Parçalı yakıt maliyeti fonksiyonlarına sahip çevresel ekonomik güç dağıtımı problemlerinin çözümüne yeni bir yaklaşım: Konik skalerleştirme metodu

Gelişen dünyada elektrik enerjisine olan ihtiyaç her geçen gün artmaktadır. Fosil yakıt kullanan elektriküretim birimleri çevre kirliliğine yol açmaktadır. Bu nedenle optimal güç dağıtımı problemleri çözülürkençevre kirliliği de dikkate alınmalıdır. Çevre kirliliğini dikkate alan bu tür problemlere çevresel ekonomik güçdağıtımı problemleri adı verilmektedir. Bu çalışmada çok amaçlı çevresel ekonomik güç dağıtım problemikonik skalerleştirme metodu (KSM) kullanılarak tek amaçlı optimizasyon problemine dönüştürülmüştür.Skalerleştirilen problemin çözümü için genetik algoritma (GA) metodu kullanılmıştır. Uygulama için elealınan örnekler, konveks ve konveks olmayan parçalı yakıt maliyeti fonksiyonlarına sahip üretimbirimlerinden oluşan kayıplı güç sistemleridir. Örnek problemlerde farklı ağırlık değerleri için toplam yakıtmaliyeti ve toplam NOx emisyon değerlerine ait en iyi çözüm değerleri elde edilmiştir (Pareto optimaldeğerler) ve sonuçlar tartışılmıştır.

A new approach in the solution of the environmental economic power dispatch problems with piecewise quadratic fuel cost function: Conic scalarization method

The need for electric power is increasing day by day in the developing world. Power generation units using fossil fuel cause environmental problems. Therefore, environmental pollution must be taken into consideration while solving optimum power dispatch problems. This kind of problems considering the environmental pollution are called environmental economic power dispatch problems. In this study, multiobjective environmental economic power dispatch problem has been transformed into single-objective optimization problem by using conic scalarization method (CSM). Genetic algorithm (GA) method has been used for the solution of the scalarized problem. The samples handled for practice are lossy power systems formed of generation units with convex and non-convex piecewise fuel cost functions. In the sample problems the best solution values belonging to total fuel cost and NOx emission values (pareto optimal values) have been obtained for different weight values and the results have been discussed.

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Goldberg D.E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, 1989.
Üstün Ö., Çok amaçlı portföy optimizasyon problemi ve çözüm yaklaşımları, Doktora Tezi, Eskişehir Osmangazi Üniversitesi, Fen Bilimleri Enstitüsü, Eskişehir, 2007.
Çanakoğlu A., Yetgin A.G., Temurtaş H., Turan M., Induction motor parameter estimation using metaheuristic methods, Turkish Journal of Electrical Engineering & Computer Sciences, 22 (5), 1177-1192, 2014.
Eke İ., Tezcan S.S., Çelik C., Solving economic load dispatch problem valve-point effects using filled function, Journal of the Faculty of Engineering and Architecture of Gazi University, 32 (2), 429-438, 2017.
Dağdeviren U., Kaymak B., Investigation of affecting optimum cost design of reinforced concrete retaining walls using artificial bee colony algorithm, Journal of the Faculty of Engineering and Architecture of Gazi University, 33 (1), 239-253, 2018.
Niknam T., Narimani M.R., Jabbari M., Malekpour A.R., A modified shuffle frog leaping algorithm for multi-objective optimal power flow, Energy, 36, 6420- 6432, 2011.
Ah King R.T.F., Rughooputh H.C.S., Deb K., Evolutionary multi-objective environmental/economic dispatch: Stochactic vs. deterministic approaches, Lect. Notes Comput. Sci., 34 (10):677-691, 2005.
Abido M.A., A niched pareto genetic algorithm for multiobjective environmental economic power dispatch, Int. J. Electr. Power Energy Syst., 25 (2), 97-105, 2003.
Abido M.A., Multiobjective evolutionary algorithm for electric power dispatch problem, IEEE Trans. Evol. Comput., 10 (3), 315-329, 2006.
Abido M.A., Multiobjective particle swarm optimization for environmental economic dispatch problem, Electr. Power Syst. Res., 79 (7), 1105-1113, 2009.
Cai J., Ma X., Li Q., Li L., Peng H., A multi-objective chaotic ant swarm optimization for environmental/economic dispatch, Int. J. Electr. Power Energy Syst., 32 (5), 337-344, 2010.
Guo C.X., Zhan J.P., Wu Q.H., Dynamic economic emission dispatch based on group search optimizer with multiple producers, Electr. Power Syst. Res., 86, 8-16, 2012.
Dhanalakshmi S., Kannan S., Mahadevan K., Baskar S., Application of modified NSGA-II algorithm to combined economic and emission dispatch problem, Int. J. Electr. Eng. Comput., 33 (4), 992-1002, 2011.
Alawode K.O., Jubril A.M., Komolafe O.A., Multiobjective optimal power flow using hybrid evolutionary algorithm, Int. J. Electr. Electron. Eng., 4 (7) 506-511, 2010.
Zhang H., Yue D., Xie X., Hu S., Weng S., Multi-elite guide hybrid differential evolution with simulated annealing technique for dynamic economic emission dispatch, Appl. Soft Comput., 34, 312-323, 2015.
Basu M., Economic environmental dispatch using multiobjective differential evolution, Appl. Soft Comput., 11 (2), 2845-2853, 2011.
Liu T., Jiao L., Ma W., Ma J., Shang R., Cultural quantum-behaved particle swarm optimization for environmental/economic dispatch, Appl. Soft Comput., 48, 597-611, 2016.
Zhang Y., Gong D., Ding Z., A bare-bones multiobjective particle swarm optimization algorithm for environmental/economic dispatch, Information Sciences, 192, 213-227, 2012.
Hota P.K., Barisal A.K., Chakrabarti R., Economic emission load dispatch through fuzzy based bacterial foraging algorithm, Int. J. Electr. Power Energy Syst., 32 (7), 794-803, 2010.
Pandi V.R., Panigrahi B.K., Hong W.C., Sharma R., A multiobjective bacterial foraging algorithm to solve the environmental economic dispatch problem, Energy Sources, Part B, 9, 236–247, 2014.
Abdelaziz A.Y., Ali E.S., Abd Elazim S.M., Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems, Energy, 101, 506-518, 2016.
Palanichamy C., Babu N.S., Analytical solution for combined economic and emissions dispatch, Electr. Power Syst. Res., 78 (7), 1129-1137, 2008.
Özyön S., Yaşar C., Durmuş B., Temurtaş H., Opposition-based gravitational search algorithm applied to economic power dispatch problems consisting of thermal units with emission constraints, Turk. J. Electr. Eng. Comput. Sci., 23, 2278-2288, 2015.
Ma H., Yang Z., You P., Fei M., Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging, Energy, 135, 101-111, 2017.
Bhattacharya A., Chattopadhyay P.K., Hybrid differential evolution with biogeography-based optimization algorithm for solution of economic emission load dispatch problems, Expert Syst. Appl., 38 (11), 14001-14010, 2011.
Yu X., Yu X., Lu Y., Sheng J., Economic and emission dispatch using ensemble multi-objective differential evolution algorithm, Sustainability, 10, 418-465, 2018.
Augusteen W.A., Kumari R., Rengaraj, R., Economic and various emission dispatch using differential evolution algorithm, 3rd International Conference on Electrical Energy Systems (ICEES), Chennai, 74-78. 2016.
Yaşar C., A pseudo spot price of electricity algorithm applied to environmental economic active power dispatch problem, Turk. J. Elec. Eng. and Comp. Sci., 20, (6), 990-1005, 2012.
Yaşar C., Özyön S., Solution to scalarized environmental economic power dispatch problem by using genetic algorithm, Int. J. Electr. Power Energy Syst., 38 (1), 54-62, 2012.
Gasimov R.N., Characterization of the Benson proper efficiency and scalarization in nonconvex vector optimization, Lect. Notes Econ. Math. Syst., 507, 189- 198, 2001.
Modiri-Delshad M., Abd Rahim N., Multi-objective backtracking search algorithm for economic emission dispatch problem, Appl. Soft Comput., 40, 479-494, 2016.
Aydın D., Özyön S., Yaşar C., Liao T., Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem, Int. J. Electr. Power Energy Syst., 54, 144-153, 2014.
Özyön S., Temurtaş H., Durmuş B., Kuvat G., Charged system search algorithm for emission constrained economic power dispatch problem, Energy, 46, 420- 430, 2012.
Wood A.J. ve Wollenberg B.F., Power Generation Operation and Control, John Wiley & Sons, New York, A.B.D., 1996.
Özyön S., Genetik algoritmanın bazı çevresel ekonomik güç dağıtım problemlerine uygulanması, Yüksek Lisans Tezi, Dumlupınar Üniversitesi, Fen Bilimleri Enstitüsü, Kütahya, 2009.