Optimized Scheduling of Electric Vehicles Charging in Smart Grid using Deep Learning

This research presents an integrated research framework for electric vehicle charging infrastructures under smart grids. It employs Genetic Algorithms and machine learning models such as Artificial Neural Networks (ANNs), Decision Trees, and Recurrent Neural Networks. The GA is able to minimally stack waiting times strategically and, by the effective allocation of charging points to drivers at various stages in their route, maximizes usage. In contrast, requiring intensive accurate training on massive data sets are the ML methods, which can also predict future demand for efficient use of resources. Results of the method demonstrate the strength of these techniques, with ANNs reaching nearly 99% accuracy. By applying this comprehensive methodology, operational metrics such as waiting time for EV owners are significantly improved and sustainable power management becomes more feasible. These findings underscore the potential for artificial intelligence to revolutionize energy distribution-so that it balances consumer convenience with tighter environmental targets and ways of working even now taking shape concerning electric vehicle charging.