration," Applied Energy, Elsevier, vol. 329(C).
    • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s0306261922014258
    DOI: 10.1016/j.apenergy.2022.120168
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    References listed on IDEAS

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    1. Wu, Pan & Huang, Wentao & Tai, Nengling & Liang, Shuo, 2018. "A novel design of architecture and control for multiple microgrids with hybrid AC/DC connection," Applied Energy, Elsevier, vol. 210(C), pages 1002-1016.
    2. Christos-Spyridon Karavas & Konstantinos Arvanitis & George Papadakis, 2017. "A Game Theory Approach to Multi-Agent Decentralized Energy Management of Autonomous Polygeneration Microgrids," Energies, MDPI, vol. 10(11), pages 1-22, November.
    3. Luis Gabriel Marín & Mark Sumner & Diego Muñoz-Carpintero & Daniel Köbrich & Seksak Pholboon & Doris Sáez & Alfredo Núñez, 2019. "Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control," Energies, MDPI, vol. 12(23), pages 1-19, November.
    4. Lidula, N.W.A. & Rajapakse, A.D., 2011. "Microgrids research: A review of experimental microgrids and test systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 186-202, January.
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    Cited by:

    1. Jiankai Gao & Yang Li & Bin Wang & Haibo Wu, 2023. "Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm," Energies, MDPI, vol. 16(7), pages 1-21, April.
    2. Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).

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