Optimal Charging Scheduling of Electric vehicles for Smart Grid Operations Employing Demand Side Management strategy with Battery Storage System

This study dives into the critical area of smart grid optimization, with an emphasis on cost minimization through the integration of demand side management (DSM) and electric vehicle (EV) dynamics. The development of renewable energy sources, as well as the increasing use of electric vehicles, have created challenging situations for traditional grid management. Using sophisticated optimization techniques, this paper presents a complete framework to improve the efficiency and economic feasibility of smart grid operations. By carefully integrating DSM tactics with EV charging patterns, the suggested model aims to reduce peak demand stresses, reduce grid congestion, and optimize energy distribution. To accomplish these objectives, a load scheduling-based DSM has been applied in this work. With the application of DSM using Frilled Lizard Optimization (FLO) has achieved 3.7% of cost reduction and 33.46% reduction in peak load when Battery was not considered. When battery is considered, there is 0.7% reduction in cost whereas the peak load has been reduced significantly by up to 48.18 %. The suggested optimization technique for DSM application has produced superior results in fewer iterations.