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

被引:5
作者
de Oliveira, Joao Fausto L. [1 ]
Neto, Paulo S. G. de Mattos [2 ]
Siqueira, Hugo Valadares [3 ]
Santos, Domingos S. de O. [2 ]
Lima, Aranildo R.
Madeiro, Francisco [1 ]
Dantas, Douglas A. P. [1 ]
Cavalcanti, Mariana de Morais [1 ]
Pereira, Alex C. [4 ]
Marinho, Manoel H. N. [1 ]
机构
[1] Univ Pernambuco, Escola Politecn Pernambuco, BR-50720001 Recife, PE, Brazil
[2] Univ Fed Pernambuco, Ctr Informat, BR-50740560 Recife, PE, Brazil
[3] Fed Univ Technol, Grad Program Elect Engn, BR-84017220 Ponta Grossa, PR, Brazil
[4] Sao Francisco Hydroelect Co Chesf, BR-50761901 Recife, PE, Brazil
关键词
solar irradiance forecasting; battery energy storage system; prediction models; SOLAR-ENERGY; INTEGRATION; OPERATION;
D O I
10.3390/en16186638
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
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页数:20
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共 71 条
  • [1] A reliable and cost-effective planning framework of rural area hybrid system considering intelligent weather forecasting
    Abazari, Ahmadreza
    Soleymani, Mohammad Mahdi
    Kamwa, Innocent
    Babaei, Masoud
    Ghafouri, Mohsen
    Muyeen, S. M.
    Foley, Aoife M.
    [J]. ENERGY REPORTS, 2021, 7 : 5647 - 5666
  • [2] Agathokleous C, 2019, 2019 IEEE MILAN POWERTECH
  • [3] A Two-Level Model Predictive Control-Based Approach for Building Energy Management including Photovoltaics, Energy Storage, Solar Forecasting and Building Loads
    Agharazi, Hanieh
    Prica, Marija D.
    Loparo, Kenneth A.
    [J]. ENERGIES, 2022, 15 (10)
  • [4] Comparison of different operation strategies for PV battery home storage systems including forecast-based operation strategies
    Angenendt, Georg
    Zurmuehlen, Sebastian
    Axelsen, Hendrik
    Sauer, Dirk Uwe
    [J]. APPLIED ENERGY, 2018, 229 : 884 - 899
  • [5] Badigenchala R., 2021, P 2021 INT C INT TEC, P1
  • [6] Bakhtvar M, 2020, IEEE INT ENER CONF, P238, DOI 10.1109/ENERGYCon48941.2020.9236559
  • [7] Predictive Energy Control Strategy for Peak Switch and Shifting Using BESS and PV Generation Applied to the Retail Sector
    Barchi, Grazia
    Pierro, Marco
    Moser, David
    [J]. ELECTRONICS, 2019, 8 (05):
  • [8] Heat and power generation augmentation planning of isolated microgrid
    Basu, M.
    [J]. ENERGY, 2021, 223
  • [9] An MPC-based power management of standalone DC microgrid with energy storage
    Batiyah, Salem
    Sharma, Roshan
    Abdelwahed, Sherif
    Zohrabi, Nasibeh
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 120
  • [10] Battery size determination for photovoltaic capacity firming using deep learning irradiance forecasts
    Beltran, Hector
    Cardo-Miota, Javier
    Segarra-Tamarit, Jorge
    Perez, Emilio
    [J]. JOURNAL OF ENERGY STORAGE, 2021, 33