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Automated Differential Evolution for Solving Dynamic Economic Dispatch Problems

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Intelligent and Evolutionary Systems

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 5))

Abstract

The objective of a dynamic economic dispatch problem is to determine the optimal power generation from a number of generating units by minimizing the fuel cost. The problem is considered a high-dimensional complex constrained optimization problem. Over the last few decades, many differential evolution variants have been proposed to solve this problem. However, such variants were highly dependent on the search operators, control parameters and constraint handling techniques used. Therefore, to tackle with this shortcoming, in this paper, a new differential evolution framework is introduced. In it, the appropriate selection of differential evolution operators is linked to the proper combination of control parameters (scaling factor and crossover rate), while the population size is adaptively updated. To add to this, a heuristic repair approach is introduced to help obtaining feasible solutions from infeasible ones, and hence enhancing the convergence rate of the proposed algorithm. The algorithm is tested on three different dynamic dispatch problems with 12 and 24 hours planning horizons. The results demonstrate the superiority of the proposed algorithm to the state-of-the-art algorithms.

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References

  1. Wood, W.G.: Spinning reserve constrained static and dynamic economic dispatch. IEEE Transactions on Power Apparatus and Systems PAS–101(2), 381–388 (1982)

    Article  Google Scholar 

  2. Zaman, M., Elsayed, S., Ray, T., Sarker, R.: Evolutionary algorithms for dynamic economic dispatch problems. IEEE Transactions on Power Systems PP(99), 1–10 (2015)

    Google Scholar 

  3. Niknam, T., Azizipanah-Abarghooee, R., Zare, M., Bahmani-Firouzi, B.: Reserve constrained dynamic environmental/economic dispatch: A new multiobjective self-adaptive learning bat algorithm. IEEE Systems Journal 7(4), 763–776 (2013)

    Article  Google Scholar 

  4. Victoire, T.A., Jeyakumar, A.E.: Reserve constrained dynamic dispatch of units with valve-point effects. IEEE Transactions on Power Systems 20(3), 1273–1282 (2005)

    Article  Google Scholar 

  5. Hemamalini, S., Simon, S.P.: Dynamic economic dispatch using artificial bee colony algorithm for units with valve-point effect. European Transactions on Electrical Power 21(1), 70–81 (2011)

    Article  Google Scholar 

  6. Zhang, H., Yue, D., Xie, X., Hu, S., Weng, S.: Multi-elite guide hybrid differential evolution with simulated annealing technique for dynamic economic emission dispatch. Applied Soft Computing 34, 312–323 (2015)

    Article  Google Scholar 

  7. Elsayed, S.M., Sarker, R.A., Essam, D.L.: Multi-operator based evolutionary algorithms for solving constrained optimization problems. Computers & Operations Research 38(12), 1877–1896 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  8. Sarker, R., Elsayed, S., Ray, T.: Differential evolution with dynamic parameters selection for optimization problems. IEEE Transactions on Evolutionary Computation 18(5), 689–707 (2014)

    Article  Google Scholar 

  9. Zamuda, A., Brest, J.: Population reduction differential evolution with multiple mutation strategies in real world industry challenges. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds.) Swarm and Evolutionary Computation, pp. 154–161. Springer (2012)

    Google Scholar 

  10. Tvrdík, J., Polakova, R.: Competitive differential evolution for constrained problems. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)

    Google Scholar 

  11. Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, vol. 3. ICSI Berkeley (1995)

    Google Scholar 

  12. Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15(1), 4–31 (2011)

    Article  Google Scholar 

  13. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  14. Deb, K.: An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186(2), 311–338 (2000)

    Article  MATH  Google Scholar 

  15. Zhang, Y., Gong, D.W., Geng, N., Sun, X.Y.: Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects. Applied Soft Computing 18, 248–260 (2014)

    Article  Google Scholar 

  16. Niu, Q., Li, K., Irwin, G.: Differential evolution combined with clonal selection for dynamic economic dispatch. Journal of Experimental & Theoretical Artificial Intelligence 27(3), 325–350 (2015)

    Article  Google Scholar 

  17. Zhang, J., Sanderson, A.C.: Jade: adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13(5), 945–958 (2009)

    Article  Google Scholar 

  18. Panigrahi, C.K., Chattopadhyay, P.K., Chakrabarti, R.N., Basu, M.: Simulated annealing technique for dynamic economic dispatch. Electric Power Components and Systems 34(5), 577–586 (2006)

    Article  Google Scholar 

  19. Hemamalini, S., Simon, S.P.: Dynamic economic dispatch using artificial immune system for units with valve-point effect. International Journal of Electrical Power & Energy Systems 33(4), 868–874 (2011)

    Article  Google Scholar 

  20. Dieu, V.N., Ongsakul, W.: Economic dispatch with emission and transmission constraints by augmented lagrange hopfield network. Transaction in Power System Optimization (GJOT) 1 (2010)

    Google Scholar 

  21. Babu, G.S., Das, D.B., Patvardhan, C.: Dynamic economic dispatch solution using an enhanced real-quantum evolutionary algorithm. In: Joint International Conference on Power System Technology and IEEE Power India Conference. POWERCON 2008, pp. 1–6. IEEE (2008)

    Google Scholar 

  22. Attaviriyanupap, P., Kita, H., Tanaka, E., Hasegawa, J.: A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Power Engineering Review 22(4), 77 (2002)

    Article  Google Scholar 

  23. Liao, G.C.: A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power. Energy 36(2), 1018–1029 (2011)

    Article  Google Scholar 

  24. Yuan, X., Wang, L., Yuan, Y., Zhang, Y., Cao, B., Yang, B.: A modified differential evolution approach for dynamic economic dispatch with valve-point effects. Energy Conversion and Management 49(12), 3447–3453 (2008)

    Article  Google Scholar 

  25. He, D., Dong, G., Wang, F., Mao, Z.: Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms. Energy Conversion and Management 52(2), 1026–1032 (2011)

    Article  Google Scholar 

  26. Immanuel Selvakumar, A.: Enhanced cross-entropy method for dynamic economic dispatch with valve-point effects. International Journal of Electrical Power & Energy Systems 33(3), 783–790 (2011)

    Article  Google Scholar 

  27. Wang, Y., Zhou, J., Qin, H., Lu, Y.: Improved chaotic particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects. Energy Conversion and Management 51(12), 2893–2900 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, Canberra, Australia

    Saber Elsayed, Md Forhad Zaman & Ruhul Sarker

Authors
  1. Saber Elsayed
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  2. Md Forhad Zaman
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  3. Ruhul Sarker
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Corresponding author

Correspondence to Saber Elsayed .

Editor information

Editors and Affiliations

  1. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Kittichai Lavangnananda

  2. School of Computing and Informatics, Institut Teknologi Brunei, Gadong, Brunei Darussalam

    Somnuk Phon-Amnuaisuk

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Worrawat Engchuan

  4. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

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© 2016 Springer International Publishing Switzerland

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Elsayed, S., Zaman, M.F., Sarker, R. (2016). Automated Differential Evolution for Solving Dynamic Economic Dispatch Problems. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_29

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  • DOI: https://doi.org/10.1007/978-3-319-27000-5_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26999-3

  • Online ISBN: 978-3-319-27000-5

  • eBook Packages: EngineeringEngineering (R0)

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