Uncertainty in Offshore Wind Power Forecasts: A Regional Climate Modeling Approach for the North Sea

Abstract. With the transition towards green energies gaining momentum, the expansion of wind farm areas and associated technologies is growing faster. The North Seas Energy Cooperation group has set an ambitious target to increase the offshore wind-generated power capacity from 26 GW in 2022 to 300 GW by 2050 in the geographical areas of the North Seas. With this goal, an extensive offshore infrastructure is planned to be deployed in the region. Studies have been carried out to assess the power production of such future development. However, the uncertainty of such assessments has not been fully addressed. Wake effects have been identified as the primary source of power losses. They are often studied within individual wind farms or small clusters, but the dynamics of large wind farm clusters at a regional scale are only beginning to be explored. In this study, we address uncertainties of power output derived from projected wind farm areas at the North Sea in scenarios that encompass different turbine setups and atmospheric conditions. To achieve this, we used COSMO6.0-CLM, the newest version of the regional climate model COSMO-CLM, and further improved the existing wind farm module to extend the model's capability to design more flexible and realistic scenarios. This allows us to quantify impacts from different factors that contribute to power output uncertainties. Our results show that wake dynamics resulting from different turbine density distributions can account for up to 5 % of the variability of the generated power, while wind regimes at different hub heights contribute an additional 2 %. Approximately 6 % of the variability is attributed to discrepancies in atmospheric circulation states inherent to the reanalysis datasets used to force the simulations. The total uncertainty in power output accounts for 13 %. In a scenario with an installed capacity of 150 GW the total power output would range from 58 to 74 GW, corresponding to an uncertainty of 20 GW. Since economic and environmental studies rely on such scenarios, it is crucial to consider these uncertainties.