MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541122489 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy Vishwavidyapeeth; Malla Reddy University; Malla Reddy College Of Engineering And Technology; Malla Reddy Engineering College For Women; and Dr N. Manikanda Devarajan, Medchal-Malkajgiri, Telangana, on Dec. 5, 2025, for 'self-optimizing smart grid node using predictive load intelligence.'
Inventor(s) include Dr N. Manikanda Devarajan; Dr. K. Apoorva; Dr. Krati Kulshrestha; Dr. D. Damodara Reddy; Mr. V. Brahmam Yadav; Dr. Jella Sandhya; Dr. Kesavan Gopal; and Potaraju Yugender.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "The current invention reveals a self-optimizing smart grid node that will improve the reliability of operations, energy efficiency, and responsiveness of the modern electrical distribution networks. The conventional grid nodes use fixed-point regulations and old-fashioned load balancing policies that cannot project a sudden shift on the consumer demand, renewable-generation variations, or components load levels. These restrictions usually result in localised overloading, ineffective power routing and delayed fault detection. The given invention will resolve these issues by introducing a predictive load intelligence engine that will be located inside every grid node of the grid, and will provide the real-time evaluation of consumption behaviour, future load changes, and supply-demand limitations. The system enables every node to make an independent optimization of its operational parameters by using predictive insights as opposed to the reactive switching process. The invention has a multi-layered sensing and analytics infrastructure, which gathers voltage, current, frequency variations, harmonic and consumption patterns at high time resolution. These inputs are fed to a distributed prediction model that is able to learn seasonal, hourly and event driven variations in residential, commercial and industrial areas. They are also a contrast to traditional SCADA-controlled control, which is based on centralized decision-making: the disclosed grid node does local computation and local load allocation, phase balancing, and supply routing adjustments on-the-fly. This leads to lower latency, high resiliency, and better handling of micro level disturbances before they lead to inefficiencies at the network wide scale. The self-optimizing node also has a contextual decision module which analyzes indicators of grid health, transformer load margins, feeder conditions and the presence of renewable energy in its surroundings. The node, through its predictive analysis, will perform dynamically the control measures into demand shaping, load shifting, selective prioritization of critical circuits, and the optimization of dispatch of distributed energy sources. The decision framework will work with the neighbouring nodes to provide a better coordination of stability throughout the broader grid ecosystem. This invention is changing grid nodes into smart, adaptive agents that are able to ensure steady operational performance even when the load is volatile. The system, by incorporating the predictive load intelligence on a node level, makes the entire grid more efficient, lowers the chance of outage, and provides a more reliable supply of power to the end user."
Disclaimer: Curated by HT Syndication.
