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MFOA-ABC hybrid optimization method for dynamic economic dispatch of the 150 kV sulselbar electrical system

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Warianty tytułu
PL
Hybrydowa metoda optymalizacji MFOA-ABC dla dynamicznej ekonomicznej dystrybucji systemu elektrycznego 150 kV
Języki publikacji
EN
Abstrakty
EN
This paper proposed a Modified Fruit Fly Optimization Algorithm-Artificial Bee Colony (MFOA-ABC) hybrid optimization method to solve the problem of dynamic economic dispatch (DED) of the 150 kV Sulselbar electrical systems by using two objective functions as tested parameters and considering power balanced, power limits of the generator, and generator ramp rate as constraints. Besides, the voltage profile, the L index voltage stability, and loading margin V-P on critical buses were evaluated. Results simulation of the MFOA-ABC optimization method were compared with other methods and it was obtained that the proposed method was better.
W artykule zaproponowano hybrydową metodę optymalizacji zmodyfikowanego algorytmu optymalizacji muszki owocowej i sztucznej kolonii pszczół (MFOA-ABC) w celu rozwiązania problemu dynamicznej dystrybucji ekonomicznej (DED) systemów elektrycznych 150 kV Sulselbar przy użyciu dwóch funkcji obiektywnych jako testowanych parametrów i biorąc pod uwagę moc zrównoważony, limity mocy generatora i prędkość narastania generatora jako ograniczenia. Ponadto oceniono profil napięciowy, stabilność napięciową wskaźnika L i margines obciążenia V-P na krytycznych szynach. Porównano wyniki symulacji metody optymalizacji MFOA-ABC z innymi metodami i stwierdzono, że proponowana metoda jest lepsza.
Rocznik
Strony
70--78
Opis fizyczny
Bibliogr. 38 poz., rys., tab.
Twórcy
autor
  • Hasanuddin University, Gowa,Indonesia
  • Makassar State University, Makassar,Indonesia
autor
  • Hasanuddin University, Gowa,Indonesia
  • Hasanuddin University, Gowa,Indonesia
  • Hasanuddin University, Gowa,Indonesia
Bibliografia
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  • [11] S. Ieng, Y. S. Akil, and I. C. Gunadin, “Hydrothermal Economic Dispatch Using Hybrid Big Bang-Big Crunch (HBB-BC) Algorithm,” J. Phys. Conf. Ser., vol. 1198, no. 5, pp. 7–13, 2019.
  • [12] A. S. I. Tayeb and H. Bouzeboudja, “Application of a New Meta-Heuristic Algorithm Using Egyptian Vulture Optimization for Economic,” Prz. Elektrotechniczny, vol. 95, no. 6, pp. 56– 65, 2019.
  • [13] Z. Jizhong, “Optimization of Power System Operation,” in Journal of Chemical Information and Modeling, vol. 53, no. 9, IEEE Press, 2013, pp. 1689–1699.
  • [14] Haripuddin, A. Suyuti, S. M. Said, and Y. S. Akil, “Dynamic Economic Dispatch for 150 kV Sulselbar Power Generation Systems Using Artificial Bee Colony Algorithm,” Proc. Int. Conf. Inf. Commun. Technol., pp. 817–822, 2019.
  • [15] R. Saravanan, S. Subramanian, V. Dharmalingam, and S. Ganesan, “Economic Dispatch with Integrated Wind-Thermal using Particle Swarm Optimization,” Int. J. Adv. Res. Innov., vol. 5, no. 1, pp. 100–103, 2017.
  • [16] J. Mei and J. Zhao, “An Enhanced Quantum-Behaved Particle Swarm Optimization for Security Constrained Economic Dispatch,” Proc. Int. Symp. Distrib. Comput. Appl. Bus. Eng. Sci., no. 1, pp. 221–224, 2018.
  • [17] X. Jiang, J. Zhou, H. Wang, and Y. Zhang, “Dynamic environmental economic dispatch using multiobjective differential evolution algorithm with expanded double selection and adaptive random restart,” Electr. Power Energy Syst., vol. 49, no. 1, pp. 399–407, 2013.
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  • [20] P. Nema and S. Gajbhiye, “Application of artificial intelligence technique to economic load dispatch of thermal power generation unit,” Int. J. Energy Power Eng., vol. 3, no. 5, pp. 15–20, 2014.
  • [21] N. Gamayanti, A. Alkaff, and A. Karim, “Optimization of Dynamic Economic Dispatch Using Artificial Bee Colony Algorithms,” Java J. Electr. Electron. Eng., vol. 13, no. 1, pp. 23–28, 2015.
  • [22] H. P. Singh, Y. S. Brar, and D. P.Kothari, “Reactive Power Based Fair Calculation Approach for Multiobjective Load Dispatch Problem,” Arch. Electr. Eng., vol. 68, no. 4, pp. 719– 735, 2019.
  • [23] A. A. Elsakaan, R. A. El-sehiemy, S. S. Kaddah, and M. I. Elsaid, “An Enhanced Moth-Flame Optimizer for Solving Nonsmooth Economic Dispatch Problems with Emissions,” Energy, pp. 1–24, 2018.
  • [24] S. Sadoudi, M. Boudour, and N. E. Y. Kouba, “Gravitational Search Algorithm for Solving Equal Combined Dynamic Economic-Emission Dispatch Problems in Presence of Renewable Energy Sources,” Proc. Int. Conf. Appl. Smart Syst. ICASS, no. November, pp. 1–5, 2019.
  • [25] G. Chen, C. Li, and Z. Dong, “Parallel and Distributed Computation for Dynamical Economic Dispatch,” IEEE Trans. Smart Grid, vol. 8, no. 2, pp. 1026–1027, 2017.
  • [26] N. Nwulu, “Emission Constrained Bid Based Dynamic Economic Dispatch Using Quadratic Programming,” Int. Conf. Energy, Commun. Data Anal. Soft Comput. ICECDS, pp. 213– 216, 2018.
  • [27] A. A. A. El-Ela, R. A. El-Sehiemy, R. M. Rizk-Allah, and D. A. Fatah, “Solving Multiobjective Economical Power Dispatch Problem using MO-FOA,” Proceeding Int. Middle East Power Syst. Conf., no. 1, pp. 19–24, 2018.
  • [28] I. Ziane, F. Benhamida, and A. Graa, “Simulated Annealing Algorithm for Combined Economic and Emission Power Dispatch using Max/Max Price Penalty Factor,” Neural Comput. Appl., vol. 28, pp. 197–205, 2017.
  • [29] R. K. Kaushal and T. Thakur, “Multiobjective Electrical Power Dispatch of Thermal Units with Convex and Non-Convex Fuel Cost Functions for 24 Hours Load Demands,” Int. J. Eng. Adv. Technol., vol. 9, no. 3, pp. 1534–1542, 2020.
  • [30] J. Chen and H. Imani Marrani, “An Efficient New Hybrid ICAPSO Approach for Solving Large Scale Non-Convex Multi Area Economic Dispatch Problems,” J. Electr. Eng. Technol., vol. 15, no. 3, pp. 1127–1145, 2020.
  • [31] S. Sivasubramani and K. S. Swarup, “Environmental/Economic Dispatch using Multi-Objective Harmony Search Algorithm,” Electr. Power Syst. Res., vol. 81, no. 9, pp. 1778–1785, 2011.
  • [32] A. Y. Abdelaziz, E. S. Ali, and S. M. Abd Elazim, “Flower Pollination algorithm to Solve Combined Economic and Emission Dispatch Problems,” Eng. Sci. Technol. an Int. J., vol. 19, no. 2, pp. 980–990, 2016.
  • [33] B. Bentouati, S. Chettih, and R. A. El-Sehiemy, “A Chaotic Krill Herd Algorithm for Optimal Solution of the Economic Dispatch Problem,” Int. J. Eng. Res. Africa, vol. 31, pp. 155–168, 2017.
  • [34] M. Peter Musau, N. O. Abungu, and C. W. Wekesa, “Multi Objective Dynamic Economic Dispatch with Cubic Cost Functions,” Int. J. Energy Power Eng., vol. 4, no. 3, pp. 153– 167, 2015.
  • [35] X. Jiang, J. Zhou, H. Wang, and Y. Zhang, “Dynamic Environmental Economic Dispatch using Multiobjective Differential Evolution Algorithm with Expanded Double Selection and Adaptive Random Restart,” Int. J. Electr. Power Energy Syst., vol. 49, no. 1, pp. 399–407, 2013.
  • [36] W. T. Pan, “A new Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as an Example,” Knowledge-Based Syst., vol. 26, pp. 69–74, 2012.
  • [37] D. Karaboga and B. Basturk, “On the performance of artificial bee colony ( ABC ) algorithm,” vol. 8, pp. 687–697, 2008.
  • [38] R.A.Rasyid, “Optimization of 150 kV Sulselbar Power Generation System With Integration Sidrap Wind Power Plant,” Hasanuddin University, 2018.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2072fca1-cbf6-4201-a770-b22c02b95508
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