Weathering the Energy Transition
With the global race to achieve net-zero carbon emissions intensifying, energy systems face mounting pressure to integrate renewable sources while coping with the uncertainties of climate change. A recent study published in Nature by Iacopo Savelli, postdoctoral researcher at Bocconi’s GREEN research center, Enrico Antonini, Laurent Drouet, Alice Di Bella and Massimo Tavoni (all of them of the CMCC Foundation and the last two also of the Politecnico di Milano) addresses these challenges by creating a comprehensive dataset of weather- and climate-driven power supply and demand across Europe from 1940 to 2100. This open-access database not only includes wind and solar power generation but also covers hydropower inflow, heating, and cooling demands. “Our contribution offers an internally consistent modeling framework that spans both historical data and future projections,” Iacopo Savelli explains.
Modeling the climate-energy relationship
As renewable energy reliance grows, power grids become increasingly susceptible to weather variability and climate shifts. Wind and solar power are known for their intermittency, which can disrupt supply at critical times. Hydropower, though more reliable, is also sensitive to changes in precipitation patterns and river flows. The researchers stress that “temperature fluctuations and extreme events will significantly alter energy demand,” particularly as heating and cooling become more electrified.
To meet these complexities head-on, the researchers used the ERA5 reanalysis data and EURO-CORDEX climate models to generate time series at an hourly resolution. By blending historical meteorological information with various climate scenarios, the dataset enables detailed country-level analysis across Europe.
From wind turbines to thermostats
The research highlights five key energy variables: wind and solar generation, hydropower inflow, and heating and cooling demand. For wind energy, the team used specific turbine models. Solar power estimates incorporated solar irradiance, panel efficiency, and terrain features. Hydropower inflow was calculated by linking basin runoff data with the locations of existing hydro plants, while heating and cooling demand were modeled using population-weighted temperature data and established metrics like heating and cooling degree days.
As Iacopo Savelli puts it, “Our dataset’s advantage lies in its capacity to analyze both supply and demand in a unified framework, enabling a holistic approach to energy planning.”
A tool for energy planners and policy makers
Energy planners face tough decisions: how to design grids that can withstand extreme weather, balance supply with variable demand, and reduce carbon footprints without compromising reliability. This dataset, covering a span of 160 years, offers a critical tool to tackle these challenges. Whether used for short-term forecasting or long-term policy simulations, it allows stakeholders to explore a wide array of scenarios.
One notable application of the dataset is the decade-by-decade analysis of renewable energy performance. For example, the researchers found that while onshore wind capacity shows little variation under current conditions, future climate projections indicate potential declines across much of Europe, particularly under high-emission scenarios. Solar photovoltaic performance trends more positively under moderate emission scenarios but becomes less reliable as emissions rise.
Toward resilient energy systems
The dataset is available on the Zenodo platform, ensuring transparency and broad access for further research. As Iacopo Savelli puts it, "By providing this comprehensive time series, we aim to support robust energy system designs that can withstand a wide range of climate uncertainties." Whether by refining renewable siting strategies, optimizing grid storage solutions, or improving demand response technologies, the potential applications are vast.
