Quantifying Capacity Adequacy in Energy System Modelling Through Stochastic Optimization

Energy system optimization models (ESOMs) can be helpful tools to determine the optimal structure of future energy systems. They usually optimize the expansion and dispatch of the energy system’s components through a minimization of total system costs. The obtained energy systems are designed to cover the energy demand for the specific assumptions made within the underlying scenarios. However, if such energy systems are exposed to slight deviations, such as a lower availability of wind energy, situations of uncovered demand may occur. The uncertainties in the scenario assumptions can be indirectly captured via excess generation capacities. However, the required amount of these excess capacities is unclear. This study analyzes capacity adequacy by considering uncertainties in a decarbonized German power system through stochastic optimization within an ESOM. Different uncertainties, such as technology investment costs, total annual demand and different weather conditions are considered and their influence on the power system is compared. Therefore, a variety of different assumptions for these uncertainties are extracted from literature and included in the stochastic optimization. As a result, the impact of the uncertainties on the structure of the energy system are identified and the excess capacity needed is estimated.