A Novel MOGNDO Algorithm for Security-Constrained Optimal Power Flow Problems

The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization is transformed into the MOGNDO using nondominated sorting crowding distancing mechanisms. OPF problem gets even more challenging when are integrated grid system, which unreliable fluctuating. FACTS devices also being used frequently in contemporary networks to assist reducing network demand congestion. In this study, stochastic wind source was with different devices, static VAR compensator, thyristor- driven series thyristor—driven phase shifter, together an IEEE-30 bus system. Positions ratings can be intended reduce system’s overall fuel cost. Weibull probability density curves were highlight character source. best compromise solutions obtained fuzzy decision-making approach. results on modified system compared other well-known algorithms, proved that has improved convergence, diversity, spread behavior across PFs.