Optimizing electric vehicle charging schedules and energy management in smart grids using an integrated GA-GRU-RL approach

Frontiers in Energy Research

Energy Research

Guojun Liang¹  Xinhui Zhao² 

[1] School of Information Technology, Halmstad University, Halmstad, Sweden;Zhuhai Technician College, Zhuhai, China;

关键词: smart grid;    deep learning;    electric vehicle charging scheduling;    smart city;    green energy management;    reinforcement learning;   

DOI  :  10.3389/fenrg.2023.1268513

 received in 2023-07-28, accepted in 2023-08-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: Smart grid technology is a crucial direction for the future development of power systems, with electric vehicles, especially new energy vehicles, serving as important carriers for smart grids. However, the main challenge faced by smart grids is the efficient scheduling of electric vehicle charging and effective energy management within the grid.Methods: To address this issue, we propose a novel approach for intelligent grid electric vehicle charging scheduling and energy management, integrating three powerful technologies: Genetic Algorithm (GA), Gated Recurrent Unit (GRU) neural network, and Reinforcement Learning (RL) algorithm. This integrated approach enables global search, sequence prediction, and intelligent decision-making to optimize electric vehicle charging scheduling and energy management. Firstly, the Genetic Algorithm optimizes electric vehicle charging demands while minimizing peak grid loads. Secondly, the GRU model accurately predicts electric vehicle charging demands and grid load conditions, facilitating the optimization of electric vehicle charging schedules. Lastly, the Reinforcement Learning algorithm focuses on energy management, aiming to minimize grid energy costs while meeting electric vehicle charging demands.Results and discussion: Experimental results demonstrate that the method achieves prediction accuracy and recall rates of 97.56% and 95.17%, respectively, with parameters (M) and triggers (G) at 210.04 M and 115.65G, significantly outperforming traditional models. The approach significantly reduces peak grid loads and energy costs while ensuring the fulfilment of electric vehicle charging demands and promoting the adoption of green energy in smart city environments.

【 授权许可】

Unknown   
Copyright © 2023 Zhao and Liang.

【 图 表 】

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