An Improved Self-Adaptive Teaching-learning Based Optimization for Multi-area Economic Dispatch
Authors: 
Qun Niu, Gui Xu, Lipeng TangAuthors Info & Claims
Pages 77 - 81
Abstract
The multi-area economic dispatch (MAED) is a hot and vital research topic for energy saving and emission reduction. Multi-areal economic dispatch refers to the most economical distribution of load requirement among the output units under the premise of satisfying the physical and operational constraints of multiple areas. Each area is connected by a transmission line. In this paper, an improved algorithm (SA-TLBO), which uses adaptive teaching factor to replace the teaching factor in the original teaching-learning based optimization, is developed. Since, adaptive teaching factor can achieve a good balance between convergence speed and search ability, thus improving the overall performance of the algorithm. The method is tested on a system with ten areas, and each area has a 130-unit system. Compared with other two improved strategies and conventional algorithms, the proposed SA-TLBO is shown to yield better solutions for multi-area economic dispatch problems.
References
[1]
S. Vijayaraj and R. K. Santhi. "Multi-area economic dispatch using flower pollination algorithm", 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, 2016, pp. 4355-4360.
[2]
Zhang P, Ma W and Dong Y, “Multi-area economic dispatching using improved grasshopper optimization algorithm”, Evolving Systems, 2021, 12(3): 837-847.
[3]
Kumar, Vijendra and Yadav, S.M. “A state-of-the-Art review of heuristic and metaheuristic optimization techniques for the management of water resources”, Water Supply, vol.22, pp . 3702-3728, 2022.
[4]
Basu M. “Fast Convergence Evolutionary Programming for Multi-area Economic Dispatch”, Electric Power Components and Systems, 2017, 45(15): 1629-1637.
[5]
Chen J and Marrani H I. “An Efficient New Hybrid ICA-PSO Approach for Solving Large Scale Non-convex Multi Area Economic Dispatch Problems”, Journal of Electrical Engineering & Technology,2020, 15(3): 1127-1145.
[6]
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, pp. 303-315, 2011.
[7]
R. Venkata Rao. "Review of applications of tlbo algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems", Decision Science Letters, vol. 5, pp. 1-30, 2016.
[8]
J. C. Zhai, Z. Zhao and P. zhang. “An Improved Teaching-learning Based Optimization Algorithm”, Computer Technology and Development, vol. 8, pp. 37-41, 2019.
[9]
J. Nayak, B. Naik, H. S. Behera, and A. Abraham. "Elitist teaching–learning-based optimization (ETLBO) with higher-order Jordan Pi-sigma neural network: a comparative performance analysis", Neural Computing and Applications, 2016.
[10]
Kommadath, Remya and Kotecha, Prakash. “Teaching Learning Based Optimization with Focused Learning and its Performance on CEC2017 functions”, 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings,2017, pp.2397-2403.
[11]
Shuijia Li, Wenyin Gong, Ling Wang, Xuesong Yan and Chengyu Hu. “A hybrid adaptive teaching–learning-based optimization and differential Evolution for parameter identification of photo voltaic models”, Energy Conversion and Management, 2020, 225:113474.
[12]
Vivek Patel and Vimal Savsani. “Optimization of a plate-fin heat exchanger design through an improved multi-objective teaching-learning based optimization (MO-ITLBO) algorithm”, Chemical Engineering Research and Design, 2014, pp. 2371–2382.
[13]
Leandro dos Santos Coelho and Viviana Cocco Mariani. “Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects”, Energy Conversion and Management, 2008, 49:3080-3085.
[14]
Secui D C. “Large-scale multi-area economic/emission dispatch based on a new symbiotic organisms search algorithm”, Energy Conversion and Management, 2017, 154: 203-223.
[15]
J. Kennedy and R. Eberhart. “Particle swarm optimization”, Proceedings of ICNN’95 - International Conference on Neural Networks, Perth, WA, Australia, 1995, pp. 1942-1948 vol.4.
[16]
Liu Yong and Shang Tao. “Economic dispatch of power system in-corporating wind power plant”, 2007 International Power Engineering Conference (IPEC 2007), Singapore, 2007, pp. 159-162.
[17]
R. Storn and K. Price. “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces”, vol. 11, no. 4, pp. 341-359, 1997.
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Published: 16 August 2023
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PRIS 2023
PRIS 2023: 2023 5th International Conference on Pattern Recognition and Intelligent Systems
July 28 - 30, 2023
Shenyang, China
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