Araştırma Makalesi
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Dynamic Economic Dispatch with Valve-Point Effect Using Crow Search Algorithm

Yıl 2022, Cilt: 10 Sayı: 3, 237 - 244, 30.07.2022
https://doi.org/10.17694/bajece.1075860

Öz

This paper presents a method based on meta-heuristic to solve Dynamic Economic Dispatch (DED) problem in a power system. In this paper, Crow Search Algorithm (CSA), which is one of the heuristic methods is proposed to solve the DED problem in a power system. In this study, line losses, generation limit values of generators, generation-consumption balance, valve-point effect and ramp rate limits of generator are included as constraints. The proposed algorithm was implemented on two different test cases. Finally, the CSA results were compared with the results of well-known heuristics in the literature such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Symbiotic Organism Search (SOS) algorithm, Artificial Bee Colony (ABC) algorithm, Simulated Annealing (SA), Imperial Competitive Algorithm (ICA), Modified Ant Colony Optimization (MACO) algorithm. The results show that the proposed algorithm has a better operating cost. With the results of the algorithm proposed in the test system 1, a profit of $2,056,5931 per day and $751,751,4815 per year is obtained. It is seen that with the results of the algorithm proposed in the test system 2, a daily profit of $12,279,7328 and a yearly profit of $4,482,102,472 are obtained. Test systems are operated by using less fuel with the results of the proposed algorithm and thus the harmful gas emissions released by thermal production units to the environment are also reduced.

Kaynakça

  • 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.
  • Jayabarathi, T., Jayaprakash, K., Jeyakumar, D. N., & Raghunathan, T. (2005). Evolutionary programming techniques for different kinds of economic dispatch problems. Electric power systems research, 73(2), 169-176.
  • Mandal, B., & Roy, P. K. (2021). Dynamic economic dispatch problem in hybrid wind based power systems using oppositional based chaotic grasshopper optimization algorithm. Journal of Renewable and Sustainable Energy, 13(1),013306.
  • Xiong, G., & Shi, D. (2018). Hybrid biogeography-based optimization with brain storm optimization for non-convex dynamic economic dispatch with valve-point effects. Energy, 157, 424-435.
  • Zheng, Z., Li, J., & Han, Y. (2020). An improved invasive weed optimization algorithm for solving dynamic economic dispatch problems with valve-point effects. Journal of Experimental & Theoretical Artificial Intelligence, 32(5), 805-829.
  • Dai, C., Hu, Z., & Su, Q. (2021). An adaptive hybrid backtracking search optimization algorithm for dynamic economic dispatch with valve-point effects. Energy, 122461.
  • Younes, Z., Alhamrouni, I., Mekhilef, S., & Reyasudin, M. (2021). A memory-based gravitational search algorithm for solving economic dispatch problem in micro-grid. Ain Shams Engineering Journal, 12(2), 1985-1994.
  • Yalcinöz, T., & Altun, H. (2000). Comparison of simulation algorithms for the Hopfield neural network: an application of economic dispatch. Turkish Journal of Electrical Engineering & Computer Sciences, 8(1), 67-80.
  • Zou, D., Li, S., Kong, X., Ouyang, H., & Li, Z. (2019). Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy. Applied energy, 237, 646-670.
  • Mahor, A., Prasad, V., & Rangnekar, S. (2009). Economic dispatch using particle swarm optimization: A review. Renewable and sustainable energy reviews, 13(8), 2134-2141.
  • Sonmez, Y. (2011). Multi-objective environmental/economic dispatch solution with penalty factor using Artificial Bee Colony algorithm. Scientific Research and Essays, 6(13), 2824-2831.
  • Andic, C., Ozturk, A., & Tosun, S. (2020). Türkiye’deki güc sisteminde karga arama algoritması kullanilarak ekonomik yük dagitimi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 8(1), 428-436.
  • Pradhan, M., Roy, P. K., & Pal, T. (2018). Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system. Ain Shams Engineering Journal, 9(4), 2015-2025.
  • Al-Bahrani, L., Seyedmahmoudian, M., Horan, B., & Stojcevski, A. (2021). Solving the real power limitations in the dynamic economic dispatch of large-scale thermal power units under the effects of valve-point loading and ramp-rate limitations. Sustainability, 13(3), 1274.
  • Sonmez, Y., Kahraman, H. T., Dosoglu, M. K., Guvenc, U., & Duman, S. (2017). Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects. Journal of Experimental & Theoretical Artificial Intelligence, 29(3), 495-515.
  • Sahoo, A. K., Panigrahi, T. K., Paramguru, J., & Hota, A. P. (2021). Dynamic economic dispatch using harmony search algorithm. In advances in machine learning and computational intelligence, 425-435.
  • Panigrahi, C. K., Chattopadhyay, P. K., Chakrabarti, R. N., & Basu, M. (2006). Simulated annealing technique for dynamic economic dispatch. Electric Power Components and Systems, 34, 577–586.
  • Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & Structures, 169, 1-12.
  • Hemamalini, S., & Simon, S. P. (2011a). Dynamic economic dispatch using artificial immune system for units with valve-point effect. International Journal of Electrical Power & Energy Systems, 33, 868–874.
  • Hemamalini, S., & Simon, S. P. (2011b). Dynamic economic dispatch using artificial bee colony algorithm for units with valve-point effect. European Transactions on Electrical Power, 21, 70–81.
  • Mohammadi-Ivatloo, B., Rabiee, A., Soroudi, A., & Ehsan, M. (2012). Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch. Energy, 44, 228–240.
  • Attaviriyanupap, P., Kita, H., Tanaka, E., & Hasegawa, J. (2002). A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Transactions on Power Systems, 17, 411–416.
  • Yuan, X., Su, A., Yuan, Y., Nie, H., & Wang, L. (2009). An improved PSO for dynamic load dispatch of generators with valve-point effects. Energy, 34, 67–74.
  • Basu, M. (2013). Hybridization of bee colony optimization and sequential quadratic programming for dynamic economic dispatch. International Journal of Electrical Power & Energy Systems, 44, 591–596.
  • Saber, A. Y. (2012). Economic dispatch using particle swarm optimization with bacterial foraging effect. International Journal of Electrical Power & Energy Systems, 34, 38–46.
  • He, D., Dong, G., Wang, F., & Mao, Z. (2011). Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms. Energy Conversion and Management, 52, 1026–1032.
  • Lu, P., Zhou, J., Zhang, H., Zhang, R., & Wang, C. (2014). Chaotic differential bee colony optimization algorithm for dynamic economic dispatch problem with valve-point effects. International Journal of Electrical Power & Energy Systems, 62, 130–143.
  • Secui, D. C. (2015). A method based on the ant colony optimization algorithm for dynamic economic dispatch with valve‐point effects. International Transactions on Electrical Energy Systems, 25(2), 262-287.
  • Xiong, G., & Shi, D. (2018). Hybrid biogeography-based optimization with brain storm optimization for non-convex dynamic economic dispatch with valve-point effects. Energy, 157, 424-435.
  • Gharehchopogh, F. S., Shayanfar, H., & Gholizadeh, H. (2020). A comprehensive survey on symbiotic organisms search algorithms. Artificial Intelligence Review, 53(3), 2265-2312.
  • Zou, D., & Gong, D. (2022). Differential evolution based on migrating variables for the combined heat and power dynamic economic dispatch. Energy, 238, 121664.
  • Ali Shaabani, Y., Seifi, A. R., & Kouhanjani, M. J. (2017). Stochastic multi-objective optimization of combined heat and power economic/emission dispatch. Energy, 141, 1892-1904.
  • Mohammadi-Ivatloo, B., Moradi-Dalvand, M., & Rabiee, A. (2013). Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. Electric Power Systems Research, 95, 9-18.
Yıl 2022, Cilt: 10 Sayı: 3, 237 - 244, 30.07.2022
https://doi.org/10.17694/bajece.1075860

Öz

Kaynakça

  • 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.
  • Jayabarathi, T., Jayaprakash, K., Jeyakumar, D. N., & Raghunathan, T. (2005). Evolutionary programming techniques for different kinds of economic dispatch problems. Electric power systems research, 73(2), 169-176.
  • Mandal, B., & Roy, P. K. (2021). Dynamic economic dispatch problem in hybrid wind based power systems using oppositional based chaotic grasshopper optimization algorithm. Journal of Renewable and Sustainable Energy, 13(1),013306.
  • Xiong, G., & Shi, D. (2018). Hybrid biogeography-based optimization with brain storm optimization for non-convex dynamic economic dispatch with valve-point effects. Energy, 157, 424-435.
  • Zheng, Z., Li, J., & Han, Y. (2020). An improved invasive weed optimization algorithm for solving dynamic economic dispatch problems with valve-point effects. Journal of Experimental & Theoretical Artificial Intelligence, 32(5), 805-829.
  • Dai, C., Hu, Z., & Su, Q. (2021). An adaptive hybrid backtracking search optimization algorithm for dynamic economic dispatch with valve-point effects. Energy, 122461.
  • Younes, Z., Alhamrouni, I., Mekhilef, S., & Reyasudin, M. (2021). A memory-based gravitational search algorithm for solving economic dispatch problem in micro-grid. Ain Shams Engineering Journal, 12(2), 1985-1994.
  • Yalcinöz, T., & Altun, H. (2000). Comparison of simulation algorithms for the Hopfield neural network: an application of economic dispatch. Turkish Journal of Electrical Engineering & Computer Sciences, 8(1), 67-80.
  • Zou, D., Li, S., Kong, X., Ouyang, H., & Li, Z. (2019). Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy. Applied energy, 237, 646-670.
  • Mahor, A., Prasad, V., & Rangnekar, S. (2009). Economic dispatch using particle swarm optimization: A review. Renewable and sustainable energy reviews, 13(8), 2134-2141.
  • Sonmez, Y. (2011). Multi-objective environmental/economic dispatch solution with penalty factor using Artificial Bee Colony algorithm. Scientific Research and Essays, 6(13), 2824-2831.
  • Andic, C., Ozturk, A., & Tosun, S. (2020). Türkiye’deki güc sisteminde karga arama algoritması kullanilarak ekonomik yük dagitimi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 8(1), 428-436.
  • Pradhan, M., Roy, P. K., & Pal, T. (2018). Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system. Ain Shams Engineering Journal, 9(4), 2015-2025.
  • Al-Bahrani, L., Seyedmahmoudian, M., Horan, B., & Stojcevski, A. (2021). Solving the real power limitations in the dynamic economic dispatch of large-scale thermal power units under the effects of valve-point loading and ramp-rate limitations. Sustainability, 13(3), 1274.
  • Sonmez, Y., Kahraman, H. T., Dosoglu, M. K., Guvenc, U., & Duman, S. (2017). Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects. Journal of Experimental & Theoretical Artificial Intelligence, 29(3), 495-515.
  • Sahoo, A. K., Panigrahi, T. K., Paramguru, J., & Hota, A. P. (2021). Dynamic economic dispatch using harmony search algorithm. In advances in machine learning and computational intelligence, 425-435.
  • Panigrahi, C. K., Chattopadhyay, P. K., Chakrabarti, R. N., & Basu, M. (2006). Simulated annealing technique for dynamic economic dispatch. Electric Power Components and Systems, 34, 577–586.
  • Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & Structures, 169, 1-12.
  • Hemamalini, S., & Simon, S. P. (2011a). Dynamic economic dispatch using artificial immune system for units with valve-point effect. International Journal of Electrical Power & Energy Systems, 33, 868–874.
  • Hemamalini, S., & Simon, S. P. (2011b). Dynamic economic dispatch using artificial bee colony algorithm for units with valve-point effect. European Transactions on Electrical Power, 21, 70–81.
  • Mohammadi-Ivatloo, B., Rabiee, A., Soroudi, A., & Ehsan, M. (2012). Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch. Energy, 44, 228–240.
  • Attaviriyanupap, P., Kita, H., Tanaka, E., & Hasegawa, J. (2002). A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Transactions on Power Systems, 17, 411–416.
  • Yuan, X., Su, A., Yuan, Y., Nie, H., & Wang, L. (2009). An improved PSO for dynamic load dispatch of generators with valve-point effects. Energy, 34, 67–74.
  • Basu, M. (2013). Hybridization of bee colony optimization and sequential quadratic programming for dynamic economic dispatch. International Journal of Electrical Power & Energy Systems, 44, 591–596.
  • Saber, A. Y. (2012). Economic dispatch using particle swarm optimization with bacterial foraging effect. International Journal of Electrical Power & Energy Systems, 34, 38–46.
  • He, D., Dong, G., Wang, F., & Mao, Z. (2011). Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms. Energy Conversion and Management, 52, 1026–1032.
  • Lu, P., Zhou, J., Zhang, H., Zhang, R., & Wang, C. (2014). Chaotic differential bee colony optimization algorithm for dynamic economic dispatch problem with valve-point effects. International Journal of Electrical Power & Energy Systems, 62, 130–143.
  • Secui, D. C. (2015). A method based on the ant colony optimization algorithm for dynamic economic dispatch with valve‐point effects. International Transactions on Electrical Energy Systems, 25(2), 262-287.
  • Xiong, G., & Shi, D. (2018). Hybrid biogeography-based optimization with brain storm optimization for non-convex dynamic economic dispatch with valve-point effects. Energy, 157, 424-435.
  • Gharehchopogh, F. S., Shayanfar, H., & Gholizadeh, H. (2020). A comprehensive survey on symbiotic organisms search algorithms. Artificial Intelligence Review, 53(3), 2265-2312.
  • Zou, D., & Gong, D. (2022). Differential evolution based on migrating variables for the combined heat and power dynamic economic dispatch. Energy, 238, 121664.
  • Ali Shaabani, Y., Seifi, A. R., & Kouhanjani, M. J. (2017). Stochastic multi-objective optimization of combined heat and power economic/emission dispatch. Energy, 141, 1892-1904.
  • Mohammadi-Ivatloo, B., Moradi-Dalvand, M., & Rabiee, A. (2013). Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. Electric Power Systems Research, 95, 9-18.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Cenk Andiç 0000-0003-1123-899X

Ali Öztürk 0000-0002-3609-3603

Salih Tosun 0000-0002-5698-6628

Yayımlanma Tarihi 30 Temmuz 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 10 Sayı: 3

Kaynak Göster

APA Andiç, C., Öztürk, A., & Tosun, S. (2022). Dynamic Economic Dispatch with Valve-Point Effect Using Crow Search Algorithm. Balkan Journal of Electrical and Computer Engineering, 10(3), 237-244. https://doi.org/10.17694/bajece.1075860

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