Smart EV charging

High integration of intermittent renewable energy sources (RES), predominantly wind power, has created complexities in power system operations. In addition, large fleets of Electric Vehicles (EVs) increase the uncertainty on electricity consumption significantly. Thus, an uncoordinated charging process will further complicate the grid scheduling.

We proposed a coordinated scheduling algorithm for charge/discharge of aggregated EV fleets to maximize the integration of wind generation and minimize the charging cost for EV owners in a vehicle-to-grid (V2G) setup. Challenges for people participation in V2G, such as battery degradation and insecurity about unexpected events, are also addressed. We formulated the real-time charge/discharge scheduling problem as a multi-objective mixed-integer quadratic programming problem that the aggregator takes advantage of real-time communication in smart grid to frequently update the EV schedule with updated real-time information about EV availabilities, wind generation forecast, and electricity price. 

A simulation of the proposed algorithm for different scenarios of EV characteristics, arrivals, departures, and charging requirements are performed to check the quality of solutions and schedules. The results show that the proposed model leads to significant improvement in all metrics, and benefits both the owner and the grid. Moreover, the results indicate that frequent updates of wind power forecast (and LMP) in the deterministic problem significantly reduce the effects of forecast uncertainty.