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

S Sakthivel1, M Arivukarasi2, G Charulatha3

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Summary

This study introduces an AI digital twin for smart grids, optimizing diverse energy storage to boost renewable use and cut carbon emissions by 30%. Model Predictive Control (MPC) offers a practical balance of performance and complexity for real-world application.

Keywords:
AI optimizationCarbon neutral smart gridsDigital twinModel predictive controlMulti energy storageRenewable energy

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Sustainable Energy Systems

Background:

  • Smart grids face challenges integrating renewable energy sources while maintaining stability and economic viability.
  • Heterogeneous energy storage systems (battery, thermal, hydrogen) offer a solution but require sophisticated management.
  • Achieving carbon neutrality in energy systems necessitates advanced optimization and control strategies.

Purpose of the Study:

  • To develop and evaluate an AI-enabled digital twin framework for optimizing heterogeneous energy storage systems in smart grids.
  • To assess the effectiveness of different optimization methodologies in enhancing renewable energy utilization and reducing carbon emissions.
  • To provide insights into the trade-offs between computational complexity, performance, and implementability for smart grid control.

Main Methods:

  • Development of an AI-enabled digital twin integrating battery, thermal, and hydrogen storage.
  • Implementation and comparative analysis of three optimization approaches: Rule-Based (RB) heuristic, Model Predictive Control (MPC), and multi-objective Genetic Algorithm (GA).
  • AI-driven forecasting models for addressing renewable integration challenges.

Main Results:

  • AI-optimized multi-energy storage (MES) integration significantly enhances renewable utilization.
  • Carbon emissions were reduced by approximately 30% compared to conventional approaches.
  • MPC achieved a 29.9% reduction in carbon footprint and 30% operational cost savings, with GA showing a 28.2% improvement.

Conclusions:

  • The AI-enabled digital twin framework effectively optimizes MES for smart grids, contributing to carbon neutrality.
  • MPC presents a balanced approach for practical implementation in real-world smart grid applications.
  • The study highlights the critical role of AI in next-generation smart grids for high renewable penetration, reliability, and economic growth.