Energy Management for PV-Powered EV Charging With Grid Integration and Battery Energy Storage System using Dung Beetle Optimizer

An Energy Management (EM) System for Photovoltaic (PV)-powered Electric Vehicle (EV) charging with grid integration and Battery Energy Storage System (BESS) optimizes energy consumption by integrating renewable energy, storage, and grid capabilities. However, drawbacks include the high initial costs associated with installation and infrastructure, as well as on-going operational costs for managing and maintaining both the storage and charging systems. Furthermore, the intermittency of solar energy can lead to inefficiencies in utilizing Renewable Energy Sources (RESs), requiring additional storage capacity to ensure a reliable power supply, while emissions from battery production, operation, and disposal may counteract some of the system’s environmental benefits. To overcome these drawbacks, this manuscript proposes an approach for EM for PV powered EV charging with BESS. The suggested method is Dung Beetle Optimizer (DBO). The major objective of this work is to reduce operational costs, improve energy efficiency, and lower carbon emissions by improving the use of renewable energy, minimizing grid reliance, and optimizing battery storage operations. The DBO optimizes the scheduling of EV charging, the operation of battery storage (charging and discharging cycles), and the interaction with the grid. Artificial Neural Network (ANN), Genetic Algorithm (GA), Adaptive Interaction Artificial Neural Network (AI-ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) are some of the existing methods that are compared with the suggested method once it is implemented in MATLAB. By achieving the lowest operational cost of 0.121M$/year and reducing carbon emissions to 505.73ktons, the suggested DBO method outperforms traditional approaches while maintaining high energy efficiency and environmental sustainability in PV-powered EV charging with BESS.