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Battery Energy Storage Systems in Microgrids: Modeling and Design Criteria

Author

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  • Matteo Moncecchi

    (Department of Energy, Politecnico di Milano, Via Lambruschini, 4, 20156 Milano, Italy)

  • Claudio Brivio

    (CSEM SA - Swiss Center for Electronics and Microtechnology, 2002 Neuchâtel, Switzerland)

  • Stefano Mandelli

    (CESI S.p.A., Via Raffaele Rubattino, 54, 20134 Milano Italy)

  • Marco Merlo

    (Politecnico di Milano, Department of Energy, Via Lambruschini, 4, 20156 Milano, Italy)

Abstract

Off-grid power systems based on photovoltaic and battery energy storage systems are becoming a solution of great interest for rural electrification. The storage system is one of the most crucial components since inappropriate design can affect reliability and final costs. Therefore, it is necessary to adopt reliable models able to realistically reproduce the working condition of the application. In this paper, different models of lithium-ion battery are considered in the design process of a microgrid. Two modeling approaches (analytical and electrical) are developed based on experimental measurements. The derived models have been integrated in a methodology for the robust design of off-grid electric power systems which has been implemented in a MATLAB-based computational tool named Poli.NRG (POLItecnico di Milano—Network Robust desiGn). The procedure has been applied to a real-life case study to compare the different battery energy storage system models and to show how they impact on the microgrid design.

Suggested Citation

  • Matteo Moncecchi & Claudio Brivio & Stefano Mandelli & Marco Merlo, 2020. "Battery Energy Storage Systems in Microgrids: Modeling and Design Criteria," Energies, MDPI, vol. 13(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2006-:d:347122
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Shuxin & Liu, Zhitao & Su, Hongye, 2023. "State of health estimation for lithium-ion batteries on few-shot learning," Energy, Elsevier, vol. 268(C).
    2. Matteo Spiller & Giuliano Rancilio & Filippo Bovera & Giacomo Gorni & Stefano Mandelli & Federico Bresciani & Marco Merlo, 2023. "A Model-Aware Comprehensive Tool for Battery Energy Storage System Sizing," Energies, MDPI, vol. 16(18), pages 1-24, September.
    3. Schmid, Fabian & Winzer, Joscha & Pasemann, André & Behrendt, Frank, 2021. "An open-source modeling tool for multi-objective optimization of renewable nano/micro-off-grid power supply system: Influence of temporal resolution, simulation period, and location," Energy, Elsevier, vol. 219(C).
    4. Rodriguez, Mauricio & Arcos-Aviles, Diego & Guinjoan, Francesc, 2024. "Simple fuzzy logic-based energy management for power exchange in isolated multi-microgrid systems: A case study in a remote community in the Amazon region of Ecuador," Applied Energy, Elsevier, vol. 357(C).
    5. Petrelli, Marina & Fioriti, Davide & Berizzi, Alberto & Bovo, Cristian & Poli, Davide, 2021. "A novel multi-objective method with online Pareto pruning for multi-year optimization of rural microgrids," Applied Energy, Elsevier, vol. 299(C).
    6. Youssef Elomari & Masoud Norouzi & Marc Marín-Genescà & Alberto Fernández & Dieter Boer, 2022. "Integration of Solar Photovoltaic Systems into Power Networks: A Scientific Evolution Analysis," Sustainability, MDPI, vol. 14(15), pages 1-23, July.
    7. Tobias Kull & Bernd Zeilmann & Gerhard Fischerauer, 2021. "Field-Ready Implementation of Linear Economic Model Predictive Control for Microgrid Dispatch in Small and Medium Enterprises," Energies, MDPI, vol. 14(13), pages 1-23, June.
    8. Yue Zhou & Hussein Obeid & Salah Laghrouche & Mickael Hilairet & Abdesslem Djerdir, 2020. "A Disturbance Rejection Control Strategy of a Single Converter Hybrid Electrical System Integrating Battery Degradation," Energies, MDPI, vol. 13(11), pages 1-19, June.
    9. Javier Solano & Diego Jimenez & Adrian Ilinca, 2020. "A Modular Simulation Testbed for Energy Management in AC/DC Microgrids," Energies, MDPI, vol. 13(16), pages 1-23, August.

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