Teaching-Learning-Based Optimization Algorithm for the Combined Dynamic Economic Environmental Dispatch Problem
Abstract
The Dynamic Economic Environmental Dispatch Problem (DEEDP) is a major issue in power system control. It aims to find the optimum schedule of the power output of thermal units in order to meet the required load at the lowest cost and emission of harmful gases. Several constraints, such as generation limits, valve point loading effects, prohibited operating zones, and ramp rate limits, can be considered. In this paper, a method based on Teaching-Learning-Based Optimization (TLBO) is proposed for dealing with the DEEDP problem where all aforementioned constraints are considered. To investigate the effectiveness of the proposed method for solving this discontinuous and nonlinear problem, the ten-unit system under four cases is used. The obtained results are compared with those obtained by other metaheuristic techniques. The comparison of the simulation results shows that the proposed technique has good performance.
Keywords:
dynamic economic environmental dispatch, teaching-learning-based optimization, prohibited operating zones, ramp rate limitsDownloads
References
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," International Journal of Electrical Power & Energy Systems, vol. 49, pp. 399-407, Jul. 2013. DOI: https://doi.org/10.1016/j.ijepes.2013.01.009
G. W. Chang et al., "Experiences with mixed integer linear programming based approaches on short-term hydro scheduling," IEEE Transactions on Power Systems, vol. 16, no. 4, pp. 743-749, Nov. 2001. DOI: https://doi.org/10.1109/59.962421
Z.- Liang and J. D. Glover, "A zoom feature for a dynamic programming solution to economic dispatch including transmission losses," IEEE Transactions on Power Systems, vol. 7, no. 2, pp. 544-550, May 1992. DOI: https://doi.org/10.1109/59.141757
C. Cao, J. Xie, D. Yue, J. Zhao, Y. Xiao, and L. Wang, "A distributed gradient algorithm based economic dispatch strategy for virtual power plant," in 2016 35th Chinese Control Conference (CCC), Chengdu, China, Jul. 2016, pp. 7826-7831. DOI: https://doi.org/10.1109/ChiCC.2016.7554598
Y. Labbi, D. B. Attous, and B. Mahdad, "Artificial bee colony optimization for economic dispatch with valve point effect," Frontiers in Energy, vol. 8, no. 4, pp. 449-458, Dec. 2014. DOI: https://doi.org/10.1007/s11708-014-0316-8
J. B. Park, K. S. Lee, J. R. Shin, and K. Y. Lee, "A particle swarm optimization for economic dispatch with nonsmooth cost functions," IEEE Transactions on Power Systems, vol. 20, no. 1, pp. 34-42, Feb. 2005. DOI: https://doi.org/10.1109/TPWRS.2004.831275
N. Singh and Y. Kumar, "Economic load dispatch with environmental emission using MRPSO," in 2013 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, India, Feb. 2013, pp. 995-999. DOI: https://doi.org/10.1109/IAdCC.2013.6514362
W. Jiang, Z. Yan, and Z. Hu, "A Novel Improved Particle Swarm Optimization Approach for Dynamic Economic Dispatch Incorporating Wind Power," Electric Power Components and Systems, vol. 39, no. 5, pp. 461-477, Mar. 2011. DOI: https://doi.org/10.1080/15325008.2010.528536
A. Torchani, A. Boudjemline, H. Gasmi, Y. Bouazzi, and T. Guesmi, "Dynamic Economic/Environmental Dispatch Problem Considering Prohibited Operating Zones," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4586-4590, Oct. 2019. DOI: https://doi.org/10.48084/etasr.2904
M. Basu, "Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II," International Journal of Electrical Power & Energy Systems, vol. 30, no. 2, pp. 140-149, Feb. 2008. DOI: https://doi.org/10.1016/j.ijepes.2007.06.009
I. Ziane, F. Benhamida, and A. Graa, "Simulated annealing algorithm for combined economic and emission power dispatch using max/max price penalty factor," Neural Computing and Applications, vol. 28, no. 1, pp. 197-205, Dec. 2017. DOI: https://doi.org/10.1007/s00521-016-2335-3
I. Bala and A. Yadav, "Optimal Reactive Power Dispatch Using Gravitational Search Algorithm to Solve IEEE-14 Bus System," in Communication and Intelligent Systems, Singapore, 2020, pp. 463-473. DOI: https://doi.org/10.1007/978-981-15-3325-9_36
V. Raviprabhakaran and C. S. Ravichandran, "Enriched Biogeography-Based Optimization Algorithm to Solve Economic Power Dispatch Problem," in Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Singapore, 2016, pp. 875-888. DOI: https://doi.org/10.1007/978-981-10-0451-3_78
B. Hernández-Ocaña, J. Hernández-Torruco, O. Chávez-Bosquez, M. B. Calva-Yáñez, and E. A. Portilla-Flores, "Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid," Applied Sciences, vol. 9, no. 6, p. 1261, Jan. 2019. DOI: https://doi.org/10.3390/app9061261
K. Lenin, B. R. Reddy, and M. Suryakalavathi, "Upgraded Harmony Search Algorithm for Solving Optimal Reactive Power Dispatch Problem," International Journal of Mathematics Research, vol. 4, no. 1, pp. 42-52, 2015. DOI: https://doi.org/10.18488/journal.24/2015.4.1/24.1.42.52
R. V. Rao, V. J. Savsani, and D. P. Vakharia, "Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems," Computer-Aided Design, vol. 43, no. 3, pp. 303-315, Mar. 2011. DOI: https://doi.org/10.1016/j.cad.2010.12.015
A. Farah, T. Guesmi, H. Hadj Abdallah, and A. Ouali, "A novel chaotic teaching-learning-based optimization algorithm for multi-machine power system stabilizers design problem," International Journal of Electrical Power & Energy Systems, vol. 77, pp. 197-209, May 2016. DOI: https://doi.org/10.1016/j.ijepes.2015.11.050
H. Singh Gill, B. Singh Khehra, A. Singh, and L. Kaur, "Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values," Egyptian Informatics Journal, vol. 20, no. 1, pp. 11-25, Mar. 2019. DOI: https://doi.org/10.1016/j.eij.2018.03.006
Τ. M. Kumar and Ν. A. Singh, "Environmental Economic Dispatch with the use of Particle Swarm Optimization Technique based on Space Reduction Strategy," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4605-4611, Oct. 2019. DOI: https://doi.org/10.48084/etasr.2969
T. Guesmi, A. Farah, I. Marouani, B. Alshammari, and H. H. Abdallah, "Chaotic sine-cosine algorithm for chance-constrained economic emission dispatch problem including wind energy," IET Renewable Power Generation, vol. 14, no. 10, pp. 1808-1821, 2020. DOI: https://doi.org/10.1049/iet-rpg.2019.1081
N. Pandit, A. Tripathi, S. Tapaswi, and M. Pandit, "An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch," Applied Soft Computing, vol. 12, no. 11, pp. 3500-3513, Nov. 2012. DOI: https://doi.org/10.1016/j.asoc.2012.06.011
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