A new heuristic method to solve unit commitment by using a time-variant acceleration coefficients particle swarm optimization algorithm

Unit commitment is one of the most important problems in power system operation. Because of the large amount of parameters and constraints, it contains a high level of complexity. In this paper a new method based on a time-variant acceleration coefficients particle swarm optimization algorithm has been proposed to solve the unit commitment problem. Integer coding (for satisfying minimum up/down constraints) and binary coding (for satisfying spinning reserve constraint) have been utilized in the proposed method. Simulations in the different cases have been done with different sizes. Numerical results have shown the superiority and better convergence of the proposed method in comparison with other methods.

A new heuristic method to solve unit commitment by using a time-variant acceleration coefficients particle swarm optimization algorithm

Unit commitment is one of the most important problems in power system operation. Because of the large amount of parameters and constraints, it contains a high level of complexity. In this paper a new method based on a time-variant acceleration coefficients particle swarm optimization algorithm has been proposed to solve the unit commitment problem. Integer coding (for satisfying minimum up/down constraints) and binary coding (for satisfying spinning reserve constraint) have been utilized in the proposed method. Simulations in the different cases have been done with different sizes. Numerical results have shown the superiority and better convergence of the proposed method in comparison with other methods.

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  • [1] H.Y. Yamin, “Review on methods of generation scheduling in electric power systems,” Electric Power System Research, Vol. 69, pp. 227–248, 2004.
  • [2] H.W. Kuhn, A.W. Tucker, “Nonlinear programming,” in Proceedings of the Second Berkeley Symposium on Mathematical Programming Statistics and Probability, Berkeley, CA, USA, University of California Press, 1951.
  • [3] C.K. Pang, G.B. Sheble, F. Albuyeh, “Evaluation of dynamic programming based methods and multiple area representation for thermal unit commitments,” IEEE Transactions On Power Systems, Vol. 100, pp. 1212–1218, 1981.
  • [4] A.M. Geoffrion, “Lagrangian relaxation for integer programming problems,” Mathematical Programming Study, Vol. 2, pp. 82–114, 1974.
  • [5] S.A. Kazarlis, A.G. Bakirtzis, V. Petridis, “A genetic algorithm solution to the unit commitment problem,” IEEE Transactions on Power Systems, Vol. 11, pp. 83–92, 1996.
  • [6] I.G. Damousis, A.G. Bakirtzis, P.S. Dokopoulos, “A solution to the unit commitment problem using integer-coded genetic algorithm,” IEEE Transactions on Power Systems, Vol. 19, pp. 1165–1172, 2004.
  • [7] J. Ebrahimi, S.H. Hosseinian, G.B. Gharehpetian, “Unit commitment problem solution using shuffled frog leaping algorithm,” IEEE Transactions on Power Systems, Vol. 26, pp. 573–581, 2011.
  • [8] X. Yuan, H. Nie, A. Su, L. Wanga, Y. Yuan, “An improved binary particle swarm optimization for unit commitment problem,” Expert Systems with Applications, Vol. 36, pp. 8049–8055, 2009.
  • [9] X. Yuan, H. Nie, A. Su, L. Wanga, Y. Yuan, “Application of enhanced discrete differential evolution approach to unit commitment problem,” Energy Conversion and Management, Vol. 50, pp. 2449–2456, 2009.
  • [10] Y.W. Jeong, J.B. Park, S.H. Jang, K.Y. Lee, “A new quantum-inspired binary PSO: application to unit commitment problems for power systems,” IEEE Transactions on Power Systems, Vol. 25, pp. 1486–1495, 2010.
  • [11] K.A. Juste, H. Kita, E. Tanaka, J. Hasegawa, “An evolutionary programming solution to the unit commitment problem,” IEEE Transactions on Power Systems, Vol. 14, pp. 1452–1459, 1999.
  • [12] N.D. Simopoulos, D.S. Kavatza, D. Vournas, “Unit commitment by an enhanced simulated annealing algorithm,” IEEE Transactions on Power Systems, Vol. 21, pp. 68–76, 2006.
  • [13] M. Eslamian, S.H. Hosseinian, B. Vahidi, “Bacterial foraging based solution to the unit-commitment problem,” IEEE Transactions on Power Systems, Vol. 24, pp. 1478–1488, 2009.
  • [14] M.M. Hadji, B. Vahidi, “A solution to the unit commitment problem using imperialistic competition algorithm,” IEEE Transactions on Power Systems, Vol. 27, pp. 117–124, 2012.
  • [15] B. Pavez-Lazo, J. Soto-Cartes, “A deterministic annular crossover genetic algorithm optimisation for the unit commitment problem,” Expert Systems with Applications, Vol. 38, pp. 6523–6529, 2011.
  • [16] S. Najafi, Y. Pourjamal, “A new heuristic algorithm for unit commitment problem,” Energy Procedia, Vol. 14, pp. 2005–2011, 2012.
  • [17] C.C. Su, Y.Y. Hsu, “Fuzzy dynamic programming: an application to unit commitment,” IEEE Transactions on Power Systems, Vol. 6, pp. 1231–1237, 1991.
  • [18] K.A. Juste, H. Kita, E. Tanaka, J. Hasegawa, “An evolutionary programming solution to the unit commitment problem,” IEEE Transactions on Power Systems, Vol. 14, pp. 1452–1459, 1999.
  • [19] C. Wang, S.M. Shahidehpour, “Effects of ramp rate limits on unit commitment and economic dispatch,” IEEE Transactions on Power Systems, Vol. 8, pp. 1341–1350, 1993.
  • [20] J. Kennedy, R.C. Eberhart, “Particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Networks (ICNN’95), Perth, Australia, Vol. 4, pp. 1942–1948, 1995.
  • [21] K.T. Chaturvedi, M. Pandit, L. Srivastava, “Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch,” International Journal of Electrical Power Energy Systems, Vol. 31, pp. 249–257, 2009.
  • [22] A.J. Wood, B.F. Wollenberg, Power Generation Operation and Control, New York, Wiley, 1984.
Turkish Journal of Electrical Engineering and Computer Science-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK
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