An Optimized Bidirectional Power Management Framework for Vehicle-to-Grid Systems Using Intelligent Energy Scheduling Algorithms

Abstract

The fact of the swift introduction of electric vehicles (EVs) into the power systems of the modern world has heightened the necessity of the effective Vehicle-to-Grid (V2G) energy management of the energy system to maintain the balance of the grid and the optimal use of energy sources. Current V2G systems either lack any intelligent coordination and use the unidirectional charging mode, or do not support unidirectional charging, resulting in inefficient power scheduling, greater grid stress and shorter battery life. The proposed study bridges the research gap by introducing an optimized self-managed power management system that incorporates smart energy management over scheduling in self-managed utilization of the grid. The methodology offered uses a hybrid optimization model of reinforcement and adaptive load prediction to control the process of charging and discharging. The most important methods are real-time state-of-charge prediction, demand-response modeling, and predictive energy dispatch based on a deep Q-network. The test environment has a simulated smart grid with several EV nodes that are modeled in MATLAB/Simulink and include real-time load profiles and renewable energy sources. Experiments indicate that energy consumption is 28.6 percent more efficient, the maximum load demand is down by 22.4 percent, and the battery usage is increased by 17.9 percent in comparison with traditional ways. This work has given a significant contribution in the formulation of a scalable intelligent V2G system which boosts grid resilience, reduces operation expense and enables the integration of sustainable energy in future-proof smart grids.