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As the popularity of electric vehicles increases, the demand for more power can increase more rapidly than our ability to install additional generating capacity. In the long term we expect that the supply and demand will become balanced. However, in the interim the rate at which electric vehicles can be deployed will depend on our ability to charge these vehicles without inconveniencing their owners. In this paper, we investigate using fairness mechanisms to distribute power to electric vehicles on a smart grid. We assume that during peak demand there is insufficient power to charge all the vehicles simultaneously. In each five minute interval of time we select a subset of the vehicles to charge, based upon information about the vehicles. We evaluate the selection mechanisms using published data on the current demand for electric power as a function of time of day, current driving habits for commuting, and the current rates at which electric vehicles can be charged on home outlets. We found that conventional selection strategies, such as first-come-first-served or round robin, may delay a significant fraction of the vehicles by more than two hours, even when the total available power over the course of a day is two or three times the power required by the vehicles. However, a selection mechanism that minimizes the maximum delay can reduce the delays to a few minutes, even when the capacity available for charging electric vehicles exceeds their requirements by as little as 5%.
Electric field induced nucleation is introduced as a possible mechanism to realize a metallic phase of hydrogen. Analytical expressions are derived for the nucleation probabilities of both thermal and quantum nucleation in terms of material parameter
As an environment-friendly substitute for conventional fuel-powered vehicles, electric vehicles (EVs) and their components have been widely developed and deployed worldwide. The large-scale integration of EVs into power grid brings both challenges an
The growing use of electric vehicles (EVs) may hinder their integration into the electricity system as well as their efficient operation due to the intrinsic stochasticity associated with their driving patterns. In this work, we assume a profit-maxim
We pose the aggregators problem as a bilevel model, where the upper level minimizes the total operation costs of the fleet of EVs, while each lower level minimizes the energy available to each vehicle for transportation given a certain charging plan.
We design a coordination mechanism for truck drivers that uses pricing-and-routing schemes that can help alleviate traffic congestion in a general transportation network. We consider the user heterogeneity in Value-Of-Time (VOT) by adopting a multi-c