Stochastic Optimal Selection and Analysis of Allowable Photovoltaic Penetration Level for Grid-Connected Systems Using a Hybrid NSGAII-MOPSO and Monte Carlo Method

Generally, the main focus of grid-linked photovoltaic systems is to scale up penetration level ensure full electricity consumption coverage. However, due stochasticity and nondispatchable nature its generation, significant adverse impacts such as power overloading, voltage, harmonics, current, frequency instabilities on utility grid arise. These vary in severity a function degree system. Thus, design problem involves optimizing two conflicting objectives presence uncertainty without violating grid’s operational limitations. Nevertheless, existing studies avoid technical impact scalarize stochastic into single objective lessen complexity problem. This study proposes multiobjective methodology decide optimum allowable for an system at cost system’s constraints. Five cutting-edge optimization algorithms were implemented compared using hypervolume metric, execution time, nonparametric statistical analysis obtain quality solution. The results indicated that Hybrid NSGAII-MOPSO had better convergence, diversity, time capacity handle complex obtained optimal solution shows practical could accurately maximum match energy demand any minimum collapsing limitations even under fluctuating weather conditions. Comparatively, approach enables development more sustainable affordable grid-connected