Optimizing FACTS devices location and sizing in integrated wind power networks using Tuna Swarm Optimization Algorithm

• Published in: Journal of thermal analysis and calorimetry 2024, Vol.149 (13), p.7135-7153
• Main Authors: Mohamed, Amal Amin, Kamel, Salah, Hassan, Mohamed H., Kamalov, Firuz, Safaraliev, Murodbek
• Format: Article
• Language: English
• Subjects: Algorithms; Analytical Chemistry; Chemistry; Chemistry and Materials Science; Cost function; Electric power loss; Electrical networks; Heuristic methods; Inorganic Chemistry; Measurement Science and Instrumentation; Optimization; Optimization algorithms; Parameter identification; Physical Chemistry; Polymer Sciences; Power flow; Probability density functions; Weibull density functions; Wind power; Wind power generation
• ISSN: 1388-6150; 1588-2926
• DOI: 10.1007/s10973-024-12909-y

This study aims to address the problem of optimal power flow (OPF) in electrical networks by integrating wind power production FACTS devices. The main objectives of this study include minimizing generating costs, reducing power loss, enhancing the voltage profile of the system, and increasing its load ability. In this study, the tuna swarm optimization algorithm (TSO) is utilized as a novel meta-heuristic technique to optimize electrical networks to incorporate FACTS devices and stochastic wind energy production. To account for wind power, a model based on the Weibull probability density function is utilized to identify the optimal values of the decision variables. The study compares several objective functions, including minimization of fuel cost and active power loss across the transmission system, and simulates the test system consisting of TCSC, TCPS, and SVC using the IEEE 30-bus system as a network for examining system parameters. The efficacy of the TSO methodology is explored and compared to other traditional approaches in the paper. The simulation findings of the study show that by lowering the overall power cost and power losses, TSO is more successful in determining the OPF's ideal solution. The results show that the TSO algorithm performs better than driving training-based optimization (DTBO), Coulomb–Franklin’s algorithm (CFA), and whale optimization algorithm (WOA) since it can handle more difficult OPF issues with a lower convergence rate. When comparing the four methods, for instance, TSO produced a noticeable improvement. It was able to successfully reduce the cost function to 807.454 $/h which is better than 807.9545 $/h for CFA, 809.4945 for DTBO, and 828.89 $/h for WOA, while lowering the power loss to 1.842 MW which is lower than the losses incurred by CFA, DTBO, and WOA, which are 1.9137 MW, 2.703 MW, and 2.757 MW, respectively. Furthermore, in Case 3, the gross cost reduced to 914.3735 $/h.