Implimentation of Evolutionary Particle Swarm Optimization in Distributed Generation Sizing

The size of Distributed Generation (DG) is very important in order to reduce the impact of installing a DG in the distribution Network. Without proper connection and sizing of DG, it will cause the power loss to increase and also might cause the voltage in the network to operate beyond the acceptable limit. Therefore, most researchers have concentrated on the optimization technique to regulate the DG’s output to compute its optimal size. In this paper, the concept of Evolutionary Particle Swarm Optimization (EPSO) method is implemented in sizing the DG units. By substituting the concept of Evolutionary Programming (EP) in some part of Particle Swarm Optimization (PSO) algorithm process, it will make the process of convergence become faster. The algorithm has been tested in 33bus distribution system with 3 units of DG that operate in PV mode. Its performance was compared with the performance when using the traditional PSO and without using any optimization method. In terms of power loss reduction and voltage profile, the EPSO can give similar performance as PSO. Moreover, the EPSO requires less number of iteration and computing time to converge. Thus, it can be said that the EPSO is superior in term of speed, while maintaining the same performance. DOI: http://dx.doi.org/10.11591/ijece.v2i1.227

[1]  Sakti Prasad Ghoshal,et al.  Optimal sizing and placement of distributed generation in a network system , 2010 .

[2]  Mehdi Ehsan,et al.  Efficient immune-GA method for DNOs in sizing and placement of distributed generation units , 2011 .

[3]  G.B. Gharehpetian,et al.  DG allocation using an analytical method to minimize losses and to improve voltage security , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[4]  Magdy M. A. Salama,et al.  Optimum siting and sizing of a large distributed generator in a mesh connected system , 2010 .

[5]  M. Kotb,et al.  Genetic Algorithm for Optimum Siting and Sizing of Distributed Generation , 2010 .

[6]  M. E. El-Hawary,et al.  Heuristic curve-fitted technique for distributed generation optimisation in radial distribution feeder systems , 2011 .

[7]  Gerard Ledwich,et al.  Optimal sitting and sizing of distributed generators based on a modified primal-dual interior point algorithm , 2011, 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT).

[8]  A. Akbarimajd,et al.  A Method for Placement of DG Units in Distribution Networks , 2008, IEEE Transactions on Power Delivery.

[9]  Hazlie Mokhlis,et al.  Combined voltage stability index for Charging Station effect on distribution network , 2011 .

[10]  Tuba Gozel,et al.  An analytical method for the sizing and siting of distributed generators in radial systems , 2009 .

[11]  Chukwudi Anyakoha,et al.  A review of particle swarm optimization. Part I: background and development , 2007, Natural Computing.

[12]  Pierluigi Siano,et al.  Distributed Generation Capacity Evaluation Using Combined Genetic Algorithm and OPF , 2007 .

[13]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[14]  Chukwudi Anyakoha,et al.  A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications , 2008, Natural Computing.

[15]  V. C. Veera Reddy,et al.  OPTIMAL DG PLACEMENT FOR MINIMUM REAL POWER LOSS IN RADIAL DISTRIBUTION SYSTEMS USING PSO , 2010 .

[16]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).