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Designing an optimal energy system with large shares of renewable energysources is computationally challenging. Considering greater spatial horizon andlevel of detail, during the design, exacerbates this challenge. This paperinvestigates spatial and technology aggregation of energy system model, as acomplexity-reduction technique. To that end, a novel two-step aggregationscheme based on model parameters such as Variable Renewable Energy Sources(VRES) time series and capacities, transmission capacities and distances, etc,is introduced. First, model regions are aggregated to obtain reduced regionset. The aggregation is based on a holistic approach that considers all modelparameters and spatial contiguity of regions. Next, technology aggregation isperformed on each VRES, present in each newly-defined region. Each VRES isaggregated based on the temporal profiles to obtain a representative set. Theimpact of these aggregations on accuracy and computational complexity of acost-optimal energy system design is analyzed for a European energy systemscenario.The aggregations are performed to obtain different combinations ofnumber of regions and VRES types, and the results are benchmarked againstinitial spatial resolution of 96 regions and 68 VRES types in each region. Theresults show that the system costs deviate significantly when lower number ofregions and/or VRES types are considered. As the spatial resolution isincreased in terms of both number of regions and VRES types, the system costfluctuates at first and stabilizes at some point, approaching the benchmarkvalue. Optimal combination can be determined based on an acceptable costdeviation and the point of stabilization. For instance, if <5% deviation isacceptable, 33 regions and 38 VRES types in each region is optimal. With thissetting, the system cost is under-estimated by 4.42% but the run time isreduced by 92.95%.

Impact of Spatial and Technology Aggregation on Optimal Energy System Design