Carbon-Aware Intelligent Electrical Distribution Network Using Digital Twins and Edge Artificial Intelligence - Milan Bharatkumar Makwana (Eastasouth Institute, 2024)
The escalating integration of distributed energy resources and the urgency of decarbonization targets have exposed the limitations of conventional, centrally managed electrical distribution networks. This paper synthesizes findings from twenty-three studies to examine how digital twin technology and edge artificial intelligence can be combined to form a carbon-aware intelligent distribution network. The problem addressed concerns the inability of legacy supervisory control and data acquisition architectures to deliver the millisecond-scale responsiveness, emission transparency, and adaptive reconfiguration required by high-penetration renewable grids. A layered conceptual framework is proposed, integrating field sensing, edge inference, digital twin synchronisation, and carbon-weighted reinforcement learning control. Evidence drawn from the literature indicates that hybrid edge-cloud architectures reduce control-loop latency from several hundred milliseconds to below 100 milliseconds relative to cloud-only deployment, while safe deep reinforcement learning controllers for Volt-VAR optimisation converge within 300 to 500 training episodes and reduce voltage violations substantially. Carbon emission flow modelling combined with temporally shifted, carbon-aware scheduling is shown to yield emission reductions ranging from approximately 10 per cent to more than 25 per cent when co-optimised with digital twin state estimation. The synthesis further identifies bandwidth reduction of up to 82 per cent and inference accuracy gains of nearly 5 percentage points for hybrid configurations. Implications include improved grid resilience, measurable decarbonization, and a pathway toward regulatory-compliant, self-optimising distribution networks, with future work directed toward standardised digital twin interoperability and federated edge learning.