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

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 Cavalcanti, Alex C. Pereira and Manoel H. N. Marinho
Additional contact information
João Fausto L. de Oliveira: Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, PE, Brazil
Paulo S. G. de Mattos Neto: Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil
Hugo Valadares Siqueira: Graduate Program in Electrical Engineering, Federal University of Technology, Ponta Grossa 84017-220, PR, Brazil
Domingos S. de O. Santos: Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil
Aranildo R. Lima: Independent Researcher, Vancouver, BC V5K 0A3, Canada
Francisco Madeiro: Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, PE, Brazil
Douglas A. P. Dantas: Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, PE, Brazil
Mariana de Morais Cavalcanti: Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, PE, Brazil
Alex C. Pereira: São Francisco Hydroelectric Company (Chesf), Recife 50761-901, PE, Brazil
Manoel H. N. Marinho: Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, PE, Brazil

Energies, 2023, vol. 16, issue 18, 1-20

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.

Keywords: solar irradiance forecasting; battery energy storage system; prediction models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/18/6638/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/18/6638/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:18:p:6638-:d:1240890

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().