AI powered Battery Energy Storage solution for DMRC, leveraging Digital Twin technology and Battery AI to unlock energy savings, regenerative braking capture, and AI-optimized peak shaving across one of the world's largest metro systems.
Delhi Metro Rail Corporation (DMRC), one of the world's largest metro systems carrying over 6 million passengers daily, engaged Ingro Energy to deploy an AI powered Battery Energy Storage solution for optimizing energy consumption across its operations. The project leverages Ingro's Digital Twin technology and Battery AI to unlock energy savings, improve power quality, and enable regenerative braking energy capture, turning metro infrastructure into an intelligent, self-optimizing energy asset.
Massive energy consumption, traction power for hundreds of daily train services across 390+ km and 285+ stations
Regenerative braking waste, significant electrical energy dissipated as heat in braking resistors rather than captured and reused
Demand charge burden, sharp load peaks during rush hours drive up maximum demand charges
Power quality on traction feeders, rapid load swings create voltage fluctuations and harmonic distortion
No unified energy visibility, consumption data siloed across substations, traction systems, and station loads
Digital Twin Modeling: Comprehensive digital replica of DMRC's electrical infrastructure, modeling substations, train schedules, and energy flows
Regenerative Braking Capture: BESS integration to absorb braking energy from decelerating trains for reuse during acceleration events
AI-Optimized Dispatch: Battery AI analyzes train schedules and real-time data to pre-position battery state-of-charge for optimal capture and release
Peak Demand Management: Automated peak shaving that anticipates rush-hour spikes to flatten the demand curve
Unified Energy Dashboard: Single cloud-based monitoring platform across all energy assets, substations, BESS, station loads, and traction feeders
Metro Transit, Delhi, India
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