Binks Institute for Sustainability: Risk and sustainability dimensions of renewable energy infrastructure in the UK and Ireland in a changing climate

The transition from the primary reliance on exhaustible fossil fuel energy sources to renewable energy sources - decarbonisation of energy production - is an essential element of the government pledge to achieve Net Zero by 2050. In essence, renewable energy installations and associated infrastructure are crucial for sustainable development of our society. However, wind, solar and all other non-exhaustible energy sources can be highly heterogeneous (in space) and/or variable (in time) with rapidly evolving extreme events (tails of distributions) as our climate changes. More specifically, heatwaves, floods, droughts, etc. are becoming more intense and/or likely in many parts of the world (Seneviratne, et al., 2021, doi:10.1017/9781009157896.013). The World Meteorological Organisation has reported that extreme weather disasters have increased over past 50 years causing global damages on average US$202 millions per day. In Europe, floods (38%) and storms (32%) were the most prevalent cause in estimated total of US$476.5 billion in economic damages from 1970–2019 (WMO-No. 1267, https://library.wmo.int/idurl/4/57564 ) 

Specific regional and local changes in the mean environmental conditions as well as different classes of extreme events are often strongly influenced by evolving global climate, orography, and land use. This project will apply 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, etc.). Their function strongly depends on multi-dimensional environmental conditions, and they can be adversely impacted by various extreme events (e.g., high- and low-precipitation events, winter storms, etc.). 

We will combine a wide set of remote (e.g., NASA IMERG precipitation, https://gpm.nasa.gov/data/imerg) and in-situ observations (e.g., Met Office HadUK-Grid datasets) and reanalysis products (e.g., Hersbach, et al., 2020, doi:10.1002/qj.3803; Beck et al., 2022, doi:10.1175/BAMS-D-21-0145.1) with the state-of-the-art multi-model historical simulations and future climate CIMP6 projections (e.g., Eyring, et al., 2016, doi:10.5194/gmd-9-1937-2016) - particularly HighResMIP simulations (e.g., Haarsma, et al., 2016, doi:10.5194/gmd-9-4185-2016). Novel machine learning methods (e.g., Hseih, 2023, doi:10.1017/9781107588493) will enable generate of user-oriented post-processed (i.e., bias corrected and downscaled, e.g., Maraun and Widmann, 2018, doi:10.1017/9781107588783) climate information at regional and local scales. This will allow us to determine 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. 

Such robust probabilistic information on weather conditions in a changing climate is not yet available at such high-resolution and at longer time horizons in the 21st century. We will be in position to merge new climate information with geographic information system (GIS) mapping of selected elements of the current and planned energy infrastructure for user-oriented risk assessment (e.g., SSE Tools and Maps Portal, UK Power Networks Open Data Portal, etc.). Moreover, in potential collaboration with SSE and some other stakeholders, we will be in position to utilise combined climate-energy system data for strengthening of infrastructure resilience and adaptation planning that will benefit the sustainable development of the UK and Ireland’s energy production and distribution. 

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