PSO-Based Planning of Distribution Systems with Distributed Generations
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.
[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
[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
@article{"International Journal of Electrical, Electronic and Communication Sciences:64529", author = "Amin Hajizadeh and Ehsan Hajizadeh", title = "PSO-Based Planning of Distribution Systems with Distributed Generations", 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", volume = "2", number = "9", pages = "2079-6", }
