  Power sector capacity expansion models (CEMs) that are used for studying
future low-carbon grid scenarios must incorporate detailed representation of
grid operations. Often CEMs are formulated to model grid operations over
representative periods that are sampled from the original input data using
clustering algorithms. However, such representative period selection (RPS)
methods are limited by the declining efficacy of the clustering algorithm with
increasing dimensionality of the input data and do not consider the relative
importance of input data variations on CEM outcomes. Here, we propose a RPS
method that addresses these limitations by incorporating dimensionality
reduction, accomplished via neural network based autoencoders, prior to
clustering. Such dimensionality reduction not only improves the performance of
the clustering algorithm, but also facilitates using additional features, such
as estimated outputs produced from parallel solutions of simplified versions of
the CEM for each disjoint period in the input data (e.g. 1 week). The impact of
incorporating dimensionality reduction as part of RPS methods is quantified
through the error in outcomes of the corresponding reduced-space CEM vs. the
full space CEM. Extensive numerical experimentation across various networks and
range of technology and policy scenarios establish the superiority of the
dimensionality-reduction based RPS methods.
