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

Abstract

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.

Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles

“…The control output that affects the charging process (Zhao and Liang, 2023) Charging current, charging voltage (Emodi et al, 2023) Defuzzification Method…”

A customer satisfaction-based optimization model for the charging and discharging path and battery swapping stations’ site selection of electric vehicles

“…Cheng et al (2023) proposed an EV charging load prediction method based on variational mode decomposition and the Prophet-LSTM neural network to solve the problem of the charging station location. Zhao and Liang (2023) proposed a new charging scheduling and energy management approach for smart grid electric vehicles based on genetic algorithms (GAs), gated recurrent unit (GRU) neural networks, and reinforcement learning (RL) algorithms. Wang et al (2020) proposed a BSS site selection framework based on the MCDM (multi-project decision method), which takes into account the lack of information in the site selection process of the replacement station and uses triangular fuzzy numbers to deal with uncertainty.…”