Genetic Algorithm-Based Strategies for Enhanced Power Management in Hybrid Charging Systems
DOI:
https://doi.org/10.24113/ijoscience.v10i6.524Abstract
The integration of renewable energy sources with advanced hybrid energy storage systems for performance optimization of electric vehicle charging stations will be discussed. The system suggested here will consist of a hybrid solar-wind power generation along with the combination of a flywheel energy storage system and permanent magnet synchronous machine for innovative energy storage. This hybrid configuration efficiently balances the energy supply-demand curve, thereby overcoming the challenges associated with the variability of renewable energy and the growing energy demands of EVs. Genetic algorithms (GAs) are used to optimize power flow management, thus ensuring efficient energy distribution, reduced operational costs, and stable DC bus voltage under dynamic conditions. Simulation results show substantial impacts, such as a 15% cost reduction in operational cost and 10% enhancement in overall energy efficiency. It further ensures effective renewable energy sources integration, minimizing dependence on the traditional grid, hence promoting sustainability. The outcomes further indicate dynamic load balancing and stable energy storage output with SOC pattern improvement. These accomplishments emphasize the capability of the system to deliver reliable and economic energy supplies, contributing to a cleaner and more resilient energy ecosystem. This framework offers a scalable solution for the development of sustainable EV charging infrastructures across the globe
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