Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method

Abstract

Conventional energy sources are a major source of pollution. Major efforts are being made by global organizations to reduce CO 2 emissions. Research shows that by 2030, EVs can reduce CO 2 emissions by 28%. However, two major obstacles affect the widespread adoption of electric vehicles: the high cost of EVs and the lack of charging stations. This paper presents a comprehensive data-driven approach based demand-side management for a solar-powered electric vehicle charging station connected to a microgrid. The proposed approach utilizes a solar-powered electric vehicle charging station to compensate for the energy required during peak demand, which reduces the utilization of conventional energy sources and shortens the problem of fewer EVCS in the current scenario. PV power stations, commercial loads, residential loads, and electric vehicle charging stations were simulated using the collected real-time data. Furthermore, a deep learning approach was developed to control the energy supply to the microgrid and to charge the electric vehicle from the grid during off-peak hours. Furthermore, two different machine learning approaches were compared to estimate the state of charge estimation of an energy storage system. Finally, the proposed framework of the demand management system was executed for a case study of 24 hours. The results reflect that peak demand has been compensated with the help of an electric vehicle charging station during peak hours.INDEX TERMS CO 2 emission, data-driven approach, deep learning, demand-side management, electric vehicle charging station, peak clipping.

Review of Power Quality and Stability of EV Charging with Grid and PV Solar

“…In order to manage the microgrid's energy supply and the grid's charge of the electric vehicle during off‐peak hours, a deep learning approach was devised. [ 42 ] Artificial neural networks and long‐short‐term memory model‐based deep learning algorithms are used and contrasted to estimate the EV charging load from the station's perspective. [ 43 ] Among deep learning models, the recurrent neural network (RNN) is extremely popular.…”

“…[ 45 ] To assess the condition of charge of an energy stockpiling device, two alternative ML methods were contrasted. [ 42 ] By generating real‐time charging decisions based on a variety of auxiliary data, such as driving, environment, pricing, and demand time series, ML tools for EV charging reduce the overall cost of vehicle energy. [ 46 ]…”

Season Aware Solar Photovoltaic-Based Efficient EV Charging System Using A Novel JFL-BiLSTM And RF-SHO

“…Moreover, for upgrading energy utilization, Battery Storage Systems (BSS) are integrated with S-PV and utility grid. The routine of the EV charging systems is mostly affected by stability, grid interruption, blackout, and uncertainty problems [8][9]. Thus, to conquer the instabilities and uncertainties, more efficient controlling strategies and optimization approaches are used [10].…”

Data-Driven Energy Management of an Electric Vehicle Charging Station Using Deep Reinforcement Learning

“…The reinforcement learning (RL) method is a generalized machine learning (ML) method in which an agent learns from past actions in the environment without requiring an environment model. It is a powerful tool for decision-making problems [18]. An MDP model of the EV charging problem was addressed in most publications in order to construct an RL model capable of accomplishing a certain goal for the EVCS [19].…”