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Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies

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  • Faria, Victor A.D.
  • Rodrigo de Queiroz, Anderson
  • DeCarolis, Joseph F.

Abstract

This work proposes an analytical decision-making framework considering scenario generation using artificial neural networks and risk-averse stochastic programming to define renewable offshore portfolios of wind, wave, and ocean current technologies. For the scenario generation, a generative adversarial neural network is developed to generate synthetic energy scenarios considering resources distributed over large geographic regions. These scenarios are then fed to a stochastic model, which objective to determine the optimal location and number of turbines for each technology. In the stochastic model formulation, a representation of the limits in the portfolio Levelized Cost of Energy and the maximization of the five percent lower energy generation conditions, also known as Conditional Value at Risk, is presented. The framework proposed here is tested considering data from a portion of the U.S. East coast, where the generative model was successful in creating energy scenarios statistically consistent with the historical data for wind, wave, and ocean current resources at more than 500 sites. Furthermore, the Conditional Value at Risk portfolio optimization model was used to construct efficient frontiers for a combination of different technologies, showing the significance of resource diversification as a tool to improve system security.

Suggested Citation

  • Faria, Victor A.D. & Rodrigo de Queiroz, Anderson & DeCarolis, Joseph F., 2023. "Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies," Energy, Elsevier, vol. 270(C).
    • Handle: RePEc:eee:energy:v:270:y:2023:i:c:s0360544223003407
    DOI: 10.1016/j.energy.2023.126946
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    References listed on IDEAS

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    1. Aquila, Giancarlo & Coelho, Eden de Oliveira Pinto & Bonatto, Benedito Donizeti & Pamplona, Edson de Oliveira & Nakamura, Wilson Toshiro, 2021. "Perspective of uncertainty and risk from the CVaR-LCOE approach: An analysis of the case of PV microgeneration in Minas Gerais, Brazil," Energy, Elsevier, vol. 226(C).
    2. Díaz, Guzmán & Gómez-Aleixandre, Javier & Coto, José, 2016. "Wind power scenario generation through state-space specifications for uncertainty analysis of wind power plants," Applied Energy, Elsevier, vol. 162(C), pages 21-30.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Stoutenburg, Eric D. & Jenkins, Nicholas & Jacobson, Mark Z., 2010. "Power output variations of co-located offshore wind turbines and wave energy converters in California," Renewable Energy, Elsevier, vol. 35(12), pages 2781-2791.
    5. Li, Binghui & de Queiroz, Anderson Rodrigo & DeCarolis, Joseph F. & Bane, John & He, Ruoying & Keeler, Andrew G. & Neary, Vincent S., 2017. "The economics of electricity generation from Gulf Stream currents," Energy, Elsevier, vol. 134(C), pages 649-658.
    6. Santos-Alamillos, F.J. & Thomaidis, N.S. & Usaola-García, J. & Ruiz-Arias, J.A. & Pozo-Vázquez, D., 2017. "Exploring the mean-variance portfolio optimization approach for planning wind repowering actions in Spain," Renewable Energy, Elsevier, vol. 106(C), pages 335-342.
    7. Shakouri, Mahmoud & Lee, Hyun Woo & Kim, Yong-Woo, 2017. "A probabilistic portfolio-based model for financial valuation of community solar," Applied Energy, Elsevier, vol. 191(C), pages 709-726.
    8. Sinsel, Simon R. & Yan, Xuqian & Stephan, Annegret, 2019. "Building resilient renewable power generation portfolios: The impact of diversification on investors’ risk and return," Applied Energy, Elsevier, vol. 254(C).
    9. Camal, S. & Teng, F. & Michiorri, A. & Kariniotakis, G. & Badesa, L., 2019. "Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications," Applied Energy, Elsevier, vol. 242(C), pages 1396-1406.
    10. de Faria, Victor A.D. & de Queiroz, Anderson R. & DeCarolis, Joseph F., 2022. "Optimizing offshore renewable portfolios under resource variability," Applied Energy, Elsevier, vol. 326(C).
    11. Silva, Allan Rodrigues & Pimenta, Felipe Mendonça & Assireu, Arcilan Trevenzoli & Spyrides, Maria Helena Constantino, 2016. "Complementarity of Brazil׳s hydro and offshore wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 413-427.
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