  Designing an optimal energy system with large shares of renewable energy
sources is computationally challenging. Considering greater spatial horizon and
level of detail, during the design, exacerbates this challenge. This paper
investigates spatial and technology aggregation of energy system model, as a
complexity-reduction technique. To that end, a novel two-step aggregation
scheme 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 region
set. The aggregation is based on a holistic approach that considers all model
parameters and spatial contiguity of regions. Next, technology aggregation is
performed on each VRES, present in each newly-defined region. Each VRES is
aggregated based on the temporal profiles to obtain a representative set. The
impact of these aggregations on accuracy and computational complexity of a
cost-optimal energy system design is analyzed for a European energy system
scenario.The aggregations are performed to obtain different combinations of
number of regions and VRES types, and the results are benchmarked against
initial spatial resolution of 96 regions and 68 VRES types in each region. The
results show that the system costs deviate significantly when lower number of
regions and/or VRES types are considered. As the spatial resolution is
increased in terms of both number of regions and VRES types, the system cost
fluctuates at first and stabilizes at some point, approaching the benchmark
value. Optimal combination can be determined based on an acceptable cost
deviation and the point of stabilization. For instance, if <5% deviation is
acceptable, 33 regions and 38 VRES types in each region is optimal. With this
setting, the system cost is under-estimated by 4.42% but the run time is
reduced by 92.95%.
