A multi-subpopulation bat optimization algorithm for economic dispatch problem with non-essential demand response

Yanjun Shen; Bo Yang; Xiongfeng Huang; Yujiao Zhang; Chao Tan

Kybernetika (2019)

  • Volume: 55, Issue: 5, page 809-830
  • ISSN: 0023-5954

Abstract

top
In this paper, we propose a new economic dispatch model with random wind power, demand response and carbon tax. The specific feature of the demand response model is that the consumer's electricity demand is divided into two parts: necessary part and non-essential part. The part of the consumer's participation in the demand response is the non-essential part of the electricity consumption. The optimal dispatch objective is to obtain the minimum total cost (fuel cost, random wind power cost and emission cost) and the maximum consumer's non-essential demand response benefit while satisfying some given constraints. In order to solve the optimal dispatch objective, a multi-subpopulation bat optimization algorithm (MSPBA) is proposed by using different search strategies. Finally, a case of an economic dispatch model is given to verify the feasibility and effectiveness of the established mathematical model and proposed algorithm. The economic dispatch model includes three thermal generators, two wind turbines and two consumers. The simulation results show that the proposed model can reduce the consumer's electricity demand, reduce fuel cost and reduce the impact on the environment while considering random wind energy, non-essential demand response and carbon tax. In addition, the superiority of the proposed algorithm is verified by comparing with the optimization results of CPLEX+YALMIP toolbox for MATLAB, BA, DBA and ILSSIWBA.

How to cite

top

Shen, Yanjun, et al. "A multi-subpopulation bat optimization algorithm for economic dispatch problem with non-essential demand response." Kybernetika 55.5 (2019): 809-830. <http://eudml.org/doc/295055>.

@article{Shen2019,
abstract = {In this paper, we propose a new economic dispatch model with random wind power, demand response and carbon tax. The specific feature of the demand response model is that the consumer's electricity demand is divided into two parts: necessary part and non-essential part. The part of the consumer's participation in the demand response is the non-essential part of the electricity consumption. The optimal dispatch objective is to obtain the minimum total cost (fuel cost, random wind power cost and emission cost) and the maximum consumer's non-essential demand response benefit while satisfying some given constraints. In order to solve the optimal dispatch objective, a multi-subpopulation bat optimization algorithm (MSPBA) is proposed by using different search strategies. Finally, a case of an economic dispatch model is given to verify the feasibility and effectiveness of the established mathematical model and proposed algorithm. The economic dispatch model includes three thermal generators, two wind turbines and two consumers. The simulation results show that the proposed model can reduce the consumer's electricity demand, reduce fuel cost and reduce the impact on the environment while considering random wind energy, non-essential demand response and carbon tax. In addition, the superiority of the proposed algorithm is verified by comparing with the optimization results of CPLEX+YALMIP toolbox for MATLAB, BA, DBA and ILSSIWBA.},
author = {Shen, Yanjun, Yang, Bo, Huang, Xiongfeng, Zhang, Yujiao, Tan, Chao},
journal = {Kybernetika},
keywords = {economic dispatch; non-essential demand response; random wind power; bat algorithm; multi-subpopulation},
language = {eng},
number = {5},
pages = {809-830},
publisher = {Institute of Information Theory and Automation AS CR},
title = {A multi-subpopulation bat optimization algorithm for economic dispatch problem with non-essential demand response},
url = {http://eudml.org/doc/295055},
volume = {55},
year = {2019},
}

TY - JOUR
AU - Shen, Yanjun
AU - Yang, Bo
AU - Huang, Xiongfeng
AU - Zhang, Yujiao
AU - Tan, Chao
TI - A multi-subpopulation bat optimization algorithm for economic dispatch problem with non-essential demand response
JO - Kybernetika
PY - 2019
PB - Institute of Information Theory and Automation AS CR
VL - 55
IS - 5
SP - 809
EP - 830
AB - In this paper, we propose a new economic dispatch model with random wind power, demand response and carbon tax. The specific feature of the demand response model is that the consumer's electricity demand is divided into two parts: necessary part and non-essential part. The part of the consumer's participation in the demand response is the non-essential part of the electricity consumption. The optimal dispatch objective is to obtain the minimum total cost (fuel cost, random wind power cost and emission cost) and the maximum consumer's non-essential demand response benefit while satisfying some given constraints. In order to solve the optimal dispatch objective, a multi-subpopulation bat optimization algorithm (MSPBA) is proposed by using different search strategies. Finally, a case of an economic dispatch model is given to verify the feasibility and effectiveness of the established mathematical model and proposed algorithm. The economic dispatch model includes three thermal generators, two wind turbines and two consumers. The simulation results show that the proposed model can reduce the consumer's electricity demand, reduce fuel cost and reduce the impact on the environment while considering random wind energy, non-essential demand response and carbon tax. In addition, the superiority of the proposed algorithm is verified by comparing with the optimization results of CPLEX+YALMIP toolbox for MATLAB, BA, DBA and ILSSIWBA.
LA - eng
KW - economic dispatch; non-essential demand response; random wind power; bat algorithm; multi-subpopulation
UR - http://eudml.org/doc/295055
ER -

References

top
  1. Abdelaziz, A. Y., Ali, E. S., Elazim, S. M. A., 10.1016/j.energy.2016.02.041, Energy 101 (2016), 506-518. DOI10.1016/j.energy.2016.02.041
  2. Chakri, A., Khelif, R., Benouaret, M., al., et, 10.1016/j.eswa.2016.10.050, Expert Systems Appl. 69 (2017), 159-175. DOI10.1016/j.eswa.2016.10.050
  3. Chen, C. L., Vempati, V. S., Aljaber, N., 10.1016/0377-2217(93)e0228-p, Europ. J. Oper. Res. 80 (1995), 389-396. DOI10.1016/0377-2217(93)e0228-p
  4. Cheng, C. T., Liao, S. L., Tang, Z. T., al., et, 10.1016/j.enconman.2009.07.020, Energy Conversion Management 50 (2009), 3007-3014. DOI10.1016/j.enconman.2009.07.020
  5. Chen, F., Zhou, J., Wang, C., al., et, 10.1016/j.energy.2017.01.010, Energy 121 (2017), 276-291. DOI10.1016/j.energy.2017.01.010
  6. Das, S., Suganthan, P. N., 10.1109/tevc.2010.2059031, IEEE Trans. Evolutionary Comput. 15 (2011), 4-31. MR3032010DOI10.1109/tevc.2010.2059031
  7. Dorigo, M., Maniezzo, V., Colorni, A., 10.1109/3477.484436, IEEE Trans. Systems, Man, Cybernetics, Part B (Cybernetics) 26 (1996), 29-41. DOI10.1109/3477.484436
  8. Fahrioglu, M., Alvarado, F. L., 10.1109/59.898098, IEEE Trans. Power Systems 15 (2000), 1255-1260. DOI10.1109/59.898098
  9. Fahrioglu, M., Alvarado, F. L., 10.1109/59.918305, IEEE Trans. Power Systems 16 (2001), 317-322. DOI10.1109/59.918305
  10. Gan, C., Cao, W., Wu, M., al., et, 10.1016/j.eswa.2018.03.015, Expert Systems Appl. 104 (2018), 202-212. DOI10.1016/j.eswa.2018.03.015
  11. Gandomi, A. H., Yang, X. S., 10.1016/j.jocs.2013.10.002, J. Comput. Sci. 5 (2014), 224-232. MR3173261DOI10.1016/j.jocs.2013.10.002
  12. Gandomi, A. H., Yang, X. S., Alavi, A. H., al., et, 10.1007/s00521-012-1028-9, Neural Computing Appl. 22 (2013), 1239-1255. DOI10.1007/s00521-012-1028-9
  13. Ghasemi, M., Ghavidel, S., Ghanbarian, M. M., al., et, 10.1016/j.energy.2014.10.007, Energy 78 (2014), 276-289. DOI10.1016/j.energy.2014.10.007
  14. Guo, Y., Tong, L., Wu, W., al., et, 10.1109/tpwrs.2017.2655442, IEEE Trans. Power Systems 32 (2017), 3736-3746. DOI10.1109/tpwrs.2017.2655442
  15. Guo, F., Wen, C., Mao, J., al., et, 10.1109/tsg.2015.2434831, IEEE Trans. Smart Grid 7 (2016), 1572-1583. DOI10.1109/tsg.2015.2434831
  16. He, X. S., Ding, W. J., Yang, X. S., 10.1007/s00521-013-1518-4, Neural Comput. Appl. 25 (2014), 459-468. DOI10.1007/s00521-013-1518-4
  17. Hetzer, J., Yu, D. C., Bhattarai, K., 10.1109/tec.2007.914171, IEEE Trans. Energy Conversion 23 (2008), 603-611. DOI10.1109/tec.2007.914171
  18. Jabr, R., Coonick, A. H., Cory, B. J., 10.1109/59.871715, IEEE Trans. Power Syst. 15 (2000), 930-936. DOI10.1109/59.871715
  19. Jeddi, B., Vahidinasab, V., 10.1016/j.enconman.2013.11.027, Energy Conversion Management 78 (2014), 661-675. DOI10.1016/j.enconman.2013.11.027
  20. Ji, M., Tang, H., 10.1016/j.chaos.2003.12.032, Chaos Solitons Fractals 21 (2004), 933-941. MR2076025DOI10.1016/j.chaos.2003.12.032
  21. Kennedy, J., Eberhart, R., 10.1109/icnn.1995.488968, In: Proc. ICNN'95 - International Conference on Neural Networks, Perth 1995, 4, pp. 1942-1948. DOI10.1109/icnn.1995.488968
  22. Lee, K. Y., Park, Y. M., Ortiz, J. L., 10.1049/ip-c.1984.0012, IEE Proceedings. Part C: Generation, Transmission and Distribution. 131 (1984), 85-93. DOI10.1049/ip-c.1984.0012
  23. Li, M., Hou, J., Niu, Y., al., et, 10.1109/icca.2016.7505381, In: International Conference on Control and Automation, IEEE 2016, pp. 830-835. DOI10.1109/icca.2016.7505381
  24. Liang, H., Liu, Y., Shen, Y., al., et, 10.1109/tpwrs.2018.2812711, IEEE Trans. Power Syst. 33 (2018), 5052-5061. DOI10.1109/tpwrs.2018.2812711
  25. Liu, X., Xu, W., 10.1109/tpwrs.2010.2042085, IEEE Trans. Power Systems 25 (2010), 1705-1713. DOI10.1109/tpwrs.2010.2042085
  26. al., I. Mazhoud et, 10.1016/j.engappai.2013.02.002, Engrg. Appl. Artif. Intell. 26 (2013), 1263-1273. DOI10.1016/j.engappai.2013.02.002
  27. Nwulu, N. I., Fahrioglu, M., 10.1109/eeeic.2011.5874776, In: International Conference on Environment and Electrical Engineering, IEEE 2011, pp. 1-4. DOI10.1109/eeeic.2011.5874776
  28. Nwulu, N. I., Fahrioglu, M., Power system demand management contract design: A comparison between game theory and artificial neural networks., Int. Rev. Modell. Simul. 4 (2011), 104-112. 
  29. Nwulu, N. I., Xia, X., 10.1016/j.renene.2016.08.026, Renewable Energy 101 (2017), 16-28. DOI10.1016/j.renene.2016.08.026
  30. Park, J. B., Lee, K. S., Shin, J. R., al., et, 10.1109/tpwrs.2004.831275, IEEE Trans. Power Syst. 20 (2005), 34-42. DOI10.1109/tpwrs.2004.831275
  31. Pavlyukevich, I., 10.1109/tpwrs.2004.831275, J. Comput. Physics 226 (2007), 1830-1844. MR2356396DOI10.1109/tpwrs.2004.831275
  32. Sen, T., Mathur, H. D., 10.1016/j.ijepes.2015.11.121, Int. J. Electr. Power Energy Systems 78 (2016), 735-744. DOI10.1016/j.ijepes.2015.11.121
  33. Walters, D. C., Sheble, G. B., 10.1109/59.260861, IEEE Trans. Power Systems 8 (1993), 1325-1332. DOI10.1109/59.260861
  34. Wood, A. J., Wollenberg, B. F., 10.1016/0140-6701(96)88715-7, Fuel Energy Abstracts 37 (1996), 195. DOI10.1016/0140-6701(96)88715-7
  35. Yang, X. S., 10.1007/978-3-642-12538-6_6, Comput. Knowledge Technol. 284 (2010), 65-74. DOI10.1007/978-3-642-12538-6_6
  36. Yang, X. S., Deb, S., 10.1504/ijmmno.2010.035430, Int. J. Math. Modell. Numer. Optim. 1 (2010), 330-343. DOI10.1504/ijmmno.2010.035430
  37. Yang, X., Gandomi, A. H., 10.1108/02644401211235834, Engrg. Computations 29 (2012), 464-483. MR3206205DOI10.1108/02644401211235834
  38. Yang, H., Yi, J., Zhao, J., al., et, 10.1016/j.neucom.2011.12.054, Neurocomputing 102 (2013), 154-162. DOI10.1016/j.neucom.2011.12.054
  39. Yao, F., Dong, Z. Y., Meng, K., al., et, 10.1109/tii.2012.2210431, IEEE Trans. Industr. Inform. 8 (2012), 880-888. DOI10.1109/tii.2012.2210431

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.