Improving wind power forecasts in the Belgian North Sea with a wind farm parameterization and a neural network

Abstract. In order to forecast the impact of meteorological events, such as large wind storms, on the Belgian offshore wind energy production and mitigate its impact on the high-voltage electricity grid, the Royal Meteorological Institute of Belgium (RMI) has in the past developed a dedicated storm forecast tool for Elia, the Belgian transmission system operator (TSO). The storm forecast tool, which has been operational since November 2018, provides 15 min wind speed and wind power forecasts for each wind farm in the Belgian offshore wind energy zone (BOZ), together with cut-out probabilities and uncertainty quantification, by combining the RMI high-resolution (4 km) ALARO model with the ENS ensemble forecasts of the European Centre for Medium Range Weather Forecasting (ECMWF). Since the completion of the first Belgian offshore wind energy zone in 2020, for an installed capacity of 2.26 GW, a significant amount of wind energy is now available in the Belgian part of the North Sea. There are considerable wake losses in the BOZ, as all wind farms lie close together in a narrow band, and each wind farm has a high density, in terms of number of turbines, and/or installed power per area. Moreover, the adjacent Dutch Borssele Wind Farm Zone, completed in 2021, can also significantly influence the BOZ (and vice versa). We report on two approaches to improve RMI's offshore wind power forecasts, and in particular to take into account wake losses. First the Fitch et al. wind farm parameterization (WFP) was implemented in the ALARO model, based on an earlier implementation by KNMI into HARMONIE-AROME. Both these models are being developed in the ACCORD consortium, and use the same dynamical core to some extent, with IFS/ARPEGE global codes as basis, but differ greatly in the different physics parameterizations used, and the physics-dynamics coupling (tendencies vs fluxes). Secondly, we investigated using an artificial neural network trained on Elia wind power production data and NWP forecasts. Verification of the improved wind and power forecasts is based on lidar data at an anonymous wind farm, and power data from Elia. Each method is found to improve forecast accuracy and able to capture certain wake effects in the BOZ. A combination of both methods gives the best results on average, and leads to competitive forecast scores.