Integrating electric vehicles (EVs) into homes and the electrical grid creates complex dynamics that traditional planning tools struggle to address. Accurately estimating residential EV charging demand typically requires resource-intensive agent-based simulations reliant on substantial input data, limiting scalability. We present REVI-Twin, an AI-driven digital twin of residential EV infrastructure that scales without computationally-intensive simulations. It encompasses: ( i ) household-level EV ownership; ( i i ) user behavior and charging preferences; ( i i i ) hourly power consumption; and ( i v ) planned trips. Our framework performs two tasks: ( i ) predicts EV adoption using transfer learning, semi-supervised learning, and Bayesian optimization; ( i i ) synthesizes hourly consumption with active-learning multi-output Gaussian processes from < 1% of data. We also release a comprehensive hourly integrated residential energy dataset. Our case study indicates that each 1% of battery adoption reduces Virginia’s net imports by ~ 0.06% daily and ~ 0.08% during peak hours. REVI-Twin assists policymakers and planners in analyzing adoption and infrastructure needs for resilient electrification.
Kishore et al. (Thu,) studied this question.
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