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

• URL: http://arxiv.org/abs/2108.03762v1
• Date: Mon, 9 Aug 2021 00:23:47 GMT
• Title: EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations
• Authors: Robert Buechler, Emmanuel Balogun, Arun Majumdar and Ram Rajagopal
• Abstract summary: We develop generative adversarial networks (GANs) to learn of electric vehicle (EV) charging sessions and disentangled representations. We show that this model structure successfully parameterizes unlabeled temporal and power patterns without supervision and is able to generate synthetic data conditioned on these parameters.
• Score: 4.273017002805776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The nexus between transportation, the power grid, and consumer behavior is more pronounced than ever before as the race to decarbonize the transportation sector intensifies. Electrification in the transportation sector has led to technology shifts and rapid deployment of electric vehicles (EVs). The potential increase in stochastic and spatially heterogeneous charging load presents a unique challenge that is not well studied, and will have significant impacts on grid operations, emissions, and system reliability if not managed effectively. Realistic scenario generators can help operators prepare, and machine learning can be leveraged to this end. In this work, we develop generative adversarial networks (GANs) to learn distributions of electric vehicle (EV) charging sessions and disentangled representations. We show that this model structure successfully parameterizes unlabeled temporal and power patterns without supervision and is able to generate synthetic data conditioned on these parameters. We benchmark the generation capability of this model with Gaussian Mixture Models (GMMs), and empirically show that our proposed model framework is better at capturing charging distributions and temporal dynamics.

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