Print Email Facebook Twitter Optimal chartering decisions for vessel fleet to support offshore wind farm maintenance operations Title Optimal chartering decisions for vessel fleet to support offshore wind farm maintenance operations Author Li, M. (TU Delft Transport Engineering and Logistics; University of Strathclyde) Bijvoet, Bas (Student TU Delft) Wu, Kangjie (Student TU Delft) Jiang, X. (TU Delft Transport Engineering and Logistics) Negenborn, R.R. (TU Delft Transport Engineering and Logistics) Date 2024 Abstract Offshore wind energy is expected to be the most significant source of future electricity supply in Europe. Offshore wind farms are located far from the shores, requiring a fleet of various types of vessels to access sites when maintaining offshore wind turbines. The employment of the vessels is costly, accounting for the majority of the total O&M costs for offshore wind energy. Therefore, configuring the size and mix of the vessel fleet to support maintenance operations in a cost-effective manner is an issue of importance to enhance economics of offshore wind sector. In this paper, a discrete event simulation based model is proposed to present how a mixed vessel fleet with the specific configuration, including crew transfer vessels, field support vessels, and heavy lift vessels, performs maintenance for an offshore wind farm. The economic performance of the vessel fleet under a predetermined condition-based opportunistic maintenance strategy is investigated by using the model. A metaheuristic algorithm, simulated annealing, is employed to find the optimal fleet size and mix to make leasing decisions with the minimum costs. The performance of the developed approaches is evaluated by using a generic offshore wind farm in the North Sea. The sensitivity analysis is performed to investigate the most influential O&M factors. Subject Condition-based opportunistic maintenanceOffshore wind energyOperation and maintenanceVessel fleet To reference this document use: http://resolver.tudelft.nl/uuid:97aeb242-8aa7-415b-9c45-84e0023e5eae DOI https://doi.org/10.1016/j.oceaneng.2024.117202 ISSN 0029-8018 Source Ocean Engineering, 298 Part of collection Institutional Repository Document type journal article Rights © 2024 M. Li, Bas Bijvoet, Kangjie Wu, X. Jiang, R.R. Negenborn Files PDF 1-s2.0-S0029801824005390-main.pdf 3.23 MB Close viewer /islandora/object/uuid:97aeb242-8aa7-415b-9c45-84e0023e5eae/datastream/OBJ/view