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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. ; ISSN:0306-2619 ; ISSN:1872-9118

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.