Economic power dispatch of power systems with pollution control using artificial bee colony optimization

This paper presents a solution for the emission-controlled economic dispatch (ECED) problem of medium-sized power systems via an artificial bee colony algorithm. The ECED problem, which accounts for the minimization of both the fuel cost and the emission, is a multiple objective function problem. The objective is to minimize the total fuel cost of the generation and environmental pollution caused by fossil-based thermal generating units and to also maintain an acceptable system performance in terms of the limits on the generator's real and reactive power outputs, bus voltages, shunt capacitors/reactors, and power flow of transmission lines. The proposed algorithm is validated on an IEEE 30-bus system with 6 generating units. The results of the proposed technique are compared with that of the particle swarm optimization technique. The proposed approach is also tested on the Algerian 59-bus network and compared with global optimization methods (fuzzy genetic algorithm and ant colony optimization). The results show that the approach proposed can converge to a near solution and obtain a competitive solution in a critical situation and within a reasonable time.

Economic power dispatch of power systems with pollution control using artificial bee colony optimization

This paper presents a solution for the emission-controlled economic dispatch (ECED) problem of medium-sized power systems via an artificial bee colony algorithm. The ECED problem, which accounts for the minimization of both the fuel cost and the emission, is a multiple objective function problem. The objective is to minimize the total fuel cost of the generation and environmental pollution caused by fossil-based thermal generating units and to also maintain an acceptable system performance in terms of the limits on the generator's real and reactive power outputs, bus voltages, shunt capacitors/reactors, and power flow of transmission lines. The proposed algorithm is validated on an IEEE 30-bus system with 6 generating units. The results of the proposed technique are compared with that of the particle swarm optimization technique. The proposed approach is also tested on the Algerian 59-bus network and compared with global optimization methods (fuzzy genetic algorithm and ant colony optimization). The results show that the approach proposed can converge to a near solution and obtain a competitive solution in a critical situation and within a reasonable time.

___

  • A. Momoh, M.E. El-Hawary, R. Adapa, “A review of selected optimal power flow literature to 1993. Part I: Nonlinear and quadratic programming approaches”, IEEE Transactions on Power Systems, Vol. 14, pp. 96–104, 1999.
  • A. Momoh, M.E. El-Hawary, R. Adapa, “A review of selected optimal power flow literature to 1993. Part II: Newton, linear programming and interior point methods”, IEEE Transactions on Power Systems, Vol. 14, pp. 105–111, 1999. H.W. Dommel, W.F. Tinney, “Optimal power flow solutions”, IEEE Transactions on Power Apparatus and Systems, Vol. 87, pp. 1866–1876, 1968.
  • T. Bouktir, M. Belkacemi, “Object-oriented optimal power flow”, Electric Power Components and Systems, Vol. 31, pp. 525–534, 2003.
  • T. Bouktir, R. Labdani, L. Slimani, “Economic power dispatch of power system with pollution control using multiobjective particle swarm optimization”, Journal of Pure and Applied Sciences, Vol. 4, pp. 57–77, 2007
  • T. Bouktir, L. Slimani, “Optimal power flow of the Algerian electrical network using ant colony optimization method”, 9th IASTED International Conference on Artificial Intelligence and Soft Computing, pp. 352–357, 2005 B. Mahdad, T. Bouktir, K. Srairi, “Fuzzy controlled genetic algorithm for environmental/economic dispatch with shunt FACTS devices”, IEEE/PES Transmission and Distribution Conference and Exposition, pp. 1–8, 2008.
  • A.J. Wood, B.F. Wollenberg, Power Generation, Operation, and Control, 2nd ed., New York, Wiley, 1996.
  • G.W. Stagg, A.H. El Abiad, Computer Methods in Power Systems Analysis, New York, McGraw-Hill, 1981.
  • L. Slimani, T. Bouktir, “Economic power dispatch of power system with pollution control using multiobjective ant colony optimization”, International Journal of Computational Intelligence Research, Vol. 3, pp. 145–153, 2007
  • B. Mahdad, T. Bouktir, K. Srairi, “OPF with environmental constraints with multi shunt dynamic controllers using decomposed parallel GA: application to the Algerian network”, Journal of Electrical Engineering and Technology, Vol. 4, pp. 55–65, 2009.
  • D. Karaboga, B. Basturk, “Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems”, Proceedings of the 12th International Fuzzy Systems Association World Congress on Foundations of Fuzzy Logic and Soft Computing, LNAI 4529, pp. 789–798, 2007.
  • D. Karaboga, B. Basturk, “On the performance of artificial bee colony (ABC) algorithm”, Applied Soft Computing, Vol. 8, pp. 687–697, 2007.
  • B. Mahdad, K. Srairi, T. Bouktir, “Improved parallel PSO solution to economic dispatch with practical generator constraints”, 15th IEEE Mediterranean Electrotechnical Conference, pp. 314–319, 2010.
  • L. Vanfretti, F. Milano, “Experience with PSAT (Power System Analysis Toolbox) as free and open-source software for power system education and research”, International Journal of Electrical Engineering Education, Vol. 47, pp. 47–62, 2010.
  • R.D. Zimmerman, C.E. Murillo-S´ anchez, R.J. Thomas, “MATPOWER steady-state operations, planning, analysis tools for power systems research and education”, IEEE Transactions on Power Systems, Vol. 26, pp. 12–19, 2011.
Turkish Journal of Electrical Engineering and Computer Science-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK