Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO)

Abstract

Renewable Energy Sources (RESs) have been growing continuously until they become the second source of electricity after coal. However, most of RESs have intermittent nature of electricity production due to the high dependency on some external conditions like weather which changes seasonally. This intermittent nature has a negative impact on security and stability, voltage profile, and increasing the power losses in radial distribution power networks which contain uncertain power sources. Therefore, this paper presents a novel technique based on Genetic Algorithm (GA) combined with Particle Swarm Optimization (PSO). The goal of utilizing the GA is to track the maximum power point of uncertain power sources such as Solar/Photovoltaic (PV) and Wind Turbine (WT). Then, PSO starts its execution to determine the optimum configuration of power networks in order to minimize the power losses, maintain voltage profile, and increase the overall system stability and security. Different test cases are considered for testing different operation conditions. The simulation work has implemented by using MATLAB 2016b software. The results are tested on standard IEEE 33 bus systems and validated with other conventional method to verify the correctness of the proposed technique. Results show a significant improvement in voltage profile, reduction in the power losses, and hence increment in the overall system stability and security.