A multi strategy optimization framework using AI digital twins for smart grid carbon emission reduction.

This research presents an AI-enabled digital twin framework to achieve carbon neutrality in smart grids through optimal management of heterogeneous energy storage systems. The proposed structure integrates battery, thermal, and hydrogen storage technologies with AI-driven forecasting models to address the challenge of renewable integration, while maintaining grid stability and economic viability. This paper presents a comparative analysis of three distinct optimization methodologies, like a rule-based (RB) heuristic approach, Model Predictive Control (MPC) with look-ahead capability, and a multi-objective Genetic Algorithm (GA). Simulation results that demonstrate the AI-optimized multi-energy storage (MES) integration significantly enhance the renewable utilization and reduce carbon emissions by approximately 30% compared to conventional approaches. Specifically, the MPC achieves a 29.9% reduction in carbon footprint (1741.1 kgCO₂ vs. 2485.2 kgCO₂ baseline) with corresponding operational cost savings of 30%, while GA shows a comparable 28.2% improvement. The comparative analysis discloses a critical trade-off between computational complexity, optimization performance, and practical implementability, with MPC emerging as a balanced method for a real-world application. This work has contributed to sustainable energy systems by providing a comprehensive framework for MES optimization, imparting treasured insights for grid operators and policymakers. The outcomes highlight the important role of AI-enabled digital twin in designing next-generation smart grid infrastructure, which is capable for supporting excessive renewable penetration at the same time as ensuring reliability and sustainable economic growth.