Coordinated Charging Scheduling for Electric Vehicles and Optimal Tuning of the Controller for Frequency Regulation under Uncertain Environment

This paper proposes a Multilevel Coordinated Controlled Charging Algorithm (MLCCA), by which the cost for Electric Vehicles (EV) charging can be minimized and as well as the Dynamic Frequency Regulation (DFR) under uncertain environment can be done by minimizing the Area Control Error Signal (ACES) by optimizing the control parameters of PID controllers in two area interconnected power system. Along with the EVs, Conventional FR resources ( CFR) and Battery Energy Storage (BES) has been integrated in priority basis while performing the DFR as per the operating states. The coordinated charging of EVs has adopted both Grid to Vehicle (G2V) and Vehicle to Grid (V2G) mode of operation. The uncertainties related with the driving cycles has been tackled by 2m Point Estimation (2m-PEM). To establish the effectiveness of EV/BES as FR sources, along with CFR, dynamic simulation has been performed for AGC of two area interconnected system.

Automated summary

This paper proposes a Multilevel Coordinated Controlled Charging Algorithm (MLCCA) to minimize the cost for Electric Vehicles (EV) charging and Dynamic Frequency Regulation (DFR) by optimizing the control parameters of PID controllers in two area interconnected power system. The coordinated charging of EVs adopts both Grid to Vehicle (G2V) and Vehicle to Grid (V2G) operation modes, while uncertainties related to driving cycles are handled with 2m Point Estimation (2m-PEM). Dynamic simulations for AGC of two area interconnected system validate the effectiveness of EV/BES as Frequency Regulation (FR) sources in addition to CFR.