Optimal Distributed Generation allocation in distirbution systems employing ant colony to reduce losses

This paper presents a method for optimal allocation of distributed generation in distribution systems. In this paper, our aim would be optimal distributed generation allocation for loss reduction in distribution network. Ant colony search algorithm (ACSA) was used as solving tool. ACSA is inspired from the natural behaviour of the ant colonies on how they find the food source and bring them back to their nest by building the unique trail formation. This algorithm is used to minimize an objective function. For applying ACSA, a soft ware is programmed under Matlab software is prepared. This proposed ACSA method and genetic algorithm (GA) are implemented on IEEE 34 bus system, and the results show that the proposed method is better than the other two methods. Using the proper and optimal allocation of DG has many advantages, but the lack of it has disadvantages, such as: increasing losses, voltage flicker, and harmonic.

[1]  J.-H. Teng,et al.  A Network-Topology-Based Three-Phase Load Flow for Distribution System , 2000 .

[2]  T. Thakur,et al.  A New Approach to Load Flow Solutions for Radial Distribution System , 2006, 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America.

[3]  Ji-Pyng Chiou,et al.  Optimal Capacitor Placement in Distribution Systems Employing Ant Colony Search Algorithm , 2005 .

[4]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[5]  P.P. Barker,et al.  Determining the impact of distributed generation on power systems. I. Radial distribution systems , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[6]  Ji-Pyng Chiou,et al.  Distribution network reconfiguration for loss reduction by ant colony search algorithm , 2005 .

[7]  M. Gandomkar,et al.  A Genetic–Based Tabu Search Algorithm for Optimal DG Allocation in Distribution Networks , 2005 .

[8]  M. Vakilian,et al.  A combination of genetic algorithm and simulated annealing for optimal DG allocation in distribution networks , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[9]  Chanan Singh,et al.  Dispersed generation planning using improved Hereford ranch algorithm , 1998 .

[10]  Taher Niknam,et al.  A NEW APPROACH BASED ON ANT ALGORITHM FOR VOLT/VAR CONTROL IN DISTRIBUTION NETWORK CONSIDERING DISTRIBUTED GENERATION , 2005 .

[11]  Lachlan D. Kuhn Ant Colony Optimization for Continuous Spaces , 2002 .

[12]  S. Hossein Cheraghi,et al.  Ant algorithms: web-based implementation and applications to manufacturing system problems , 2006, Int. J. Comput. Integr. Manuf..