Adequacy assessment using data-driven models to account for aerodynamic losses in offshore wind generation
Nguyen, T.-H.; Toubeau, J.-F.; De Jaeger, E.; Vallée, F. (2022). Adequacy assessment using data-driven models to account for aerodynamic losses in offshore wind generation. Electric Power Systems Research 211: 108599. https://dx.doi.org/10.1016/j.epsr.2022.108599 In: Electric Power Systems Research. ELSEVIER SCIENCE SA: Lausanne. ISSN 0378-7796; e-ISSN 1873-2046, more |  |
| Keyword | | | Author keywords | Adequacy; Machine Learning; Offshore wind generation; Wake effects |
| Abstract | Offshore wind generation has developed rapidly in the past few years, leading to an increasing importance in power systems. Therefore, it becomes essential to properly account for aerodynamic effects that affect the power extracted from the wind, and to assess their impact on the power system adequacy. In adequacy studies, due to computational constraints, the power output of offshore wind farms is currently modelled in a simple and approximate way, neglecting important factors such as turbulence and wake effects. This may lead to erroneous, and thus misleading adequacy estimations. Hence, the focus of this paper is to develop data-driven proxy models able to learn the complex relation between free flow wind information and the aggregated power of wind farms. Those Machine Learning-based models are used as fast and reliable surrogates of numerical simulations based on computational fluid dynamics. The developed models are then included in an adequacy study built upon sequential Monte-Carlo simulations. The obtained outcomes are compared with traditional modelling approaches, which allows to quantify the value of the proposed procedure. |
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