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An MILP-Based Distributed Energy Management for Coordination of Networked Microgrids

Author

Listed:
  • Guodong Liu

    (Grid Components & Control Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Maximiliano F. Ferrari

    (Grid Components & Control Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Thomas B. Ollis

    (Grid Components & Control Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Kevin Tomsovic

    (Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA)

Abstract

An MILP-based distributed energy management for the coordination of networked microgrids is proposed in this paper. Multiple microgrids and the utility grid are coordinated through iteratively adjusted price signals. Based on the price signals received, the microgrid controllers (MCs) and distribution management system (DMS) update their schedules separately. Then, the price signals are updated according to the generation–load mismatch and distributed to MCs and DMS for the next iteration. The iteration continues until the generation–load mismatch is small enough, i.e., the generation and load are balanced under agreed price signals. Through the proposed distributed energy management, various microgrids and the utility grid with different economic, resilient, emission and socio-economic objectives are coordinated with generation–load balance guaranteed and the microgrid customers’ privacy preserved. In particular, a piecewise linearization technique is employed to approximate the augmented Lagrange term in the alternating direction method of multipliers (ADMM) algorithm. Thus, the subproblems are transformed into mixed integer linear programming (MILP) problems and efficiently solved by open-source MILP solvers, which would accelerate the adoption and deployment of microgrids and promote clean energy. The proposed MILP-based distributed energy management is demonstrated through various case studies on a networked microgrids test system with three microgrids.

Suggested Citation

  • Guodong Liu & Maximiliano F. Ferrari & Thomas B. Ollis & Kevin Tomsovic, 2022. "An MILP-Based Distributed Energy Management for Coordination of Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-20, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6971-:d:922848
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    References listed on IDEAS

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    1. Changsen Feng & Fushuan Wen & Lijun Zhang & Chenbo Xu & Md. Abdus Salam & Shi You, 2018. "Decentralized Energy Management of Networked Microgrid Based on Alternating-Direction Multiplier Method," Energies, MDPI, vol. 11(10), pages 1-18, September.
    2. Hirsch, Adam & Parag, Yael & Guerrero, Josep, 2018. "Microgrids: A review of technologies, key drivers, and outstanding issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 402-411.
    3. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    4. Liu, Guodong & Jiang, Tao & Ollis, Thomas B. & Zhang, Xiaohu & Tomsovic, Kevin, 2019. "Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics," Applied Energy, Elsevier, vol. 239(C), pages 83-95.
    5. Guodong Liu & Thomas B. Ollis & Bailu Xiao & Xiaohu Zhang & Kevin Tomsovic, 2017. "Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics," Energies, MDPI, vol. 10(10), pages 1-19, October.
    6. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    7. Muhammad Zahid Khan & Chaoxu Mu & Salman Habib & Waleed Alhosaini & Emad M. Ahmed, 2021. "An Enhanced Distributed Voltage Regulation Scheme for Radial Feeder in Islanded Microgrid," Energies, MDPI, vol. 14(19), pages 1-19, September.
    8. Ruiming Liu & Shengtie Wang & Guangchen Liu & Sufang Wen & Jianwei Zhang & Yuechao Ma, 2022. "An Improved Virtual Inertia Control Strategy for Low Voltage AC Microgrids with Hybrid Energy Storage Systems," Energies, MDPI, vol. 15(2), pages 1-21, January.
    9. Guodong Liu & Thomas B. Ollis & Maximiliano F. Ferrari & Aditya Sundararajan & Kevin Tomsovic, 2022. "Robust Scheduling of Networked Microgrids for Economics and Resilience Improvement," Energies, MDPI, vol. 15(6), pages 1-19, March.
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    1. Oscar G. Duarte & Javier A. Rosero & María del Carmen Pegalajar, 2022. "Data Preparation and Visualization of Electricity Consumption for Load Profiling," Energies, MDPI, vol. 15(20), pages 1-30, October.

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