||Abstract Long-term energy systems often simplify temporal detail resolution. However, this simplification is not appropriate for systems with an increasing share of intermittent renewable energy sources (IRES). The low temporal resolution causes a systemic bias in the model’s outputs. The model overpredicts the share of the IRES on the total generation. This entails an underprediction of the total system costs, underprediction of the amount of greenhouse gas emissions and a bias in the energy mix. This could lead to an ill-informed energy policy and eventually cause a failure in the decarbonization of the energy sector. This thesis increases the temporal resolution of the TIMES-CZ model from 12 seasonal times slices and 3 daynite time slices to 24 daynite time slices and compares 8 scenarios with 1, 4 and 12 seasonal time slices. This thesis compares three representative days selection methods – simple heuristics, hierarchical clustering centroid selection method and historical day closest to the centroid selection method. The electricity generation of IRES and the total production in base year (2012) is the most accurately approximated by the 12 seasonal time slices selected by the hierarchical clustering centroid method.