Abstract
Electric vehicles (EVs) have gained in popularity over the years. The charging of a high number of EVs harms the distribution system. As a result, increased transformer overloads, power losses, and voltage fluctuations may occur. Thus, management of EVs is required to address these challenges. An EV charging management system based on machine learning (ML) is utilized to route EVs to charging stations to minimize the load variance, power losses, voltage fluctuations, and charging cost whilst considering conventional charging, fast charging, and vehicle-to-grid (V2G) technologies. A number of ML algorithms are contrasted in terms of their performances in optimization since ML has the ability to create accurate future decisions based on historical data, which are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Long Short-Term Memory (LSTM) and Deep Neural Networks (DNN). The results verify the reliability of the use of LSTM for the management of EVs to ensure high accuracy. The LSTM model successfully minimizes power losses and voltage fluctuations and achieves peak shaving by flattening the load curve. Furthermore, the charging cost is minimized. Additionally, the efficiency of the management system proved to be robust against the uncertainty of the load data that is used as an input to the ML system.
Highlights
Electric vehicles (EVs) have become an integral part of the automobile industry
We present an EV charging management system that considers the use of conventional charging, fast charging and V2G technologies, with a high degree of robustness against the load data uncertainty that may occur with the input data of the machine learning (ML) system to ensure reliability and effectiveness
The proposed system in this paper focuses on managing the charging of EVs by optimizing the voltage profile, the power losses, the transformer loading and the charging cost using ML, which will, in turn, reduce power overloads in the grid and enhance the voltage profile of the grid
Summary
Electric vehicles (EVs) have become an integral part of the automobile industry. EV sales reached 2.1 million in 2019, continuing the mean 40% yearly increase in EV sales [1]. The large demand on the use of EVs causes substantial strain of the distribution grid due to the large power demands during the charging process of such vehicles. Such demand is expected to increase, with the increasing use of EVs as more methods of decreasing operating costs for users are explored through different driving techniques [6]. An EV managing and routing solution is presented for optimizing the operation of the distribution grid, considering conventional charging, fast charging and vehicle-to-grid (V2G) technologies. ML algorithm as an optimization technique, as well as minimize load variance, power losses, voltage fluctuations and charging cost. Nomenclature lists the nomenclature and symbols used in the paper
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