Optimal power quality enhancement of photovoltaic and plug-in electric vehicle for smart grid using HBA-GJO technique

The increasing complexity of power distribution systems, coupled with the growing stress on power production and the proliferation of nonlinear loads, poses significant challenges to enhancing smart grid power quality for photovoltaic (PV) systems and plug-in electric vehicles (PEVs). This paper proposes a hybrid method for optimal power quality enhancement of PV and PEV systems, combining the honey badger algorithm (HBA) and golden jackal optimization (GJO), often known as the HBA-GJO methodology. The proposed technique's goal is to compensate for harmonic and reactive currents, regulate the main grid frequency, and smooth peak demands under full loads. The HBA method is used to identify nonlinear load currents and determine compensating currents for PV and PEV converters, while the GJO method controls phase frequency and manages power exchange to ensure grid frequency stability. On the MATLAB platform, the performance of the HBA-GJO approach is evaluated and contrasted with other strategies that are currently in use. The HBA-GJO method achieves a total harmonic distortion value of 28%, which is significantly lesser, compared to other methods such as the enhanced artificial gorilla troops optimizer, particle swarm optimization-based artificial neural network (PSO-ANN), and generalized predictive control technique techniques. These results exhibit the performance of the HBA-GJO technique in improving power quality in smart grid applications.