A Novel Data-Driven Tool Based on Non-Linear Optimization for Offshore Wind Farm Siting

Marina Polykarpou (), Flora Karathanasi (), Takvor Soukissian, Vasiliki Loukaidi and Ioannis Kyriakides
Additional contact information
Marina Polykarpou: Cyprus Marine and Maritime Institute, Vasileos Pavlou Square, Larnaca 6023, Cyprus
Flora Karathanasi: Cyprus Marine and Maritime Institute, Vasileos Pavlou Square, Larnaca 6023, Cyprus
Takvor Soukissian: Hellenic Centre for Marine Research, 46.7 km Athens Sounio Ave., 190 13 Anavyssos, Greece
Vasiliki Loukaidi: Hellenic Hydrocarbons and Energy Resources Management Company, Dim. Margari 18, 115 25 Athens, Greece
Ioannis Kyriakides: Cyprus Marine and Maritime Institute, Vasileos Pavlou Square, Larnaca 6023, Cyprus

Energies, 2023, vol. 16, issue 5, 1-17

Abstract: One preliminary key step for developing an offshore wind farm is identifying favorable sites. The process of sitting involves multiple requirements and constraints, and therefore, its feasible implementation requires either approximating assumptions or an optimization method that is capable of handling non-linear relationships and heterogeneous factors. A new optimization method is proposed to address this problem that efficiently and accurately combines essential technical criteria, such as wind speed, water depth, and distance from shore, to identify favorable areas for offshore wind farm development through a user-friendly data-driven tool. Appropriate ranks and weighting factors are carefully selected to obtain realistic results. The proposed methodology is applied in the central Aegean Sea, which has a high offshore wind energy potential. The application of the proposed optimization method reveals large areas suitable for developing floating wind energy structures. The algorithm matches the accuracy of the exhaustive search method. It, therefore, produces the optimum outcome, however, at a lower computational expense demonstrating the proposed method’s potential for larger spatial-scale analysis and use as a decision support tool.

Keywords: sequential Monte Carlo method; simulation; K-means clustering; floating installations; site selection; Aegean Sea (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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