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All Journal International Journal of Renewable Energy Development
Delu Li
Jiangsu Vocational Institute of Architecture Technology, Xuzhou 221116, Jiangsu, China
Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Enhance EV Station Charge and Discharge Based on Deep Learning Forecasting Method Incorporating Renewable Energy Sources and Multi-Objective Optimization Algorithm Delu Li
International Journal of Renewable Energy Development Accepted Articles
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.61916

Abstract

Electric vehicle charging stations must be efficient and sustainable to meet increasing demand. This exploration recommends the incorporation of RESs, energy storage systems (ESS), and vehicle-to-grid (V2G) technology to boost charging and discharging operations using DL-based forecasting. The framework predicts energy generation, electric vehicle (EV) demand, and storage dynamics using an RNN to capture non-linear relationships and temporal dependencies. The model optimizes charge and discharge cycles in real time to reduce grid reliance, operational costs, and emissions, while accounting for energy pricing and grid stress. The system incorporates solar and wind energy generation along with fuel cells and battery storage to create a coordinated configuration that adapts to fluctuations in renewable energy availability. Battery storage retains excess energy during periods of low demand and high renewable availability, while V2G technology enables EVs to supply energy back to the grid during peak demand, improving grid stability and operational efficiency. This hybrid setup reduces dependency on the grid, particularly during periods of high costs and emissions. A comprehensive simulation is conducted using data that includes solar, wind, and load profiles, in addition to real-time pricing and grid conditions. Outcomes showcase a 6.7% drop in operational costs and a 4.4% decrease in emissions compared to the baseline scenario. Utilizing battery energy storage systems (BESS) and V2G technology enables better grid load balancing, especially during peak demand periods. This article presents a novel methodology for optimizing EV charging and discharging, offering a sustainable, economical, and scalable solution for future energy infrastructures.
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