The report, titled “2025 Predictions – Energy Transition & Utilities Technology and Industry Trends in India,” highlights key areas where technology and policy shifts are expected to drive India’s energy transition over the next two years.

The confluence of the internet of things (IoT) with smart grids has ushered in a paradigm shift in energy management, promising unparalleled efficiency, economic robustness and unwavering reliability. However, this integrative evolution has concurrently amplified the grid's susceptibility to cyber intrusions, casting shadows on its foundational security and structural integrity. Machine learning (ML) and deep learning (DL) emerge as beacons in this landscape, offering robust methodologies to navigate the intricate cybersecurity labyrinth of IoT-infused smart grids. While ML excels at sifting through voluminous data to identify and classify looming threats, DL delves deeper, crafting sophisticated models equipped to counteract avant-garde cyber offensives. Both of these techniques are united in their objective of leveraging intricate data patterns to provide real-time, actionable security intelligence. Yet, despite the revolutionary potential of ML and DL, the battle against the ceaselessly morphing cyber threat landscape is relentless. The pursuit of an impervious smart grid continues to be a collective odyssey. In this review, we embark on a scholarly exploration of ML and DL's indispensable contributions to enhancing cybersecurity in IoT-centric smart grids. We meticulously dissect predominant cyber threats, critically assess extant security paradigms, and spotlight research frontiers yearning for deeper inquiry and innovation.

Network planning Power electronics (HVDC/FACTS) Bulk renewable integration Energy Management System (EMS) Smart substation automation and protection Integrated Substation Condition Monitoring (ISCM) Communication Solutions Distribution Management System (DMS) Distribution automation and protection Distributed Energy Resources (DER) Decentralized Energy Management System... Read more

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Boosting Energy Efficiency through Smart Grids

IEEE / Institute of Electrical and Electronics Engineers Incorporated

IEEE / Institute of Electrical and Electronics Engineers Incorporated

The Tea Party and a few others don't like the idea of 'Big Brother' monitoring our electricity usage.

A method for estimating loss rates of links in a smart grid includes identifying end-to-end passage rates from a source to each of multiple receiver and/or load meters in a smart grid, and estimating a loss in at least one link of the grid by identifying the sparsest solution of link loss rates that matches the end-to-end passage rates. Also, a system includes a memory, a processor coupled thereto, and software modules executing on the processor, including a monitoring module for monitoring power flow of receiver and/or load meters in a smart grid, a measurement collector module for collecting measurements to compute end-to-end passage rates from a source to each receiver and/or load meter, and a processing module for estimating a loss in a link of the grid by identifying the sparsest solution of link loss rates matching the end-to-end passage rates.

IBM collaborates with academia and industry to educate and prepare IT professionals, telecommunications engineers, and electrical power engineers for the electricity networks of the future

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