scholarly journals Application of genetic algorithm for problem of optimizing location and capacity of distributed generation considering distributed network reconfiguration

2018 ◽  
Vol 20 (K7) ◽  
pp. 5-14
Author(s):  
Linh Tung Nguyen ◽  
Thuan Thanh Nguyen ◽  
Trieu Ngoc Ton ◽  
Anh Viet Truong ◽  
Xuan Anh Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Distribution Network ◽  
Network Reconfiguration ◽  
Distributed Network ◽  
Network Configuration ◽  
Second Stage ◽  
Simulation Results ◽  
Two Stages ◽  
Radial Network

This paper presents a method of determining the location and size of distributed generation (DG) considering to operate the configuration of distribution network to minimize the real power loss. The proposed method which is based on the genetic algorithm (GA) is divided into two stages. In the first stage, GA is used to optimize the location and size of DG in the mesh distribution network, while in the second stage, GA is used to determine the radial network configuration after installing DG. The simulation results on the 33-nodes and 69-nodes systems show that the proposed method can be an efficient method for the placing DG problem and that is considering to solve the problem of distribution network reconfiguration.

Network Reconfiguration for Distribution System with Distributed Generations

2011 ◽  
Vol 403-408 ◽  
pp. 3271-3276
Author(s):  
Hong Jiang Liu ◽  
Lin Chuan Li ◽  
Xin Chen
Keyword(s):  
Genetic Algorithm ◽  
Distribution System ◽  
Configuration Model ◽  
Binary Particle Swarm Optimization ◽  
Network Reconfiguration ◽  
Network Configuration ◽  
Swarm Optimization ◽  
Distributed Generations ◽  
Search Speed ◽  
Simulation Results

Recently more and more distributed generations(DG) are accessed in distribution system. So we analyze how DG influence the distribution system. In order to make the result of network reconfiguration more closely to the reality, we build a network configuration model for distribution system with DG which contains three load modes, and we bring forth a composite algorithm which contains improved binary particle swarm optimization based on loop circuit and genetic algorithm, and combine with the taboo algorithm to avoid circuitous search. This composite algorithm can not only increase the search speed, but also effectively overcome the precocity. The simulation results show that after the network reconfiguration, the network loss has been greatly reduced, and this verifies the effectiveness of this algorithm.

Distribution Network Reconfiguration with Distributed Generation Based on Cloud Genetic Algorithm

2012 ◽  
Vol 529 ◽  
pp. 306-310
Author(s):  
Long Hui Liu ◽  
Yan Wang ◽  
Shu Jun Yao ◽  
Lu Yao Ma ◽  
Jing Yang
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Distribution Network ◽  
Test System ◽  
Utilization Rate ◽  
Mathematics Model ◽  
Network Reconfiguration ◽  
Distribution Network Reconfiguration ◽  
Network Change

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.

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