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

Representative period selection for power system planning using autoencoder-based dimensionality reduction