Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over Northern Europe

Abstract

Abstract. As wind energy increases its share of total electricity generation and its integration into the power system becomes more challenging, accurately representing the spatio-temporal variability in wind data becomes crucial. Wind fluctuations impact power and energy systems, e.g., energy system planning, vulnerability to storm shutdowns, and available voltage stability support. To analyze such fluctuations and their spatio-temporal dependencies, time series of wind speeds at hourly time-frequency or higher are needed. We provide a comprehensive evaluation of the global and mesoscale-model derived wind time series against observations by using a set of metrics that we present as requirements for wind energy integration studies. We also perform a sensitivity analysis to find the best model setup of the Weather Research and Forecasting (WRF) model, focusing on evaluating the wind speed fluctuation metrics. The results show that using higher spatial resolution in the WRF model simulations improves the representation of temporal fluctuations; however, higher-resolution simulations often lower the correlations of wind time series with measurements. We also show that the nesting strategy is an important consideration, and a smoother transition from the forcing data to the nested domains improves the correlations with measurements. All mesoscale model simulations overestimate the value of the spatial correlations in wind speed with respect to their observed values. Still, the spatial correlations and the wind speed distributions are insensitive to the model configuration tested in this study.

Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over northern Europe

“…The code modifications, namelists, and table files we used are available from the NEWA GitHub repository (https://doi.org/10.5281/zenodo.3709088; Hahmann et al, 2020a). The WRF model namelists and geofiles used in the experiments described in this paper are available at the DTU Data webpage (https://doi.org/10.11583/ DTU.19375490.v1;Luzia, 2022). The code used in the calculation of EMD metric is available from https://pypi.org/project/pyemd/ (Pele and Werman, 2009).…”