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Robust Scheduling of Networked Microgrids for Economics and Resilience Improvement

Author

Listed:
  • Guodong Liu

    (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)

  • Maximiliano F. Ferrari

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

  • Aditya Sundararajan

    (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

The benefits of networked microgrids in terms of economics and resilience are investigated and validated in this work. Considering the stochastic unintentional islanding conditions and conventional forecast errors of both renewable generation and loads, a two-stage adaptive robust optimization is proposed to minimize the total operating cost of networked microgrids in the worst scenario of the modeled uncertainties. By coordinating the dispatch of distributed energy resources (DERs) and responsive demand among networked microgrids, the total operating cost is minimized, which includes the start-up and shut-down cost of distributed generators (DGs), the operation and maintenance (O&M) cost of DGs, the cost of buying/selling power from/to the utility grid, the degradation cost of energy storage systems (ESSs), and the cost associated with load shedding. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The results of case studies demonstrate the advantages of networked microgrids over independent microgrids in terms of reducing total operating cost and improving the resilience of power supply.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2249-:d:774811
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jin-Li Hu, 2022. "Energy Resilience in Presence of Natural and Social Uncertainties," Energies, MDPI, vol. 15(18), pages 1-3, September.
    2. Luisa Fernanda Escobar-Orozco & Eduardo Gómez-Luna & Eduardo Marlés-Sáenz, 2023. "Identification and Analysis of Technical Impacts in the Electric Power System Due to the Integration of Microgrids," Energies, MDPI, vol. 16(18), pages 1-29, September.
    3. Monir Sadat AlDavood & Abolfazl Mehbodniya & Julian L. Webber & Mohammad Ensaf & Mahdi Azimian, 2022. "Robust Optimization-Based Optimal Operation of Islanded Microgrid Considering Demand Response," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    4. 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.
    5. Guodong Liu & Maximiliano F. Ferrari & Thomas B. Ollis & Aditya Sundararajan & Mohammed Olama & Yang Chen, 2023. "Distributed Energy Management for Networked Microgrids with Hardware-in-the-Loop Validation," Energies, MDPI, vol. 16(7), pages 1-27, March.
    6. Ahmed Sulaiman Alsafran, 2023. "A Feasibility Study of Implementing IEEE 1547 and IEEE 2030 Standards for Microgrid in the Kingdom of Saudi Arabia," Energies, MDPI, vol. 16(4), pages 1-15, February.
    7. Shen, Haotian & Zhang, Hualiang & Xu, Yujie & Chen, Haisheng & Zhang, Zhilai & Li, Wenkai & Su, Xu & Xu, Yalin & Zhu, Yilin, 2024. "Two stage robust economic dispatching of microgrid considering uncertainty of wind, solar and electricity load along with carbon emission predicted by neural network model," Energy, Elsevier, vol. 300(C).
    8. Pinciroli, Luca & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2023. "Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning," Applied Energy, Elsevier, vol. 352(C).

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