Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Srinivas, Chodagam; Margaret, Vijaya Title A name given to the resource Hybrid Quantum Network with Snow Geese-Elk Herd Optimization for Smart Load Shedding in Grids with Electric Vehicles and Photovoltaic Systems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Iranian Journal of Science and Technology - Transactions of Electrical Engineering; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s40998-025-00925-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s40998-025-00925-z</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019073241?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019073241?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Srinivas C., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST Deemed to be University, Kengeri Campus, Banglore, India; Margaret V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST Deemed to be University, Kengeri Campus, Banglore, India Description An account of the resource The increasing penetration of variable renewable energy and the growth of electric vehicles (EV) have created an urge for more sophisticated load management methods to ensure grid stability. Conventional load shedding (LS) methods are typically not equipped to manage the unpredictability brought about by these modern additions to the grid. This study introduces an innovative smart load-shedding strategy that uses a hybrid optimization model. At its core is a Quantum Neural Network (QNN), which enables intelligent and data-based load prioritization by evaluating factors such as load criticality, energy usage, responsiveness to demand, and operational flexibility. The required LS amount is calculated through a combined use of Snow Geese Optimization (SGO) and the Elk Herd Optimizer (EHO), with specific attention given to the flexibility offered by EVs to address the variability in photovoltaic (PV) power generation. Testing has been performed on the IEEE 33-bus network reveal a notable decrease in total load demand by around 33%, contributing to improved grid stability, with voltage levels staying close to 0.99 p.u. Additionally, the average load across the network buses dropped by roughly 52%. This hybrid approach not only ensures better performance but also achieves quicker convergence compared to existing optimization methods. The proposed intelligent LS method presents an effective strategy for preserving grid stability amid growing integration of renewables and EV by incorporating QNN with SGO and EHO while accounting for EV adaptability. The Author(s), under exclusive licence to Shiraz University 2025. Subject The topic of the resource Demand response potential; Energy consumption patterns; Inflexible building load; Load criticality; Photovoltaic output; Real-time load flexibility Publisher An entity responsible for making the resource available Springer Nature Relation A related resource ISSN: 22286179; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online