Energy Management in PV Powered Electric Vehicle Charging Stations using Honey Badger Algorithm with Battery Backup and Vehicle-to-Grid

Photovoltaic (PV) Powered Electric Vehicle Charging Stations (EVCSs) have an important role in integrating Renewable Energy (RE) into transport, enabling sustainable mobility and efficient energy utilization. RE systems can be problematic due to intermittent solar power, battery degradation, bidirectional power management, and demand variations that affect the energy stability and grid integration. This paper proposes a Honey Badger Algorithm (HBA) for optimizing EM in PV-powered EVCSs by enhancing RE integration. The HBA is used to minimize Total Harmonic Distortion (THD) in EVCS. By optimizing the operational parameters, HBA effectively reduces harmonic distortions, leading to improved EM in EVCS. Its adaptive optimization ensures that THD is minimized across various operating conditions, while maintaining a balanced power distribution among the Photovoltaic (PV), the Electric Vehicles (EVs), and the grid. The proposed HBA method is executed in MATLAB platform and evaluated their performance with many existing methods including Artificial Neural Network with Particle Swarm Optimization (ANN-PSO), Water Filling Algorithm (WFA), Hunger Games Search Optimization Algorithm (HGSOA), Genetic algorithm (GA), and Robust Optimization (RO). The HBA method proposed outshone all other competitors, with a THD level of 1.04%, indicating its utmost capacity for the inhibition of harmonic distortion.