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Comparative Analysis of Degradation Assessment of Battery Energy Storage Systems in PV Smoothing Application

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  • Valentin Silvera Diaz

    (Renewable Energy Center, Itaipu Technological Park Foundation—FPTI, 85867-900 Paraná City, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE/CNPq), Federal University of Latin American Integration—UNILA, 85867-000 Paraná City, Brazil)

  • Daniel Augusto Cantane

    (Renewable Energy Center, Itaipu Technological Park Foundation—FPTI, 85867-900 Paraná City, Brazil)

  • André Quites Ordovás Santos

    (Research Group on Energy & Energy Sustainability (GPEnSE/CNPq), Federal University of Latin American Integration—UNILA, 85867-000 Paraná City, Brazil)

  • Oswaldo Hideo Ando Junior

    (Research Group on Energy & Energy Sustainability (GPEnSE/CNPq), Federal University of Latin American Integration—UNILA, 85867-000 Paraná City, Brazil)

Abstract

Photovoltaic (PV) generation depends on the availability of solar resources, being directly influenced by the variation in irradiance due to the presence of clouds over the PV panels, causing a variation in the power output. The use of battery energy storage systems integrated with the PV showed to be a technically feasible solution to mitigate these power output fluctuations within the maximum ramp limit. Most articles reported in the literature on smoothing PV power output, by coupling with battery degradation as performance indicators of the control strategy, used the event-oriented model that considers only the number of cycles and depth of discharge. This paper presents on the comparative analysis of two approaches to battery degradation models, an event-oriented model based on the Rainflow counting and a semiempirical model, and applies to photovoltaic power smoothing by using a wide range of restrictions and installed PV plant capacities. The semi-empirical degradation model revealed higher battery degradation for all simulated cases. For Strategy 2, the order was 50% higher than the event-oriented model, probably due to severe DR and RR, which increases the stress on the battery. For Strategy 1, the difference was greater, between 100% and 300%. The event-based model indicated that Strategy 1 implied less battery degradation, but the semi-empirical model indicated the opposite. Considering that the semi-empirical model considers more parameters of degradation, the fact that Strategy 2 implies less degradation is more reliable. Moreover, the result obtained by the SimSES model corroborates that the accelerated lithium cell battery degradation takes place, as the operation is at high SoC. Maintaining Ebat, reference is SoC 80% decreased the degradation in at least 25% with respect to degradation, maintaining Ebat, reference is SoC 100%. For this, Ebat, reference of the SoC control, can be designed to avoid operating under a high load state. The results demonstrated that choosing a simplified degradation model approach can lead to an error in the conclusion of which strategies are the best since calendar life effects are very important in the application of PV power smoothing.

Suggested Citation

  • Valentin Silvera Diaz & Daniel Augusto Cantane & André Quites Ordovás Santos & Oswaldo Hideo Ando Junior, 2021. "Comparative Analysis of Degradation Assessment of Battery Energy Storage Systems in PV Smoothing Application," Energies, MDPI, vol. 14(12), pages 1-16, June.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3600-:d:576513
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    References listed on IDEAS

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    1. Nouha Mansouri & Abderezak Lashab & Dezso Sera & Josep M. Guerrero & Adnen Cherif, 2019. "Large Photovoltaic Power Plants Integration: A Review of Challenges and Solutions," Energies, MDPI, vol. 12(19), pages 1-16, October.
    2. Javier Marcos & Iñigo De la Parra & Miguel García & Luis Marroyo, 2014. "Control Strategies to Smooth Short-Term Power Fluctuations in Large Photovoltaic Plants Using Battery Storage Systems," Energies, MDPI, vol. 7(10), pages 1-27, October.
    3. Shivashankar, S. & Mekhilef, Saad & Mokhlis, Hazlie & Karimi, M., 2016. "Mitigating methods of power fluctuation of photovoltaic (PV) sources – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1170-1184.
    4. João Martins & Sergiu Spataru & Dezso Sera & Daniel-Ioan Stroe & Abderezak Lashab, 2019. "Comparative Study of Ramp-Rate Control Algorithms for PV with Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-15, April.
    5. van Haaren, Rob & Morjaria, Mahesh & Fthenakis, Vasilis, 2015. "An energy storage algorithm for ramp rate control of utility scale PV (photovoltaics) plants," Energy, Elsevier, vol. 91(C), pages 894-902.
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    1. Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.

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