New Optimization Algorithms for Application to Environmental Economic Load Dispatch in Power Systems
Öz
DOI:
10.5152/iujeee.2018.1825
The determination of the most economical
generation dispatch in an electrical power system has become a very important
issue globally. However, economical dispatch can no longer be considered alone
because of environmental problems that are derived from emissions such as
nitrogen oxide, carbon dioxide, and sulfur dioxide. In this study, the problem
of environmental economic load dispatch (EELD) in a power system of six
generators is addressed both by neglecting and including line transmission
losses using a modified genetic algorithm and a modified artificial bee colony
optimization method. The results of these modified algorithms are compared with
those of the unmodified versions. The results demonstrate that the proposed new
methods have better economic and environmental distribution performances.
Accordingly, it can be concluded that the new methods are more effective and
should be adopted.
Anahtar Kelimeler
Environmental economic dispatch optimization power systems genetic algorithm artificial bee colony
Kaynakça
- 1. L.D.S. Coelho, C. Lee, “Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches”, Electrical Power and Energy Systems, vol. 30, no. 5, pp. 297-307, 2008. 2. J. Novacheck, J.X. Johnson, “The environmental and cost implications of solar energy preferences in Renewable Portfolio Standards”, Energy Policy, vol. 86, pp. 250-261, November 2015. 3. H. Zhong, Q. Xia, Y. Chen, C. Kang, “Energy-saving generation dispatch toward a sustainable electric power industry in China”, Energy Policy, vol. 83, pp. 14-25, 2015. 4. A. Galetovic, C.M. Munoz, “Wind, coal, and the cost of environmental externalities”, Energy Policy, vol. 62, pp. 1385-1391, 2013. 5. D. Gupta, “Environmental Economic Load Dispatch Using Hopfield Neural Network”, M.S. thesis, Thapar University, Patiala, 2008. 6. Y.H. Song, G.S. Wang, P.Y. Wang, A.T. Johns, “Environmental/economic dispatch using fuzzy logic controlled genetic algorithms”, IEE Proc. Gener. Transm. Distrib, vol. 144, no. 4, pp. 377-382, 1997. 7. M. Pandit, L. Srivastava, M. Sharma, ”Environmental economic dispatch in multi-area power system employing improved differential evolution with fuzzy selection”, Applied Soft Computing, vol. 28, pp. 498-510, 2015. 8. S. Sivasubramani, K.S. Swarup, “Environmental/economic dispatch using multi-objective harmony search algorithm”, Electric Power Systems Research, vol. 81, no. 9, pp. 1778-1785, 2011. 9. U. Güvenç, Y. Sönmez, S. Duman, N. Yörükeren, ”Combined economic and emission dispatch solution gravitational search algorithm”, Scientia Irenica, vol. 19, no. 6, pp. 1754-1762, 2012. 10. A. Bhattacharya, P.K. Chattopadhyay, “Solving economic emission load dispatch problems using hybrid differential evolution”, Applied Soft Computing, vol. 11, no. 2, pp. 2526-2537, 2011. 11. A. Y. Abdelaziz, E. S. Ali, S. M. Abd Elazim, “Combined economic and emission dispatch solution using flower pollination algorithm”, International Journal of Electrical Power & Energy Systems, vol. 80, pp. 264-274, 2016. 12. F. Chen, G. H. Huang, Y. R. Fan, R. F. Liao, “A nonlinear fractional programming approach for environmental-economic power dispatch”, International Journal of Electrical Power & Energy Systems, vol. 78, pp. 463-469, June 2016. 13. N. Singh, Y. Kumar, “Multiobjective Economic Load Dispatch Problem Solved by New PSO”, Advances in Electrical Engineering, vol. 2015, 2015. 14. Y. A. Gherbi, H. Bouzeboudja, F. Z. Gherbi, “The combined economic environmental dispatch using new hybrid metaheuristic”, Energy, vol. 115, part 1, pp. 468-477, 2016. 15. T. Liu, L. Jiao, W. Ma, J. Ma, R. Shang, “Cultural quantum-behaved particle swarm optimization for environmental/economic dispatch”, Applied Soft Computing, vol. 48, pp. 597-611, 2016. 16. K.K. Mandal, S. Mandal, B. Bhattacharya, B., N. Chakraborty, ”Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique”, Applied Soft Computing, vol. 28, pp. 188-195, 2015. 17. P.S. Kulkarni, A.G. Kothari, D. P. Kothari, “Combined economic and emission dispatch using improved backpropagation neural network”, Electrical Machines & Power Systems, vol. 28, no. 1, pp. 31-44, 2010. 18. H. Saadat, “Power Systems Analysis”, McGraw Hill, Boston, 1999. 19. J. H. Holland, “Adaptation in Natural and Artificial Systems”, The University of Michigan Press, Ann Arbor, 1975. 20. A. Kunjur, A. Genetic Algorithms in Mechanical Synthesis. Available: http://www.ecs.umass.edu/mie/labs/mda/mechanism/papers/genetic.html. 21. D. Karaboğa, “An idea based on honey bee swarm for numerical optimization”, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005. 22. D. Karaboğa, B. Baştürk, “Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems”, IFSA ‘07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing, 2007, pp. 789-798. 23. D. Karaboğa, “Yapay Zeka Optimizasyon Algoritmaları”, Nobel Yayın Dağıtım, 2004. 24. M. Özdemir. Artificial Bee Colony Algorithm. Available: http://www.muhlisozdemir.com/blog/yapay-ari-kolonisi-algoritmasi. 25. J. S. Dhillon, S. C. Parti, D. P. Kothari, “Stochastic economic emission load dispatch”, Electric Power Systems Research, vol. 26, no. 3, pp. 179-186, 1993. 26. Ö. P. Arslan, Y. Arıkan, E. Çam, İ. Eke, “An application of environmental economic dispatch using genetic algorithm”, TOJSAT, vol. 6, no. 1, pp. 1-4, 2016.
New Optimization Algorithms for Application to Environmental Economic Load Dispatch in Power Systems
Öz
DOI: 10.5152/iujeee.2018.1825
The determination of the most economical generation dispatch in an electrical power system has become a very important issue globally. However, economical dispatch can no longer be considered alone because of environmental problems that are derived from emissions such as nitrogen oxide, carbon dioxide, and sulfur dioxide. In this study, the problem of environmental economic load dispatch (EELD) in a power system of six generators is addressed both by neglecting and including line transmission losses using a modified genetic algorithm and a modified artificial bee colony optimization method. The results of these modified algorithms are compared with those of the unmodified versions. The results demonstrate that the proposed new methods have better economic and environmental distribution performances. Accordingly, it can be concluded that the new methods are more effective and should be adopted.
Anahtar Kelimeler
Environmental economic dispatch optimization power systems genetic algorithm artificial bee colony
Kaynakça
- 1. L.D.S. Coelho, C. Lee, “Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches”, Electrical Power and Energy Systems, vol. 30, no. 5, pp. 297-307, 2008. 2. J. Novacheck, J.X. Johnson, “The environmental and cost implications of solar energy preferences in Renewable Portfolio Standards”, Energy Policy, vol. 86, pp. 250-261, November 2015. 3. H. Zhong, Q. Xia, Y. Chen, C. Kang, “Energy-saving generation dispatch toward a sustainable electric power industry in China”, Energy Policy, vol. 83, pp. 14-25, 2015. 4. A. Galetovic, C.M. Munoz, “Wind, coal, and the cost of environmental externalities”, Energy Policy, vol. 62, pp. 1385-1391, 2013. 5. D. Gupta, “Environmental Economic Load Dispatch Using Hopfield Neural Network”, M.S. thesis, Thapar University, Patiala, 2008. 6. Y.H. Song, G.S. Wang, P.Y. Wang, A.T. Johns, “Environmental/economic dispatch using fuzzy logic controlled genetic algorithms”, IEE Proc. Gener. Transm. Distrib, vol. 144, no. 4, pp. 377-382, 1997. 7. M. Pandit, L. Srivastava, M. Sharma, ”Environmental economic dispatch in multi-area power system employing improved differential evolution with fuzzy selection”, Applied Soft Computing, vol. 28, pp. 498-510, 2015. 8. S. Sivasubramani, K.S. Swarup, “Environmental/economic dispatch using multi-objective harmony search algorithm”, Electric Power Systems Research, vol. 81, no. 9, pp. 1778-1785, 2011. 9. U. Güvenç, Y. Sönmez, S. Duman, N. Yörükeren, ”Combined economic and emission dispatch solution gravitational search algorithm”, Scientia Irenica, vol. 19, no. 6, pp. 1754-1762, 2012. 10. A. Bhattacharya, P.K. Chattopadhyay, “Solving economic emission load dispatch problems using hybrid differential evolution”, Applied Soft Computing, vol. 11, no. 2, pp. 2526-2537, 2011. 11. A. Y. Abdelaziz, E. S. Ali, S. M. Abd Elazim, “Combined economic and emission dispatch solution using flower pollination algorithm”, International Journal of Electrical Power & Energy Systems, vol. 80, pp. 264-274, 2016. 12. F. Chen, G. H. Huang, Y. R. Fan, R. F. Liao, “A nonlinear fractional programming approach for environmental-economic power dispatch”, International Journal of Electrical Power & Energy Systems, vol. 78, pp. 463-469, June 2016. 13. N. Singh, Y. Kumar, “Multiobjective Economic Load Dispatch Problem Solved by New PSO”, Advances in Electrical Engineering, vol. 2015, 2015. 14. Y. A. Gherbi, H. Bouzeboudja, F. Z. Gherbi, “The combined economic environmental dispatch using new hybrid metaheuristic”, Energy, vol. 115, part 1, pp. 468-477, 2016. 15. T. Liu, L. Jiao, W. Ma, J. Ma, R. Shang, “Cultural quantum-behaved particle swarm optimization for environmental/economic dispatch”, Applied Soft Computing, vol. 48, pp. 597-611, 2016. 16. K.K. Mandal, S. Mandal, B. Bhattacharya, B., N. Chakraborty, ”Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique”, Applied Soft Computing, vol. 28, pp. 188-195, 2015. 17. P.S. Kulkarni, A.G. Kothari, D. P. Kothari, “Combined economic and emission dispatch using improved backpropagation neural network”, Electrical Machines & Power Systems, vol. 28, no. 1, pp. 31-44, 2010. 18. H. Saadat, “Power Systems Analysis”, McGraw Hill, Boston, 1999. 19. J. H. Holland, “Adaptation in Natural and Artificial Systems”, The University of Michigan Press, Ann Arbor, 1975. 20. A. Kunjur, A. Genetic Algorithms in Mechanical Synthesis. Available: http://www.ecs.umass.edu/mie/labs/mda/mechanism/papers/genetic.html. 21. D. Karaboğa, “An idea based on honey bee swarm for numerical optimization”, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005. 22. D. Karaboğa, B. Baştürk, “Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems”, IFSA ‘07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing, 2007, pp. 789-798. 23. D. Karaboğa, “Yapay Zeka Optimizasyon Algoritmaları”, Nobel Yayın Dağıtım, 2004. 24. M. Özdemir. Artificial Bee Colony Algorithm. Available: http://www.muhlisozdemir.com/blog/yapay-ari-kolonisi-algoritmasi. 25. J. S. Dhillon, S. C. Parti, D. P. Kothari, “Stochastic economic emission load dispatch”, Electric Power Systems Research, vol. 26, no. 3, pp. 179-186, 1993. 26. Ö. P. Arslan, Y. Arıkan, E. Çam, İ. Eke, “An application of environmental economic dispatch using genetic algorithm”, TOJSAT, vol. 6, no. 1, pp. 1-4, 2016.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 3 Ağustos 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 18 Sayı: 2 |
