  The rapid escalation in plug-in electric vehicles (PEVs) and their
uncoordinated charging patterns pose several challenges in distribution system
operation. Some of the undesirable effects include overloading of transformers,
rapid voltage fluctuations, and over/under voltages. While this compromises the
consumer power quality, it also puts on extra stress on the local voltage
control devices. These challenges demand for a well-coordinated and power
network-aware charging approach for PEVs in a community. This paper formulates
a real-time electric vehicle charging scheduling problem as an mixed-integer
linear program (MILP). The problem is to be solved by an aggregator, that
provides charging service in a residential community. The proposed formulation
maximizes the profit of the aggregator, enhancing the utilization of available
infrastructure. With a prior knowledge of load demand and hourly electricity
prices, the algorithm uses a moving time horizon optimization approach,
allowing the number of vehicles arriving unknown. In this realistic setting,
the proposed framework ensures that power system constraints are satisfied and
guarantees desired PEV charging level within stipulated time. Numerical tests
on a IEEE 13-node feeder system demonstrate the computational and performance
superiority of the proposed MILP technique.
