  With the electrification of transportation, the rising uptake of electric
vehicles (EVs) might stress distribution networks significantly, leaving their
performance degraded and stability jeopardized. To accommodate these new loads
cost-effectively, modern power grids require coordinated or ``smart'' charging
strategies capable of optimizing EV charging scheduling in a scalable and
efficient fashion. With this in view, the present work focuses on reservation
management programs for large-scale, networked EV charging stations. We
formulate a time-coupled binary optimization problem that maximizes EV users'
total welfare gain while accounting for the network's available power capacity
and stations' occupancy limits. To tackle the problem at scale while retaining
high solution quality, a data-driven optimization framework combining
techniques from the fields of Deep Learning and Approximation Algorithms is
introduced. The framework's key ingredient is a novel input-output processing
scheme for neural networks that allows direct extrapolation to problem sizes
substantially larger than those included in the training set. Extensive
numerical simulations based on synthetic and real-world data traces verify the
effectiveness and superiority of the presented approach over two representative
scheduling algorithms. Lastly, we round up the contributions by listing several
immediate extensions to the proposed framework and outlining the prospects for
further exploration.
