PSO-Based Planning of Distribution Systems with Distributed Generations

Authors: Amin Hajizadeh, Ehsan Hajizadeh

Abstract:

This paper presents a multi-objective formulation for optimal siting and sizing of distributed generation (DG) resources in distribution systems in order to minimize the cost of power losses and energy not supplied. The implemented technique is based on particle swarm optimization (PSO) and weight method that employed to obtain the best compromise between these costs. Simulation results on 33-bus distribution test system are presented to demonstrate the effectiveness of the proposed procedure.

Keywords: Distributed generation, distribution networks, particle swarm optimization, reliability, weight method

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1085225

References:

[1] T. Griffin, K. Tomsovic, D. Secrest, A. Law, "Placement of dispersed Generations Systems for Reduced Losses", Proceedings of the 33rd Hawaii International Conference on System Sciences - 2000
[2] Jen-Hao Teng, Tain-Syh Luor, and Yi-Hwa Liu, "Strategic Distributed Generator Placements for Service Reliability Improvements", 2002 IEEE
[3] D.H. Popovic, J.A. Greatbanks, M. Begovic, A. Pregelj, "Placement of distributed generators and reclosers for distribution network security and reliability", 2005 Elsevier
[4] M.Mardaneh ,G.B.Gharehpetian " Siting and Sizing of DG Units Using GA and OPF Based Technique " , IEEE Region 10 Int. Conference on Computers, Communications Control and Power Engineering, IEEE TENCON 2004, 21-24 Nov. 2004, Chiang Mai, Thailand
[5] B Kuri, M A Redfern, F Li, "Optimisation of Rating and Positioning of Dispersed Generation with Minimum Network Disruption", 2004 IEEE
[6] Carlos A. Coello Coello, "An Updated Survey of Evolutionary Multiobjective Optimization Techniques: State of the Art and Future Trends" , Proceedings of the 1999 congress on evolutionary computation (CEC 1999), Washington USA, IEEE press, 3-13, 1999.
[7] J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, vol. IV, 1995, pp. 1942-1948.
[8] R.C. Eberhart, Y. Shi, Evolving artificial neural networks, in: Proceedings of the International Conference on Neural Networks and Brain, 1998, pp. PL5-PL13.
[9] V. Tandon, Closing the gap between CAD/CAM and optimized CNC end milling, Master thesis, Purdue School of Engineering and Technology, Indiana University, Purdue University, Indianapolis, 2000.
[10] H. Yoshida, K. Kawata, Y. Fukuyama, Y. Nakanishi, A particle swarm optimization for reactive power and voltage control considering voltage stability, in: Proceedings of the International Conference on Intelligent System Application to Power Systems, 1999, pp. 117-121.
[11] N. Shigenori, G. Takamu, Y. Toshiku, F. Yoshikazu, A hybrid particle swarm optimization for distribution state estimation, IEEE Transaction on Power Systems 18 (2003) 60-68.
[12] M. Clerc, J. Kennedy, The particle swarm explosion, stability, and convergence in a multidimensional complex space, IEEE Transaction on Evolutionary Computation 6 (2002) 58-73.
[13] H. Saadat , " Power System Analysis " , McGraw-Hill , 2002