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  •   Iman Niazazari

  •   Oveis Asgari Gashteroodkhani

  •   Amir Niaz Azari

Abstract

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

Hetzer, J., David, C. Y., & Bhattarai, K. (2008). An economic dispatch model incorporating wind power. IEEE Transactions on energy conversion, 23(2), 603-611.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

Abido, M. A. (2003). A novel multiobjective evolutionary algorithm for environmental/economic power dispatch. Electric power systems research, 65(1), 71-81.

Noman, N., & Iba, H. (2008). Differential evolution for economic load dispatch problems. Electric power systems research, 78(8), 1322-1331.

Basu, M. (2011). Economic environmental dispatch using multi-objective differential evolution. Applied soft computing, 11(2), 2845-2853.

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).

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.

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.

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.

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.

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.

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.

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

D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 2 ed. Piscataway, NJ: IEEE Press, 2000.

Krishna, K., & Murty, N. M. (1999). Genetic K-means algorithm. IEEE Transactions on Systems Man And Cybernetics-Part B: Cybernetics, 29(3), 433-439.

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.

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.

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.

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.

Tan, Y. and Zhu, Y., 2010. Fireworks algorithm for optimization. Advances in swarm intelligence, pp.355-364.

Zheng, S., Janecek, A. and Tan, Y., 2013, June. Enhanced fireworks algorithm. In Evolutionary Computation (CEC), 2013 IEEE Congress on (pp. 2069-2077).

A. J. Wood and B. F. Wollenbergy, Power Generation, Operation, and Control. New York: Wiley, 1984.

Available at http://www.pserc.cornell.edu/matpower/

Available at pierrepinson.com/31761/Projects/Project2/IEEE-RTS-24.pdf

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How to Cite
[1]
Niazazari, I., Gashteroodkhani, O. and Niaz Azari, A. 2019. A Novel Economic Dispatch in Power Grids Based on Enhanced Firework Algorithm. European Journal of Electrical Engineering and Computer Science. 3, 4 (Jun. 2019). DOI:https://doi.org/10.24018/ejece.2019.3.4.96.