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

TL;DR

This paper introduces GAMES, a graph autoencoder method that efficiently extracts representative demand patterns from interdependent power and natural gas networks, enabling cost-effective joint system planning under emissions constraints.

Contribution

The paper presents a novel graph autoencoder approach for capturing spatio-temporal demand patterns in joint energy systems, improving computational efficiency and planning accuracy.

Findings

GAMES effectively reduces computational burden in GTEP.

Representative days from GAMES lead to lower-cost joint planning.

Method accurately captures interdependencies in power and NG systems.

Abstract

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.