%0 Journal Article %1 GonzalezAparicio2017Simulating %A González-Aparicio, I. %A Monforti, F. %A Volker, P. %A Zucker, A. %A Careri, F. %A Huld, T. %A Badger, J. %D 2017 %J Applied Energy %K wind climatology energy renewables downscaling %P 155--168 %R 10.1016/j.apenergy.2017.04.066 %T Simulating European wind power generation applying statistical downscaling to reanalysis data %U http://dx.doi.org/10.1016/j.apenergy.2017.04.066 %V 199 %X Wind speed spatial resolution highly influences calculated wind power peaks and ramps. Reduction of wind power generation uncertainties using statistical downscaling. Publicly available dataset of wind power generation hourly time series at NUTS2. The growing share of electricity production from solar and mainly wind resources constantly increases the stochastic nature of the power system. Modelling the high share of renewable energy sources – and in particular wind power – crucially depends on the adequate representation of the intermittency and characteristics of the wind resource which is related to the accuracy of the approach in converting wind speed data into power values. One of the main factors contributing to the uncertainty in these conversion methods is the selection of the spatial resolution. Although numerical weather prediction models can simulate wind speeds at higher spatial resolution (up to 1 × 1 km) than a reanalysis (generally, ranging from about 25 km to 70 km), they require high computational resources and massive storage systems: therefore, the most common alternative is to use the reanalysis data. However, local wind features could not be captured by the use of a reanalysis technique and could be translated into misinterpretations of the wind power peaks, ramping capacities, the behaviour of power prices, as well as bidding strategies for the electricity market. This study contributes to the understanding what is captured by different wind speeds spatial resolution datasets, the importance of using high resolution data for the conversion into power and the implications in power system analyses. It is proposed a methodology to increase the spatial resolution from a reanalysis. This study presents an open access renewable generation time series dataset for the EU-28 and neighbouring countries at hourly intervals and at different geographical aggregation levels (country, bidding zone and administrative territorial unit), for a 30 year period taking into account the wind generating fleet at the end of 2015.

@article{GonzalezAparicio2017Simulating, abstract = { Wind speed spatial resolution highly influences calculated wind power peaks and ramps. Reduction of wind power generation uncertainties using statistical downscaling. Publicly available dataset of wind power generation hourly time series at NUTS2. The growing share of electricity production from solar and mainly wind resources constantly increases the stochastic nature of the power system. Modelling the high share of renewable energy sources – and in particular wind power – crucially depends on the adequate representation of the intermittency and characteristics of the wind resource which is related to the accuracy of the approach in converting wind speed data into power values. One of the main factors contributing to the uncertainty in these conversion methods is the selection of the spatial resolution. Although numerical weather prediction models can simulate wind speeds at higher spatial resolution (up to 1 × 1 km) than a reanalysis (generally, ranging from about 25 km to 70 km), they require high computational resources and massive storage systems: therefore, the most common alternative is to use the reanalysis data. However, local wind features could not be captured by the use of a reanalysis technique and could be translated into misinterpretations of the wind power peaks, ramping capacities, the behaviour of power prices, as well as bidding strategies for the electricity market. This study contributes to the understanding what is captured by different wind speeds spatial resolution datasets, the importance of using high resolution data for the conversion into power and the implications in power system analyses. It is proposed a methodology to increase the spatial resolution from a reanalysis. This study presents an open access renewable generation time series dataset for the EU-28 and neighbouring countries at hourly intervals and at different geographical aggregation levels (country, bidding zone and administrative territorial unit), for a 30 year period taking into account the wind generating fleet at the end of 2015.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Gonz\'{a}lez-Aparicio, I. and Monforti, F. and Volker, P. and Zucker, A. and Careri, F. and Huld, T. and Badger, J.}, biburl = {https://www.bibsonomy.org/bibtex/27a33e293550cb9cc1b874af72cccafa3/pbett}, citeulike-article-id = {14355006}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.apenergy.2017.04.066}, doi = {10.1016/j.apenergy.2017.04.066}, interhash = {55d8fd180336f70881172e972a03ecdb}, intrahash = {7a33e293550cb9cc1b874af72cccafa3}, issn = {03062619}, journal = {Applied Energy}, keywords = {wind climatology energy renewables downscaling}, month = aug, pages = {155--168}, posted-at = {2017-05-10 15:39:41}, priority = {2}, timestamp = {2018-06-22T18:36:55.000+0200}, title = {Simulating European wind power generation applying statistical downscaling to reanalysis data}, url = {http://dx.doi.org/10.1016/j.apenergy.2017.04.066}, volume = 199, year = 2017 }