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How explain on-grid PV systems diffusion? Review and application in Brazil

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  • Rigo, Paula Donaduzzi
  • Lunardi, Gabriel Machado
  • Siluk, Julio Cezar Mairesse
  • Schneider, Paulo Schmidt
  • Nascimento, Felipe Moraes do
  • Thomasi, Virgínia
  • Funke, Edson

Abstract

This paper examines the remarkable growth of photovoltaic (PV) solar energy technology in Brazil, which stands out among the leading countries in photovoltaic installations. The recent implementation of Law 14300 on grid-connected PV systems has caused a stir in the Brazilian market, with investors anticipating installations to avoid reductions in benefits. This phenomenon, previously observed in Europe, instigates an understanding of the variables underlying the exponential growth of the Brazilian market. This study seeks to identify the predictors that explain the PV systems diffusion in Brazil, in two stages: a systematic review of the literature to map approaches and methods, followed by the construction of a database and the application of predictive models. The literature revealed 24 methods in six approaches, with 44 predictors. For Brazil, the database contains 90,129 instances, 22 predictors, and covers all 5570 municipalities. The results highlight the crucial importance of the electricity tariff as the main predictor, followed by irradiation and municipal Gross Domestic Product. Predictors such as the number of companies, minimum wage, HDI education, demographic density, and vehicle fleet prove to be relevant. In conclusion, the spatially explicit analysis highlights the complexity of the market, offering valuable implications for energy planners and stakeholders.

Suggested Citation

  • Rigo, Paula Donaduzzi & Lunardi, Gabriel Machado & Siluk, Julio Cezar Mairesse & Schneider, Paulo Schmidt & Nascimento, Felipe Moraes do & Thomasi, Virgínia & Funke, Edson, 2024. "How explain on-grid PV systems diffusion? Review and application in Brazil," Renewable Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:renene:v:230:y:2024:i:c:s0960148124009303
    DOI: 10.1016/j.renene.2024.120862
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    References listed on IDEAS

    as
    1. Rediske, Graciele & Siluk, Julio Cezar M. & Michels, Leandro & Rigo, Paula D. & Rosa, Carmen B. & Cugler, Gilberto, 2020. "Multi-criteria decision-making model for assessment of large photovoltaic farms in Brazil," Energy, Elsevier, vol. 197(C).
    2. Reddy, B. Sudhakara, 2018. "Economic dynamics and technology diffusion in indian power sector," Energy Policy, Elsevier, vol. 120(C), pages 425-435.
    3. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    4. Jha, Ashutosh & Saha, Debashis, 2020. "“Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: A comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models”," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Rosa, Carmen B. & Wendt, João Francisco M. & Chaves, Daniel M.S. & Thomasi, Virginia & Michels, Leandro & Siluk, Julio Cezar M., 2020. "Mathematical modeling for the measurement of the competitiveness index of Brazil south urban sectors for installation of photovoltaic systems," Energy Policy, Elsevier, vol. 136(C).
    7. Thormeyer, Christoph & Sasse, Jan-Philipp & Trutnevyte, Evelina, 2020. "Spatially-explicit models should consider real-world diffusion of renewable electricity: Solar PV example in Switzerland," Renewable Energy, Elsevier, vol. 145(C), pages 363-374.
    8. Karakaya, Emrah, 2016. "Finite Element Method for forecasting the diffusion of photovoltaic systems: Why and how?," Applied Energy, Elsevier, vol. 163(C), pages 464-475.
    9. Andrade, Jorge Vleberton Bessa de & Rodrigues, Bruno Noronha & Santos, Ivan Felipe Silva dos & Haddad, Jamil & Tiago Filho, Geraldo Lúcio, 2020. "Constitutional aspects of distributed generation policies for promoting Brazilian economic development," Energy Policy, Elsevier, vol. 143(C).
    10. Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo & Monteiro, Claudio & Garcia-Garrido, Eduardo & Zorzano-Santamaria, Pedro, 2011. "Spatial long-term forecasting of small power photovoltaic systems expansion," Renewable Energy, Elsevier, vol. 36(12), pages 3499-3506.
    11. Sinitskaya, Ekaterina & Gomez, Kelley J. & Bao, Qifang & Yang, Maria C. & MacDonald, Erin F., 2020. "Designing linked journey maps to understand the complexities of the residential solar energy market," Renewable Energy, Elsevier, vol. 145(C), pages 1910-1922.
    12. Romeo Danielis & Mariangela Scorrano & Alessandro Massi Pavan & Nicola Blasuttigh, 2023. "Simulating the Diffusion of Residential Rooftop Photovoltaic, Battery Storage Systems and Electric Cars in Italy. An Exploratory Study Combining a Discrete Choice and Agent-Based Modelling Approach," Energies, MDPI, vol. 16(1), pages 1-20, January.
    13. Candas, Soner & Siala, Kais & Hamacher, Thomas, 2019. "Sociodynamic modeling of small-scale PV adoption and insights on future expansion without feed-in tariffs," Energy Policy, Elsevier, vol. 125(C), pages 521-536.
    14. Mukisa, Nicholas & Zamora, Ramon & Lie, Tek Tjing, 2021. "Diffusion forecast for grid-tied rooftop solar photovoltaic technology under store-on grid scheme model in Sub-Saharan Africa: Government role assessment," Renewable Energy, Elsevier, vol. 180(C), pages 516-535.
    15. Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting the diffusion of renewable electricity considering the impact of policy and oil prices: The case of South Korea," Applied Energy, Elsevier, vol. 197(C), pages 29-39.
    16. Rao, K. Usha & Kishore, V.V.N., 2010. "A review of technology diffusion models with special reference to renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1070-1078, April.
    17. Hanna, Richard & Leach, Matthew & Torriti, Jacopo, 2018. "Microgeneration: The installer perspective," Renewable Energy, Elsevier, vol. 116(PA), pages 458-469.
    18. Huh, Sung-Yoon & Lee, Chul-Yong, 2014. "Diffusion of renewable energy technologies in South Korea on incorporating their competitive interrelationships," Energy Policy, Elsevier, vol. 69(C), pages 248-257.
    19. Mesak, HI & Coleman, RW, 1992. "Modeling the effect of subsidized pricing policy on new product diffusion," Omega, Elsevier, vol. 20(3), pages 303-312, May.
    20. Yamaguchi, Yohei & Akai, Kenju & Shen, Junyi & Fujimura, Naoki & Shimoda, Yoshiyuki & Saijo, Tatsuyoshi, 2013. "Prediction of photovoltaic and solar water heater diffusion and evaluation of promotion policies on the basis of consumers’ choices," Applied Energy, Elsevier, vol. 102(C), pages 1148-1159.
    21. dos Santos, L.L.C. & Canha, L.N. & Bernardon, D.P., 2018. "Projection of the diffusion of photovoltaic systems in residential low voltage consumers," Renewable Energy, Elsevier, vol. 116(PA), pages 384-401.
    22. Rosa, Carmen Brum & Rigo, Paula Donaduzzi & Rediske, Graciele & Moccellin, Ana Paula & Mairesse Siluk, Julio Cezar & Michels, Leandro, 2021. "How to measure organizational performance of distributed generation in electric utilities? The Brazilian case," Renewable Energy, Elsevier, vol. 169(C), pages 191-203.
    23. Rediske, G. & Burin, H.P. & Rigo, P.D. & Rosa, C.B. & Michels, L. & Siluk, J.C.M., 2021. "Wind power plant site selection: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    24. Minhi Hahn & Sehoon Park & Lakshman Krishnamurthi & Andris A. Zoltners, 1994. "Analysis of New Product Diffusion Using a Four-Segment Trial-Repeat Model," Marketing Science, INFORMS, vol. 13(3), pages 224-247.
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