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This paper is focused at providing a solution to optimal power flow problem in power systems by using soft computing approaches. The proposed approach finds the optimal setting of OPF control variables which include generator active output, generator bus voltages, transformer tap-setting and shunt devices with the objective function of minimizing the fuel cost.   Soft computing optimization methods have been implemented based on genetic algorithm and particle swarm optimization. The proposed soft computing techniques are modelled to be flexible for implementation to any power systems with the given system line,    bus    data,    generator fuel    cost parameter and forecasted load demand. Proposed soft computing optimization techniques have been analyzed and tested on the standard benchmark IEEE 30-bus system. Results obtained after applying both optimization techniques on American Electric IEEE 30-bus system with the same control variable maximum & minimum limits and system data have been compared and analyzed.   Proposed methods efficiently optimize and solve the optimal power flow problem with high efficiency and wide flexibility for implementation and analysis on different power system networks.

This paper is focused at providing a solution to optimal power flow problem in power systems by using soft computing approaches. The proposed approach finds the optimal setting of OPF control variables which include generator active output, generator bus voltages, transformer tap-setting and shunt devices with the objective function of minimizing the fuel cost.   Soft computing optimization methods have been implemented based on genetic algorithm and particle swarm optimization. The proposed soft computing techniques are modelled to be flexible for implementation to any power systems with the given system line,    bus    data,    generator fuel    cost parameter and forecasted load demand. Proposed soft computing optimization techniques have been analyzed and tested on the standard benchmark IEEE 30-bus system. Results obtained after applying both optimization techniques on American Electric IEEE 30-bus system with the same control variable maximum & minim um limits and system data have been compared and analyzed.   Proposed methods efficiently optimize and solve the optimal power flow problem with high efficiency and wide flexibility for implementation and analysis on different power system networks.