  A rapid transformation of current electric power and natural gas (NG)
infrastructure is imperative to meet the mid-century goal of CO2 emissions
reduction requires. This necessitates a long-term planning of the joint
power-NG system under representative demand and supply patterns, operational
constraints, and policy considerations. Our work is motivated by the
computational and practical challenges associated with solving the generation
and transmission expansion problem (GTEP) for joint planning of power-NG
systems. Specifically, we focus on efficiently extracting a set of
representative days from power and NG data in respective networks and using
this set to reduce the computational burden required to solve the GTEP. We
propose a Graph Autoencoder for Multiple time resolution Energy Systems (GAMES)
to capture the spatio-temporal demand patterns in interdependent networks and
account for differences in the temporal resolution of available data. The
resulting embeddings are used in a clustering algorithm to select
representative days. We evaluate the effectiveness of our approach in solving a
GTEP formulation calibrated for the joint power-NG system in New England. This
formulation accounts for the physical interdependencies between power and NG
systems, including the joint emissions constraint. Our results show that the
set of representative days obtained from GAMES not only allows us to tractably
solve the GTEP formulation, but also achieves a lower cost of implementing the
joint planning decisions.
