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Multi-objective Differential-Based Brain Storm Optimization for Environmental Economic Dispatch Problem

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Brain Storm Optimization Algorithms

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 23))

Abstract

Environmental Economic Dispatch (EED) problem has been paid more attention in recent years as it can save the cost of the fuel while reducing the environmental pollution. A novel Multi-objective Differential Brain Storm Optimization (MDBSO) algorithm is proposed to solve EED problem in this chapter. Different from the classical BSO, the clustering operation is designed in the objective space instead of solution space to improve the computing efficiency. The difference mutation operation is also adopted in the proposed algorithm to replace the Gaussian mutation in the original BSO algorithm for increasing the diversity of the population and improving the speed of convergence. The performance of the proposed algorithm is verified by two test systems with 6 units and 40 units in the literature. The simulation results show that comparing with other intelligent optimization method, MDBSO can maintain the diversity of Pareto optimal solutions and show better convergence at the same time.

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References

  1. Abido, M.A.: Multi objective particle swarm optimization for environmental/economic dispatch problem. Electr. Power Syst. Res. 79(7), 1105–1113 (2009)

    Article  Google Scholar 

  2. Yalcinoz, T., Köksoy, O.: A multi objective optimization method to environmental economic dispatch. Electr. Power Syst. Res. 29(1), 42–50 (2007)

    Article  Google Scholar 

  3. Qu, B.Y., Zhu, Y.S., Jiao, Y.C., et al.: A Survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems. Swarm Evol. Comput. 38, 1–11 (2018)

    Article  Google Scholar 

  4. Alomoush, M.I., Oweis, Z.B.: Environmental-economic dispatch using stochastic fractal search algorithm. Int. Trans. Electr. Energ. Syst. 3, e2530 (2018)

    Article  Google Scholar 

  5. Zhao, B.: Research on Swarm Intelligence Computation and Multi-agent Technique and their Applications to Optimal Operation of Electrical Power System. Zhejiang University, Zhejiang (2005)

    Google Scholar 

  6. Abido, A.A.: A new multiobjective evolutionary algorithm for environmental/economic power dispatch. Electr. Power Syst. Res. 65(1), 71–81 (2003)

    Article  Google Scholar 

  7. Rose, L.R., Selvi, B.D.A., Singh, R.L.R.: A novel approach to solve environmental economic dispatch problem using Gauss Newton based genetic algorithm 6(6), 1561 (2016)

    Google Scholar 

  8. El-Sehiemy, R.A., El-Hosseini, M.A., Hassanien, A.E.: Multiobjective real-coded genetic algorithm for economic/environmental dispatch problem. Stud. Inf. Control 22(2), 113–122 (2013)

    Google Scholar 

  9. Lu, Y., Zhou, J., Hui, Q., et al.: Environmental/economic dispatch problem of power system by using an enhanced multi-objective differential evolution algorithm. Energy Convers. Manag. 52(2), 1175–1183 (2011)

    Article  Google Scholar 

  10. Basu, M.: Economic environmental dispatch using multi-objective differential evolution. Appl. Soft Comput. J. 11(2), 2845–2853 (2011)

    Article  Google Scholar 

  11. Pandit, M., Srivastava, L., Sharma, M.: Environmental economic dispatch in multi-area power system employing improved differential evolution with fuzzy selection. Appl. Soft Comput. 28, 498–510 (2015)

    Article  Google Scholar 

  12. Agrawal, S., Panigrahi, B.K., Tiwari, M.K.: Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch. IEEE Trans. Evol. Comput. 12(5), 529–541 (2008)

    Article  Google Scholar 

  13. Rose, R.L., Selvi, B.D.A., Singh, R.L.R.: Development of hybrid algorithm based on PSO and NN to solve economic emission dispatch problem. Circuits Syst. 07(9), 2323–2331 (2016)

    Article  Google Scholar 

  14. Zou, D., Li, S., Li, Z., et al.: A new global particle swarm optimization for the economic emission dispatch with or without transmission losses. Energy Convers. Manag. 139, 45–70 (2017)

    Article  Google Scholar 

  15. Roy, P.K., Bhui, S.: Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem. Int. J. Electr. Power Energy Syst. 53(4), 937–948 (2013)

    Article  Google Scholar 

  16. Lu, Z.G., Feng, T., Li, X.P.: Low-carbon emission/economic power dispatch using the multi-objective bacterial colony chemotaxis optimization algorithm considering carbon capture power plant. Int. J. Electr. Power Energy Syst. 53(1), 106–112 (2013)

    Article  Google Scholar 

  17. Shi, Y.: Brain storm optimization algorithm. In: Advances in Swarm Intelligence Second International Conference, ICSI 2011, pp. 1–3. Chongqing, China, 12–15 June 2011

    Google Scholar 

  18. Xue, J., Wu, Y., Shi, Y., et al.: Brain storm optimization algorithm for multi-objective optimization problems. Lect. Notes Comput. Sci. 7331(4), 513–519 (2012)

    Article  Google Scholar 

  19. Wu, Y, Xie, L, Liu, Q.: Multi-objective brain storm optimization based on estimating in knee region and clustering in objective-space. In: International Conference in Swarm Intelligence, pp. 479–490. Springer, Cham (2016)

    Google Scholar 

  20. Guo, X., Wu, Y., Xie, L., et al.: An adaptive brain storm optimization algorithm for multiobjective optimization problems. Lect. Notes Comput. Sci. 9140(4), 365–372 (2015)

    Article  Google Scholar 

  21. Walters, D.C., Sheblé, G.B.: Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans. Power Syst. 8(3), 1325–1332 (1993)

    Article  Google Scholar 

  22. Farag, A., Al-Baiyat, S., Cheng, T.C.: Economic load dispatch multiobjective optimization procedures using linear programming techniques. IEEE Trans. Power Syst. 10(2), 731–738 (1995)

    Article  Google Scholar 

  23. Yokoyama, R., Bae, S.H., Morita, T., Sasaki, H.: Multiobjective generation dispatch based on probability security criteria. IEEE Trans. Power Syst. 3(1), 317–324 (1988)

    Article  Google Scholar 

  24. Cheng, S, Shi, Y, Qin, Q, et al.: Solution clustering analysis in brain storm optimization algorithm. In: 2013 IEEE Symposium on Swarm Intelligence (SIS), pp. 1391–1399 (2013)

    Google Scholar 

  25. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  26. Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

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Acknowledgements

This chapter is supported by National Youth Foundation of China with Grant Number 61503299.

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Authors and Affiliations

  1. Faculty of Automation and Information Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Yali Wu, Xinrui Wang, Yingruo Xu & Yulong Fu

  2. Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an, 710048, China

    Yali Wu, Xinrui Wang, Yingruo Xu & Yulong Fu

Authors
  1. Yali Wu
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  2. Xinrui Wang
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  3. Yingruo Xu
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  4. Yulong Fu
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Corresponding author

Correspondence to Yali Wu .

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Editors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Shi Cheng

  2. Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Wu, Y., Wang, X., Xu, Y., Fu, Y. (2019). Multi-objective Differential-Based Brain Storm Optimization for Environmental Economic Dispatch Problem. In: Cheng, S., Shi, Y. (eds) Brain Storm Optimization Algorithms. Adaptation, Learning, and Optimization, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-15070-9_4

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