Optimal Power Flow For Non-Smooth Cost Function Using Particle Swarm Optimization On 150 Kv System

Optimal power flow by considering the non-smooth cost curve using the meta-heuristic algorithm method, namely particle swarm optimization (PSO) in the 150 kV Sulselrabar electrical system. In this study, the PSO algorithm was used to optimize optimal power flow so that the cheapest generation price was obtained with a non-smooth cost curve and still considered the limitations of similarity and inequality. In this study, the PSO algorithm was used to optimize optimal power flow so that the cheapest generation price was obtained with a non-smooth cost curve and still considered the limitations of similarity and inequality. From the results of generation optimization using the Particle Swarm method, it produces the cheapest generation costs from other methods, namely Rp. 93,498,916.1,- / hour to generate power of 270.14 MW with losses of 25.73 MW. The Particle Swarm Optimization (PSO) method is able to reduce the cost of generating the Sulselrabar system by Rp. 34,382,857.58 / hour or 26.89%. From the results of generation optimization using the Ant Colony method, it resulted in a total generation cost of Rp. 94670335.98 / hour to generate power of 270,309 MW with losses of 25.91 MW. The Ant Colony method is able to reduce the cost of generating the Sulselrabar system by Rp. 33,211,437.70 / hour or 25.98%. From the results of generation optimization using the lagrange method, it resulted in a total generation cost of Rp. 117,121,631.08 / hour to generate power of 339.4 MW with losses of 25,016 MW. The lagrange method is able to reduce the cost of generating the Sulselrabar system by Rp. 10,760,142.60 / hour or 8.41%. The artificial intelligence method based on Particle Swarm Optimization (PSO) can well perform optimization of Optimal Power Flow, from the results of the analysis obtained the cheapest generation cost compared to the comparison method, Lagrange Method and Ant Colony artificial intelligence method.