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Optimizing Fuel Efficiency on an Islanded Microgrid under Varying Loads

Author

Listed:
  • Joo Won Lee

    (Department of Operations Research, Naval Postgraduate School, Monterey, CA 93943, USA)

  • Emily Craparo

    (Department of Operations Research, Naval Postgraduate School, Monterey, CA 93943, USA)

  • Giovanna Oriti

    (Department of Electrical and Computer Engineering, Naval Postgraduate School, Monterey, CA 93943, USA)

  • Arthur Krener

    (Department of Applied Mathematics, Naval Postgraduate School, Monterey, CA 93943, USA)

Abstract

Past studies of microgrids have been based on measurements of fuel consumption by generators under static loads. There is little information on the fuel efficiency of generators under time-varying loads. To help analyze the impact of time-varying loads on optimal generator operation and fuel consumption, we formulate a mixed-integer linear optimization model to plan generator and energy storage system (ESS) operation to satisfy known demands. Our model includes fuel consumption penalty terms on time-varying loads. We exercise the model on various scenarios and compare the resulting optimal fuel consumption and generator operation profiles. Our results show that the change in fuel efficiency between scenarios with the integration of ESS is minimal regardless of the imposed penalty placed on the generator. However, without the assistance of the ESS, the fuel consumption increases dramatically with the penalty imposed on the generator. The integration of an ESS improves fuel consumption because the ESS allows the generator to minimize power output fluctuation. While the presence of a penalty term has a clear impact on generator operation and fuel consumption, the exact type and weight of the penalty appears insignificant; this may provide useful insight for future studies in developing a real-time controller.

Suggested Citation

  • Joo Won Lee & Emily Craparo & Giovanna Oriti & Arthur Krener, 2022. "Optimizing Fuel Efficiency on an Islanded Microgrid under Varying Loads," Energies, MDPI, vol. 15(21), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7943-:d:953575
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    References listed on IDEAS

    as
    1. Daniel Reich & Giovanna Oriti, 2021. "Rightsizing the Design of a Hybrid Microgrid," Energies, MDPI, vol. 14(14), pages 1-22, July.
    2. Craparo, Emily & Karatas, Mumtaz & Singham, Dashi I., 2017. "A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts," Applied Energy, Elsevier, vol. 201(C), pages 135-147.
    3. Kapetanović, Marko & Núñez, Alfredo & van Oort, Niels & Goverde, Rob M.P., 2021. "Reducing fuel consumption and related emissions through optimal sizing of energy storage systems for diesel-electric trains," Applied Energy, Elsevier, vol. 294(C).
    4. Craparo, E.M. & Sprague, J.G., 2019. "Integrated supply- and demand-side energy management for expeditionary environmental control," Applied Energy, Elsevier, vol. 233, pages 352-366.
    5. Petros Siritoglou & Giovanna Oriti & Douglas L. Van Bossuyt, 2021. "Distributed Energy-Resource Design Method to Improve Energy Security in Critical Facilities," Energies, MDPI, vol. 14(10), pages 1-20, May.
    6. Rae-Kyun Kim & Mark B. Glick & Keith R. Olson & Yun-Su Kim, 2020. "MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations," Energies, MDPI, vol. 13(8), pages 1-17, April.
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