Emission constrained unit commitment of Kuwait power generation system using genetic algorithm

  • Mohammad A. Alsaffar Department of Electrical Engineering, Kuwait University
  • Mohamed A. El-Sayed Department of Electrical Engineering, Kuwait University
Keywords: Economic Dispatch, Unit Commitment, Genetic Algorithm, Optimization Techniques, Greenhouse Gases

Abstract

Emission Constrained Unit Commitment (ECUC) is an extension of the conventional Unit Commitment (UC) problem that takes into consideration the minimization of the amount of greenhouse gases emitted from generating units in power plants. This paper presents a Genetic Algorithm (GA) solution of ECUC for thermal power plants in Kuwait. GA is an efficient optimization technique based on the principle of biological evolution. This complicated nonlinear ECUC problem is solved in two stages; the first stage uses GA to perform the Economic Dispatch (ED) taken into account all system constraints, while the second stage uses also GA to decide the ON/OFF status of the generating units. The simulation results indicated the efficient convergent of the GA to the final optimal solution of the ECUC in Kuwait generation system.

Author Biographies

Mohammad A. Alsaffar, Department of Electrical Engineering, Kuwait University
Mohammad A. Alsaffar was born in Kuwait in 1974. He earned a B.S. degree in Electrical Engineering from University of Kentucky – Lexington, USA in December 1997. He worked as an Electrical Engineer at Kuwait National Petroleum Company KNPC in May 1998. He earned a M.S. degree in Electrical Engineering from University of Wisconsin – Madison, USA in May 2002. He earned a Ph.D. degree in Electrical Engineering from University of Minnesota – Twin Cities, USA in November 2005. Since January 2006, he is working as a faculty member at the Electrical Engineering Department at Kuwait University.
Mohamed A. El-Sayed, Department of Electrical Engineering, Kuwait University
Mohamed A. El-Sayed was born in Egypt 1946. He earned a B.S. degree in Electrical Power Engineering from Cairo University in 1968. He earned a M.S. degree in Electrical Power Engineering from Cairo University in 1971. He earned a Ph.D. degree in Electrical Power Engineering from RWTH University – Aachen, Germany, 1979. Since September 2010, he is working as a faculty member at the Electrical Engineering Department at Kuwait University.

References

A. J. Wood and B. F. Wollenberg, Power Generation Operation And Control, 2nd ed. John Wiley, 1996.

N. P. Padhy, “Unit commitment—a bibliographical survey,” IEEE Transactions on Power Systems, vol. 19, no. 2,

pp. 1196–1205, May 2004.

R. M. Burns and C. A. Gibson, “Optimization of priority lists for a unit commitment program,” in Proceeding

IEEE Power Engineering Society Summer Meeting, 1975.

F. N. Lee and Q. Feng, “Multi-area unit commitment,” IEEE Transactions on Power Systems, vol. 7, pp. 591–599,

May 1992.

W. L. Snyder, H. D. Powell, and J. C. Rayburn, “Dynamic programming approach to unit commitment,” IEEE

Transactions on Power Systems, vol. 2, pp. 339–347, May 1987.

P. G. Lower, “Generating unit commitment by dynamic programming,” IEEE Transactions on Power Systems,

vol. PAS-85, pp. 422–426, May 1966.

S. Virmani, E. C. Adrian, K. Imhof, and S. Muhherjee, “Implementation of a lagrangian based unit commitment

problem,” IEEE Transactions on Power Systems, vol. 4, no. 4, pp. 1373–1380, November 1989.

F. Zhuang and F. D. Galiana, “Towards a more rigorous and practical unit commitment by lagrangian relaxation,”

IEEE Transactions on Power Systems, vol. 3, no. 2, pp. 763–773, May 1988.

M. Madrigal and V. H. Quintana, “An interior point/cutting-plane method to solve unit commitment problem,”

IEEE Transactions on Power Systems, vol. 15, pp. 1022–1027, August 2000.

I. A. Farhat and M. E. El-Hawary, “Interior point method application in optimum operation scheduling of electric

power systems,” IET Generation, Transmission and Distribution, vol. 3, no. 11, pp. 1020–1029, 2009.

H. Mori and O. Matsuzaki, “Embedding the priority into tabu search for unit commitment,” in Proceeding IEEE

Winter Meeting, August 2000.

A. Rajan, C. C. Mohan, and M. R. Manivannan, “Neural based tabu search method for solving unit commitment

problem,” in Proceeding 5th International Conference on Power Systems Managment and Control, 2002, pp.

–185.

H. Sasaki, M. Watanabe, and R. Yokoyama, “A solution method of unit commitment by artificial neural

networks,” IEEE Transactions on Power Systems, vol. 7, pp. 974–981, August 1992.

JOURNAL OF ENGINEERING RESEARCH

R. H. Liang and F. C. Kang, “Thermal generating unit commitment using an extended mean field annealing

neural network,” in Proceeding Inst. Electrical Engineering, vol. 147, May 2000, pp. 164–170.

J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceeding IEEE Conference Neural Networks,

vol. 4, 1995, pp. 1942–1948.

T. O. Ting, M. V. C. Rao, and C. K. Loo, “A novel approach for unit commitment problem via an effective

hybrid particle swarm optimization,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 411–418, February

K. S. Swarup and S. Yamashiro, “Unit commitment solution methodology using genetic algorithm,” IEEE

Transactions on Power Systems, vol. 17, pp. 87–91, February 2002.

X. Ma, A. A. El-Keib, R. E. Smith, and H. Ma, “A genetic algorithm based approach to thermal unit commitment

of electric power systems,” Electric Power System Research, vol. 34, pp. 29–36, 1995.

S. O. Orero and M. R. Irving, “A genetic algorithm for generator scheduling in power systems,” Electric Power

Energy Systems, vol. 18, no. 1, pp. 19–26, 1996.

T. T. Maifeld and G. B. Sheble, “Genetic based unit commitment algorithm,” IEEE Transactions on Power

Systems, vol. 11, pp. 1359–1370, August 1996.

Y. R. Sood, N. P. Padhy, and H. O. Gupta, “Discussion on optimal power flow by enhanced genetic algorithm,”

IEEE Transactions on Power Systems, vol. 18, p. 1219, August 2003.

I. C. O. P. W. Group, “Potential impacts of clean air regulations on system operations,” IEEE Transactions on

Power Systems, vol. 10, pp. 647–653, 1995.

P. S. Kulkarni, A. G. Kothari, and D. P. Kothari, “Combined economic and emission dispatch using improved

backpropagation neural network,” Electric Machines and Power Systems, vol. 28, pp. 31–44, 2000.

H. Cai, M. Wang, A. Elgowainy, and J. Han, “Updated greenhouse gas and criteria air pollutant emission factors

and their probability distribution functions for electric generating units,” Argonne National Laboratory, Tech.

Rep., May 2012.

MATLAB, Matlab Optimization Toolbox User’s Guide R2011b. The MathWorks Inc., 2011.

S. Department and I. Center, Statistical Year Book (Electrical Energy), 36th ed. Ministry of Electricity and

Water - State of Kuwait, 2011.

Published
2014-05-14
Issue
Vol 2 No 2 (2014)
Section
Electrical Engineering