Spatial concentration of renewables in energy system optimization models

Energy system models with high resolutions for time and space can contribute to establishing technically feasible and economically viable energy system designs with high shares of renewable energy. However, the spatial concentration of renewables in cost-optimal spatially-distributed model results is a known effect which provides solutions with low public acceptance and often in contrast to political goals. Existing approaches focus on either cost-optimality or equity, whereas integrated methods to establish energy system designs with low costs and a fair distribution of renewable energy are scarce. In this study, we analyze the spatial concentration in a cost-optimal energy system model to identify the most relevant parameters for renewable distribution. Furthermore, two model extensions are presented to overcome this concentration. First, we examine the standard approach of regional average solar and onshore wind energy yields. We find that the intra-regional energy yield distribution can reduce extreme renewable distributions. Second, we consider quadratic environmental costs as the result of space resistance due to land consumption by renewable energy capacities. This approach sets incentives for the partial utilization of capacity potential, thereby reducing spatial concentration of renewable energy. Both model extensions highlight the importance of the renewable energy potential.