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

Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints