Mixed Integer Programming models for planning maintenance at offshore wind farms under uncertainty

Albert Schrotenboer*, Evrim Ursavas, Iris Vis

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

2 Citaten (Scopus)
65 Downloads (Pure)

Samenvatting

We introduce the Stochastic Maintenance Fleet Transportation Problem for Offshore wind farms (SMFTPO), in which a maintenance provider determines an optimal, medium-term planning for maintaining multiple wind farms while controlling for uncertainty in the maintenance tasks and weather conditions. Since the maintenance provider is typically not the owner of a wind farm, it needs to adhere minimum service requirements that specify the required service. We consider three of such settings: 1) perform all maintenance tasks, 2) allow for a fraction of unscheduled tasks, and 3) incentivize to perform maintenance rather quickly. We provide a two-stage stochastic mixed integer programming model for the three SMFTPO settings, and solve it by means of Sample Average Approximation. In addition, we provide an overview of the, what we discovered, non-aligned modeling assumptions in the literature regarding operational decisions. By providing a series of special cases of the second-stage problem resembling the different modeling assumptions, we aim to establish a common consensus regarding the key modeling decisions to be taken in maintenance planning problems for offshore wind farms. We provide newly constructed, and publicly available, benchmark sets. We extensively compare the different SMFTPO settings and its special cases on those benchmark sets, and we show that the special case reformulations are very effective for solving the second-stage problems. In addition, we find that for particular cases, established modeling techniques result in overestimations and increased running times.
Originele taal-2English
Pagina's (van-tot)180-202
Aantal pagina's37
TijdschriftTransportation Research. Part C: Emerging Technologies
Volume112
DOI's
StatusPublished - mrt-2020

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