An immunity-based ant colony algorithm for solving distribution network planning problem

2006 ◽  
Author(s):  
Yongfeng Dong ◽  
Junhua Gu ◽  
Xiangdan Hou ◽  
Weili Yan
Keyword(s):  
Ant Colony Algorithm ◽  
Distribution Network ◽  
Network Planning ◽  
Ant Colony ◽  
Planning Problem ◽  
Distribution Network Planning

scholarly journals An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach

2019 ◽  
Vol 9 (6) ◽  
pp. 1212 ◽  
Author(s):  
Deyang Yin ◽  
Fei Mei ◽  
Jianyong Zheng
Keyword(s):  
Hybrid Algorithm ◽  
Ant Colony Algorithm ◽  
Distribution Network ◽  
Convergence Speed ◽  
Ant Colony ◽  
Planning Problem ◽  
Dc Distribution ◽  
Convergence Results ◽  
Proposed Model ◽  
Searching Ability

The planning problem of distributed generators (DG) accessing the AC/DC distribution network is a hot research topic at present. In this paper, a location and volume model of DG is established that considers DG operation and maintenance costs, DG investment costs, system network loss costs, fuel costs, pollution compensation costs, and environmental protection subsidies. Furthermore, voltage and power constraints are also considered in the model. To solve the proposed model, a hybrid algorithm called the GA-ACO algorithm is presented that combines the ant colony algorithm (ACO) and the genetic algorithm (GA). On one hand GA has good robustness, good adaptability, and quick global searching ability but it also has some disadvantages such as premature convergence and low convergence speed. On the other hand, ACO has the ability of parallel processing and global searching but its convergence speed is very low at the beginning. The IEEE-33 node distribution network is taken as a basic network to verify the rationale of the proposed model and the effectiveness of the proposed hybrid algorithm. Simulation results show that the proposed model is very in line with reality, the hybrid algorithm is very effective in solving the model and it has advantages in both convergence speed and convergence results compared to ACO and GA.

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