Designing a sector-coupled European energy system robust to 60 years of historical weather data

TL;DR

This study develops a robust European energy system design that accounts for 62 years of weather variability, demonstrating how to optimize infrastructure for resilience and cost-effectiveness amidst climate uncertainties.

Contribution

The paper introduces a method to optimize and evaluate energy infrastructure across multiple weather years, highlighting the importance of compound weather events for robustness.

Findings

Weather variability causes ±10% system cost fluctuation.

Designs including compound weather years improve robustness and cost-effectiveness.

Backup generation marginally increases CO2 emissions, maintaining low levels.

Abstract

As energy systems transform to rely on renewable energy and electrification, they encounter stronger year-to-year variability in energy supply and demand. However, most infrastructure planning is based on a single weather year, resulting in a lack of robustness. In this paper, we optimize energy infrastructure for a European energy system designed for net-zero CO$_2$ emissions in 62 different weather years. Subsequently, we fix the capacity layouts and simulate their operation in every weather year, to evaluate resource adequacy and CO$_2$ emissions abatement. We show that interannual weather variability causes variation of $\pm$10\% in total system cost. The most expensive capacity layout obtains the lowest net CO$_2$ emissions but not the highest resource adequacy. Instead, capacity layouts designed with years including compound weather events result in a more robust and cost-effective design. Deploying CO$_2$-emitting backup generation is a cost-effective robustness measure, which only increase CO$_2$ emissions marginally as the average CO$_2$ emissions remain less than 1\% of 1990 levels. Our findings highlight how extreme weather years drive investments in robustness measures, making them compatible with all weather conditions within six decades of historical weather data.