Physics-Informed AI Digital Twins For Ultra-Fast, Scalable Time-Series Power Flow And Automated Grid Constraint Resolution

Siva Prakash Sunkara

doi:10.29284/ecrg7s57

Authors

Siva Prakash Sunkara Utility GIS & Smart Grid Technology Consultant (SME) , Smart Grid & Grid Operations Technology , Tata Consultancy Services,Glen Ellyn ,Illinois ,60137,USA

DOI:

https://doi.org/10.29284/ecrg7s57

Keywords:

Digital twin, physics-informed neural network, voltage prediction, distribution networks, constraint monitoring, load adjustment, real-time power flow, and IEEE 33-bus system.

Abstract

This paper presents a physics-informed AI digital twin framework for real-time voltage monitoring and corrective control in distribution networks. By combining a neural network surrogate with AC power flow constraints, the system predicts bus voltages, detects violations, and automatically adjusts loads. Validation on the IEEE 33-bus system demonstrates high accuracy (MAE ≈ 0.070 pu) and rapid computation, enabling reliable and scalable grid operations.

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