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Martina Fischetti: Operations Research + Machine Learning for the design of future offshore wind farms

Abstract: Sustainability is a key focus in our society that is today working to change towards a greener future. Wind energy, in particular, is attracting always more attention as source of renewable energy. In this picture, Vattenfall is working towards the ambitious goal of becoming fossil free within one generation. To achieve this goal, innovation (and optimization!) is of key importance.
This talk presents how Vattenfall is using advanced operations research and analytics for designing cheaper and more profitable offshore wind farms. The talk will focus on the design phase of offshore wind farms, explain in details the optimization challenges faced by companies as Vattenfall. In particular, we will focus on the Offshore Wind Farm Design problem, that is the task of deciding how to position turbines offshore in order to increase the overall farm production and reduce costs. This task is particularly challenging due to the interference effects among turbines, due to the stochasticity of wind and due to the high dimensionality of the problem in real applications. Mixed Integer Programming models and other state-of-the-art optimization techniques have been developed to solve this problem. These tools are nowadays fully deployed in Vattenfall and used for the design of all offshore wind farms. They have been used, for example, for the design of Hollandse Kust Zuid in the Netherlands, which will be the first offshore wind farm ever constructed without any subsidies. This is a huge milestone for the whole wind energy business.
These advanced optimization tools allowed Vattenfall to think out of the box, take more informed decision and perform different what-if-analyses. In particular, we can foresee the number of what-if analyses to quickly grow in the future. Therefore we have looked into Machine Learning techniques. In the specific, we propose a combination of Mathematical Optimization and Machine Learning to estimate the value of optimized solutions. We investigate if a machine, trained on a large number of optimized solutions, could accurately estimate the value of the optimized solution for new (unseen) instances. This research question could be of general interest for the OR community, but we focus on the wind farm layout application in our research. Given the complexity of the wind farm layout problem and the big difference in production between optimized/non optimized solutions, it is not trivial to understand the potential value of a new site without running a complete optimization. This could be too time consuming if a lot of sites need to be evaluated, therefore we propose to use Machine Learning to quickly estimate the potential of new sites (i.e., to estimate the optimized production of a site without explicitly running the optimization). To do so, we trained and tested different Machine Learning models on a dataset of 3000+ optimized layouts found by the optimizer. Our results show that Machine Learning is able to efficiently estimate the value of optimized instances for the offshore wind farm layout problem.