  A key motivation to fasten roll-out of electric vehicles (EVs) to the market
is to implement Vehicle-to-Grid (V2G) functionalities. With V2G in place, EV
owners can have extra freedom to interact their battery energy with power
grids, namely by selling their energy to the grid when their EVs are not in
use. On the other hand, EV aggregators and utility companies can leverage the
flexibility of the collected energy to implement various ancillary services to
the grids, which may significantly reduce costs of, for instance, running
spinning reserve of traditional power plants on the grid side. However, this
extra freedom also poses practical challenges in terms of how to devise a
discharging strategy for a group of EVs that is fair and in some sense optimal.
In this paper, we present a new design of EV discharging strategy in a typical
V2G energy trading framework whilst leveraging the whale optimization algorithm
in a decentralized manner, a metaheuristic algorithm that has been shown
effective in solving large-scale centralized optimization problems. We
demonstrate that by using simple ideas of data shuffling and aggregation, one
can design an EV discharging strategy in a fair, optimal and privacy-aware
manner, where the privacy refers to the fact that no critical information of
EVs should be exchanged with the EV aggregator, and vice versa. The fairness
implies that a common discharge rate needs to be sought for all EVs so that no
one gets better benefits than others in the same V2G programme. Simulation
results are presented to illustrate the efficacy of our proposed system.
