Accelerating the Green Transition: Integrating Next-Generation Energy Storage, Carbon Capture, and AI-Optimized Smart Grids for a Sustainable Future

Abstract—Achieving a sustainable energy future requires an integrated approach that addresses the limitations of current infrastructure in storage, distribution, and emissions management. This research investigates the synergistic integration of next-generation energy storage technologies, carbon capture systems, and artificial intelligence (AI)-optimized smart grids to accelerate the global green transition. The study begins by identifying key inefficiencies in traditional grid networks and energy systems, particularly in managing intermittent renewable sources and mitigating carbon emissions. Advanced battery chemistries—including solid-state, flow, and sodium-ion storage—are evaluated for scalability, efficiency, and lifecycle impact. Simultaneously, modular carbon capture units based on direct air capture (DAC) and point-source filtration are assessed for deployment compatibility. AI-driven optimization models, employing machine learning algorithms and real-time sensor networks, are developed to enhance grid flexibility, predictive maintenance, and load balancing. The integrated framework is tested through simulations using Python and MATLAB, with performance evaluated through metrics such as energy throughput, CO₂ reduction potential, and grid reliability. Results indicate that the convergence of these technologies can significantly improve energy efficiency, reduce emissions, and support decentralized, resilient power systems. This study provides a systems-level blueprint for accelerating decarbonization while improving operational performance, with implications for policymakers, utilities, and global sustainability initiatives. Keywords—green transition, energy storage, carbon capture, smart grids, artificial intelligence, sustainable energy, AI optimization, grid reliability, decarbonization, renewable integration, direct air capture, machine learning in energy systems