AN AI-AUGMENTED HYBRID RENEWABLE ENERGY SYSTEM FOR INDIAN RAILWAY STATIONS: A CASE STUDY AT MUMBAI CENTRAL TERMINUS

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🔬研究者:AI-augmented energy management for hybrid renewables: validates LSTM, reinforcement learning, and predictive maintenance in a real-world railway setting.

🏢実務担当者:Railway and facility energy managers can adopt this software-only approach to increase renewable self-consumption and reduce grid reliance.

🏛政策担当者:Transport decarbonization strategies can leverage AI retrofits as low-cost, scalable solutions for achieving net-zero targets.

The Indian Railways consumes about 20 billion units of electricity in a year, constituting about 2.4 percent of the national electricity demand. The Railway Ministry has pledged to achieve net-zero carbon emissions from its operations by 2030. Most of the electricity consumed by Railways is for traction. However, considerable demand exists at the station level. Therefore, in the absence of any traction-related savings, an on-site renewable energy system at major railway stations can help the Ministry achieve its net-zero carbon emissions goal. The present study focuses on sizing and simulating an integrated renewable energy system at Mumbai Central Terminus. The system is designed to incorporate 1.41 MWp of rooftop solar PV, piezoelectric energy harvesting from footfalls of passengers, piezoelectric energy harvesting from rail vibrations, and thermoelectric energy recovery from the waste heat of the air conditioning system. The system is buffered by a 3.1 megawatt-hour lithium-iron-phosphate battery and connected through a 1.5 megavolt-ampere inverter to the grid. Each subsystem is sized from first principles using internationally accepted methods, with all governing equations and substituted values reported explicitly so the calculations can be reproduced. The same physical hardware is then simulated under two control strategies in MATLAB and Simulink. The conventional version uses today's standard controllers, while an artificial-intelligence-augmented version replaces them with a long short-term memory forecaster, neural-network maximum-power-point trackers, a Twin-Delayed Deep Deterministic Policy Gradient reinforcement-learning energy manager, and an XGBoost-with-One-Class-Support-Vector-Machine predictive maintenance pipeline. Across a 24-hour and 30-day benchmarking horizon, the AI layer raised annual renewable yield by 5.9 percent, reduced grid imports by 26 percent, cut daily battery cycling by 23 percent, and provided multi-week warning of four common failure modes. Renewable share of station load grew from 51 percent to 58 percent, all at zero additional capital cost on the physical plant. Replicated across more than two thousand already solarised stations on the Indian Railways network, the same software-only upgrade represents roughly 270 gigawatt-hours of additional renewable energy each year, comparable to a 150-megawatt utility-scale solar plant, with no extra panels, land, or batteries.

• Zenodo https://zenodo.org/records/20369329first seen 2026-05-25 04:13:47 · last seen 2026-06-03 04:22:33