Abstract
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.
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Data and code availability
The datasets, code, and materials generated during this study have been deposited in public repositories to ensure reproducibility. Source Code: MATLAB scripts for all optimization algorithms (RB, MPC, GA), LSTM forecasting model, and digital twin framework are available at: [*https://github.com/sakthi0707/Digital-twin-mes-optimization*](https:/github.com/sakthi0707/Digital-twin-mes-optimization)Archived Version (with DOI): A preserved, citable version of the code and data is available via Zenodo: [*https://doi.org/10.5281/zenodo.17824324*](https:/doi.org/10.5281/zenodo.17824324). License: Code is released under the MIT License; data under CC BY 4.0. Reproducibility Package : Complete set of scripts to regenerate all figures and tables from the paperThe code is released under the MIT License, and the data under the CC BY 4.0 license. Detailed documentation and run instructions are provided in the repository README files.
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Acknowledgements
The authors gratefully acknowledge the support and resources provided by Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences(SIMATS), Chennai, Tamil Nadu, India. Special thanks to the Department of Electronics and Communication Engineering and Department of Computer Science and Engineering for their academic support and infrastructure.
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Sakthivel S: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization.M. Arivukarasi: Supervision, Project administration, Resources, Writing – review & editing.G. Charulatha: Supervision, Validation, Writing – review & editing.J. Nithisha: Supervision, Writing – review & editing.Abirami B: Supervision, Writing – review & editing.Jaithunbi A K: Supervision, Writing – review & editing.V. Suresh Kumar: Supervision, Resources, Writing – review & editing **.**All authors have read and approved the final manuscript.
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Sakthivel, S., Arivukarasi, M., Charulatha, G. et al. A multi strategy optimization framework using AI digital twins for smart grid carbon emission reduction. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38720-3
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DOI: https://doi.org/10.1038/s41598-026-38720-3
