Research Information System
IEEE Access, vol.13, pp.133321-133337, 2025 (SCI-Expanded, Scopus)
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.