Multi-objective ant lion optimizer for solving environmental/economic dispatch

• Autorzy: Hardiansyah Hardiansyah , Junaidi Junaidi

Identyfikatory

DOI

10.15199/48.2021.02.32

Warianty tytułu

PL

ieloobiektowy algorytm mrowkolwowaty do rozwiązywania problemu ekonomicznego rozsyłu energii z uwzględnieniem środowiska

• Języki publikacji: EN

Abstrakty

EN

In this paper, a new meta-heuristic algorithm, called multi-objective ant lion optimizer (MOALO) is presented to solve environmental economic dispatch (EED) problem considering transmission losses. MOALO is inspired by the hunting mechanism of ant lions in nature. It has fast convergence speed due to the use of roulette wheel selection technique. The effectiveness of the proposed algorithm has been tested on the standard IEEE 30-bus test system and the results were compared with other methods reported in recent literature. The simulation results show that the proposed algorithm outperforms previous optimization methods.

PL

Przedstawiono nowy meta-heurystyczny algorytm MOALO do rozwiązywania problemu ekonomicznego rozsyłu energii z uwzględnieniem warunków środowiskowych. Algorytm jest inspirowany mechanizmem polowania. Daje on szybkie rozwiązanie z wykorzystaniem zasady koła w ruletce. Algorytm sprawdzono wykorzystując system testowy IEEE-30-bus.

Słowa kluczowe

EN

multi-objective ant lion optimizer; economic dispatch; emission dispatch; combined economic emission dispatch

PL

ekonomiczny rozsył energii; owady mrówkolwaowate; emisja zanieczyszczeń

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2021
• Tom: R. 97, nr 2
• Strony: 153--158
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Hardiansyah Hardiansyah

• Faculty of Engineering, University of Tanjungpura, Pontianak 78124, Indonesia

autor

Junaidi Junaidi

• Faculty of Engineering, University of Tanjungpura, Pontianak 78124, Indonesia

Bibliografia

• [1] M. A. Abido, “Environmental/economic power dispatch using multiobjective evolutionary algorithms”, IEEE Transactions on Power Systems, 18(2003), No. 4, 1529-1537.
• [2] S. Krishnamurthy, and R. Tzoneva, “Multi objective dispatch problem with valve point effect loading of fuel cost and emission criterion”, International Journal of Computer and Electrical Engineering, 4(2012), No. 5, 775-784.
• [3] S. Y. Lim, M. Montakhab, and H. Nouri, “Economic dispatch of power system using particle swarm optimization with constriction factor”, International Journal of Innovations in Energy Systems and Power, 4(2009), No. 2, 29-34. 158 PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 97 NR 2/2021
• [4] Z. L. Gaing, “Particle swarm optimization to solving the economic dispatch considering the generator constraints”, IEEE Transactions on Power Systems, 18(2003), No. 3, 1187- 1195.
• [5] Wanchai KHAMSEN and Chiraphon TAKEANG, “Hybrid of lamda and bee colony optimization for solving economic dispatch”, PRZEGLAD ELEKTROTECHNICZNY, 9(2016), 220-223.
• [6] T. Ratniyomchai, A. Oonsivilai, P. Pao-La-Or, and T. Kulworawanichpong, “Particle swarm optimization for solving combined economic and emission dispatch problems”, in Proceedings of the 5th IASME/WSEAS International Conference of Energy and Environment: 211-216, 2010.
• [7] C. Palanichamy, and N. S. Babu, “Analytical solution for combined economic and emissions dispatch”, Electric Power Systems Research, 78(2008), No. 7, 1129-1137.
• [8] N. Cetinkaya, “Optimization algorithm for combined economic and emission dispatch with security constraints”, International Conference on Computer Science and Application (ICCSA 2009), 150-153.
• [9] L. H. Wu, Y. N. Wang, X. F. Yuan, and S. W. Zhou, “Environmental/economic power dispatch problem using multiobjective differential evolution algorithm”, Electric Power Systems Research, 80(2010), No. 9, 1171-1181.
• [10] L. A. Koridak, M. Rahli, and M. Younes, “Hybrid optimization of the emission and economic dispatch by the genetic algorithm”, Leonardo Journal of Sciences, (2008), Issue 14, 193-203.
• [11] U. Güvenç, “Combined economic emission dispatch solution using genetic algorithm based on similarity crossover”, Scientific Research and Essays, 5(2010), No. 17, 2451-2456.
• [12] Simon Dinu, Ioan Odagescu, and Maria Moise, “Environmental economic dispatch optimization using a modified genetic algorithm”, International Journal of Computer Applications, 20(2011), No. 2, 7-14.
• [13] J. Sasikala, and M. Ramaswamy, “Optimal λ based economic emission dispatch using simulated annealing”, International Journal of Computer Applications, 1(2010), No. 10, 55-63.
• [14] P. K. Roy, S. P. Ghoshal, and S. S. Thakur, “Combined economic and emission dispatch problems using biogeography-based optimization”, Electrical Engineering, 92(2010), No. 4-5, 173-184.
• [15] P. K. Hota, A. K. Barisal, and R. Chakrabarti, “Economic emission load dispatch through fuzzy based bacterial foraging algorithm”, International Journal of Electrical Power and Energy Systems, 32 (2010), No. 7, 794-803.
• [16] M. A. Abido, “Multi-objective particle swarm optimization for environmental/economic dispatch problem”, Electric Power Systems Research, 79(2009), 1105-1113.
• [17] Y. M. Chen, and W. S. Wang, “A particle swarm approach to solve environmental/economic dispatch problem”, International Journal of Industrial Engineering Computations, 1(2010), 157-172.
• [18] Anurag Gupta, K. K. Swarnkar, and K. Wadhwani, “Combined economic emission dispatch problem using particle swarm optimization”, International Journal of Computer Applications, 49(2012), No. 6, 1-6.
• [19] S. Hemamalini, and S. P. Simon, “Economic/emission load dispatch using artificial bee colony algorithm”, ACEEE International Journal on Electrical and Power Engineering, 1(2010), No. 2, 27-33.
• [20] Y. Sonmez, “Multi-objective environmental/economic dispatch solution with penalty factor using artificial bee colony algorithm”, Scientific Research and Essays, 6(2011), No. 3, 2824-2831.
• [21] D. Aydin, S. Ozyon, C. Yasar, and T. Liao, “Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem”, International Journal of Electrical Power, 54(2014), 144-153.
• [22] Jordan Radosavljevic, “Gravitational search algorithm for solving combined economic and emission dispatch”, Infoteh- Jahorina, 14(2015), 148-153.
• [23] M. Jevtic, N. Jovanovic, J. Radosavljevic, and D. Klimenta, “Moth swarm algorithm for solving combined economic and emission dispatch problem”, Elektron Elektrotech, 23(2017), No. 5, 21-28.
• [24] M. Jevtic, N. Jovanovic, and J. Radosavljevic, “Solving a combined economic emission dispatch problem using adaptive wind driven optimization”, Turkis Journal of Electrical Engineering & Computer Sciences, 26(2018), 1747-1758.
• [25] S. Mirjalili, “The ant lion optimizer”, Advances in Engineering Software, 83(2015), 80-98.
• [26] S. Mirjalili, P. Jangir and S. Saremi, “Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems”, Applied Intelligence, 46(2017), No. 1, 79-95.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).