Contributors: A. Troccoli (WEMC), L. Sanger (WEMC), C. Goodess (WEMC), J. Ogonji (WEMC), L. Dubus (WEMC), R. Vautard F Pons (CEA), X. Jin (CEA), G. Levavasseur (CEA), R. Legrand (MF), L. Grigis (MF), S. Martinoni-Lapierre (MF), C. Viel (MF), S. Parey (EDF), B. Oueslati (EDF), Y-M. Saint-Drenan (ASSOCIATION POUR LA RECHERCHE ET LE DEVELOPPEMENT DES METHODES ET PROCESSUS INDUSTRIELS, ARMINES, FRANCE), J. Mendes (MO), J. Osborne (MO), G.Guentchev (MO)
1. General
1.1. Description
Datasets of Wind Power generation are computed through a simple physical model. Wind Power generation is available as two components:
- Wind Power Onshore (WON)
- Wind Power Offshore (WOF)
Wind Power capacity factor (CFR), defined as the ratio of actual generation over installed capacity, is calculated at grid point level, considering one single wind turbine type for onshore wind, and one for offshore wind. It is assumed that one turbine is located at each grid point, the turbine type depending only on the grid point type (land or ocean). All turbines are assumed to have a hub height of 100 m. This was not meant to faithfully represent wind farms locations and characteristics, but rather to illustrate the use of C3S indicators. As a consequence, the estimated capacity factors are generally overestimated compared to observed ones as i) the real turbines installed have various characteristics, including lower hub height, lower installed capacity, etc. and ii) our estimation does not take into account turbines' unavailability for maintenance or failures.
WON and WOF are available for three streams:
Historical: This dataset is based on the ERA5 (for GHI and 2 m air temperature) at 0.25° resolution. Averages for NUTS0 (country) and NUTS2 (sub-country) areas are also available. |
Seasonal Forecasts: This dataset is currently available for ECMWF System 5, Météo-France ARPEGE System 7 and MetOffice GloSEA5 System 14. Seasonal Forecasts are retrieved from the CDS and are subsequently bias adjusted via quantile mapping. The forecast datasets, 1° resolution, are updated monthly when the raw forecast of each system is available. Averages for NUTS0 (country) are also provided. |
Projections: This dataset is available for eleven regional climate model (RCM)/global climate model (GCM) combinations for the EURO-CORDEX project (Table 1) and for two Representative Concentration Pathways (RCP4.5 and RCP8.5). Historical forcing is used from 1979-2005, and RCP forcing from 2006-2100. The original EURO-CORDEX simulations of the needed input variables are retrieved from the producers and are subsequently bias adjusted by applying the Cumulative Distribution Function transform (CDFt) method. Further processing involved interpolation to a standard 0.25° latitude/longitude grid, and averages for NUTS0 (country) and NUTS2 (sub-country) areas are also available. |
1.2. Units
WON and WOF as capacity factor: unitless, labelled as CFR in the output files. WON and WOF as energy: MWh, labelled as NRG in the output files.
WON and WOF as mean power: MW, labelled as PWR in the output files.
1.3. Links
Data will eventually be available via the C3S Energy/CDS demonstrator.
1.4. Data format
Gridded data (0.25° for HIST and PROJ, 1° for SEAS): NetCDF NUTS0, NUTS2, MAR0 and MAR1 averages: CSV
1.5. Keywords
Wind Power Onshore (WON) and Wind Power Offshore (WOF).
Wind Power, Onshore, Offshore, Capacity Factor, Power, Energy, ENTSO-E, Wind Turbine.
1.6. Contact
Please raise a ticket through the ECMWF Support Portal (ECMWF login required).
2. Dataset coverage
2.1. Geographic area
C3S Energy domain is: 26.5° N to 72.5° N by 22.0° W to 45.5° E.
2.2. Temporal resolution
Historical: hourly for gridded data. Averages for daily, monthly, seasonal (DJF, MAM, JJA, SON) and annual as NUTS0, NUTS2, MAR0 and MAR1. |
Seasonal Forecasts: 6-hour time step (gridded data). Averages for monthly, seasonal (DJF, MAM, JJA, SON) and annual are also computed for NUTS0 and MAR0 mean values. |
Projections: 3-hour time step (gridded data). Daily NUTS0, NUTS2, MAR0 and MAR1 averages are also computed. |
2.3. Time period
Historical: 1979 to present (ca. 1 month behind real time); updated every month based on ERA5T data availability. | ||||
Seasonal Forecasts: The hindcasts cover the period 1993-2016 for the three models used by C3S Energy: ECMWF Sys5, Météo-France Sys7 and UK Met Office Sys14. ECMWF has all the month, MTFR SY07 currently covers October to April with the rest of the months to be completed by mid- May 2020 as this is a new system. METO SY14 ranges from June to April (notice that METO SY14 is a rolling release model, hindcast have to be computed each months). The forecasts updated have been run every month from the first month of availability in the current version on the CDS: | ||||
Forecast availability | Forecast horizon | |||
ECMWF Sys5 | From January 2017 | 215 days, or 860 six-hourly time steps | ||
MTFR Sys7 | From October 2019 | 211 days, or 844 six-hourly time steps | ||
METO Sys14 | From June 2019 | 215 days, or 860 six-hourly time steps | ||
Projections: 1980 to 2098 (common period), with historical forcing to 2005, then RCP forcing. | ||||
2.4. Spatial resolution
Historical: 0.25° by 0.25° latitude/longitude, NUTS0 and NUTS2 (onshore), MAR0 and MAR1 (offshore). |
Seasonal Forecasts: 1° by 1° latitude/longitude and at country scale. The underlying forecast systems run at different resolutions. Data are also available as averages for more than 30 European countries as NUTS0 areas (onshore) and MAR0 (offshore). |
Projections: 0.25° by 0.25° latitude/longitude, NUTS0 and NUTS2 (onshore), MAR0 and MAR1 (offshore) |
3. Lineage statement
3.1. Original data source
Energy data: Wind turbines power curves: Wind Power database (commercial) as a broad reference for typical wind turbine; ENTSO-E Power Statistics.
Historical: ERA5 |
Seasonal Forecasts:
|
Projections: original data come from CORDEX simulations available via the ESGF portal. |
3.2. Tools used in the production of indicators
R Studio Version 1.1.463 – © 2009-2018 RStudio, Inc. R version 3.6.1 (2019-07-05)
R packages (latest versions): chron, corrr, easyNCDF, fields, glue, lubridate, maptools, ncdf4, raster, readxl, rgdal, rgeos, seas, sf, shapefiles, sp, tidyverse
NCO netCDF Operators version 4.7.5: ncks
4. Data Quality
The Wind Power CFR data reconstruction quality has been assessed for each individual country over the historical period, using ENTSO-E and some other Transmission System Operators data. Due to the assumptions made, it is not expected that the Wind Power CFR closely resembles the observed one, but its temporal evolution is realistic.
5. Appendix
Table 1: List of the 11 EURO-CORDEX simulations and the institutes that provided the data.
Short name | Driving GCM | RCM | Contact Institute | Period |
HIIC | ECEARTH | HIRAM | Danish Meteorological Institute (DMI) | 1951 - 2100 |
RAIC | ECEARTH | RACMO | Royal Netherlands National Meteorological Institute (KNMI) | 1950 - 2100 |
RCIC | ECEARTH | RCA | Swedish Meteorological and Hydrological Institute (SMHI) | 1970 - 2100 |
RAMO | HADGEM | RACMO | KNMI | 1951 - 2098 |
RCMO | HADGEM | RCA | SMHI | 1970 - 2098 |
ReMO | HADGEM | REGCM | International Centre for Theoretical Physics (ICTP) | 1971 - 2099 |
WRIP | IPSL | WRF 381P | Institute Pierre Simon Laplace (IPSL) | 1951 - 2100 |
RCMP | MPI | RCA | SMHI | 1970 - 2100 |
CCMP | MPI | CCLM | Climate Limited-area Modelling Community | 1950 - 2100 |
HINC | NORESM | HIRAM | Danish Meteorological Institute (DMI) | 1951 - 2100 |
ALCN | CNRM | ALADIN63 | CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France) | 1952 - 2100 |
