In the forthcoming decades, significant advancements will shape the construction and operations of distribution systems. Particularly, the increasing prominence of photovoltaic (PV) systems in the power industry will impact the security of these systems. This study employs Monte Carlo Simulation (MCS) in conjunction with genetic algorithm (GA) and differential evolution (DE) to address uncertainties. The probability density functions (pdf) for total voltage harmonic distortion (UTHD), individual voltage harmonic distortion (UIHDh), and RMS voltage (URMS) have been determined for utilization in chance constrained framework. In addition, the uncertainty effects of PV systems on grid losses for various solar radiation conditions are also investigated. Specifically, the paper aims to evaluate these impacts within the context of stochastic limits. The PV system sizing problem has been addressed inside the distribution system using a chance-constrained framework. A key contribution is the integration of GA, DE, and MCS into a cohesive approach, and the study evaluates the benefits of this approach through an analysis of outcomes derived from the stochastic method. The simulation results illustrate the advantages of the proposed stochastic GA methodology.

Several types of power quality problems can be observed in power network. These power quality problems include voltage unbalance, voltage flicker, voltage sags and swells, interruptions, and voltage and current harmonics. Voltage sag is one of the vital power quality issues. This problem can cause a decrease in power grid voltage over a short time horizon. The aforementioned problem is caused by various types of short circuits and the widespread use of sensitive devices. These problems have a significant impact on power systems. In this review article, the different ways to prevent voltage sag have been presented by considering literature studies. Concerning voltage sags in radial and mesh grids, the equipment used, and methods have all been taken into account. In these studies, the articles about voltage sag-related problems have been put into groups based on certain criteria. These criteria include the consideration of uncertainties in electricity demand or renewable sources. The recommendations related to the voltage sag studies have been presented. These suggestions are based on the inclusion of electricity consumption and renewable energy source uncertainties. The future works have been mentioned by considering these load and renewable system uncertainties.

This study investigates the impact that uncertainties in photovoltaic (PV) performance under a variety of solar irradiance scenarios have on the expected power losses that occur when probabilistic distribution network restrictions are taken into consideration. Through the use of Teaching–Learning–Based Optimization (TLBO) and Monte Carlo Simulation (MCS) in conjunction with an emphasis on chance-constrained approaches, this study contributes to the enhancement of the body of literature for optimal PV installation in distribution networks. The outputs of the optimization are validated using MCS under a variety of uncertainty scenarios, and the variables of the distribution network are evaluated in terms of the probability of exceeding constraints. This is done in order to demonstrate that the suggested technique is effective. A comparison is made between the outcomes of TLBO and the implementation of Artificial Bee Colony (ABC) technique, which make use of probabilistic evaluation and modeling. It has been shown via simulation that the TLBO methodology is superior than the ABC method in terms of efficiently minimizing power losses.