Energetica India Magazine

el intelligence: aggregating thousands of rooftop systems and farm pumps into something a DISCOM can actually forecast and balance, much like a virtual power plant. It also needs far better visibility down to the feeder and transformer lev- el – which is why efforts like the ISA–AVVNL initiative on GIS-based network mapping and digital twins matter. DER integration is as much a metering and data-standardisation challenge as it is a control-systems challenge. Get the data model right, and the AI layer on top becomes much easier to build. Q What are the key technologies enabling the shift from conventional grids to intelligent, self-healing smart grids, and how ready are Indian utilities to adopt them? Meenakshi Vashist: Three building blocks matter most: ad- vanced metering infrastructure to generate granular, re- al-time data (India has already crossed roughly 60 million smart meters); ADMS and DERMS platforms that give operators real-time visibility and automated control; and self-healing capability itself – what the industry calls FLISR or FDIR – where the network detects a fault, isolates it, and reroutes power automatically, without a human in the loop. Digital twins are the layer that ties all of this together, letting utilities simulate and plan before they act. On readiness, I’d give a balanced picture. The institutional foundation is solid – the National Smart Grid Mission has been active since 2015, and RDSS has earmarked over INR 3 lakh crore for distribution modernisation with a target of 250 million smart meters. The results are showing up in the numbers: AT&C losses have fallen from nearly 22 percent to about 15 percent over four years, and DISCOMs posted their first-ever aggregate profit. But most utilities today are still further along on metering and billing digitisation than on real-time ADMS/DERMS deployment. Fully automated, self-healing grids remain concentrated in a small set of pro- gressive DISCOMs rather than being the norm. Q How is AI-powered predictive maintenance helping utili- ties reduce outages, optimise asset performance, and im- prove grid reliability? Meenakshi Vashist: The industry-wide numbers are com- pelling: predictive maintenance programmes typically cut unplanned downtime by 35-50 percent, reduce unexpected equipment failures by up to 70-75 percent, and lower main- tenance costs by roughly a quarter, while extending asset life by up to 17 percent. In power generation specifically, facilities have recovered the full cost of implementing AI-based predic- tive maintenance within six months of deployment. WOMAN INFLUENCER energetica INDIA- Jul-Aug_2026 63