  The concept of plug-in electric vehicles (PEV) are gaining increasing
popularity in recent years, due to the growing societal awareness of reducing
greenhouse gas (GHG) emissions, and gaining independence on foreign oil or
petroleum. Large-scale deployment of PEVs currently faces many challenges. One
particular concern is that the PEV charging can potentially cause significant
impacts on the existing power distribution system, due to the increase in peak
load. As such, this work tries to mitigate the impacts of PEV charging by
proposing a decentralized smart PEV charging algorithm to minimize the
distribution system load variance, so that a `flat' total load profile can be
obtained. The charging algorithm is myopic, in that it controls the PEV
charging processes in each time slot based entirely on the current power system
states, without knowledge about future system dynamics. We provide theoretical
guarantees on the asymptotic optimality of the proposed charging algorithm.
Thus, compared to other forecast based smart charging approaches in the
literature, the charging algorithm not only achieves optimality asymptotically
in an on-line, and decentralized manner, but also is robust against various
uncertainties in the power system, such as random PEV driving patterns and
distributed generation (DG) with highly intermittent renewable energy sources.
