Novel control-based design optimization of smart energy distribution and Management in Vehicle-to-Grid Integrated Microgrid

The growing reliance on fossil fuels for energy and transportation accelerates environmental degradation and depletes natural resources. Integrating Renewable Energy Sources (RESs) and Electric Vehicles (EVs) into microgrids offers a sustainable alternative; however, effective energy management remains challenging due to the intermittent nature of renewable generation and variable load demands. Some studies encounter challenges in effectively managing generation variability, load fluctuations, and uncertainties using advanced algorithms. This paper presents a novel hybrid controller—Model Predictive Control-Tuned Imitation Learning with Long Short-Term Memory (MPC-TIL-LSTM)—designed to optimize energy distribution, storage operations, and vehicle-to-grid interactions. The proposed controller predicts optimal energy allocation, mitigates fluctuations from intermittent RESs using storage systems, and ensures efficient load scheduling to maintain microgrid stability. Performance is evaluated under diverse scenarios involving RESs integration, EV charging/discharging, and varying load conditions. The MPC-TIL-LSTM controller achieves a 70.86 % reduction in fuel consumption and a 91.63 % decrease in electricity costs, outperforming the Deep Q-Network (DQN) algorithm. Sensitivity analysis across multiple locations demonstrates the controller's adaptability to uncertain conditions, enhancing both operational reliability and economic efficiency. By intelligently managing energy flows, the proposed strategy supports sustainable, cost-effective, and resilient microgrid operations, contributing to the development of advanced smart energy distribution systems with reduced environmental and economic impacts.