Multi-objective Optimal Power Flow Using Fuzzy Satisfactory Stochastic Optimization

The optimal power flow (OPF) has been widely used in power system operation and planning of power systems. Recently, many advanced optimization methods have been used to solve the OPF problem with different objectives. However, traditional OPF cannot effectively handle multiple objectives, at the same time. Therefore, the main contribution of this is to propose the fuzzy multi-objective optimal power flow (FMOPF) using the stochastic search optimization technique. In the proposed method, particle swarm optimization (PSO) is used to solve the FMOPF by incorporating to minimizing the objective function's fuzzy satisfaction function, including the total system cost, active power loss, and voltage magnitude deviation. The proposed FMOPF is applied to solve the optimal condition for the IEEE 30-bus test system for verification. The simulation results showed that the proposed FMOPF using the PSO method could potentially and effectively determine the best solution for single-objective OPF compared to existing methods. From the results, the proposed method gave the compromise solution among different objectives in the proposed FMOPF in a fuzzy manner by comparing the results obtained with the existing method under the same system data, and control variables.