COMPARATIVE ANALYSIS OF A METAHEURISTIC OPTIMIZER APPROACH FOR THE SOLUTION OF OPTIMAL POWER FLOW PROBLEM

This work uses population-based particle swarm optimization (PSO) and teaching-learning- based optimization (TLBO) methodologies to solve the optimal power flow problem, and the outcomes of both methods are contrasted. One issue that needs to be addressed in power systems is financial loss. Appropriate scheduling of energy produced by different generation sources in the power network is necessary to address the aforementioned issue. This paper formulates an optimal power flow (OPF) issue and solves it to find the optimal values for the control variables. In this case study, five objective functions are developed for five distinct scenarios to verify the effectiveness of the proposed methodology in MATLAB application. The five objectives are as follows: minimizing fuel costs, improving voltage profiles, reducing active and reactive power losses on transmission lines, and improving voltage stability. The fitness function is considered as a single-objective function based on the control parameters. In order to assess the applicability of the proposed method, it has been used to the IEEE 30 bus test system to investigate the performance of the power system for certain objective functions. According to results from PSO and TLBO optimization techniques as well as results from the techniques mentioned in the literature, the Teaching-Learning-Based Optimization technique offers an effective and dependable solution when tackling the optimal power flow problem with a variety of complexities. To demonstrate how rapidly the offered technique may converge to optimal and useful global solutions and how it can handle the problem's various complexities, the achieved optimal solutions are contrasted