Abstract
This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios.
Highlights
The distribution system (DS) is the final stage in the construction and planning electrical power system, which delivers the power between the transmission and end-user consumer
This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-Grey Wolf Optimizer (GWO)) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs)
Analysis of the results obtained from the hybrid cuckoo search (CS)-GWO algorithm shows that simultaneous DNR, DG installation process in RDSs is a more effective method in minimizing system power loss and improving the system's voltage profile than results obtained by other optimization methods reported earlier in the literature
Summary
The distribution system (DS) is the final stage in the construction and planning electrical power system, which delivers the power between the transmission and end-user consumer. Many meta-heuristic algorithms have been introduced to solve DG allocation problems for last decades, such as genetic algorithm and particle swarm optimization [22], modified bacterial foraging optimization algorithm [23], analytical approach [24], invasive weed optimization algorithm [25], quasi-oppositional teaching-learning based optimization [26], intelligent water drop algorithm [27], Krill herd algorithm [28], Flower Pollination algorithm [29], Shuffled Bat algorithm [30], Stud Krill herd Algorithm [31], hyper-spherical search algorithm [32], one rank cuckoo search algorithm [33], symbiotic organism search-based method [34], stochastic fractal search algorithm [35], combined evolutionary algorithm [36], mutated salp swarm algorithm [37], Multi-Objective Hybrid Teaching–Learning Based Optimization-Grey Wolf Optimizer [38], coyote optimization algorithm (COA) and electrical transient analyzer program (ETAP)[39], improved spotted hyena algorithm [40] These methods are easy to implement and widely used in the DS to obtain a global optimum solution with less computation time. This algorithm's obtained results are better than various optimization methods in literature such as HSA, GA, RGA, FWA and UVDA
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