Comparing different methods which improve security of supply in a decarbonized European electricity system under climate uncertainty

To limit negative effects of climate change, decarbonizing energy systems is essential. However, climate change itself will impact energy systems by influencing both demand and supply. To ensure security of supply, climate change uncertainty must be included in energy system optimization. Therefore, in this study, climate data from six climate models, three human greenhouse gas emission scenarios and three years is used to plan a decarbonized European electricity system. Thereby, three methods assessing climate uncertainty are compared: analysis of single climate years, a time clustering algorithm with a focus on maintaining high security of supply, and a decomposition approach which divides the optimization problem into a top-level and several sub-problems and solves them iteratively. While single year analysis results in high levels of lost load, the time clustering algorithm and the decomposition approach achieve higher security of supply, reducing lost load by at least 94%. The decomposition approach more reliably abates lost load and achieves lower abatement costs than the time clustering algorithm but requires more computational resources. Compared to single year analysis, lost load abatement costs for both advanced methods remain below values of lost load reported in the literature, leading to the recommendation to researchers and policy makers to use advanced methods for assessing climate uncertainty in long term energy system planning as security of supply can be increased with reasonable costs. Furthermore, all methods agree on 1990 GW of invested capacities showing that the general design of a decarbonized European electricity system is independent from climate uncertainty.