Solution Economic Power Dispatch Problems by an Ant Colony Optimization Approach

Authors: Navid Mehdizadeh Afroozi, Khodakhast Isapour, Mojtaba Hakimzadeh, Abdolmohammad Davodi

Abstract:

The objective of the Economic Dispatch(ED) Problems of electric power generation is to schedule the committed generating units outputs so as to meet the required load demand at minimum operating cost while satisfying all units and system equality and inequality constraints. This paper presents a new method of ED problems utilizing the Max-Min Ant System Optimization. Historically, traditional optimizations techniques have been used, such as linear and non-linear programming, but within the past decade the focus has shifted on the utilization of Evolutionary Algorithms, as an example Genetic Algorithms, Simulated Annealing and recently Ant Colony Optimization (ACO). In this paper we introduce the Max-Min Ant System based version of the Ant System. This algorithm encourages local searching around the best solution found in each iteration. To show its efficiency and effectiveness, the proposed Max-Min Ant System is applied to sample ED problems composed of 4 generators. Comparison to conventional genetic algorithms is presented.

Keywords: Economic Dispatch (ED), Ant Colony Optimization, Fuel Cost, Algorithm.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1097335

References:

[1] X. Guan, P.B. Luh, L. Zhang, Nonlinear approximation method in Lagrangian relaxation based algorithms for hydrothermal scheduling, IEEE Trans. Power Systems, Vol. 10, (2), pp. 772-778, 1995.
[2] A.J. Wood, B.F. Wollenberg, Power Generation, Operation and Control, John Wiley & Sons, New York 1996.
[3] A.M. Chebbo, M.R. Irving, Combined active and reactive dispatch, Proc IEE, Pt.C, (4), pp. 393-405, 1995.
[4] S. Granville, Optimal reactive dispatch through interior point methods, IEEE Summer Meeting, Paper No. 92, SM 416-8 PWRS, 1992.
[5] D.C. Walter and G.B. Sheble, “Genetic algorithm solution of economic dispatch with valve point loading,” IEEE Trans. Power Syst., vol. 8, no. 3, pp. 1325–1332, Aug. 1999.
[6] K.P. Wong and C.C. Fung, “Simulated annealing based economic dispatch algorithm,” Proc. Inst. Elect. Eng. C., Gen., Transm., Distrib., vol. 140, no. 6, pp. 505–519, Nov. 1993.
[7] N. Sinha, R. Chakrabarti, and P. K. Chattopadhyay, “Evolutionary programming techniques for economic load dispatch,” IEEE Trans. Evol. Comput., vol. 7, no. 1, pp. 83–94, Feb. 2003.
[8] W.M. Lin, F.S. Cheng, and M.T. Tsay, “An improved tabu search for economic dispatch with multiple minima,” IEEE Trans. Power Syst., vol. 17, no. 1, pp. 108–112, Feb. 2002.
[9] T. Stutzle, and H.H. Hoos, Max-Min Ant System, Future Generation Computer Systems, 16, pp.889-914, 2001.
[10] E. Bonabenn, M. Dorigo, G. Theraulaz, Swarm Intelligence from natural to Artificial systems, Sante Fe Institute studies in the Sciences of complexity, Oxford University Press, 1999.
[11] M. Dorigo, G. Di Caro, The Ant Colony Optimization Meta-Heuristic, In D.Come, M.Dorigo, and F.Glover, editors, New Ideas in optimization, Mc Graw-Hill, 2001.
[12] T. Stutzle, H. Hoos, The MAX-MIN Ant System and local search for the traveling Salesman Problem, Proceedings of the IEEE International conference on Evolutionary Computaion , ICEC ’97, pp.309-314. 1997.
[13] T. Stutzle, An Ant Approach to the Flow Shop Problem, Proceedings of the 6th European Congress on Intelligent Techniques and soft Computing (EUFIT ’98), (3), Verlag Mainz, Aachem, pp.1560-1564, 1997.
[14] M. Dorigo, L.M. Gambardella, Ant Algorithms for Discrete Optimization, Artificial Life, volume 5, no.2, pp.137-172, 1998.
[15] M. Dorigo , V. Maniezzo, and K. Colorni, The ant system: optimization by a colony of cooperating agents, IEE Transactions on Systems, Man, and Cybernetics, Part B, Cybernetics, 26(1), pp. 29-44, 2000.
[16] T. Stutzle and H.H. Hoos, MAX-MIN Ant System, Future Generation Computer Systems, (16), pp.889-914, 2003