Optimizing Electric Vehicle Integration in Virtual Power Plants: A Stochastic Optimization Framework With MDNN Integration

The integration of electric vehicles (EVs) into the power grid presents both opportunities and challenges, necessitating efficient management of their charging and discharging activities. Virtual Power Plants (VPPs) have emerged as a promising solution to aggregate and manage distributed energy resources, including EV batteries, in a coordinated manner. This paper proposes a novel optimization framework combining Stochastic Receding-Horizon Convex Optimization with Mixture Density Neural Networks (MDNNs) to address the scheduling of EV batteries within VPPs. The framework considers uncertainties such as renewable energy generation, EV availability, and market prices. Through comprehensive modeling, simulation, and real-world data analysis, the effectiveness of the proposed approach in maximizing revenue generation for VPPs is demonstrated. Integration of MDNNs enhances prediction accuracy and decision-making under uncertainty, showcasing the transformative potential of advanced optimization techniques and machine learning methodologies in shaping the future of energy management systems. Overall, this study contributes a pioneering approach tailored for VPPs, highlighting its practical feasibility and effectiveness in enhancing grid reliability and efficiency.