Modeling of EV charging circuit by integrating renewable energy sources Solar, Wind with Grid
Journal Title: International Journal for Modern Trends in Science and Technology - Year 2024, Vol 10, Issue 11
Abstract
This paper presents a comprehensive framework for optimizing electric vehicle (EV) charging systems through the integration of solar wind and an advanced grid system. Central to this framework is the utilization of an Artificial Neural Network (ANN) controller, which dynamically adjusts energy extraction from renewable sources by employing the Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm. The system operates across four key modes: Firstly, in the Grid-to-Vehicle (G2V) mode, EV batteries are efficiently charged from the conventional power grid, ensuring optimal energy delivery to meet evolving demand patterns. Secondly, the Vehicle-to-Grid (V2G) mode enables the controlled discharge of surplus energy from EV batteries back to the grid, contributing to grid stability and facilitating bidirectional energy flow. Thirdly, the Renewable-to-Grid (R2G) mode directs energy generated by solar photovoltaic (PV) panels into the grid, promoting renewable energy integration and reducing reliance on fossil fuels. Lastly, the Renewable-to-Vehicle (R2V) mode prioritizes the utilization of solar energy to directly charge EV batteries, minimizing grid stress during peak demand periods. Through comprehensive simulations encompassing various environmental conditions and grid operation scenarios, the proposed framework's effectiveness is rigorously evaluated, considering metrics such as energy efficiency, grid stability, and economic viability. This paper proposes underscores the potential of integrating solar wind, ANN control, and advanced grid systems to optimize EV charging infrastructure, drive renewable energy adoption, and enhance the overall sustainability and resilience of modern energy grids.
Authors and Affiliations
Dr. K. V. R. B. Prasad, Y. Jayachandra, G. Chnadramohan Reddy, V. Ajith kumar Reddy, D. Raj kumar and V. Gangadhar Reddy
Keywords
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- EP ID EP752216
- DOI https://doi.org/10.46501/IJMTST1011007
- Views 35
- Downloads 0
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