Summary
Delhi’s distribution grid is witnessing rapid growth in air conditioning, EV charging, and rooftop solar, much of which aligns with peak demand, intensifying stress on distribution transformers (DTs) and low-voltage (LV) feeders with limited real-time visibility. DT failure rates of 12–15% are reported in stressed networks due to overloading and thermal stress [1, 2, 3]. Although BRPL reduced its DT failure rate from 0.26% in FY 24–25 to 0% in the first two quarters of FY 25–26 [4], LV congestion and overloading risks persist in dense urban areas.
Read more Read lessThis paper presents a practical roadmap based on BRPL’s deployment of a distribution digital twin and predictive asset management framework—one of India’s first large-scale LV implementations. The system integrates network data, consumer-to-DT mapping, AMI, LV sensors, outage records, and drone imagery into a dynamic model enabling real-time visibility, state estimation, and simulation for operations and planning.
Benefits include faster fault localization, improved overload management, and enhanced hosting capacity assessment for EVs and solar. UNEP reports that predictive insights can reduce unnecessary DT upgrades and emergency replacements, saving several thousand dollars per transformer annually [3].
A DT health monitoring layer uses sensor data to derive Loss of Life (LoL) and Remaining
Useful Life (RuL) indices. EV charging introduces current harmonics of 8–10% and voltage
THD of ~2.3–2.4%, consistent with recent studies [9]. Combined with loading and connectivity data, this enables risk-based asset prioritization.
Key enablers include robust GIS data, accurate mapping, LV sensing, reliable communications, strong data governance, and skilled teams. Cyber-security is critical due to expanded attack surfaces; literature highlights vulnerabilities in EV chargers, inverters, and embedded devices, necessitating secure APIs, segmentation, encryption, and anomaly detection [12].
Additional informations
| Publication type | Session Materials |
|---|---|
| Reference | A3_10271_2026 |
| Publication year | |
| Publisher | CIGRE |
| Country | India |
| Study committees | |
| File size | 1 MB |
| Price for non member | 30 € |
| Price for member | 30 € |
Authors
KUMAR* Avinash - BSES Rajdhani Power Limited , India; NAGARAJAN Adarsh - BSES Rajdhani Power Limited , India; WADHERA Sugandhita - BSES Rajdhani Power Limited , India; KHETARPAUL Shaleen - BSES Rajdhani Power Limited , India; RANJAN Abhishek - BSES Rajdhani Power Limited , India
Keywords
Digital, Twin, Predictive, Asset, Management, Distribution, Grids
