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Dynamic economic dispatch based on improved differential evolution algorithm

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Abstract

Dynamic economic dispatch optimum scheduling of power plant generation is of great importance to electric utility systems, it is difficult to solve because of its complex structure, variable parameter, nonlinear characteristics et al. Based on analysis of DE searching mechanism, an improved differential evolution (IDE) algorithm based DE/target-to-best is presented,which adopts an improved mutation strategy that a random vector and the previous best vector is used instead of the current vector in case the DE algorithm may be in early maturity or decline in convergence speed.The algorithm is applied to solve the generators dynamic load economic dispatch problems taking into account the incremental fuel cost function and the valve-point effects. Computer simulation test shows that IDE algorithm provides better solution of less cost In the case of less generations and outperforms GA, PSO and DE.

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References

  1. Hu, Z., Wang, H., He, J., et al.: Improved control method for solar auto-trackingbasedon difference evolution algorithm. Acta Energiae Solaris Sinica 35(6), 1016–1021 (2014)

    Google Scholar 

  2. Mohamed, A.W.: Restart differential evolution algorithm with local search mutation for global numerical optimization. Egypt. Inform. J. 15(3), 175–188 (2014)

    Article  Google Scholar 

  3. Qin, H., Wei, H.: A quantum-inspired approximate dynamic programming algorithm for large-scale unit commitment problems. Proc. CSEE 35(19), 4918–4927 (2015)

    Google Scholar 

  4. Xiong, W., Xu, M., Xu, B.: Differential bee colony algorithm for non–convex economic load dispatch. Control Decis. 26(12), 1813–1823 (2011)

    MathSciNet  Google Scholar 

  5. Zhao, J., Wang, M.: Dynamic economic dispatch of microgrid based on dynamic programming. J. Northeast Dianli Univ. 36(2), 19–25 (2016)

    Google Scholar 

  6. Bin, Z., Yan, S., Jinming, L., et al.: Multiobjective optimal generation dispatch using equilibria-based multi-group synergistic searching algorithm. Trans. China Electro Tech. Soc. 30(22), 181–189 (2015)

    Google Scholar 

  7. Daqing, W., Liu, L., Jianguo, Z., et al.: Environmental economic power dispatch based on multi-objective evolution algorithm with adaptive space partition. Control Decis. 30(11), 1974–1980 (2015)

    MATH  Google Scholar 

  8. Lianghong, W., Yaolan, W., Xiaofang, Y., et al.: Fast self-adaptive differential evolution algorithm for power economic load dispatch. Control Decis. 28(4), 557–562 (2013)

    Google Scholar 

  9. Wu, X., Wang, X., Wang, J., et al.: Economicgeneration scheduling of a microgrid using mixed integer programming. Proc. CSEE 33(28), 1–9 (2013)

    Google Scholar 

  10. Zheng, H., Zheng, H., Yang, Y., et al.: A novel quasi pr composite control strategy applied in photovoltaic grid-connecteid inverter. Acta Energiae Solaris Sinica 37(5), 1190–1196 (2016)

    Google Scholar 

  11. Zheng, H., Yang, Y., Zheng, H., et al.: Grid frequency tracking technology of double closed-loop orthogonal vector lock strategy. Acta Energiae Solaris Sinica 37(10), 2497–2504 (2016)

    Google Scholar 

  12. Heng-Dong, X., Hao, Z., Wei, G., et al.: Active droplet sorting in microfluidics: a review. Lab Chip 17, 751–771 (2017)

    Article  Google Scholar 

  13. Meng, A., Hu, H., Lu, H.: Crisscross optimization algorithm for large scale dynamic economic dispatch. Power Syst. 23, 18–23 (2015)

    Google Scholar 

  14. Chen, Z., Hu, Z.: A modified hybrid PSO-BBO algorithm for dynamic economic dispatch. Power Syst. Prot. Control 18, 44–49 (2014)

    Google Scholar 

  15. Zheng, X.: Multi-area economic dispatch of power system based on artificial bee colony optimization. Comput. Eng. Sci 37(8), 1533–1539 (2015)

    Google Scholar 

  16. Basu, M.: Teaching-learning-based optimization algorithm for multi-area economic dispatch. Energy 68(8), 21–28 (2014)

    Article  Google Scholar 

  17. Secui, D.C.: The chaotic global best artificial bee colony algorithm for the multi-area economic /emission dispatch. Energy 93, 2518–2545 (2015)

    Article  Google Scholar 

  18. Basu, M.: Quasi-oppositional group search optimization for multi-area dynamic economic dispatch. Electr. Power Energy Syst. 78, 356–367 (2016)

    Article  Google Scholar 

  19. Li, Z., Wu, W., Zhang, B., et al.: Decentralized multi-area dynamic economic dispatch using modified generalized benders decomposition. Power Syst. 31(1), 526–538 (2016)

    Article  Google Scholar 

  20. Meng, A., Hu, H., Yin, H., et al.: Crisscross optimization algorithm for large-scale dynamic economic dispatch problem with valve-point effects[. Energy 93, 2175–2190 (2015)

    Article  Google Scholar 

  21. Anbo, Meng, Peng, Mei, Haiming, Lu: Crisscross Optimization Algorithm for Combined Heat and Power EconomicDispatch[J]. Power System Protection and Control 44(6), 90–97 (2016)

    Google Scholar 

  22. Jang, H., Dong, Yao, Wang, Jiang Zhou, et al.: Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation. Energy Convers. Manage. 95, 42–58 (2015)

    Article  Google Scholar 

  23. Anbo, A., Longfei, Y., Xing, L., et al.: Research on reactive power optimization using auantum evolutionary algorithm based on NW small world model. Electric Power 48(1), 107–114 (2015)

    Google Scholar 

Download references

Acknowledgements

Zheng Hongfeng gratefully acknowledge the support through Zhejiang public welfare projects Grant (2016c31055) and Shaoxing science and technology innovation team Grant (2016).

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  1. Zhejiang Industry Polytechnic College, Shaoxing, China

    Zheng Hongfeng

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  1. Zheng Hongfeng
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Correspondence to Zheng Hongfeng.

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Hongfeng, Z. Dynamic economic dispatch based on improved differential evolution algorithm. Cluster Comput 22 (Suppl 4), 8241–8248 (2019). https://doi.org/10.1007/s10586-018-1733-y

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  • DOI: https://doi.org/10.1007/s10586-018-1733-y

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