Optimal management of vehicle-to-grid and grid-to-vehicle strategies for load profile improvement in distribution system

Abstract

This paper develops a penalty factor-based objective function for scheduling the strategy of coordinated charging and discharging of Electric Vehicles (EVs). Firstly, EVs owner behaviors, such as arrival time, departure time, duration of EVs at the parking lots, and the EVs' battery capacity, are modeled and estimated using Monte Carlo Simulation (MCS). The proposed objective function is minimized with the help of the Opposition-based Competitive Swarm Optimizer (OCSO) algorithm to obtain EVs' optimal charging and discharging scheduling with minimum daily load variance or flatten daily load curve. The effectiveness of the proposed method is tested on IEEE 69-bus radial distribution system. It is found that the proposed OCSO based controlled scheduling, in conjunction with MCS, reduces the load variance up to 32.67% compared to uncontrolled scheduling. Also, the parametric sensitivity studies are executed to determine the optimal values of free parameters for the OCSO algorithm. Finally, the results obtained with the proposed technique are compared with the other contemporary optimization algorithms to demonstrate its efficacy. The comparative analysis shows that the optimal load variance for the controlled scheduling, as obtained with the proposed method, is 0.89% to 3.71%, smaller than the existing controlled scheduling techniques. Further, it is found that the proposed work provides a flattened load curve and a faster convergence rate with a 100% success rate.