Optimal design of EV aggregator for real-time peak load shaving and valley filling

Nowadays, extensive Electric Vehicle (EV) aggregator research has been carried out to design smart charging-discharging and scheduling of EVs. The EV aggregator plays a vital role to coordinate the energy management in between power grid and the EVs. This paper presents the optimal design of an EV aggregator in real-time for peak load shaving and valley filling. In practice, uncoordinated charging-discharging of grid-connected EVs may create serious issues to the power grid by introducing new peak demand and fluctuating load profile. In order to overcome the above issue, the EV aggregator's energy management system was optimized using Genetic Algorithm (GA) with constraints such as power balance and State-of-Charge (SOC). Furthermore, 24 hours dynamic load profile was considered in the optimization algorithm to manage the real-time power demand at each time instance. The deterministic optimization technique was tested in MATLAB-SIMULINK model for real-time application. The results showed that the proposed optimization algorithm can effectively reduce the peaks and fill valleys in compliance with various constraints in real-time.