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A BESS Sizing Strategy for Primary Frequency Regulation Support of Solar Photovoltaic Plants

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  • Diego Mejía-Giraldo

    (Grupo en Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia (UdeA), Calle 70 No. 52-21, Medellín 050010, Colombia)

  • Gregorio Velásquez-Gomez

    (Empresa de Energía del Pacífico S.A. E.S.P (EPSA)-Celsia S.A. E.S.P, Carrera 43A No. 1 sur-143, Medellín 050021, Colombia)

  • Nicolás Muñoz-Galeano

    (Grupo en Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia (UdeA), Calle 70 No. 52-21, Medellín 050010, Colombia)

  • Juan Bernardo Cano-Quintero

    (Grupo en Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia (UdeA), Calle 70 No. 52-21, Medellín 050010, Colombia)

  • Santiago Lemos-Cano

    (Empresa de Energía del Pacífico S.A. E.S.P (EPSA)-Celsia S.A. E.S.P, Carrera 43A No. 1 sur-143, Medellín 050021, Colombia)

Abstract

This paper proposes a strategy for sizing a battery energy storage system ( BESS ) that supports primary frequency regulation ( PFR ) service of solar photo-voltaic plants. The strategy is composed of an optimization model and a performance assessment algorithm. The optimization model includes not only investment costs, but also a novel penalty function depending on the state of charge ( SoC ). This function avoids the existence of a potential inappropriate SoC trajectory during BESS operation that could impede the supply of PFR service. The performance assessment algorithm, fed by the optimization model sizing results, allows the emulation of BESS operation and determines either the success or failure of a particular BESS design. The quality of a BESS design is measured through number of days in which BESS failed to satisfactorily provide PFR and its associated penalization cost. Battery lifetime, battery replacements, and SoC are also key performance indexes that finally permit making better decisions in the election of the best BESS size. The inclusion of multiple BESS operational restrictions under PFR is another important advantage of this strategy since it adds a realistic characterization of BESS to the analysis. The optimization model was coded using GAMS/CPLEX, and the performance assessment algorithm was implemented in MATLAB. Results were obtained using actual frequency data obtained from the Colombian power system; and the resulting BESS sizes show that the number of BESS penalties, caused by failure to provide PFR service, can be reduced to zero at minimum investment cost.

Suggested Citation

  • Diego Mejía-Giraldo & Gregorio Velásquez-Gomez & Nicolás Muñoz-Galeano & Juan Bernardo Cano-Quintero & Santiago Lemos-Cano, 2019. "A BESS Sizing Strategy for Primary Frequency Regulation Support of Solar Photovoltaic Plants," Energies, MDPI, vol. 12(2), pages 1-16, January.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:317-:d:199345
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    References listed on IDEAS

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    1. Roberto Benato & Sebastian Dambone Sessa & Maura Musio & Francesco Palone & Rosario Maria Polito, 2018. "Italian Experience on Electrical Storage Ageing for Primary Frequency Regulation," Energies, MDPI, vol. 11(8), pages 1-12, August.
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    Cited by:

    1. Bonginkosi A. Thango & Pitshou N. Bokoro, 2022. "Battery Energy Storage for Photovoltaic Application in South Africa: A Review," Energies, MDPI, vol. 15(16), pages 1-21, August.
    2. Lai, Chun Sing & Locatelli, Giorgio, 2021. "Economic and financial appraisal of novel large-scale energy storage technologies," Energy, Elsevier, vol. 214(C).
    3. Fulin Fan & Giorgio Zorzi & David Campos-Gaona & Graeme Burt & Olimpo Anaya-Lara & John Nwobu & Ander Madariaga, 2021. "Sizing and Coordination Strategies of Battery Energy Storage System Co-Located with Wind Farm: The UK Perspective," Energies, MDPI, vol. 14(5), pages 1-21, March.
    4. Sandro Sitompul & Goro Fujita, 2021. "Impact of Advanced Load-Frequency Control on Optimal Size of Battery Energy Storage in Islanded Microgrid System," Energies, MDPI, vol. 14(8), pages 1-18, April.
    5. Shaobo Wen & Yipeng Gong & Zhendong Zhao & Xiufeng Mu & Sufang Zhao, 2025. "Power Grid Primary Frequency Control Strategy Based on Fuzzy Adaptive and State-of-Charge Self-Recovery of Flywheel–Battery Hybrid Energy Storage System," Energies, MDPI, vol. 18(6), pages 1-23, March.
    6. Akram, Umer & Nadarajah, Mithulananthan & Shah, Rakibuzzaman & Milano, Federico, 2020. "A review on rapid responsive energy storage technologies for frequency regulation in modern power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    7. D’Ettorre, F. & Banaei, M. & Ebrahimy, R. & Pourmousavi, S. Ali & Blomgren, E.M.V. & Kowalski, J. & Bohdanowicz, Z. & Łopaciuk-Gonczaryk, B. & Biele, C. & Madsen, H., 2022. "Exploiting demand-side flexibility: State-of-the-art, open issues and social perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).

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