Research Article

A Novel Economic Dispatch in Power Grids Based on Enhanced Firework Algorithm

Iman Niazazari
University of Nevada, Reno,USA
* Corresponding author
Oveis Asgari Gashteroodkhani
Department of Electrical and Biomedical Engineering, University of Nevada, Reno, USA
Amir Niaz Azari
Department of Electrical Engineering, Azad University, Sari, Iran
DOI icon 10.24018/ejece.2019.3.4.96

Read Counter
768

Downloads
536

Citations

Share

Article Sidebar

Submitted 2019-05-30
Published 2019-06-09

Read counter = 768 times

Table of Contents

Article Main Content

This paper proposes a novel single objective optimization technique for economic dispatch (ED) in power grids. This new technique is developed based on firework algorithm (FWA) and is implemented in the IEEE 24 bus reliability test system. In this paper, the single-objective enhanced fireworks (EFWA) is developed to find the economic operating condition to minimize the generation cost. This method is a swarm intelligence algorithm that solves a single-objective optimization problem much faster than other well-known algorithms such as genetic algorithm (GA). The experimental results show that the proposed EFWA method is indeed capable of obtaining higher quality solutions efficiently in ED problems.

Keywords: Economic dispatch, enhanced firework algorithm, genetic algorithm, optimization

References

  1. Hetzer, J., David, C. Y., & Bhattarai, K. (2008). An economic dispatch model incorporating wind power. IEEE Transactions on energy conversion, 23(2), 603-611.
     Google Scholar
  2. Yang, H. T., Yang, P. C., & Huang, C. L. (1996). Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions. IEEE transactions on Power Systems, 11(1), 112-118.
     Google Scholar
  3. Gaing, Z. L. (2003). Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE transactions on power systems, 18(3), 1187-1195.
     Google Scholar
  4. Lin, C. E., & Viviani, G. L. (1984). Hierarchical economic dispatch for piecewise quadratic cost functions. IEEE transactions on power apparatus and systems, (6), 1170-1175.
     Google Scholar
  5. Lin, W. M., Cheng, F. S., & Tsay, M. T. (2002). An improved tabu search for economic dispatch with multiple minima. IEEE Transactions on power systems, 17(1), 108-112.
     Google Scholar
  6. Chitsazan, M. A., Fadali, M. S., & Trzynadlowski, A. M. (2019). Wind speed and wind direction forecasting using echo state network with nonlinear functions. Renewable energy, 131, 879-889.
     Google Scholar
  7. Chitsazan, M. A., Fadali, M. S., Nelson, A. K., & Trzynadlowski, A. M. (2017, May). Wind speed forecasting using an echo state network with nonlinear output functions. In 2017 American Control Conference (ACC) (pp. 5306-5311). IEEE.
     Google Scholar
  8. Alsumait, J. S., Sykulski, J. K., & Al-Othman, A. K. (2010). A hybrid GA?PS?SQP method to solve power system valve-point economic dispatch problems. Applied Energy, 87(5), 1773-1781.
     Google Scholar
  9. Niazazari, I., Abyaneh, H. A., Farah, M. J., Safaei, F., & Nafisi, H. (2014, May). Voltage profile and power factor improvement in PHEV charging station using a probabilistic model and flywheel. In 2014 19th Conference on Electrical Power Distribution Networks (EPDC) (pp. 100-105). IEEE.
     Google Scholar
  10. Coelho, L. S., & Mariani, V. C. (2006). Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Transactions on power systems, 21(2), 989-996.
     Google Scholar
  11. S. M. M. H. N., S. Heydari, H. Mirsaeedi, A. Fereidunian, and A. R. Kian, ?Optimally operating microgrids in the presence of electric vehicles and renewable energy resources,? in 2015 Smart Grid Conference (SGC), Dec 2015, pp. 66?72.
     Google Scholar
  12. Zakeri, A. S., Gashteroodkhani, O. A., Niazazari, I., & Askarian-Abyaneh, H. (2019). The effect of different non-linear demand response models considering incentive and penalty on transmission expansion planning. European Journal of Electrical Engineering and Computer Science, 3(1).
     Google Scholar
  13. Abido, M. A. (2003). A novel multiobjective evolutionary algorithm for environmental/economic power dispatch. Electric power systems research, 65(1), 71-81.
     Google Scholar
  14. Noman, N., & Iba, H. (2008). Differential evolution for economic load dispatch problems. Electric power systems research, 78(8), 1322-1331.
     Google Scholar
  15. Basu, M. (2011). Economic environmental dispatch using multi-objective differential evolution. Applied soft computing, 11(2), 2845-2853.
     Google Scholar
  16. Aranizadeh, A., Niazazari, I., & Mirmozaffari, M. (2019). A Novel Optimal Distributed Generation Planning in Distribution Network using Cuckoo Optimization Algorithm. European Journal of Electrical Engineering and Computer Science, 3(3).
     Google Scholar
  17. Vlachogiannis, J. G., & Lee, K. Y. (2009). Economic load dispatch?A comparative study on heuristic optimization techniques with an improved coordinated aggregation-based PSO. IEEE Transactions on Power Systems, 24(2), 991-1001.
     Google Scholar
  18. Bomze, I. M., & De Klerk, E. (2002). Solving standard quadratic optimization problems via linear, semidefinite and copositive programming. Journal of Global Optimization, 24(2), 163-185.
     Google Scholar
  19. Pindoriya, N. M., Singh, S. N., & Lee, K. Y. (2010, July). A comprehensive survey on multi-objective evolutionary optimization in power system applications. In IEEE PES General Meeting (pp. 1-8). IEEE.
     Google Scholar
  20. A. Abbaskhani-Davanloo, M. Amini, M. S. Modarresi, F. Jafarishiadeh, ?Distribution System Reconfiguration for Loss Reduction Incorporating Load and Renewable Generation Uncertainties,? 2019 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, 2019.
     Google Scholar
  21. O. A. Gashteroodkhani and B. Vahidi, "Application of Imperialistic Competitive Algorithm to Fault Section Estimation Problem in Power Systems," in The International Conference in New Research of Electrical Engineering and Computer Science, Iran, Sep 2015.
     Google Scholar
  22. O. A. Gashteroodkhani, M. Majidi, M. Etezadi-Amoli, A. F. Nematollahi, B. Vahidi, "A hybrid SVM-TT transform-based method for fault location in hybrid transmission lines with underground cables" Electric Power Systems Research, vol. 170, pp. 205-214, 2019.
     Google Scholar
  23. S. Heydari, SM. Mohammadi-Hosseininejad, H. Mirsaeedi, A. Fereidunian, H. Lesani, ?Simultaneous placement of control and protective devices in the presence of emergency demand response programs in smart grid,? International Transactions on Electrical Energy, 2018; e2537. https://doi.org/10.1002/etep.2537
     Google Scholar
  24. D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 2 ed. Piscataway, NJ: IEEE Press, 2000.
     Google Scholar
  25. Krishna, K., & Murty, N. M. (1999). Genetic K-means algorithm. IEEE Transactions on Systems Man And Cybernetics-Part B: Cybernetics, 29(3), 433-439.
     Google Scholar
  26. Imran, A. M., & Kowsalya, M. (2014). A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using fireworks algorithm. International Journal of Electrical Power & Energy Systems, 62, 312-322.
     Google Scholar
  27. Zheng, Y. J., Song, Q., & Chen, S. Y. (2013). Multiobjective fireworks optimization for variable-rate fertilization in oil crop production. Applied Soft Computing, 13(11), 4253-4263.
     Google Scholar
  28. Sarfi, V., Niazazari, I., & Livani, H. (2016, September). Multiobjective fireworks optimization framework for economic emission dispatch in microgrids. In 2016 North American Power Symposium (NAPS) (pp. 1-6). IEEE.
     Google Scholar
  29. Bacanin, N., & Tuba, M. (2015, May). Fireworks algorithm applied to constrained portfolio optimization problem. In 2015 IEEE Congress on evolutionary computation (CEC) (pp. 1242-1249). IEEE.
     Google Scholar
  30. Tan, Y. and Zhu, Y., 2010. Fireworks algorithm for optimization. Advances in swarm intelligence, pp.355-364.
     Google Scholar
  31. Zheng, S., Janecek, A. and Tan, Y., 2013, June. Enhanced fireworks algorithm. In Evolutionary Computation (CEC), 2013 IEEE Congress on (pp. 2069-2077).
     Google Scholar
  32. A. J. Wood and B. F. Wollenbergy, Power Generation, Operation, and Control. New York: Wiley, 1984.
     Google Scholar
  33. Available at http://www.pserc.cornell.edu/matpower/
     Google Scholar
  34. Available at pierrepinson.com/31761/Projects/Project2/IEEE-RTS-24.pdf
     Google Scholar