  Effective investment planning decisions are crucial to ensure cyber-physical
infrastructures satisfy performance requirements over an extended time horizon.
Computing these decisions often requires solving Capacity Expansion Problems
(CEPs). In the context of regional-scale energy systems, these problems are
prohibitively expensive to solve due to large network sizes, heterogeneous node
characteristics, and a large number of operational periods. To maintain
tractability, traditional approaches aggregate network nodes and/or select a
set of representative time periods. Often, these reductions do not capture
supply-demand variations that crucially impact CEP costs and constraints,
leading to suboptimal decisions. Here, we propose a novel graph convolutional
autoencoder approach for spatio-temporal aggregation of a generic CEP with
heterogeneous nodes (CEPHN). Our architecture leverages graph pooling to
identify nodes with similar characteristics and minimizes a multi-objective
loss function. This loss function is tailored to induce desirable spatial and
temporal aggregations with regard to tractability and optimality. In
particular, the output of the graph pooling provides a spatial aggregation
while clustering the low-dimensional encoded representations yields a temporal
aggregation. We apply our approach to generation expansion planning of a
coupled 88-node power and natural gas system in New England. The resulting
aggregation leads to a simpler CEPHN with 6 nodes and a small set of
representative days selected from one year. We evaluate aggregation outcomes
over a range of hyperparameters governing the loss function and compare
resulting upper bounds on the original problem with those obtained using
benchmark methods. We show that our approach provides upper bounds that are 33%
(resp. 10%) lower those than obtained from benchmark spatial (resp. temporal)
aggregation approaches.
