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Site selection for offshore renewable energy platforms: A multi-criteria decision-making approach

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  • Bao, Minghan
  • Arzaghi, Ehsan
  • Abaei, Mohammad Mahdi
  • Abbassi, Rouzbeh
  • Garaniya, Vikram
  • Abdussamie, Nagi
  • Heasman, Kevin

Abstract

This paper presents a novel methodology for site selection of Offshore Renewable Energy (ORE) systems, addressing the growing global energy demand and the need for sustainable solutions to climate change. Focusing on the wave energy, a relatively untapped renewable energy source, this research focuses on optimizing the Wave Energy Converter (WEC) deployment amidst the uncertainties of the offshore environment. The study involves a comprehensive evaluation of potential sites, considering key factors like power generation capacity, mooring system fatigue life, and tether response to extreme loads. Initial wave data analysis for various locations is followed by numerical simulations of a point absorber WEC under different environmental conditions. A Bayesian Network (BN) model is then employed to integrate uncertainties into a multi-criteria decision-making (MCDM), enhancing the robustness of site selection. This approach facilitates the calculation of utility values for various sites, leading to the identification of the optimal decision alternative based on maximum expected utility. This work provides a detailed framework for renewable energy stakeholders, helping in the assessment of both profitability and survivability of WECs in chosen locations. It significantly contributes to minimizing economic and performance risks associated with ORE system installations, promoting efficient and sustainable energy production from ocean resources.

Suggested Citation

  • Bao, Minghan & Arzaghi, Ehsan & Abaei, Mohammad Mahdi & Abbassi, Rouzbeh & Garaniya, Vikram & Abdussamie, Nagi & Heasman, Kevin, 2024. "Site selection for offshore renewable energy platforms: A multi-criteria decision-making approach," Renewable Energy, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:renene:v:229:y:2024:i:c:s096014812400836x
    DOI: 10.1016/j.renene.2024.120768
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