Optimal Scheduling of Charging/Discharging Power and EVs Pattern Using Stochastic Techniques in V2G System

Nowadays power demands penetration increasing day by day due to the global adaptation of Electric Vehicles (EVs). It has been observed that a rising penetration rate of electric vehicles (EVs) fleet could cause undervoltage and congestions in the power grid network. Under such circumstances, optimal scheduling of EVs charging/discharging power proved to be one of the best alternatives. The prime objective of this paper, to investigate the impact of charging and discharging on load variations/peaks. This paper develops a load estimation model and EVs charging model under the various scenario of vehicle travelling patterns to predict their impacts on charging behavior. This paper also demonstrates a Markov Chain Monte Carlo (MCMC) based charging/discharging algorithm that efficiently deals with the prediction of EVs driving patterns and manages the issues of high-power demand during peak time. In vehicle to grid (V2G) technologies, the stored energy supplies power to the grid at a certain time when required. Further, to increase the flexibility of the grid storage system and user trip schedule using a proposed algorithm. The proposed probabilistic approach demonstrates an effective estimation of power demands and controls the peak regulation, without changing the performance of the scheduled vehicle owner's trip.