International Journal of Science, Engineering and TechnologyAuthors: Adarsh Kumar Bhardwaj, Jaya shukla
Abstract: The increasing integration of electric vehicles (EVs) into smart grids necessitates advanced control strategies for efficient bidirectional power flow management. Vehicle-to-Grid (V2G) systems enable EVs to act as distributed energy resources, supporting grid stability and economic operation. However, challenges such as stochastic load variations, renewable intermittency, and battery degradation limit the effectiveness of conventional control approaches. This paper proposes a multi-objective stochastic model predictive control (MO-SMPC) framework integrated with adaptive power sharing between battery and supercapacitor storage systems for optimal V2G operation. The proposed method simultaneously minimizes electricity cost, grid power fluctuations, and battery aging while ensuring system constraints. A novel adaptive droop-based predictive control strategy is introduced to dynamically allocate power between battery and supercapacitor, enhancing transient response and extending battery life. Stochastic modeling is incorporated to address uncertainties in load demand, EV availability, and solar generation. MATLAB-based simulations under multiple operating scenarios demonstrate improved peak shaving, reduced operational cost, and enhanced battery lifespan compared to conventional battery-only systems. Results show up to 40% reduction in cost, 30% improvement in grid stability, and significant mitigation of battery stress. The proposed framework offers a scalable and practical solution for intelligent energy management in next-generation smart grids.
