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Improved Offshore Wind Generation Modelling in Power Systems Adequacy Evaluation Using Machine Learning

The research is aimed at improving the way offshore wind farms are considered within the current adequacy tools. A fast and accurate modelling of the offshore wind generation yields a better assessment of the power system ability to provide an adequate provision of electricity.

Offshore wind energy is one of the building block for the energy transition. However, the power output of offshore wind farms is highly fluctuating as the wind conditions at sea are not constant. Therefore, an increased share of offshore wind generation may lead to challenges within modern power systems to supply electricity in a reliable way.


This research aims at improving the way offshore wind parks are considered within the currently used adequacy tools. It has three main objectives:

- Account for the wind conditions variability as well as intra-park aerodynamic effects such as wake and turbulence by using Machine Learning models

- Capture the inter-park effects, i.e. time-space correlations in wind conditions and power output between neighbouring wind farms

- Integrate the improved offshore wind generation modelling in adequacy studies (risk-based approach to evaluate the long-term ability of the power system to cover the load) and assess the impact of the improvement on the adequacy results.


An improved estimation of offshore wind generation leads to more accurate adequacy results. This in turn could help decision-making authorities to define suited investment policies and/or incentives to ensure an adequate provision of electricity for the future.

Thuy-Hai Nguyen
University of Mons 
Academic promoter:
Prof. François Vallée (UMons)
Prof. Emmanuel De Jaeger (UCL)
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