A Hybrid Approach for Smart Energy Management in Microgrids With Electric Vehicle Charging Using Snow Ablation Optimization and Cascade Chaotic Neural Network

Abstract

Integration of Renewable Energy Sources (RES) with Electric Vehicles (EVs) elucidates a crucial area in Energy Management (EM) for Microgrids (MGs). Probably the most difficult job is stochastic behavior from RES together with unpredictable EV charging demands, aspires towards grid stability, and destabilizes prompt frequency control. This article introduces a hybrid methodology designed for intelligent EM in MGs with EV charging. Proposed method integrates Snow Ablation Optimization (SAO) and Cascade Chaotic Neural Network (CCNN); therefore, it is called the SAO-CCNN technique. The aim is to improve economic performance of the MG integrated by EV charging by minimize the Operating Cost. SAO optimizes the utilization of RES and EVs, improving overall energy management. The CCNN is employed to predict the participation probability of EVs in grid support activities, thereby aiding in the accurate forecasting of energy demand. The suggested SAO-CCNN technique is implemented on MATLAB platform and evaluated against existing optimization methods, including Firefly Optimization Algorithm (FOA), Particle Swarm Optimization (PSO), Robust Optimization Algorithm (ROA), Multi Objective Optimization (MOO), and Whale Optimization Algorithm (WOA). The operating cost achieved using the proposed method is $17 184.1, demonstrating improved cost-efficiency compared to optimization methods.