Incorporating wake effects in Belgian offshore wind and power forecasts

The Royal Meteorological Institute of Belgium (RMI) has been delivering offshore wind and power forecasts to Elia, the Belgian transmission system operator for high-voltage electricity, as part of a dedicated storm forecast tool, in an operational setting since November 2018. With an installed capacity of 2.26 GW fully completed by the end of 2020, the Belgian offshore zone (BOZ) is one of the highest density wind energy zones in the world. Each Belgian wind farm has a relatively high number of turbines and/or installed power per area. Moreover, due to lack of space in the Belgian North Sea, all Belgian wind farms lie close together in a narrow band, with the Dutch Borssele wind farm zone nearby. There is thus a considerable impact of intra-farm and inter-farm wakes on both power production and mesoscale wind. 

In order to improve offshore wind and power forecasts in the Belgian North Sea, the BeFORECAST research project was funded by the Energy Transition Funds of the Belgian federal government, from 01 November 2022 until 31 October 2025. The project was coordinated by the von Karman Institute for Fluid Dynamics (VKI), in a consortium with KU Leuven, the Royal Meteorological Institute of Belgium (RMI), SABCA, 3E and Vrije Universiteit Brussel (VUB).

We give an overview of RMI's main results in the BeFORECAST project over the past 3 years. In particular a wind farm parameterization was implemented in RMI's operational weather model ALARO, and an artificial neural network for power forecasting was trained on power production data and NWP forecasts. Both wind and power forecasts were further compared with VKI's mesoscale WRF model, and against real-world observations from turbine SCADA, lidar and power production data. The influence of the planned second Belgian offshore zone, the future Princess Elisabeth zone (PEZ), on the BOZ production was also studied. Additionally, 3DVar assimilation of Doppler radar radial wind (VRAD) in ALARO was tested, with very promising results on offshore wind speed forecasts, showing a positive impact up to 24 hours in forecast lead time. Finally, two methods for postprocessing wind speed NWP forecasts using historical lidar and SCADA data were investigated. We developed a neural network for postprocessing of deterministic ALARO forecasts, and a modified member-by-member approach with special emphasis on storm events for the ensemble forecasts of ECMWF.