Journal article
Multi-scale optimization of the design of offshore wind farms
Operations Research, Management Science, Department of Technology, Management and Economics, Technical University of Denmark1
Management Science, Department of Technology, Management and Economics, Technical University of Denmark2
Department of Technology, Management and Economics, Technical University of Denmark3
Vattenfall Vindkraft A/S4
University of Twente5
Swiss Federal Institute of Technology Zurich6
The traditional optimization of a wind farm layout consisted of arranging the wind turbines inside a designated area. In contrast, the 2021 tender from the UK government, Offshore Wind Leasing Round 4 (“UK Round-4”), and upcoming bids only specify large regions where the wind farm can be built. This leads to the new challenge of selecting the wind farm shape and area out of a larger region to maximize its profitability.
We introduce this problem as the “wind farm area selection problem” and present a novel optimization framework to solve it efficiently. Specifically, our framework combines three scales of design: (i) on a macro-scale, choosing the approximate location of the wind farm out of larger regions, (ii) on a meso-scale, generating the optimal shape of the wind farm, and (iii) on a micro-scale, choosing the exact position of the turbines within the shape.
In particular, we propose a new constructive heuristic to choose the best shape of a wind farm at the meso-scale, which is scarcely studied in the literature. Moreover, while macro and micro-scales have already been investigated, our framework is the first to integrate them. We perform a detailed computational analysis using real-life data and constraints from the recent UK Round-4 tender.
Compared to the best rectangular-shaped wind farm at the same location, our results show that optimizing the shape increases profitability by 1.1% on average and up to 2.8%, corresponding to 46 and 109 million Euro respectively.
Language: | English |
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Year: | 2022 |
Pages: | 118830 |
ISSN: | 03062619 and 18729118 |
Types: | Journal article |
DOI: | 10.1016/j.apenergy.2022.118830 |
ORCIDs: | Cazzaro, Davide , 0000-0002-2614-5051 and Pisinger, David |