Methods for assessing climate uncertainty in energy system models — A systematic literature review

Abstract

Due to anthropological climate change, climate scientists project a significant change in climate in the coming years. Despite the advances in climate modeling, future climate is still subject to large uncertainties. To mitigate greenhouse gas emissions and slow down climate change, the energy system must undergo a fundamental transition. Thereby, energy system modeling tools are used to optimize the design of future energy systems. As many components of an energy system depend on climate variables, changing climate and its uncertainties influence the outcome of energy system models. Energy systems with high shares of renewable energies are thereby especially vulnerable to changing climate conditions. To find how the changing climate and its uncertainties are assessed in energy system modeling, a systematic literature review was conducted. Thereby we address three major issues: First, we depict the most common techniques to convert climate data into energy system input data. As large amounts of climate data are necessary to depict future climate developments, we secondly review data reduction techniques which can be used to restrain computational time. Around 46% of the studies in this review use those techniques with scenario building being the most common one. Finally, we review methods to explicitly assess climate uncertainty in energy system models. The majority of energy system optimization models searches for individual solutions for the scenarios, while only 22% use methods that include the uncertainty explicitly in the optimization process. We see the need for further research especially in the use of more advanced optimization methods and the combination of those with well known data reduction techniques. The review provides a wide synopsis of different methods for the three major issues and can therefore enable energy system modelers to choose the right methods for the specific challenges.