Tarık KOÇ, İbrahim EKE, Suleyman Sungur TEZCAN

Parçacık Sürü Optimizasyonu ve Genetik Algoritma Kullanılarak Birleşik Isı ve Güç Ekonomik Dağıtımı Probleminin Çözümü

Enerji kaynaklarının artan maliyeti ve çevre sorunları nedeniyle birleşik ısı ve güç birimleri gibi daha yüksek verimlilikte çalışan sistemler daha popüler hale gelmektedir. Birleşik ısı ve güç ünitelerinin doğrusal ve dışbükey olmayan özelliklere sahip olmaları nedeniyle optimum çalışması giderek karmaşıklaşmaktadır. Bahsi geçen bu problemin zorlukları bizi sezgisel ve evrimsel yöntemleri kullanmaya yöneltmektedir. Bu çalışmada, parçacık sürü optimizasyon (PSO) algoritması ve genetik algoritma (GA), birleşik ısı ve güç birimlerinin ekonomik dağıtımına(ED) uygulanmaktadır. ED probleminin temel amacı, toplam üretim maliyeti en aza indirilirken ve sistem operasyonel kısıtlamaları yerine getirilirken her bir ünitenin optimum çıkış gücü ve ısısını elde etmektir. Sonuçlar bu algoritmaların birleşik ısı ve güç sistemlerinin ekonomik dağıtımı problemini çözmedeki yeteneklerinin gösterilmesi ve karşılaştırılmasıdır.

Anahtar Kelimeler:

Birleşik Isı ve Güç, Parçacık Sürü Optimizasyon, Genetik Algoritma, Ekonomik Dağıtım

Solution of the Economic Distribution of Combined Heat and Power Problem Using Particle Swarm Optimization and Genetic Algorithm

Due to the increasing cost of energy resources and environmental problems, higher efficiency systems such as combined heat and power units are becoming more popular. Due to the linear and non-convex properties of combined heat and power units, their optimum operation is becoming increasingly complex. The difficulties of this problem lead us to use intuitive and evolutionary methods. In this study, particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are applied to the economic distribution (ED) of combined heat and power units. The main purpose of the ED problem is to obtain the optimum output power and temperature of each unit while minimizing the total cost of production and fulfilling system operational constraints. The results are to demonstrate and compare the capabilities of these algorithms in solving the problem of economic distribution of combined heat and power systems.

Keywords:

Combined Heat and Power, Particle Swarm Optimization, Genetic Algorithm, Economic Distribution,

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Abido, M. A. (2002). Optimal power flow using particle swarm optimization. International Journal of Electrical Power and Energy Systems 24(7): 563–71. doi: 10.1016/S0142-0615(01)00067-9
Abido, M. A. (2009). Multiobjective particle swarm optimization for environmental/economic dispatch problem. Electric Power Systems Research 79(7): 1105–13. doi: 10.1016/j.epsr.2009.02.005
Basu, M. (2011). Bee colony optimization for combined heat and power economic dispatch. Expert Systems with Applications 38(11): 13527–31. doi: 10.1016/j.eswa.2011.03.067
Basu, M. (2016). Group search optimization for combined heat and power economic dispatch. International Journal of Electrical Power and Energy Systems 78: 138–47. doi: 10.1016/j.ijepes.2015.11.069
Gaing, Z. L. (2003). Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Transactions on Power Systems 18(3): 1187–95. doi: 10.1109/TPWRS.2003.814889
Henwood, T. G. & Mark I. (1996). An algorithm for combined heat and power economic dispatch. IEEE Transactions on Power Systems 11(4): 1778–84. doi: 10.1109/59.544642
Kaur, A., Harinder, P. S., & Abhishek B. (2014). Analysis of Economic Load Dispatch Using Genetic Algorithm. International Journal of Application or Innovation in Engineering & Management (IJAIEM) 3(3): 240–46.
Kennedy, J., & Russell E. (1995). Particle swarm optimization. International Conference on Neural Networks 4: 1942–48. http://ieeexplore.ieee.org/document/488968/ (02 Ocak 2021).
Mohammadi-Ivatloo, B., Mohammad M., & Abbas R. (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. doi: 10.1016/j.epsr.2012.08.005
Özsağlam, M. Y.,, & Çunkaş, M. (2008). Optimizasyon Problemlerinin Çözümü için Parçaçık Sürü Optimizasyonu Algoritması. Politeknik Dergisi Journal of Polytechnic Cilt 11(4): 299–305.
Park, J. B., Ki S. L., Joong R. S., & Kwang Y. L. (2005). A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Transactions on Power Systems 20(1): 34–42. doi: 10.1109/TPWRS.2004.831275
Rezaie, H., Kazemi-Rahbar M.H., Behrooz V., & Hasan R. (2019). Solution of combined economic and emission dispatch problem using a novel chaotic improved harmony search algorithm. Journal of Computational Design and Engineering 6(3): 447–67. https://academic.oup.com/jcde/article/6/3/447/5732314.
Shi, B., Lie X. Y., & Wei W. (2013). Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction. Energy 56: 135–43. doi: 10.1016/j.energy.2013.04.066
Sohrabi F., Jabari F., Pourghasem P. & Mohammadi-Ivatloo B. (2020). Combined Heat and Power Economic Dispatch Using Particle Swarm Optimization. Optimization of Power System Problems. Studies in Systems, Decision and Control, vol 262. 127-141. Springer, Cham. doi: 10.1007/978-3-030-34050-6_6
Song, Y. H., Chou, C. S., & Stonham, T. J. (1999). Combined heat and power economic dispatch by improved ant colony search algorithm. Electric Power Systems Research 52(2): 115–21. doi: 10.1016/S0378-7796(99)00011-5
Vasebi, A., Fesanghary, M., & Bathaee S. M. T. (2007). Combined heat and power economic dispatch by harmony search algorithm. International Journal of Electrical Power and Energy Systems 29(10): 713–19. doi: 10.1016/j.ijepes.2007.06.006
Yazdani, A., Jayabarathi, T., Ramesh, V., & Raghunathan, T. (2013). Combined heat and power economic dispatch problem using firefly algorithm. Frontiers in Energy 7(2): 133–139. doi: 10.1007/s11708-013-0248-8
Yoshida, H. (2000). A Particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transactions on Power Systems 15(4): 1232–1239. doi: 10.1109/59.898095