DOI: https://doi.org/10.61435/ijred.2026.61916

Enhance EV Station Charge and Discharge Based on Deep Learning Forecasting Method Incorporating Renewable Energy Sources and Multi-Objective Optimization Algorithm

Jiangsu Vocational Institute of Architecture Technology, Xuzhou 221116, Jiangsu, China, China

Received: 27 Oct 2025; Published: 19 Aug 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2025 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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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.

Keywords: Electric vehicle charging stations; Deep learning; Recurrent neural network; Renewable energy source; Battery energy storage system; Vehicle-to-Grid

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Article Info
Section: Original Research Article
Language : EN
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