Most existing energy system models rely on input data available at country-level, or at the level of administrative divisions. This is due to the difficulty of obtaining data in higher resolutions, and the way energy policies are implemented. However, there is usually no correlation between the distribution of data such as solar radiation, wind speed, and electrical load on one hand, and the administrative divisions on the other hand. The goal of the research is to measure the impact of the spatial resolution on the results of system optimization models. A novel clustering methodology for high-resolution data is presented and applied to define new geographical regions for an energy system model, which optimizes expansion planning and unit commitment. The new model regions take into account the bottlenecks in the transmission system and their effect on the expansion of renewable energy sources. We compare the obtained total costs and curtailment levels against models using administrative divisions. The results highlight the importance of the distribution of load, wind and solar resources on energy system models. Possible applications of the new model region definitions are discussed to emphasize their utility for modelers and policy-makers.     «

Most existing energy system models rely on input data available at country-level, or at the level of administrative divisions. This is due to the difficulty of obtaining data in higher resolutions, and the way energy policies are implemented. However, there is usually no correlation between the distribution of data such as solar radiation, wind speed, and electrical load on one hand, and the administrative divisions on the other hand. The goal of the research is to measure the impact of the spat...     »