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Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis

Author

Listed:
  • Giuliano Rancilio

    (European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027 Ispra (VA), Italy
    Politecnico di Milano–Department of Energy, Piazza Leonardo Da Vinci 32, I-20133 Milano, Italy)

  • Alexandre Lucas

    (European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027 Ispra (VA), Italy)

  • Evangelos Kotsakis

    (European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027 Ispra (VA), Italy)

  • Gianluca Fulli

    (European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027 Ispra (VA), Italy)

  • Marco Merlo

    (Politecnico di Milano–Department of Energy, Piazza Leonardo Da Vinci 32, I-20133 Milano, Italy)

  • Maurizio Delfanti

    (Ricerca sul Sistema Energetico–RSE S.p.A., Via R. Rubattino, I-20134 Milano, Italy)

  • Marcelo Masera

    (European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027 Ispra (VA), Italy)

Abstract

The interest in modeling the operation of large-scale battery energy storage systems (BESS) for analyzing power grid applications is rising. This is due to the increasing storage capacity installed in power systems for providing ancillary services and supporting nonprogrammable renewable energy sources (RES). BESS numerical models suitable for grid-connected applications must offer a trade-off, keeping a high accuracy even with limited computational effort. Moreover, they are asked to be viable in modeling for real-life equipment, and not just accurate in the simulation of the electrochemical section. The aim of this study is to develop a numerical model for the analysis of the grid-connected BESS operation; the main goal of the proposal is to have a test protocol based on standard equipment and just based on charge/discharge tests, i.e., a procedure viable for a BESS owner without theoretical skills in electrochemistry or lab procedures, and not requiring the ability to disassemble the BESS in order to test each individual component. The BESS model developed is characterized by an experimental campaign. The test procedure itself is framed in the context of this study and adopted for the experimental campaign on a commercial large-scale BESS. Once the model is characterized by the experimental parameters, it undergoes the verification and validation process by testing its accuracy in simulating the provision of frequency regulation. A case study is presented for the sake of presenting a potential application of the model. The procedure developed and validated is replicable in any other facility, due to the low complexity of the proposed experimental set. This could help stakeholders to accurately simulate several layouts of network services.

Suggested Citation

  • Giuliano Rancilio & Alexandre Lucas & Evangelos Kotsakis & Gianluca Fulli & Marco Merlo & Maurizio Delfanti & Marcelo Masera, 2019. "Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis," Energies, MDPI, vol. 12(17), pages 1-26, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3312-:d:261663
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    References listed on IDEAS

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    1. Kleijnen, Jack P. C., 1995. "Verification and validation of simulation models," European Journal of Operational Research, Elsevier, vol. 82(1), pages 145-162, April.
    2. Greenwood, D.M. & Lim, K.Y. & Patsios, C. & Lyons, P.F. & Lim, Y.S. & Taylor, P.C., 2017. "Frequency response services designed for energy storage," Applied Energy, Elsevier, vol. 203(C), pages 115-127.
    3. Hong-Chao Gao & Joon-Ho Choi & Sang-Yun Yun & Hak-Ju Lee & Seon-Ju Ahn, 2018. "Optimal Scheduling and Real-Time Control Schemes of Battery Energy Storage System for Microgrids Considering Contract Demand and Forecast Uncertainty," Energies, MDPI, vol. 11(6), pages 1-15, May.
    4. Holger C. Hesse & Michael Schimpe & Daniel Kucevic & Andreas Jossen, 2017. "Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids," Energies, MDPI, vol. 10(12), pages 1-42, December.
    5. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    6. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
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    2. Yoro, Kelvin O. & Daramola, Michael O. & Sekoai, Patrick T. & Wilson, Uwemedimo N. & Eterigho-Ikelegbe, Orevaoghene, 2021. "Update on current approaches, challenges, and prospects of modeling and simulation in renewable and sustainable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).

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