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Optimal Scheduling and Real-Time Control Schemes of Battery Energy Storage System for Microgrids Considering Contract Demand and Forecast Uncertainty

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  • Hong-Chao Gao

    (Department of Electrical Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Korea)

  • Joon-Ho Choi

    (Department of Electrical Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Korea)

  • Sang-Yun Yun

    (Department of Electrical Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Korea)

  • Hak-Ju Lee

    (Energy System Group Energy New Business Laboratory, Korea Electric Power Research Institute, Daejeon 34056, Korea)

  • Seon-Ju Ahn

    (Department of Electrical Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Korea)

Abstract

Optimal operation of the battery energy storage system (BESS) is very important to reduce the running cost of a microgrid. Rolling horizon-based scheduling, which updates the optimal decision based on the latest information, is widely applied to microgrid operation. In this paper, the optimal scheduling of a microgrid, considering the energy cost, demand charge, and the battery wear-cost, is formulated as a mixed integer linear programming (MILP) problem. This paper also deals with two practical and important issues when applying the rolling-horizon strategy to BESS scheduling. First, to mitigate the high dependency of the load forecast on the latest information, a confidence weight parameter method is proposed. Second, a new target state of charge (SOC) assignment method is proposed to avoid the depletion of BESS and to reduce the wear-cost of the battery. In addition to the optimal scheduling, a novel real-time control scheme is proposed to mitigate the effect of the forecast uncertainty. The performance of the proposed methods is tested with data measured from a campus microgrid.

Suggested Citation

  • Hong-Chao Gao & Joon-Ho Choi & Sang-Yun Yun & Hak-Ju Lee & Seon-Ju Ahn, 2018. "Optimal Scheduling and Real-Time Control Schemes of Battery Energy Storage System for Microgrids Considering Contract Demand and Forecast Uncertainty," Energies, MDPI, vol. 11(6), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1371-:d:149401
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    References listed on IDEAS

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    Cited by:

    1. Aslam Amir & Hussain Shareef & Falah Awwad, 2023. "Energy Management in a Standalone Microgrid: A Split-Horizon Dual-Stage Dispatch Strategy," Energies, MDPI, vol. 16(8), pages 1-25, April.
    2. Mohammed Abdullah H. Alshehri & Youguang Guo & Gang Lei, 2023. "Energy Management Strategies of Grid-Connected Microgrids under Different Reliability Conditions," Energies, MDPI, vol. 16(9), pages 1-22, May.
    3. Amad Ali & Hafiz Abdul Muqeet & Tahir Khan & Asif Hussain & Muhammad Waseem & Kamran Ali Khan Niazi, 2023. "IoT-Enabled Campus Prosumer Microgrid Energy Management, Architecture, Storage Technologies, and Simulation Tools: A Comprehensive Study," Energies, MDPI, vol. 16(4), pages 1-19, February.
    4. Sergio Rech, 2019. "Smart Energy Systems: Guidelines for Modelling and Optimizing a Fleet of Units of Different Configurations," Energies, MDPI, vol. 12(7), pages 1-36, April.
    5. Hong-Chao Gao & Joon-Ho Choi & Sang-Yun Yun & Seon-Ju Ahn, 2020. "A New Power Sharing Scheme of Multiple Microgrids and an Iterative Pairing-Based Scheduling Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    6. Hafiz Abdul Muqeet & Hafiz Mudassir Munir & Haseeb Javed & Muhammad Shahzad & Mohsin Jamil & Josep M. Guerrero, 2021. "An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges," Energies, MDPI, vol. 14(20), pages 1-34, October.
    7. Giuliano Rancilio & Alexandre Lucas & Evangelos Kotsakis & Gianluca Fulli & Marco Merlo & Maurizio Delfanti & Marcelo Masera, 2019. "Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis," Energies, MDPI, vol. 12(17), pages 1-26, August.

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