Vehicle-to-Grid and vehicle-to-load strategies and demand response program with bender decomposition approach in electrical vehicle-based microgrid for profit profile improvement

Abstract

Electric Vehicle (EV)-based microgrid (MG) has an opportunity of using of mobile energy storage units where EV can help the MG in supporting load demands and maintaining the quality of voltage and power profile. However, in EV traffic time, MG suffers from high demands of EVs and loads. EV power scheduling can overcome the challenges of profit profile, demand support and cost profile in the MG. Two strategies as power flow from EV to the main grid (V2G) and from EV to load (V2L) can be used to reduce the cost and prevent buying energy from main grid. In high-demand times, the MG should buy energy from main grid which consequently increases the cost profile. To overcome the high-demand supporting problem, demand response program is also employed to reduce the loads in on-peak time durations or shift them to off-peak times. Applying the EV scheduling and demand response program helps MG to reduce the cost and improve the profit profile. Bender decomposition and Lagrange method provide a robust approach in this paper for optimization of MG in terms of EV scheduling and demand response program to obtain desirable cost and profit profile. As shown in simulation results, using the proposed approach, the cost profile is reduced by 14.67% compared to applying no optimized EV scheduling and no demand response program in the MG.