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Optimal Economic Dispatch of Fuel Cost Based on Intelligent Monkey King Evolutionary Algorithm

  • Jing Tang
  • Jeng-Shyang Pan
  • Yen-Ming TsengEmail author
  • Pei-Wei Tsai
  • Zhenyu Meng
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 82)

Abstract

Monkey King evolutionary algorithm (MKEA) is a new type and innovation of gene method that can be more effective evolution of the algorithm to reach goal or objective function. In this study and research is applied the monkey king evolutionary algorithm is used to apply the evolutionary particle to find the optimal power flow of system and calculate the complex power of each line, bus and to minimize power generation cost of the power plant. In order to study the practicability of the algorithm, it is applied to the standard IEEE 5bus load flow test system, and its convergence characteristic curve is observed and compared with the genetic algorithm (GA). The experimental results show that the MKEA can effectively solve the power system optimal power flow problem and this method is find the global solution not local solution that be confirmed in minimum fuel cost of generator of power plants. The minimum fuel cost obtained by MKE and GA is 5369.55 and 5422.0 US Dollars, respectively, when the number of population particles is 100 and the number of iterations is 300 that compared with GA which is 7.6% lower than GA. The results show that MKE has the obvious superiority to find the global solution.

Keywords

Monkey king evolutionary algorithm Optimal power flow Minimum fuel cost IEEE 5bus Convergence characteristic 

References

  1. 1.
    Liu, M., Sun, H., He, J., Zhang, H., Yi, J.: Research on security assessment index system for operating reserve in large interconnected power grid. Energy Power Eng. 5(4B), 785–791 (2013)CrossRefGoogle Scholar
  2. 2.
    Keskes, S., Bahloul, W., Kammoun, M.B.A.: Improvement of power system stability by static var compensator and tuning employing genetic algorithm. Int. J. Mod. Nonlinear Theor. Appl. 3(3), 113–123 (2014)CrossRefGoogle Scholar
  3. 3.
    Li, D.F.: Closeness coefficient based nonlinear programming method for interval-valued intuitionistic fuzzy multiattribute decision making with incomplete preference information. Appl. Soft Comput. 11(4), 3402–3418 (2011)CrossRefGoogle Scholar
  4. 4.
    Hatami-Marbini, A., Tavana, M.: An extension of the linear programming method with fuzzy parameters. Int. J. Math. Oper. Res. 3(1), 44–55 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Wöhrmann, A.M., Deller, J., Wang, M.: A mixed-method approach to post-retirement career planning. J. Vocat. Behav. 84(3), 307–317 (2014)CrossRefGoogle Scholar
  6. 6.
    Yi, W., Gao, L., Li, X., Zhou, Y.: A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems. Appl. Intell. 42(4), 642–660 (2015)CrossRefGoogle Scholar
  7. 7.
    Brest, J., Zamuda, A., Bošković, B.: Adaptation in the Differential Evolution, vol. 18, pp. 53–68. Springer, Heidelberg (2015)Google Scholar
  8. 8.
    Liang, J.J., Zhang, W.X., Qu, B.Y., Chen, T.J.: Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Environmental/Economic Dispatch Problem, vol. 7389, pp. 657–664. Springer, Heidelberg (2012)Google Scholar
  9. 9.
    Zhang, Y., Gong, D.W., Zhang, J.H.: Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing 103(2), 172–185 (2013)CrossRefGoogle Scholar
  10. 10.
    Ling, W.X., Wang, Y.X.: Using Modular Neural Network with Artificial Bee Colony Algorithm for Classification, vol. 7928, pp. 396–403. Springer, Heidelberg (2013)Google Scholar
  11. 11.
    Kisi, O., Ozkan, C., Akay, B.: Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. J. Hydrol. 428–429, 94–103 (2012)CrossRefGoogle Scholar
  12. 12.
    Chinnasri, W.: Adaptive probability of crossover and mutation in genetic algorithm on university course timetabling problem. In: 2013 IEEE International Conference on Computer Science and Automation Engineering, vol. 24(4), pp. 656–667 (2002)Google Scholar
  13. 13.
    Wang, S., Lu, Z., Wei, L., Ji, G., Yang, J.: Fitness-scaling adaptive genetic algorithm with local search for solving the multiple depot vehicle routing problem. Simulation 92(7), 601–616 (2016)CrossRefGoogle Scholar
  14. 14.
    Sarkheyli, A., Zain, A.M., Sharif, S.: The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review. Soft. Comput. 19(7), 2011–2038 (2015)CrossRefGoogle Scholar
  15. 15.
    Roy, P., Chakrabarti, A.: Modified shuffled frog leaping algorithm for solving economic load dispatch problem. Energy Power Eng. 334068(4), 551–556 (2011)CrossRefGoogle Scholar
  16. 16.
    Meng, Z., Pan, J.S.: Monkey king evolution: a new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization. Knowl. Based Syst. 97, 144–157 (2016)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Meng, Z., Pan, J.S.: A Simple and Accurate Global Optimizer for Continuous Spaces Optimization, Genetic and Evolutionary Computing, pp. 121–129. Springer, Heidelberg (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jing Tang
    • 1
    • 2
  • Jeng-Shyang Pan
    • 1
    • 2
  • Yen-Ming Tseng
    • 1
    • 2
    Email author
  • Pei-Wei Tsai
    • 3
  • Zhenyu Meng
    • 4
  1. 1.School of Information Science and EngineeringFujian University of TechnologyFuzhouChina
  2. 2.Fujian Provincial Key Laboratory of Big Data Mining and ApplicationFujian University of TechnologyFuzhouChina
  3. 3.Department of Computer Science and Software EngineeringSwinburne University of TechnologyMelbourneAustralia
  4. 4.Harbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina

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