Optimal allocation of solar photovoltaic distributed generation for performance enhancement of electrical distribution networks considering optimal volt-var regulation under uncertainty and high load variation

This article proposes an optimal placement and sizing of photovoltaic (PV) power systems based distributed generation (DG) in radial electrical distribution networks considering the capability of PV inverters to regulate the voltage by optimal injecting and absorbing reactive power at the point of common coupling (PCC) using honey badger algorithm (HBA), as a recent and efficient optimization algorithm to solve the complicated optimal allocation. Several objective functions are achieved for distribution system performance enhancement: minimizing the power loss and the voltage deviation index (VDI) and maximizing the voltage stability index (VSI). Based on historical data and probabilistic models, seasonal hourly solar irradiance, ambient temperature, and load variation curves have been modeled, which simultaneously consider the light, normal, and heavy load demand. The essential components of distribution power systems have been characterized. To investigate the validity of the proposed approach, IEEE 33 and IEEE 69 BUS radial distribution test systems have been considered for power flow (PF) analyses, where Newton's Raphson method has been applied to solve the PF issue. The simulation results of different numerical scenarios have shown the effectiveness and validity of the newly proposed method to solve the optimal allocation problem considering optimal volt-var regulation control of PV inverters compared to several valid and robust optimization algorithms. The obtained results confirm that incorporating decentralized generation systems, which utilize intelligent photovoltaic inverters capable of optimally injecting and absorbing reactive power at the point of common coupling, significantly enhances the performance of the distribution grid. This approach effectively manages weather uncertainties and high load variations in the optimal allocation problem. image