  The accurate representation of variable renewable generation (RES, e.g.,
wind, solar PV) assets in capacity expansion planning (CEP) studies is
paramount to capture spatial and temporal correlations that may exist between
sites and impact both power system design and operation. However, it typically
has a high computational cost. This paper proposes a method to reduce the
spatial dimension of CEP problems while preserving an accurate representation
of renewable energy sources. A two-stage approach is proposed to this end. In
the first stage, relevant sites are identified via a screening routine that
discards the locations with little impact on system design. In the second
stage, the subset of relevant RES sites previously identified is used in a CEP
problem to determine the optimal configuration of the power system. The
proposed method is tested on a realistic EU case study and its performance is
benchmarked against a CEP set-up in which the entire set of candidate RES sites
is available. The method shows great promise, with the screening stage
consistently identifying 90% of the optimal RES sites while discarding up to
54% of the total number of candidate locations. This leads to a peak memory
reduction of up to 41% and solver runtime gains between 31% and 46%, depending
on the weather year considered.
