Smart Evolutionary Algorithm for Static Economic Load Dispatch Optimization in a Thermal Generating Station

Sunny Orike^1, and Vincent I. E. Anireh¹

¹Department of Electrical/Computer Engineering, Rivers State University of Science and Technology, Port Harcourt, Nigeria

Cite this paper:
Sunny Orike and Vincent I. E. Anireh. Smart Evolutionary Algorithm for Static Economic Load Dispatch Optimization in a Thermal Generating Station. American Journal of Electrical and Electronic Engineering. 2015; 3(4):100-106. doi: 10.12691/ajeee-3-4-3

Abstract

The Economic Load Dispatch (ELD) problem is an optimization task with emphasis on how power generating companies (GENCOs) will be able to meet the power demands of the distribution companies (DISCOs) and electricity consumers, and at the same time minimize both under/over generation of electricity, and also minimize the operational costs of running the units in their various stations. This paper implemented a Smart Evolutionary Algorithm, which combines a standard Evolutionary Algorithm with a smart mutation operator that is applied to the Static ELD problem. It also investigated and analyzed three distinct variants of the smart mutation operator. The operator focused mutation on genes contributing mostly to cost of generation and penalty violations in the fitness function. Rather than using a generic off-the-shelf optimization package, the paper demonstrated a novel approach to solving certain kinds of real-world problems, contributing a method that have advanced the state of the art in solving a specific optimization problem in the area of economic load dispatch.

Keywords:
economic load dispatch evolutionary algorithm optimization smart mutation

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References:

[1]	Orike, S., “Computational Intelligence in Electrical Power Systems: A Survey of Emerging Approaches,” British Journal of Science, 12 (2). 23-45. April. 2015.
[2]	Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, 1989.
[3]	Sayah, S. and Zehar, K., “Using Evolutionary Computation to Solve the Economic Load Dispatch Problem,” Leonardo Journal of Sciences, 12. 67- 78, Jan-Jun. 2008.
[4]	Orike, S. and Corne, D.W., “Improved Evolutionary Algorithms for Economic Load Dispatch Optimisation Problems,” in 12th UK Workshop on Computational Intelligence (UKCI), Edinburgh, 5-7 Sept. 2012, IEEE.
[5]	Orike, S., “Investigating the Effects of Evolutionary Parametric Tuning for Static Economic Load Dispatch Problems,” Asian Engineering Review, 1(2). 26-35, Nov. 2014.
[6]	Hasan, B.H.F., and Saleh, M.S.M., “Evaluating the Effectiveness of Mutation Operators on the Behaviour of Genetic Algorithms Applied to Non-deterministic Polynomial Problems,” Informatica, 35. 513-518, 2011.
[7]	Korejo, I., Yang, S., and Li, C., “A Comparative Study of Adaptive Mutation Operators for Genetic Algorithms,” in 8th Metaheuristics International Conference, Hamburg, Germany, July 13-16, 2009.
[8]	Yang, S., “Statistics-Based Adaptive Non-Uniform Mutation for Genetic Algorithms,” in 2003 Genetic and Evolutionary Computation Conference, July 9- 11, 2003, Chicago, USA.
[9]	Julstrom, B., “What have you done for me lately? Adaptive Operator Probabilities in a Steady-State Genetic Algorithm,” in 6th Conference on Genetic Algorithms, San Mateo, CA, USA, 1995, Morgan Kaufmann, 81-87.
[10]	Corne, D., Ross, P., and Fang, H.L., “Genetic Algorithm Research Note 7: Fast Practical Evolutionary Timetabling,” Technical Report, Department of Artificial Intelligence, University of Edinburgh, UK, 1994.
[11]	Bäck, T., “Mutation Parameters,” in Bäck, T., Fogel, D.B., and Michalewicz, Z. (eds.), Handbook of Evolutionary Computation, E1.2.1- E1.2.7, Oxford University Press, 1997.
[12]	Wood, A.J., and Wollenberg, B.F., Power Generation, Operation and Control. 2nd Ed., John Wiley, 2012.
[13]	Woodward, J., and Swan, J., “The Automatic Generation of Mutation Operators for Genetic Algorithms,” in 2012 Genetic and Evolutionary Computation Conference, Philadelphia, USA, July 7-11, 2012.
[14]	Tao, W., Xu, C., Ding, Q., Li, R., Xiang, Y., Chung J., and Zhong, J., “A Single Point Mutation in E2 Engances Hepatitis C Virus Infectivity and Alters Lipoprotein Association of Viral Particles,” Journal of Virology, 395. 67-76, Dec. 2009.
[15]	Haupt, R.L., and Haupt, S.E., Practical Genetic Algorithms. John Wiley, 2004.