Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems

Abstract

This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units.

RETRACTED ARTICLE: On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems

“…Generally speaking, these swarm-based algorithms have some limitations and take more computational effort [42]. Mohammed et al [29] used genetic algorithm (GA) and moth swarm algorithm (MSA) for finding the optimal location and sizing of distributed generation. Kumar et al [22] used ant colony optimization (ACO) and K-means clustering algorithm to find the shortest path from source to destination for Internet of things (IoT) models.…”

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

“…In paper, 44 an attempt has been made to hybridize the quasi oppositional based learning framework where the SHO algorithm is used to enhance the optimization of SHO, this improvised framework is known as quasi oppositional SHO. Paper 126 presents a hybrid approach that relied on the GA and MSA to lessen active power generation, power losses, and gross system cost. Besides, to recuperate the least bus voltage and the yearly net saving by choosing the DGs size and their optimal locations on radial distribution systems.…”

Editor’s Note

“…Next three articles propose solutions to electrical engineering problems, focusing on power systems. The first one, by Mohamed et al [11], presents the genetic moth swarm algorithm, which is an hybrid approach based on genetic algorithms and moth swarm algorithms, for determining the optimal location and sizing of renewable distributed generation sources on radial distribution networks. The aim is to minimize the electrical power loss under security constraints.…”