Generator Capability Curve Constraint for PSO Based Optimal Power Flow
An optimal power flow (OPF) based on particle swarm
optimization (PSO) was developed with more realistic generator
security constraint using the capability curve instead of only Pmin/Pmax
and Qmin/Qmax. Neural network (NN) was used in designing digital
capability curve and the security check algorithm. The algorithm is
very simple and flexible especially for representing non linear
generation operation limit near steady state stability limit and under
excitation operation area. In effort to avoid local optimal power flow
solution, the particle swarm optimization was implemented with
enough widespread initial population. The objective function used in
the optimization process is electric production cost which is
dominated by fuel cost. The proposed method was implemented at
Java Bali 500 kV power systems contain of 7 generators and 20
buses. The simulation result shows that the combination of generator
power output resulted from the proposed method was more economic
compared with the result using conventional constraint but operated
at more marginal operating point.
[1] Sudhakaran, M., Palanivelu,T.G., "GA and PSO culled hybridtechnique
for economic dispatch problem with prohibited operating zones",
Journal of Zhejiang University, ISSN 1673-565X, pp. 896 - 903, 2007.
[2] Pablo, E., Juan, M.R., "Optimal Power Flow Subject to Security
Constraints Solved With a Particle Swarm Optimizer", IEEE
Transactions On Power Systems, Vol. 23, No. 1, pp. 33 - 40, 2008.
[3] Gaing, Z.L., Particle swarm optimization to solving the economic
dispatch considering the generator constrains, IEEE Trans. On Power
System, Vol 18. No. 3, pp. 1187 - 1195, 2003.
[4] Zimmerman,D. Ray, Murilloa E. Carlos, User's Manual A Matlab Power
System Simulation Package, Version 3.2 - September 21, PSERC, 2007.
[5] Boukir, T., Labdani, R., "Economic power dispatch of power system
with pollution control using multiobjective particle swarm
optimization", University of Sharjah Journal of Pure & Applied
Sciences, Vol.4. No..2, pp. 57 - 73, 2007.
[6] Wang, C.R., Yuan, H.J., "A modified particle swarm optimization
algorithm and its application in optimal power flow problem",
Proceedings of the fourth International Conference on machine learning
and Cybernetics, Guangzhou, 2005.
[7] Balci, H.H, Valenzuela, J.F., "Scheduling electric power generators
using particle swarm optimization combined with the lagrangian
relaxation method", AMCS Appl.Math.Comput.Sci, Vol.14. No. 14, pp.
411 - 421, 2004.
[8] Kumari, M.S., Sydulu, M., "An Improved Evolutionary Computation
Technique for Optimal Power Flow Solution", International Journal of
Innovations in Energy Systems and Power, Vol. 3, no. 1, pp. 32 - 45,
2008.
[9] Younes,M., Rahliga,M., "GA Based Optimal Power Flow Solutions",
Electrical & Instrumentation Engineering Department, Thapar
University, 2008.
[10] Piccolo, A., Vaccaro, A., "Fuzzy Logic Based Optimal Power Flow
Management in Parallel Hybrid Electric Vehicles", Iranian Journal of
Electrical and Computer Engineering, Vol. 4, no. 2, pp. 85 - 93, 2005.
[11] Wong,K.P.,Wong,S.Y.W., "Combined Genetic Algorithm/ Simulated
Annealing /Fuzzy Set to Short Term Generation Scheduling with Takeor
Pay Fuel Contract", IEEE Trans. Power Systems, Vol.11, No.1, pp.
128-136, 1996.
[12] Wong,K.P.,Wong,S.Y.W., "Hybrid Genetic/Simulated Annealing to
Short Term Multiple Fuel-Constrained Generation Scheduling", IEEE
Trans. Power Systems, Vol.12, No.2, pp. 776-784, 1997.
[1] Sudhakaran, M., Palanivelu,T.G., "GA and PSO culled hybridtechnique
for economic dispatch problem with prohibited operating zones",
Journal of Zhejiang University, ISSN 1673-565X, pp. 896 - 903, 2007.
[2] Pablo, E., Juan, M.R., "Optimal Power Flow Subject to Security
Constraints Solved With a Particle Swarm Optimizer", IEEE
Transactions On Power Systems, Vol. 23, No. 1, pp. 33 - 40, 2008.
[3] Gaing, Z.L., Particle swarm optimization to solving the economic
dispatch considering the generator constrains, IEEE Trans. On Power
System, Vol 18. No. 3, pp. 1187 - 1195, 2003.
[4] Zimmerman,D. Ray, Murilloa E. Carlos, User's Manual A Matlab Power
System Simulation Package, Version 3.2 - September 21, PSERC, 2007.
[5] Boukir, T., Labdani, R., "Economic power dispatch of power system
with pollution control using multiobjective particle swarm
optimization", University of Sharjah Journal of Pure & Applied
Sciences, Vol.4. No..2, pp. 57 - 73, 2007.
[6] Wang, C.R., Yuan, H.J., "A modified particle swarm optimization
algorithm and its application in optimal power flow problem",
Proceedings of the fourth International Conference on machine learning
and Cybernetics, Guangzhou, 2005.
[7] Balci, H.H, Valenzuela, J.F., "Scheduling electric power generators
using particle swarm optimization combined with the lagrangian
relaxation method", AMCS Appl.Math.Comput.Sci, Vol.14. No. 14, pp.
411 - 421, 2004.
[8] Kumari, M.S., Sydulu, M., "An Improved Evolutionary Computation
Technique for Optimal Power Flow Solution", International Journal of
Innovations in Energy Systems and Power, Vol. 3, no. 1, pp. 32 - 45,
2008.
[9] Younes,M., Rahliga,M., "GA Based Optimal Power Flow Solutions",
Electrical & Instrumentation Engineering Department, Thapar
University, 2008.
[10] Piccolo, A., Vaccaro, A., "Fuzzy Logic Based Optimal Power Flow
Management in Parallel Hybrid Electric Vehicles", Iranian Journal of
Electrical and Computer Engineering, Vol. 4, no. 2, pp. 85 - 93, 2005.
[11] Wong,K.P.,Wong,S.Y.W., "Combined Genetic Algorithm/ Simulated
Annealing /Fuzzy Set to Short Term Generation Scheduling with Takeor
Pay Fuel Contract", IEEE Trans. Power Systems, Vol.11, No.1, pp.
128-136, 1996.
[12] Wong,K.P.,Wong,S.Y.W., "Hybrid Genetic/Simulated Annealing to
Short Term Multiple Fuel-Constrained Generation Scheduling", IEEE
Trans. Power Systems, Vol.12, No.2, pp. 776-784, 1997.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49681", author = "Mat Syai'in and Adi Soeprijanto and Takashi Hiyama", title = "Generator Capability Curve Constraint for PSO Based Optimal Power Flow", abstract = "An optimal power flow (OPF) based on particle swarm
optimization (PSO) was developed with more realistic generator
security constraint using the capability curve instead of only Pmin/Pmax
and Qmin/Qmax. Neural network (NN) was used in designing digital
capability curve and the security check algorithm. The algorithm is
very simple and flexible especially for representing non linear
generation operation limit near steady state stability limit and under
excitation operation area. In effort to avoid local optimal power flow
solution, the particle swarm optimization was implemented with
enough widespread initial population. The objective function used in
the optimization process is electric production cost which is
dominated by fuel cost. The proposed method was implemented at
Java Bali 500 kV power systems contain of 7 generators and 20
buses. The simulation result shows that the combination of generator
power output resulted from the proposed method was more economic
compared with the result using conventional constraint but operated
at more marginal operating point.", keywords = "Optimal Power Flow, Generator Capability Curve,
Particle Swarm Optimization, Neural Network", volume = "3", number = "5", pages = "1070-6", }