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High integration of intermittent renewable energy sources (RES), inparticular wind power, has created complexities in power system operations. Onthe other hand, large fleets of Electric Vehicles (EVs) are expected to havegreat impact on electricity consumption, and uncoordinated charging processwill add load uncertainty and further complicate the grid scheduling. In thispaper, we propose a smart charging approach that uses the flexibility of EVowners to absorb the fluctuations in the output of RES in a vehicle-to-grid(V2G) setup. We propose an optimal scheduling algorithm for charge/discharge ofaggregated EV fleets to maximize the integration of wind generation as well asminimize the charging cost for EV owners. Challenges for people participationin V2G, such as battery degradation and feeling insecure for unexpected events,are also addressed. We first formulate a static model using mixed-integerquadratic programming (MIQP) with multi-objective optimization assuming thatevery parameter of the model is known a day ahead of scheduling. Subsequently,we formulate a dynamic (dis)charging schedule after EVs arrive into the systemwith updated information about EV availabilities, wind generation forecast, andenergy price in real-time, using rolling-horizon optimization. Simulationsusing a group of 100 EVs in a micro-grid with wind as primary resourcedemonstrate significant increase in wind utilization and reduction in chargingcost compared to uncontrolled charging scenario.

Leveraging Owners' Flexibility in Smart Charge/Discharge Scheduling of Electric Vehicles to Support Renewable Energy Integration