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Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review

Author

Listed:
  • João Fausto L. de Oliveira

    (Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, PE, Brazil
    These authors contributed equally to this work.)

  • Paulo S. G. de Mattos Neto

    (Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil
    These authors contributed equally to this work.)

  • Hugo Valadares Siqueira

    (Graduate Program in Electrical Engineering, Federal University of Technology, Ponta Grossa 84017-220, PR, Brazil
    These authors contributed equally to this work.)

  • Domingos S. de O. Santos

    (Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil
    These authors contributed equally to this work.)

  • Aranildo R. Lima

    (Independent Researcher, Vancouver, BC V5K 0A3, Canada
    These authors contributed equally to this work.)

  • Francisco Madeiro

    (Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, PE, Brazil
    These authors contributed equally to this work.)

  • Douglas A. P. Dantas

    (Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, PE, Brazil
    These authors contributed equally to this work.)

  • Mariana de Morais Cavalcanti

    (Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, PE, Brazil
    These authors contributed equally to this work.)

  • Alex C. Pereira

    (São Francisco Hydroelectric Company (Chesf), Recife 50761-901, PE, Brazil
    These authors contributed equally to this work.)

  • Manoel H. N. Marinho

    (Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, PE, Brazil
    These authors contributed equally to this work.)

Abstract

The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS.

Suggested Citation

  • João Fausto L. de Oliveira & Paulo S. G. de Mattos Neto & Hugo Valadares Siqueira & Domingos S. de O. Santos & Aranildo R. Lima & Francisco Madeiro & Douglas A. P. Dantas & Mariana de Morais Cavalcant, 2023. "Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review," Energies, MDPI, vol. 16(18), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6638-:d:1240890
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    1. Carlos Henrique Illa Font & Hugo Valadares Siqueira & João Eustáquio Machado Neto & João Lucas Ferreira dos Santos & Sergio Luiz Stevan & Attilio Converti & Fernanda Cristina Corrêa, 2023. "Second Life of Lithium-Ion Batteries of Electric Vehicles: A Short Review and Perspectives," Energies, MDPI, vol. 16(2), pages 1-14, January.
    2. Angenendt, Georg & Zurmühlen, Sebastian & Axelsen, Hendrik & Sauer, Dirk Uwe, 2018. "Comparison of different operation strategies for PV battery home storage systems including forecast-based operation strategies," Applied Energy, Elsevier, vol. 229(C), pages 884-899.
    3. Basu, M., 2021. "Heat and power generation augmentation planning of isolated microgrid," Energy, Elsevier, vol. 223(C).
    4. Litjens, G.B.M.A. & Worrell, E. & van Sark, W.G.J.H.M., 2018. "Assessment of forecasting methods on performance of photovoltaic-battery systems," Applied Energy, Elsevier, vol. 221(C), pages 358-373.
    5. Usman Bashir Tayab & Junwei Lu & Seyedfoad Taghizadeh & Ahmed Sayed M. Metwally & Muhammad Kashif, 2021. "Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization," Energies, MDPI, vol. 14(24), pages 1-19, December.
    6. Chalermjit Klansupar & Surachai Chaitusaney, 2022. "Optimal Sizing of Grid-Scaled Battery with Consideration of Battery Installation and System Power-Generation Costs," Energies, MDPI, vol. 15(13), pages 1-18, June.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. Olabi, A.G. & Abdelkareem, Mohammad Ali, 2022. "Renewable energy and climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    9. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Forecasting error processing techniques and frequency domain decomposition for forecasting error compensation and renewable energy firming in hybrid systems," Applied Energy, Elsevier, vol. 313(C).
    10. Konstantinos Kotsalos & Ismael Miranda & Jose Luis Dominguez-Garcia & Helder Leite & Nuno Silva & Nikos Hatziargyriou, 2020. "Exploiting OLTC and BESS Operation Coordinated with Active Network Management in LV Networks," Sustainability, MDPI, vol. 12(8), pages 1-25, April.
    11. Lai, Chun Sing & Chen, Dashen & Zhang, Jinning & Zhang, Xin & Xu, Xu & Taylor, Gareth A. & Lai, Loi Lei, 2022. "Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks," Energy, Elsevier, vol. 259(C).
    12. Henryka Danuta Stryczewska & Oleksandr Boiko & Mariusz Adam Stępień & Paweł Lasek & Masaaki Yamazato & Akira Higa, 2023. "Selected Materials and Technologies for Electrical Energy Sector," Energies, MDPI, vol. 16(12), pages 1-26, June.
    13. Yuqing Yang & Stephen Bremner & Chris Menictas & Merlinde Kay, 2019. "A Mixed Receding Horizon Control Strategy for Battery Energy Storage System Scheduling in a Hybrid PV and Wind Power Plant with Different Forecast Techniques," Energies, MDPI, vol. 12(12), pages 1-25, June.
    14. Andu Dukpa & Boguslaw Butrylo, 2022. "MILP-Based Profit Maximization of Electric Vehicle Charging Station Based on Solar and EV Arrival Forecasts," Energies, MDPI, vol. 15(15), pages 1-14, August.
    15. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.
    16. Gherboudj, Imen & Zorgati, Mohamed & Chamarthi, Phani-Kumar & Tuomiranta, Arttu & Mohandes, Baraa & Beegum, Naseema S. & Al-Sudairi, Jood & Al-Owain, Omar & Shibli, Hussain & El-Moursi, Mohamed & Ghed, 2021. "Renewable energy management system for Saudi Arabia: Methodology and preliminary results," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    17. Hanieh Agharazi & Marija D. Prica & Kenneth A. Loparo, 2022. "A Two-Level Model Predictive Control-Based Approach for Building Energy Management including Photovoltaics, Energy Storage, Solar Forecasting and Building Loads," Energies, MDPI, vol. 15(10), pages 1-21, May.
    18. Djamila Rekioua, 2023. "Energy Storage Systems for Photovoltaic and Wind Systems: A Review," Energies, MDPI, vol. 16(9), pages 1-26, May.
    19. Ji-Won Cha & Sung-Kwan Joo, 2021. "Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)," Energies, MDPI, vol. 14(21), pages 1-19, October.
    20. Manzoor Ellahi & Ghulam Abbas & Irfan Khan & Paul Mario Koola & Mashood Nasir & Ali Raza & Umar Farooq, 2019. "Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review," Energies, MDPI, vol. 12(22), pages 1-30, November.
    21. Oscar Danilo Montoya & Walter Gil-González & Luis Grisales-Noreña & César Orozco-Henao & Federico Serra, 2019. "Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models," Energies, MDPI, vol. 12(23), pages 1-20, November.
    22. Pierro, Marco & Perez, Richard & Perez, Marc & Moser, David & Cornaro, Cristina, 2020. "Italian protocol for massive solar integration: Imbalance mitigation strategies," Renewable Energy, Elsevier, vol. 153(C), pages 725-739.
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