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Rightsizing the Design of a Hybrid Microgrid

Author

Listed:
  • Daniel Reich

    (Graduate School of Defense Management, Naval Postgraduate School, Monterey, CA 93943, USA)

  • Giovanna Oriti

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

Abstract

Selecting the sizes of distributed energy resources is a central planning element when designing a microgrid. Decision makers may consider several important factors, including, but not limited to, capacity, cost, reliability and sustainability. We introduce a method for rightsizing capacity that presents a range of potential microgrid design solutions, allowing decision makers to weigh their upsides and downsides based on a variety of measurable factors. We decouple component-specific modeling assumptions, energy management system logic and objective measurements from our simulation-based nested binary search method for rightsizing to meet power loads. In doing so, we develop a flexible, customizable and extensible approach to microgrid design planning. Aspects which have traditionally been incorporated directly in optimization-centric frameworks, such as resilience and reliability, can be treated as complementary analyses in our decoupled approach. This enables decision makers to gain exposure to a wide range of relevant information and actively participate in the microgrid design assessment process.

Suggested Citation

  • Daniel Reich & Giovanna Oriti, 2021. "Rightsizing the Design of a Hybrid Microgrid," Energies, MDPI, vol. 14(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4273-:d:594766
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    References listed on IDEAS

    as
    1. 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.
    2. Ramli, Makbul A.M. & Bouchekara, H.R.E.H. & Alghamdi, Abdulsalam S., 2018. "Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 121(C), pages 400-411.
    3. Zhao, Bo & Zhang, Xuesong & Li, Peng & Wang, Ke & Xue, Meidong & Wang, Caisheng, 2014. "Optimal sizing, operating strategy and operational experience of a stand-alone microgrid on Dongfushan Island," Applied Energy, Elsevier, vol. 113(C), pages 1656-1666.
    4. Zenginis, Ioannis & Vardakas, John S. & Echave, Cynthia & Morató, Moisés & Abadal, Jordi & Verikoukis, Christos V., 2017. "Cooperation in microgrids through power exchange: An optimal sizing and operation approach," Applied Energy, Elsevier, vol. 203(C), pages 972-981.
    5. Ping Liu & Zexiang Cai & Peng Xie & Xiaohua Li & Yongjun Zhang, 2019. "A Computationally Efficient Optimization Method for Battery Storage in Grid-connected Microgrids Based on a Power Exchanging Process," Energies, MDPI, vol. 12(8), pages 1-19, April.
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    7. Fossati, Juan P. & Galarza, Ainhoa & Martín-Villate, Ander & Fontán, Luis, 2015. "A method for optimal sizing energy storage systems for microgrids," Renewable Energy, Elsevier, vol. 77(C), pages 539-549.
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    Cited by:

    1. 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.

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