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

Offshore wind energy is an essential building block for the large-scale energy transition, but it is by nature intermittent and uncertain. Future power systems with a high share of offshore wind energy may therefore face new challenges to ensure a reliable electrical supply. One important aspect of reliability is the adequacy, defined as the long-term ability of the power system to cover its load in steady-state conditions. The increase of offshore wind power capacity demands the inclusion of fast and accurate wind farm models into power systems adequacy assessment. The recent developments in Machine Learning techniques, able to capture the complex characteristics of wind generation, can be exploited to produce such models. Therefore, this work is aimed at developing data-driven models to improve the way offshore wind parks are considered within the current adequacy tools. These models are then included in an adequacy study built on sequential Monte-Carlo simulations and the impact of the improved offshore modelling on adequacy results is evaluated.

Speaker : Thuy-hai NGUYEN, UMONS