P.Gayathri, Murugan S, 2024. "Power Allocation and EV Charging System" ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume 2, Issue 4: 9-16.
IThe increasing adoption of electric vehicles (EVs) presents significant challenges to power grid management, particularly in balancing demand and supply while ensuring energy efficiency. This project explores a smart Power Allocation and EV Charging System designed to optimize energy distribution, enhance grid stability, and reduce charging costs. The proposed system integrates advanced algorithms for demand response, renewable energy utilization, and predictive analytics to dynamically allocate power to EV charging stations based on real-time data. By incorporating machine learning techniques, the system predicts peak usage times and adjusts charging schedules to minimize grid stress. A cloud-based monitoring and control system for real-time data collection and decision-making. An adaptive charging algorithm to prioritize critical loads and optimize charging rates. Integration of renewable energy sources like solar and wind to promote sustainability. This project aims to contribute to a scalable, cost-effective, and environmentally sustainable solution for managing the growing energy demands of EVs while ensuring grid reliability.
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Power Distribution, Power Management, Load Balancing, Dynamic Power Allocation, Distributed Power Systems, Demand-Side Management (DSM), Power Optimization, Electric Vehicle Charging, EV Charging Stations, Fast Charging, Slow Charging, Smart Charging, Charging Infrastructure, Charging Time Optimization.