Achieving Reliable Coordination of Residential Plug-in Electric Vehicle Charging: A Pilot Study

Wide-scale electrification of the transportation sector will require careful planning and coordination with the power grid. Left unmanaged, uncoordinated charging of electric vehicles (EVs) at increased levels of penetration will amplify existing peak loads, potentially outstripping the grid's capacity to reliably meet demand. In this paper, we report findings from the OptimizEV Project - a real-world pilot study in Upstate New York exploring a novel approach to coordinated residential EV charging. The proposed coordination mechanism seeks to harness the latent flexibility in EV charging by offering EV owners monetary incentives to delay the time required to charge their EVs. Each time an EV owner initiates a charging session, they specify how long they intend to leave their vehicle plugged in by selecting from a menu of deadlines that offers lower electricity prices the longer they're willing to delay the time required to charge their EV. Given a collection of active charging requests, a smart charging system dynamically optimizes the power being drawn by each EV in real time to minimize strain on the grid, while ensuring that each customer's car is fully charged by its deadline. Under the proposed incentive mechanism, we find that customers are frequently willing to engage in optimized charging sessions, allowing the system to delay the completion of their charging requests by more than eight hours on average. Using the flexibility provided by customers, the smart charging system was shown to be highly effective in shifting the majority of EV charging loads off-peak to fill the night-time valley of the aggregate load curve. Customer opt-in rates remained stable over the span of the study, providing empirical evidence in support of the proposed coordination mechanism as a potentially viable "non-wires alternative" to meet the increased demand for electricity driven growing EV adoption.Comment: 19 pages, 12 figure

Safe Linear Stochastic Bandits

We introduce the safe linear stochastic bandit framework---a generalization of linear stochastic bandits---where, in each stage, the learner is required to select an arm with an expected reward that is no less than a predetermined (safe) threshold with high probability. We assume that the learner initially has knowledge of an arm that is known to be safe, but not necessarily optimal. Leveraging on this assumption, we introduce a learning algorithm that systematically combines known safe arms with exploratory arms to safely expand the set of safe arms over time, while facilitating safe greedy exploitation in subsequent stages. In addition to ensuring the satisfaction of the safety constraint at every stage of play, the proposed algorithm is shown to exhibit an expected regret that is no more than $O(\sqrt{T}\log (T))$ after $T$ stages of play