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Renewable energy, grid management, and power systems
6 Problem Statements in this Domain (5 Standard + 1 Open Innovation)
Non-technical losses (theft, meter tampering, unbilled connections) remain a major source of revenue loss for rural power distribution, but most rural low-tension networks have only partial smart-meter penetration, meaning loss detection cannot rely on the dense, granular consumption data assumed by standard smart-grid theft-detection approaches. The problem requires inferring likely loss locations from a genuinely incomplete, mixed digital/manual metering environment, where much of the network remains effectively unobserved.
Rural distribution transformers fail at elevated rates during extreme summer heat combined with peak agricultural pump-load demand, but most rural feeders lack the dense sensor instrumentation that would allow direct condition-based failure prediction, forcing utilities to rely mainly on reactive replacement after failure — causing extended outages for entire villages during exactly the season when reliable power matters most for irrigation. A useful failure-risk prediction approach must work from sparse, indirect signals (ambient conditions, aggregate load data, minimal available sensor readings) rather than assuming rich per-transformer instrumentation.
Decentralized microgrids serving remote villages typically combine intermittent solar/wind generation with limited battery storage and a diesel backup generator, and optimal operation requires balancing multiple conflicting objectives — minimizing diesel/fuel cost, maximizing renewable utilization, maintaining supply reliability for critical loads (health centre, water pumping), and protecting battery lifespan — under genuinely uncertain renewable generation and unpredictable village-level demand patterns that differ significantly from urban load profiles.
When a fault occurs on a rural low-voltage distribution line, current practice often relies on customer complaint calls and manual line-walking to locate the fault, which can take hours, especially at night or across long, sparsely-populated feeder sections — extending outage duration significantly. Precise fault localization techniques used on higher-voltage, well-instrumented networks depend on infrastructure (synchronized measurement units, dense metering) that rural low-voltage networks generally lack, making the problem one of extracting maximum localization value from minimal available data (substation-level readings, sparse complaint reports, network topology).
As India's EV fleet grows, a large volume of batteries will reach end-of-automotive-life (typically at 70–80% original capacity) while still holding significant value for less-demanding stationary storage applications (e.g., pairing with rural solar microgrids), but repurposing requires reliably assessing the remaining health, safety, and expected degradation trajectory of batteries with unknown, heterogeneous prior usage histories — a genuinely hard problem given the lack of standardized battery health data across different vehicle makes, chemistries, and usage patterns feeding into the second-life supply.
Have your own innovation in Renewable Energy & Power Systems? Develop your own smart microgrid management system, solar/wind forecasting and load-balancing algorithm, battery health diagnostic tool, energy theft detection tool, or smart home power-saving automation system.