Reconstructions, forecasts and climate projections of electricity demand and renewable generation
Over the last several years, we have developed many different “meteorologically-based” datasets of nationally-aggregated demand, wind power and solar PV generation over Europe at sub-daily frequency. These now span the recent historic period (back to ~1950), future climate projections (up to ~2050) and archived sub-seasonal forecasts (multi-week ahead ensemble re-forecasts over the period 1996-2015).
Many of these datasets are freely available for use (click on the DOI links below for information/access):
- Bloomfield, Hannah and Brayshaw, David (2021): Future climate projections of surface weather variables, wind power, and solar power capacity factors across North-West Europe. University of Reading. Dataset. https://researchdata.reading.ac.uk/id/eprint/331
- Bloomfield, Hannah and Brayshaw, David (2021): ERA5 derived time series of European aggregated surface weather variables, wind power, and solar power capacity factors: hourly data from 1950-2020.
University of Reading. Dataset. https://researchdata.reading.ac.uk/id/eprint/321 - Gonzalez, Paula, Bloomfield, Hannah, Brayshaw, David and Charlton-Perez, Andrew (2020): Sub-seasonal forecasts of European electricity demand, wind power and solar power generation. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.275
- Bloomfield, Hannah, Brayshaw, David and Charlton-Perez, Andrew (2020): ERA5 derived time series of European country-aggregate electricity demand, wind power generation and solar power generation: hourly data from 1979-2019. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.275
- Bloomfield, Hannah, Brayshaw, David and Charlton-Perez, Andrew (2020): MERRA2 derived time series of European country-aggregate electricity demand, wind power generation and solar power generation. University of Reading. Dataset.http://dx.doi.org/10.17864/1947.239
- Bloomfield, Hannah, Brayshaw, David and Charlton-Perez, Andrew (2019): ERA5 derived time series of European country-aggregate electricity demand, wind power generation and solar power generation. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.227
- Drew, Daniel, Bloomfield, Hannah, Coker, Phil, Barlow, Janet and Brayshaw, David (2019): MERRA derived hourly time series of GB-aggregated wind power, solar power and demand. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.191
- Drew, Daniel, Brayshaw, David, Barlow, Janet and Coker, Phil (2016): An hourly time series of GB-aggregated wind power generation from 1980-2013, based on a future distribution of wind farms with a high level of offshore capacity. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.75
An earlier dataset reconstructing GB-aggregated wind power generation is available here and a brief introductory description is provided here. This dataset corresponds to that used in Cannon et al (2015), Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain. Renewable Energy, 75. pp. 767-778.
GB telecommunications infrastructure electricity load
Fallon et al (2023) present a new framework for using weather‐sensitive surplus power reserves in critical infrastructure based on synthesised load data for BT in the UK derived from the MERRA2 reanalysis. The data used in that paper are available here.
Efficient sampling of climate uncertainty for power systems modelling: tools, code and examples
Hilbers et al (2019) present a novel “importance subsampling” method to incorporate large-volumes of climate data into complex and computationally expensive power system models (e.g., Unit Commitment / Economic Dispatch, Generation and Transmission Expansion Planning). The data and tools used in that paper are available here.
Extensions to the methodology can be found in Hilbers et al (2020), and a set of simple “test case” and “tutorial” model can be found here.
Additional tools for exploring sampling uncertainty using computationally efficient “fast” bootstrap methods were developed in Hilbers et al (2021), with code/data/model available here.
Quasi-stationary wave climatology
Large-scale quasi-stationary atmospheric waves (QSWs) have long been known to be associated with weather extremes in the midlatitude, such as heatwaves (e.g., Europe, 2003). The NERC ODYSEA project produced a QSW climatology, as described in Wolf et al 2018 and Wolf et al 2020. The climatology dataset is available for download via the CEDA archive here.