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Comparative analysis and predictive optimization using EVISON for enhanced electric vehicle charging coordination
Abstract
Electric vehicle (EV) charging integration into modern power systems introduces challenges such as increased active power loss, voltage instability, constraint violations, and suboptimal charging infrastructure utilization. Existing strategies, Static scheduling, Rule-Based Heuristics, and Dynamic & Adaptive methods, often compromise between computational efficiency and operational reliability. This study presents a comprehensive comparative analysis and predictive evaluation of the proposed EVISON optimization framework against state-of-the-art methods. Performance metrics include Active Power Loss, Voltage Deviation, Constraint Violation Rate, Charging Station Utilization, and Execution Time. Results indicate that EVISON achieves a 45.83% reduction in active power loss, 51.11% improvement in voltage stability, 91.67% lower constraint violations, 32.86% higher charging station utilization, and 51.2% faster execution compared to the best-performing baselines. The findings highlight EVISON’s capability to simultaneously enhance operational efficiency, service reliability, and computational feasibility, establishing it as a robust solution for real-time smart grid and EV charging network management.
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DOI: https://dx.doi.org/10.21622/resd.2026.12.1.1644
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Copyright (c) 2026 Niti Shashikant Desai, Chandrakant Rathore
Renewable Energy and Sustainable Development
E-ISSN: 2356-8569
P-ISSN: 2356-8518
Published by:
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Arab Academy for Science, Technology and Maritime Transport (AASTMT)
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