Risk and sustainability dimensions of renewable energy infrastructure in a changing climate

Wind, solar and all other non-exhaustible energy sources can be highly heterogeneous and/or variable due to rapidly evolving extreme events as our climate changes. Specific regional and local changes in mean environmental conditions, as well as different classes of extreme events (e.g., high- and low-precipitation events, winter storms), are often strongly influenced by evolving global climate, orography, and land use. This project will apply a multi-method approach to modelling of actionable high-resolution (1-10 km) climate hazard and risk information of selected elements of energy infrastructure in the UK and Ireland (e.g., wind farms, hydroelectric power plants).

The research will combine a wide set of remote (e.g., NASA IMERG precipitation [1]) and in-situ observations (e.g., Met Office HadUK-Grid datasets [2]), and reanalysis products [3, 4] with the state-of-the-art multi-model historical simulations and future climate CIMP6 projections [5] - particularly HighResMIP simulations [6]. Novel machine learning methods [7] will enable generation of user-oriented post-processed (i.e., bias corrected and downscaled, e.g., [8]) climate information at regional and local scales. This will allow the determination of optimal locations with favourable environmental conditions for renewable energy production and limited exposure to impactful class of extreme events that could damage specific components of renewable energy infrastructure.

References:

[1] NASA IMERG: https://gpm.nasa.gov/data/imerg [2] UK Met Office HadUK-Grid: https://www.metoffice.gov.uk/research/climate/maps-and-data/data/haduk-grid/haduk-grid

[3] Hersbach, et al., 2020, doi:10.1002/qj.3803

[4] Beck et al., 2022, doi:10.1175/BAMS-D-21-0145.1

[5] Eyring, et al., 2016, doi:10.5194/gmd-9-1937-2016

[6] Haarsma, et al., 2016, doi:10.5194/gmd-9-4185-2016

[7] Hseih, 2023, doi:10.1017/9781107588493

[8] Maraun and Widmann, 2018, doi:10.1017/9781107588783

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