  Fluctuations in electricity tariffs induced by the sporadic nature of demand
loads on power grids has initiated immense efforts to find optimal scheduling
solutions for charging and discharging plug-in electric vehicles (PEVs) subject
to different objective sets. In this paper, we consider vehicle-to-grid (V2G)
scheduling at a geographically large scale in which PEVs have the flexibility
of charging/discharging at multiple smart stations coordinated by individual
aggregators. We first formulate the objective of maximizing the overall profit
of both, demand and supply entities, by defining a weighting parameter. We then
propose an online decentralized greedy algorithm for the formulated mixed
integer non-linear programming (MINLP) problem, which incorporates efficient
heuristics to practically guide each incoming vehicle to the most appropriate
charging station (CS). The better performance of the presented algorithm
compared to an alternative allocation strategy is demonstrated through
simulations in terms of the overall achievable profit and flatness of the final
electricity load. Moreover, the results of simulations reveal the existence of
optimal number of deployed stations at which the overall profit can be
maximized.
