IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i18p6638-d1240890.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/18/6638/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/18/6638/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yisen Niu & Ying Su & Ping Tang & Qian Wang & Yong Sun & Jifeng Song, 2025. "Estimation of Solar Irradiance Under Cloudy Weather Based on Solar Radiation Model and Ground-Based Cloud Image," Energies, MDPI, vol. 18(3), pages 1-21, February.
    2. Ruihua Si & Xintong Yan & Wanxun Liu & Ping Zhang & Mengdi Wang & Fengyong Li & Jiajia Yang & Xiangjing Su, 2025. "Hybrid Optimization-Based Sequential Placement of DES in Unbalanced Active Distribution Networks Considering Multi-Scenario Operation," Energies, MDPI, vol. 18(3), pages 1-16, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jamal, Taskin & Carter, Craig & Schmidt, Thomas & Shafiullah, G.M. & Calais, Martina & Urmee, Tania, 2019. "An energy flow simulation tool for incorporating short-term PV forecasting in a diesel-PV-battery off-grid power supply system," Applied Energy, Elsevier, vol. 254(C).
    2. Ma, Tao & Zhang, Yijie & Gu, Wenbo & Xiao, Gang & Yang, Hongxing & Wang, Shuxiao, 2022. "Strategy comparison and techno-economic evaluation of a grid-connected photovoltaic-battery system," Renewable Energy, Elsevier, vol. 197(C), pages 1049-1060.
    3. Aniello, Gianmarco & Bertsch, Valentin, 2023. "Shaping the energy transition in the residential sector: Regulatory incentives for aligning household and system perspectives," Applied Energy, Elsevier, vol. 333(C).
    4. Kar, Sumi & Basu, Kajla & Sarkar, Biswajit, 2023. "Advertisement policy for dual-channel within emissions-controlled flexible production system," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    5. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    6. Luigi Fortuna & Arturo Buscarino, 2022. "Sustainable Energy Systems," Energies, MDPI, vol. 15(23), pages 1-7, December.
    7. Muhammad Zeeshan Malik & Haoyong Chen & Muhammad Shahzad Nazir & Irfan Ahmad Khan & Ahmed N. Abdalla & Amjad Ali & Wan Chen, 2020. "A New Efficient Step-Up Boost Converter with CLD Cell for Electric Vehicle and New Energy Systems," Energies, MDPI, vol. 13(7), pages 1-14, April.
    8. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    9. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    10. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    11. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    12. Hoda M. Aboalsamh & Laith T. Khrais & Sami A. Albahussain, 2023. "Pioneering Perception of Green Fintech in Promoting Sustainable Digital Services Application within Smart Cities," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
    13. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    14. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    15. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    16. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    17. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
    18. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.
    19. Rivera, Nilza & Guzmán, Juan Ignacio & Jara, José Joaquín & Lagos, Gustavo, 2021. "Evaluation of econometric models of secondary refined copper supply," Resources Policy, Elsevier, vol. 73(C).
    20. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    21. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6638-:d:1240890. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.