scholarly journals A Quasioppositional-Chaotic Symbiotic Organisms Search Algorithm for Distribution Network Reconfiguration with Distributed Generations

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Minh-Tuan Nguyen Hoang ◽  
Bao-Huy Truong ◽  
Khoa Truong Hoang ◽  
Khanh Dang Tuan ◽  
Dieu Vo Ngoc
Keyword(s):  
Distributed Generation ◽  
Search Algorithm ◽  
Distribution Network ◽  
Distribution Networks ◽  
Network Reconfiguration ◽  
Symbiotic Organisms Search ◽  
Distributed Generations ◽  
Distribution Network Reconfiguration ◽  
Radial Distribution Networks ◽  
Symbiotic Organisms

This study suggests an enhanced metaheuristic method based on the Symbiotic Organisms Search (SOS) algorithm, namely, Quasioppositional Chaotic Symbiotic Organisms Search (QOCSOS). It aims to optimize the network configuration simultaneously and allocate distributed generation (DG) subject to the minimum real power loss in radial distribution networks (RDNs). The suggested method is developed by integrating the Quasiopposition-Based Learning (QOBL) as well as Chaotic Local Search (CLS) approaches into the original SOS algorithm to obtain better global search capacity. The proposed QOCSOS algorithm is tested on 33-, 69-, and 119-bus RDNs to verify its effectiveness. The findings demonstrate that the suggested QOCSOS technique outperformed the original SOS and provided higher-quality alternatives than many other methods studied. Accordingly, the proposed QOCSOS algorithm is favourable in adapting to the DG placement problems and optimal distribution network reconfiguration.

scholarly journals Improvement methods for solving the distribution network reconfiguration problem

Energetika ◽  
2019 ◽  
Vol 64 (4) ◽  
Author(s):  
Ha Duc Nguyen ◽  
Ilgiz Mirgalimovich Valeev
Keyword(s):  
Large Scale ◽  
Heuristic Algorithms ◽  
Distribution Network ◽  
Distribution Networks ◽  
Network Reconfiguration ◽  
Voltage Profile ◽  
Distributed Generations ◽  
Distribution Network Reconfiguration ◽  
Radial Distribution Networks ◽  
Active Power Losses

The paper presents methods for solving the distribution network reconfiguration problem based on heuristic algorithms. In particular, the study solved the distribution network reconfiguration problem for active power losses reduction. The application of the method to a sample network proved effective compared to other algorithms, especially in case of large-scale and complex systems. In addition, the paper considers the influence of the location and capacity of distributed generations on the distribution network reconfiguration problem in different cases. The results show that the distribution network reconfiguration problem combined with optimization location and size of distributed generations is the most efficient solution for minimizing power loss and enhancing voltage profile. The proposed methods have been also successfully applied in the small- and medium-scale practical radial distribution networks. The simulation results show that the proposed methods can be used as reference materials for network reconfiguration problems with single and multi-objectives.

scholarly journals Symbiotic Organism Search Algorithm for Power Loss Minimization in Radial Distribution Systems by Network Reconfiguration and Distributed Generation Placement

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Tung Tran The ◽  
Sy Nguyen Quoc ◽  
Dieu Vo Ngoc
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Power Loss ◽  
Distribution Systems ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Distribution Networks ◽  
Network Reconfiguration ◽  
Symbiotic Organism Search

This paper proposes the Symbiotic Organism Search (SOS) algorithm to find the optimal network configuration and the placement of distributed generation (DG) units that minimize the real power loss in radial distribution networks. The proposed algorithm simulates symbiotic relationships such as mutualism, commensalism, and parasitism for solving the optimization problems. In the optimization process, the reconfiguration problem produces a large number of infeasible network configurations. To reduce these infeasible individuals and ensure the radial topology of the network, the graph theory was applied during the power flow. The implementation of the proposed SOS algorithm was carried out on 33-bus, 69-bus, 84-bus, and 119-bus distribution networks considering seven different scenarios. Simulation results and performance comparison with other optimization methods showed that the SOS-based approach was very effective in solving the network reconfiguration and DG placement problems, especially for complex and large-scale distribution networks.

scholarly journals Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1544 ◽  
Author(s):  
Damir Jakus ◽  
Rade Čađenović ◽  
Josip Vasilj ◽  
Petar Sarajčev
Keyword(s):  
Distribution Network ◽  
Distribution Networks ◽  
Total Power ◽  
Computational Time ◽  
Small Scale ◽  
Network Reconfiguration ◽  
Optimal Distribution ◽  
Voltage Profile ◽  
Distribution Network Reconfiguration ◽  
Optimal Reconfiguration

This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time.

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