Optimal Placement and Sizing of DGs and Shunt Capacitor Banks Simultaneously in Distribution Networks using Particle Swarm Optimization Algorithm Based on Adaptive Learning Strategy

Abstract: Optimization of DG and capacitors is a nonlinear objective optimization problem with equal and unequal constraints, and the efficiency of meta-heuristic methods for solving optimization problems has been proven to any degree of complex it. As the population grows and then electricity consumption increases, the need for generation increases, which further reduces voltage, increases losses, and thereby decreases the voltage stability of the nodes. The best solution to these problems is to install distributed generation sources (DGs) and capacitors, the installation of such resources will prevent the establishment of new transmission and distribution lines and change the power system topology to improve power supply and improve the aforementioned problems. Therefore, in this article, optimum locating and quantification of distributed generation and parallel capacitors simultaneously in power distribution networks using Adaptive Learning practical swarm optimization Algorithm (ALPSO) with the goal of reducing active power losses, improving voltage profile profiles and stability index Voltage on standard 33-bus grid by MATLAB software. The results presented after review and comparison show the relatively desirable performance of the ALPSO algorithm for solving the optimal location problem.