Traditionally, modeling investment and dispatch problems in electricity economics has been limited by computation power. Due to this limitation, simplifications are applied. One common practice, for example, is to reduce the temporal resolution of the dispatch by clustering similar load levels. The increase of intermittent electricity from renewable energy sources (RES-E) changes the validity of this assumption. RES-E already cover a certain amount of the total demand. This leaves an increasingly volatile residual demand to be matched by the conventional power market. This paper quantifies differences in investment decisions by applying three different time-resolution residual load patterns in an investment and dispatch power system model. The model optimizes investment decisions in five year steps between today and 2030 with residual load levels for 8760, 288 and 16 time slices per year. The market under consideration is the four zone ERCOT market in Texas. The results show that investment decisions significantly differ across the three scenarios. In particular, investments into base-load technologies are substantially reduced in the high resolution scenario (8760 residual load levels) relative to the scenarios with lower temporal resolution. Additionally, the amount of RES-E curtailment and the market value of RES-E exhibit noteworthy differences.