Impacts of spatial and temporal resolutions on the near-optimal spaces of energy system optimisation models

Over the past years, the rising penetration of renewable energy in power systems has led to the need for more detailed energy system models. Specifically, spatial and temporal resolutions have become increasingly important, and multiple studies have investigated their impact on the optimal solutions to energy system optimisation problems. However, these studies have yet to be conducted for near-optimal solutions, which can
provide valuable insights to decision-makers. This paper aims to initiate this research by examining the effects of spatial and temporal resolutions on the values of necessary conditions for near-optimality. In particular, we investigate how spatiotemporal resolution changes affect minimal capacity investments in the European electricity grid. Our analysis leads to three key observations. Firstly, we show that minimal capacities for near-optimality exhibit similar trends to optimal capacities when each resolution varies. Secondly, the resolutions that result in higher optimal capacities are also the ones where minimal capacities deviate the least from the optimal capacities. Thirdly, as a result of the second observation, spatial or temporal resolution changes have a greater impact on minimal capacities for near-optimality than on optimal capacities. We conclude by suggesting solutions to expand this research track and gain a deeper understanding of
the impact of spatiotemporal resolution on near-optimal spaces.