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A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids

Author

Listed:
  • Nian Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Cheng Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Minyang Cheng

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Jie Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

Abstract

With the development of microgrids (MGs), interconnected operation of multiple MGs is becoming a promising strategy for the smart grid. In this paper, a privacy-preserving distributed optimal scheduling method is proposed for the interconnected microgrids (IMG) with a battery energy storage system (BESS) and renewable energy resources (RESs). The optimal scheduling problem is modeled to minimize the coalitional operation cost of the IMG, including the fuel cost of conventional distributed generators and the life loss cost of BESSs. By using the framework of the alternating direction method of multipliers (ADMM), a distributed optimal scheduling model and an iteration solution algorithm for the IMG is introduced; only the expected exchanging power (EEP) of each MG is required during the iterations. Furthermore, a privacy-preserving strategy for the sharing of the EEP among MGs is designed to work with the mechanism of the distributed algorithm. According to the security analysis, the EEP can be delivered in a cooperative and privacy-preserving way. A case study and numerical results are given in terms of the convergence of the algorithm, the comparison of the costs and the implementation efficiency.

Suggested Citation

  • Nian Liu & Cheng Wang & Minyang Cheng & Jie Wang, 2016. "A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids," Energies, MDPI, vol. 9(12), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1031-:d:84578
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    References listed on IDEAS

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    1. Nan Zhou & Nian Liu & Jianhua Zhang & Jinyong Lei, 2016. "Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response," Energies, MDPI, vol. 9(8), pages 1-24, July.
    2. Liu, Nian & Tang, Qingfeng & Zhang, Jianhua & Fan, Wei & Liu, Jie, 2014. "A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids," Applied Energy, Elsevier, vol. 129(C), pages 336-345.
    3. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2016. "Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building," Applied Energy, Elsevier, vol. 171(C), pages 153-171.
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    Cited by:

    1. Richard T. Meyer, 2020. "Distributed Switched Optimal Control of an Electric Vehicle," Energies, MDPI, vol. 13(13), pages 1-27, July.
    2. Pan Wu & Wentao Huang & Nengling Tai & Zhoujun Ma & Xiaodong Zheng & Yong Zhang, 2019. "A Multi-Layer Coordinated Control Scheme to Improve the Operation Friendliness of Grid-Connected Multiple Microgrids," Energies, MDPI, vol. 12(2), pages 1-21, January.
    3. Diptish Saha & Najmeh Bazmohammadi & Juan C. Vasquez & Josep M. Guerrero, 2023. "Multiple Microgrids: A Review of Architectures and Operation and Control Strategies," Energies, MDPI, vol. 16(2), pages 1-32, January.

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