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A benders decomposition approach for solving the offshore wind farm installation planning at the North Sea

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  • Ursavas, Evrim

Abstract

Wind farm installation and particularly offshore wind farm installation is highly complex due to high dependency on weather and remarkably large components. Amongst others projects at North Sea face considerable interruptions due to severe weather conditions. The problem we refer to consists of determining the renting period of the offshore installation vessels and the scheduling of the operations for building the wind farm. Planners need to make these decisions under the uncertain wind states. A deterministic offshore wind turbine installation model is not suitable to capture the uncertainties which may leave the vessel resource unused or vessel being sent offshore in unfavorable conditions. Accordingly, this study proposes a model that considers disruptions arising from uncertain weather conditions which stand as the main challenge for such projects. As the compact formulation fails to provide solutions due to the large number of scenarios, an approach based on Benders decomposition is developed. The tool is applied to two major wind farm projects, “Bard 1” and “Borkum West” at the North Sea using real weather data over two years. The two-stage model leans to conservatively use the suitable conditions in advance to avoid huge waiting costs incurred under severe weather. Motivated by experts at Wagenborg and MPI Offshore, we extend our experiments to analyze cases where plants are build further away from the shore. The tool presented in this study is suitable for being used in the planning of wind farm projects providing competent solutions.

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  • Ursavas, Evrim, 2017. "A benders decomposition approach for solving the offshore wind farm installation planning at the North Sea," European Journal of Operational Research, Elsevier, vol. 258(2), pages 703-714.
    • Handle: RePEc:eee:ejores:v:258:y:2017:i:2:p:703-714
    DOI: 10.1016/j.ejor.2016.08.057
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    Cited by:

    1. Tjaberings, Jorick & Fazi, Stefano & Ursavas, Evrim, 2022. "Evaluating operational strategies for the installation of offshore wind turbine substructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    2. Amorosi, Lavinia & Fischetti, Martina & Paradiso, Rosario & Roberti, Roberto, 2024. "Optimization models for the installation planning of offshore wind farms," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1182-1196.
    3. Rippel, Daniel & Jathe, Nicolas & Lütjen, Michael & Szczerbicka, Helena & Freitag, Michael, 2019. "Integrated domain model for operative offshore installation planning," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 25-54, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    4. Stålhane, Magnus & Halvorsen-Weare, Elin E. & Nonås, Lars Magne & Pantuso, Giovanni, 2019. "Optimizing vessel fleet size and mix to support maintenance operations at offshore wind farms," European Journal of Operational Research, Elsevier, vol. 276(2), pages 495-509.
    5. Taylor, James W. & Jeon, Jooyoung, 2018. "Probabilistic forecasting of wave height for offshore wind turbine maintenance," European Journal of Operational Research, Elsevier, vol. 267(3), pages 877-890.
    6. Daniel Rippel & Fatemeh Abasian Foroushani & Michael Lütjen & Michael Freitag, 2021. "A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions," Energies, MDPI, vol. 14(21), pages 1-21, October.
    7. Lerche, J. & Lorentzen, S. & Enevoldsen, P. & Neve, H.H., 2022. "The impact of COVID -19 on offshore wind project productivity – A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    8. Chandra Ade Irawan & Graham Wall & Dylan Jones, 2019. "An optimisation model for scheduling the decommissioning of an offshore wind farm," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(2), pages 513-548, June.

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