Abstract
This paper presents a validation of atmospheric reanalysis data sets for simulating onshore wind generation time series for large-scale energy system studies. The three reanalyses are the ERA5, the New European Wind Atlas (NEWA) and DTU’s previous generation European-level atmospheric reanalysis (EIWR). An optional scaling is applied to match the microscale mean wind speeds reported in the Global Wind Atlas version 2 (GWA2). This mean wind speed scaling is used to account for the effects of terrain on the wind speed distributions. The European wind power fleet for 2015–2018 is simulated, with commissioning of new wind power plants (WPPs) considered for each year. A generic wake model is implemented to include wake losses that are layout agnostic; the wake model captures the expected wake losses as function of wind speed given the technical characteristics of the WPP. We validate both point measurement wind speeds and generation time-series aggregated at the country-level. Wind measurements from 32 tall meteorological masts are used to validate the wind speed, while power production for four years from twelve European countries is used to validate the simulated country-level power production. Various metrics are used to rank the models according to the variables of interest: descriptive statistics, distributions, daily patterns, auto-correlation and spatial-correlation. We find that NEWA outperforms ERA5 and EIWR for the simulated wind speed, but, as expected, no model is able to fully describe the auto-correlation function of the wind speed at a single point. The mean wind speed scaling is found to be necessary to match the distribution of generation on country-level, with NEWA-GWA2 and ERA5-GWA2 showing highest accuracy and precision for simulating large-scale wind generation time-series.
Highlights
The generation of electricity from renewable sources is a key component of the climate change mitigation plans worldwide
This paper presents a validation of atmospheric reanalysis data sets for simulating onshore wind generation time series for large-scale energy system studies
This paper presents a new optic of the validation of reanalysis data focused on the specific need of large-scale wind generation simulations, and compares multiple weather data sets including microscale downscaling in European level
Summary
The generation of electricity from renewable sources is a key component of the climate change mitigation plans worldwide. For the first time in 2019, the renewable power generation grew faster than the electricity demand [1]. Accurate simulation of wind energy generation time series are needed in energy system design studies, such as the sector coupling and transmission reinforcement designs in Europe [3] and in the North Sea [4], as variability in wind power generation impacts electricity prices, and correlations in generations between countries can impact optimal transmission expansion. Regional aggregated wind generation time series can be obtained using a reanalysis data set in two approaches: (1) a bottomup modeling of the details of each plant [7], i.e. installed capacity, location, wind turbine type, hub height, rotor diameter, etc. Bias correction of either the wind speeds or wind generation is usually needed and can be done without the use of measurements, e.x. using the global wind atlas to correct mean wind speeds [7] or based on calibration of wind speed correction to match generation observations [9]
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