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On offshore wind farm maintenance scheduling for decision support on vessel fleet composition

Author

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  • Gutierrez-Alcoba, A.
  • Hendrix, E.M.T.
  • Ortega, G.
  • Halvorsen-Weare, E.E.
  • Haugland, D.

Abstract

Maintenance costs account for a large part of the total cost of an offshore wind farm. Several models have been presented in the literature to optimize the fleet composition of the required vessels to support maintenance tasks. We provide a mixed integer linear programming (MILP) description of such a model, where on the higher level, the fleet composition is decided and on the lower level the maintenance operations are scheduled for a set of weather and breakdown scenarios. A drawback of deciding an a priori information schedule for the coming year is that, the weather outcomes and breakdowns are not known in advance. Consequently, given a fleet composition, its corresponding maintenance costs are underestimated compared to what can be realised in practice under incomplete information. Therefore, we present a heuristic that simulates the practical scheduling and may provide a better cost estimate. The latter method is used to evaluate a fleet composition based on available information and it is compared with the MILP solution based on a priori information.

Suggested Citation

  • Gutierrez-Alcoba, A. & Hendrix, E.M.T. & Ortega, G. & Halvorsen-Weare, E.E. & Haugland, D., 2019. "On offshore wind farm maintenance scheduling for decision support on vessel fleet composition," European Journal of Operational Research, Elsevier, vol. 279(1), pages 124-131.
    • Handle: RePEc:eee:ejores:v:279:y:2019:i:1:p:124-131
    DOI: 10.1016/j.ejor.2019.04.020
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    References listed on IDEAS

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    1. Snyder, Brian & Kaiser, Mark J., 2009. "Ecological and economic cost-benefit analysis of offshore wind energy," Renewable Energy, Elsevier, vol. 34(6), pages 1567-1578.
    2. Irawan, Chandra Ade & Ouelhadj, Djamila & Jones, Dylan & Stålhane, Magnus & Sperstad, Iver Bakken, 2017. "Optimisation of maintenance routing and scheduling for offshore wind farms," European Journal of Operational Research, Elsevier, vol. 256(1), pages 76-89.
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    Cited by:

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    4. 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.
    5. Liu, Lujie & Xiao, Yiyong & Yang, Jun, 2024. "Daily optimization of maintenance routing and scheduling in a large-scale photovoltaic power plant with time-varying output power," Applied Energy, Elsevier, vol. 360(C).
    6. Zhou, Yifan & Miao, Jindan & Yan, Bin & Zhang, Zhisheng, 2020. "Bio-objective long-term maintenance scheduling for wind turbines in multiple wind farms," Renewable Energy, Elsevier, vol. 160(C), pages 1136-1147.
    7. Centeno-Telleria, Manu & Aizpurua, Jose Ignacio & Penalba, Markel, 2023. "Computationally efficient analytical O&M model for strategic decision-making in offshore renewable energy systems," Energy, Elsevier, vol. 285(C).
    8. Alberti, A.R. & Neto, W.A. Ferreira & Cavalcante, C.A.V. & Santos, A.C.J., 2022. "Modelling a flexible two-phase inspection-maintenance policy for safety-critical systems considering revised and non-revised inspections," Reliability Engineering and System Safety, Elsevier, vol. 221(C).

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