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Stochastic financial appraisal of offshore wind farms

Author

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  • Ioannou, Anastasia
  • Angus, Andrew
  • Brennan, Feargal

Abstract

Increasing investment activity in offshore wind energy projects has induced the need for an improved appraisal framework of the assets. As opposed to the deterministic appraisal models currently available, a probabilistic analysis can provide decision support with assigned confidence levels, taking into account uncertainties inherent in the analysis. To this end, departing from an integrated lifecycle techno-economic model developed by the authors, the present study develops a probabilistic approach considering time-dependent and independent stochastic variables. To this end, advanced numerical methods, namely Artificial Neural Network (ANN) approximation model and an Auto-Regressive Integrated Moving Average (ARIMA) time series model are combined with Monte Carlo simulations in order to assess the impact of the system uncertainties on the performance of the asset. Joint probability distributions of the output variables, namely the NPV, capital cost, annual operating cost and LCOE are presented, providing insights regarding the profitability of the asset within defined confidence intervals.

Suggested Citation

  • Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2020. "Stochastic financial appraisal of offshore wind farms," Renewable Energy, Elsevier, vol. 145(C), pages 1176-1191.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:1176-1191
    DOI: 10.1016/j.renene.2019.06.111
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    Citations

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    Cited by:

    1. Tvedt, Jostein, 2022. "Floating offshore wind and the real options to relocate," Energy Economics, Elsevier, vol. 116(C).
    2. Sara C. Pryor & Rebecca J. Barthelmie, 2024. "Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas," Energies, MDPI, vol. 17(5), pages 1-30, February.
    3. Rinaldi, Giovanni & Garcia-Teruel, Anna & Jeffrey, Henry & Thies, Philipp R. & Johanning, Lars, 2021. "Incorporating stochastic operation and maintenance models into the techno-economic analysis of floating offshore wind farms," Applied Energy, Elsevier, vol. 301(C).
    4. Wu, Yunna & Liu, Fangtong & Wu, Junhao & He, Jiaming & Xu, Minjia & Zhou, Jianli, 2022. "Barrier identification and analysis framework to the development of offshore wind-to-hydrogen projects," Energy, Elsevier, vol. 239(PB).
    5. Bórawski, Piotr & Bełdycka-Bórawska, Aneta & Jankowski, Krzysztof Jóżef & Dubis, Bogdan & Dunn, James W., 2020. "Development of wind energy market in the European Union," Renewable Energy, Elsevier, vol. 161(C), pages 691-700.
    6. Mingyu Li & Dongxiao Niu & Zhengsen Ji & Xiwen Cui & Lijie Sun, 2021. "Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net," Sustainability, MDPI, vol. 13(21), pages 1-19, November.

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