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The nexus between transportation, the power grid, and consumer behavior ismore pronounced than ever before as the race to decarbonize the transportationsector intensifies. Electrification in the transportation sector has led totechnology shifts and rapid deployment of electric vehicles (EVs). Thepotential increase in stochastic and spatially heterogeneous charging loadpresents a unique challenge that is not well studied, and will have significantimpacts on grid operations, emissions, and system reliability if not managedeffectively. Realistic scenario generators can help operators prepare, andmachine learning can be leveraged to this end. In this work, we developgenerative adversarial networks (GANs) to learn distributions of electricvehicle (EV) charging sessions and disentangled representations. We show thatthis model structure successfully parameterizes unlabeled temporal and powerpatterns without supervision and is able to generate synthetic data conditionedon these parameters. We benchmark the generation capability of this model withGaussian Mixture Models (GMMs), and empirically show that our proposed modelframework is better at capturing charging distributions and temporal dynamics.

EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations