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Portfolio optimization of renewable energy assets: Hydro, wind, and photovoltaic energy in the regulated market in Brazil

Author

Listed:
  • Pinheiro Neto, Daywes
  • Domingues, Elder Geraldo
  • Coimbra, António Paulo
  • de Almeida, Aníbal Traça
  • Alves, Aylton José
  • Calixto, Wesley Pacheco

Abstract

This study proposes a methodology for risk analysis and portfolio optimization of power generation assets with hydro, wind, and solar power, considering the Regulated Contracting Environment and the Mechanism for Reallocation of Energy in Brazil. Innovative stochastic models are used to generate synthetic time series for the random variables water inflow, wind speed, solar irradiance, temperature of the photovoltaic panel, and average generation capacity of the Mechanism for Reallocation of Energy. The simulation is implemented using the Monte Carlo method associated with a Cholesky decomposition. An economic approach is presented taking into account taxation and financing, as well as the Markowitz Portfolio theory. The results show that the initial correlation between the energy resources is altered by the cash flow model and mainly by the debt. In the diversification process, the complementarity between sources helps to reduce the economic risk. The increase in debt increases the correlation, decreases the return and risk and, consequently, affects the diversification process and economic results. The Mechanism for Reallocation of Energy significantly reduces the hydroelectric economic risk and increases the financial return, which directly benefits the formation of portfolios.

Suggested Citation

  • Pinheiro Neto, Daywes & Domingues, Elder Geraldo & Coimbra, António Paulo & de Almeida, Aníbal Traça & Alves, Aylton José & Calixto, Wesley Pacheco, 2017. "Portfolio optimization of renewable energy assets: Hydro, wind, and photovoltaic energy in the regulated market in Brazil," Energy Economics, Elsevier, vol. 64(C), pages 238-250.
    • Handle: RePEc:eee:eneeco:v:64:y:2017:i:c:p:238-250
    DOI: 10.1016/j.eneco.2017.03.020
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    Cited by:

    1. Anderson Mitterhofer Iung & Fernando Luiz Cyrino Oliveira & André Luís Marques Marcato, 2023. "A Review on Modeling Variable Renewable Energy: Complementarity and Spatial–Temporal Dependence," Energies, MDPI, vol. 16(3), pages 1-24, January.
    2. David Juárez-Luna, 2021. "Power generation portfolios: A parametric formulation of the efficient frontier," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(1), pages 1-29, Enero - M.
    3. David Juárez-Luna, 2021. "Power generation portfolios: A parametric formulation of the efficient frontier," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(1), pages 1-29, Enero - M.
    4. Unni, Arjun C. & Ongsakul, Weerakorn & Madhu M., Nimal, 2020. "Fuzzy-based novel risk and reward definition applied for optimal generation-mix estimation," Renewable Energy, Elsevier, vol. 148(C), pages 665-673.
    5. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Shahriari, Mehdi & Blumsack, Seth, 2018. "The capacity value of optimal wind and solar portfolios," Energy, Elsevier, vol. 148(C), pages 992-1005.
    7. Gabrielli, Paolo & Aboutalebi, Reyhaneh & Sansavini, Giovanni, 2022. "Mitigating financial risk of corporate power purchase agreements via portfolio optimization," Energy Economics, Elsevier, vol. 109(C).
    8. Ma, Yilin & Wang, Yudong & Wang, Weizhong & Zhang, Chong, 2023. "Portfolios with return and volatility prediction for the energy stock market," Energy, Elsevier, vol. 270(C).
    9. Chen, Chen & Liu, Dinghao & Xian, Liang & Pan, Lin & Wang, Lihua & Yang, Min & Quan, Li, 2020. "Best-case scenario robust portfolio for energy stock market," Energy, Elsevier, vol. 213(C).
    10. Ávila, Leandro & Mine, Miriam R.M & Kaviski, Eloy & Detzel, Daniel H.M., 2021. "Evaluation of hydro-wind complementarity in the medium-term planning of electrical power systems by joint simulation of periodic streamflow and wind speed time series: A Brazilian case study," Renewable Energy, Elsevier, vol. 167(C), pages 685-699.
    11. Mosquera-López, Stephania & Uribe, Jorge M., 2022. "Pricing the risk due to weather conditions in small variable renewable energy projects," Applied Energy, Elsevier, vol. 322(C).
    12. Schiochet Pinto, Luane & Pinheiro Neto, Daywes & de Leles Ferreira Filho, Anésio & Domingues, Elder Geraldo, 2020. "An alternative methodology for analyzing the risk and sensitivity of the economic viability for generating electrical energy with biogas from the anaerobic bio-digestion of vinasse," Renewable Energy, Elsevier, vol. 155(C), pages 1401-1410.
    13. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
    14. Bortoluzzi, Mirian & Furlan, Marcelo & dos Reis Neto, José Francisco, 2022. "Assessing the impact of hydropower projects in Brazil through data envelopment analysis and machine learning," Renewable Energy, Elsevier, vol. 200(C), pages 1316-1326.
    15. Aldemar Leguizamon-Perilla & Juan S. Rodriguez-Bernal & Laidi Moralez-Cruz & Nidia Isabel Farfán-Martinez & César Nieto-Londoño & Rafael E. Vásquez & Ana Escudero-Atehortua, 2023. "Digitalisation and Modernisation of Hydropower Operating Facilities to Support the Colombian Energy Mix Flexibility," Energies, MDPI, vol. 16(7), pages 1-17, March.
    16. Jung, Seunghoon & Jeoung, Jaewon & Kang, Hyuna & Hong, Taehoon, 2021. "Optimal planning of a rooftop PV system using GIS-based reinforcement learning," Applied Energy, Elsevier, vol. 298(C).
    17. Laura Casula & Guglielmo D’Amico & Giovanni Masala & Filippo Petroni, 2020. "Performance estimation of photovoltaic energy production," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 267-285, December.
    18. Aleksei V. Bogoviz & Svetlana V. Lobova & Alexander N. Alekseev, 2020. "Current State and Future Prospects of Hydro Energy in Russia," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 482-488.
    19. Jia, Rui & He, Mengjiao & Zhang, Xinyu & Zhao, Ziwen & Han, Shuo & Jurasz, Jakub & Chen, Diyi & Xu, Beibei, 2022. "Optimal operation of cascade hydro-wind-photovoltaic complementary generation system with vibration avoidance strategy," Applied Energy, Elsevier, vol. 324(C).
    20. de Jesus, Ábio Xavier Cardoso & Pinheiro Neto, Daywes & Domingues, Elder Geraldo, 2023. "Computational tool for technical-economic analysis of photovoltaic microgeneration in Brazil," Energy, Elsevier, vol. 271(C).

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    More about this item

    Keywords

    Monte Carlo method; Renewable energy; Risk analysis; Mechanism for reallocation of energy; Portfolio optimization;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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