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Engineering Microgrids Amid the Evolving Electrical Distribution System

Author

Listed:
  • Smriti Sharma

    (DTE Electric, Detroit, MI 48226, USA)

  • John O’Donnell

    (DTE Electric, Detroit, MI 48226, USA
    Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Wencong Su

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Richard Mueller

    (DTE Electric, Detroit, MI 48226, USA)

  • Line Roald

    (Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA)

  • Khurram Rehman

    (DTE Electric, Detroit, MI 48226, USA)

  • Andrey Bernstein

    (National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA)

Abstract

Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system’s resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries.

Suggested Citation

  • Smriti Sharma & John O’Donnell & Wencong Su & Richard Mueller & Line Roald & Khurram Rehman & Andrey Bernstein, 2024. "Engineering Microgrids Amid the Evolving Electrical Distribution System," Energies, MDPI, vol. 17(19), pages 1-34, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4764-:d:1484380
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    References listed on IDEAS

    as
    1. John O’Donnell & Wencong Su, 2023. "Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System," Energies, MDPI, vol. 16(15), pages 1-21, July.
    2. Dagar, Annu & Gupta, Pankaj & Niranjan, Vandana, 2021. "Microgrid protection: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Sijia Li & Arman Oshnoei & Frede Blaabjerg & Amjad Anvari-Moghaddam, 2023. "Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
    4. Shabani, Masoume & Wallin, Fredrik & Dahlquist, Erik & Yan, Jinyue, 2023. "The impact of battery operating management strategies on life cycle cost assessment in real power market for a grid-connected residential battery application," Energy, Elsevier, vol. 270(C).
    Full references (including those not matched with items on IDEAS)

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