Optimal Distribution Network Reconfiguration with Distributed Generation using a Genetic Algorithm

Author(s):  
John Penaloza ◽  
Jairo Yumbla ◽  
Julio Lopez ◽  
Antonio Padilha-Feltrin
2012 ◽  
Vol 529 ◽  
pp. 306-310
Author(s):  
Long Hui Liu ◽  
Yan Wang ◽  
Shu Jun Yao ◽  
Lu Yao Ma ◽  
Jing Yang

According to the traditional distribution network reconfiguration, the fault feeder with distributed generation (DG) will separate from the distribution network immediately while the network goes wrong. In order to improve the system power supply reliability and the utilization rate of DG, the new standard allows the distribution network change into island operation. This paper establishes the mathematics model of the distribution network reconfiguration with DG, the objective function and constraint conditions. The traditional genetic algorithm (GA) has the shortcomings of premature convergence and slow convergence speed to solve this nonlinear optimization problem. This paper applies the cloud genetic algorithm (CGA) to solve the network reconfiguration problem. The Case study on IEEE33 test system shows that the algorithm is reasonable and effective.


Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1544 ◽  
Author(s):  
Damir Jakus ◽  
Rade Čađenović ◽  
Josip Vasilj ◽  
Petar Sarajčev

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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