Akademik Veri Yönetim Sistemi
Energy Conversion and Management, cilt.349, 2026 (SCI-Expanded, Scopus)
This study presents a method that combines Monte Carlo Simulation (MCS) with Modified Honey Badger Algorithm (MHBA) for the first time to investigate the impact of photovoltaic (PV) system uncertainties on power losses under different solar radiation conditions. Moreover, this research proposes the chance – constrained probabilistic planning by generating probability distribution functions (pdf) of bus voltage and line current and taking into account probability constraints for encouraging more efficient and secure utilization of electricity. This study contributes to the information base concerning the optimal PV system placement by considering chance – constrained method. The simulation results reveal that the MHBA approach demonstrates more advantageous computational performance as compared to Differential Evolution (DE) in the IEEE 118 bus distribution system. By running both algorithms several times, MHBA method produces the best optimal power loss of 531.8649 kW in 1475.6075 s, whereas DE approach gives that of 540.9138 kW in 3373.4966 s for high radiation scenario, respectively. It has been revealed that the power loss may be further reduced by the increasing solar radiation conditions. In order to provide evidence that the proposed methodology is effective, the results of optimization are validated under different scenarios through the application of MCS. In addition, the constraints of network are investigated in order to make a determination regarding the probabilities of exceeding their limits. The validation of optimization results has concluded that the confidence levels of 0.7, 0.8, and 0.9 have been maintained for the low, medium, and high solar radiation scenarios, respectively.