Optimal Solution of the Dynamic Economic Dispatch by Improved Teaching-Learning-Based Artificial Bee Colony Algorithm

Öz

Dynamic economic dispatch is one of the most handled problem in modern power system operations. It aims to optimize the output power from thermal generating units over a specified time period to minimize the total fuel cost, while satisfying the several constraints such as generation limits, ramp rate limits, and power balance. In addition to these constraints, the prohibited operating zones and the valve-point loading effect are included the DED problem. In this case, the complexity, nonlinearity, and non-convexity of the DED problem are increases. Therefore, in order to solve the DED problem, a powerful meta-heuristic search (MHS) algorithm are proposed. In this study, an improved teaching-learning-based artificial bee colony (TLABC) algorithm, where the fitness-distance balance based TLABC (FDB-TLABC) and natural-survivor method based TLABC (NSM-TLABC) algorithms were hybridized. To prove the performance of the proposed algorithm, it was applied to solve the DED problem and benchmark problem suites. In the simulation study carried out on benchmark problems, the results of the proposed algorithm and five MHS algorithms were evaluated statistically. According to Friedman test results, the proposed algorithm ranked first with 2.2836 values among them. On the other hand, the proposed algorithm and its rival algorithms were applied to solve the two DED cases. The results of them show that the proposed algorithm achieved superior performance to find the best objective values for both case studies.

Anahtar Kelimeler

• Dynamic economic dispatch
• Natural-Survivor method
• Fitness-Distance Balance
• NSM-FDB-TLABC algorithm

Kaynakça

1. [1] B. Mohammadi-Ivatloo, A. Rabiee, A. Soroudi, M. Ehsan. "Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch." Energy, vol. 44. 1, 2012, pp 228-240.
2. [2] B. Mohammadi-Ivatloo, A. Rabiee, M. Ehsan. "Time-varying acceleration coefficients IPSO for solving dynamic economic dispatch with non-smooth cost function." Energy conversion and management, vol. 56, 2012, pp 175-183.
3. [3] Y. Sonmez, H. T. Kahraman, M. K. Dosoglu, U. Guvenc, S. Duman. "Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects." Journal of Experimental & Theoretical Artificial Intelligence, vol. 29. 3, 2017, pp 495-515.
4. [4] C. Dai, Z. Hu, Q. Su. "An adaptive hybrid backtracking search optimization algorithm for dynamic economic dispatch with valve-point effects." Energy, vol. 239, 2022, 122461.
5. [5] B. Mohammadi-Ivatloo, A. Rabiee, A. Soroudi. "Nonconvex dynamic economic power dispatch problems solution using hybrid immune-genetic algorithm." IEEE Systems Journal, vol. 7.4, 2013, pp 777-785.
6. [6] R. Azizipanah-Abarghooee. "A new hybrid bacterial foraging and simplified swarm optimization algorithm for practical optimal dynamic load dispatch." International Journal of Electrical Power & Energy Systems, vol. 49, 2013, pp 414-429.
7. [7] Y. Zhang, D. W. Gong, N. Geng, X. Y. Sun."Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects." Applied Soft Computing, vol. 18, 2014, pp 248-260.
8. [8] G. Xiong, D. Shi, "Hybrid biogeography-based optimization with brain storm optimization for non-convex dynamic economic dispatch with valve-point effects." Energy, vol. 157, 2018, pp 424-435.

9. [9] D. Zou, S. Li, X. Kong, H. Ouyang, Z. Li, "Solving the dynamic economic dispatch by a memory-based global differential evolution and a repair technique of constraint handling." Energy, vol. 147, 2018, pp 59-80.
10. [10] M. Ghasemi, E. Akbari, M. Zand, M. Hadipour, S. Ghavidel, L. Li. "An efficient modified HPSO-TVAC-based dynamic economic dispatch of generating units." Electric Power Components and Systems, vol. 47. 19-20, 2019, pp 1826-1840.
11. [11] Z. Zheng, J. Li, Y. Han. "An improved invasive weed optimization algorithm for solving dynamic economic dispatch problems with valve-point effects." Journal of Experimental & Theoretical Artificial Intelligence, vol. 32. 5, 2020, pp 805-829.
12. [12] D. Santra, A. Mukherjee, K. Sarker, S. Mondal, "Dynamic economic dispatch using hybrid metaheuristics." Journal of Electrical Systems and Information Technology, vol. 7, 2020, pp 1-30.
13. [13] W. Yang, Z. Peng, Z. Yang, Y. Guo, X. Chen, "An enhanced exploratory whale optimization algorithm for dynamic economic dispatch." Energy Reports, vol. 7, 2021, pp 7015-7029.
14. [14] Z. Hu, C. Dai, Q. Su, "Adaptive backtracking search optimization algorithm with a dual-learning strategy for dynamic economic dispatch with valve-point effects." Energy, vol. 248, 2022, 123558.
15. [15] S. Basak, B. Bhattacharyya, B. Dey, "Dynamic economic dispatch using hybrid CSAJAYA algorithm considering ramp rates and diverse wind profiles." Intelligent Systems with Applications, vol. 16, 2022, 200116.
16. [16] W. Yang, X. Zhu, F. Nie, H. Jiao, Q. Xiao, Z. Yang, "Chaos Moth Flame Algorithm for Multi-Objective Dynamic Economic Dispatch Integrating with Plug-In Electric Vehicles." Electronics, vol. 12. 12, 2023, 2742.
17. [17] W. Yang, Y. Zhang, X. Zhu, K. Li, Z. Yang, "Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm." Energies, vol. 17. 6, 2024, 1491.
18. [18] K. Nagarajan, A. Rajagopalan, M. Bajaj, R. Sitharthan, S. A. Dost Mohammadi, V. Blazek, Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management. Scientific Reports, vol. 14. 1, 2024, 3091.
19. [19] S. Duman, H. T. Kahraman, Y. Sonmez, U. Guvenc, M. Kati, S. Aras. "A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems." Engineering Applications of Artificial Intelligence, vol. 111, 2022, 104763.
20. [20] H. T. Kahraman, M. Katı, S. Aras, D. A. Taşci. "Development of the Natural Survivor Method (NSM) for designing an updating mechanism in metaheuristic search algorithms." Engineering Applications of Artificial Intelligence, vol. 122, 2023, 106121.
21. [21] X. Chen, B. Xu, C. Mei, Y. Ding, K. Li. "Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation." Applied Energy, vol. 212, 2018, pp 1578-1588.
22. [22] H. T. Kahraman, S. Aras, E. Gedikli. "Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms." Knowledge-Based Systems, vol. 190, 2020, 105169.
23. [23] N. H. Awad, M. Z. Ali, J. J. Liang, B. Y. Qu, P. N. Suganthan."Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization." Technical Report, 2016.
24. [24] C. T. Yue, K. V. Price, P. N. Suganthan, J. J. Liang, M. Z. Ali, B. Y. Qu,... , P. P. Biswas. "Problem definitions and evaluation criteria for the CEC 2020 special session and competition on single objective bound constrained numerical optimization." Technical Report, 2019.
25. [25] F. A. Hashim, R. R. Mostafa, A. G. Hussien, S. Mirjalili, K. M. Sallam. "Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization." Knowledge-Based Systems, vol. 260, 2023, 110146.
26. [26] M. H. Sulaiman, Z. Mustaffa, M. M. Saari, H. Daniyal, S. Mirjalili. "Evolutionary mating algorithm. "Neural Computing and Applications, vol. 35. 1, 2023, pp 487-516.
27. [27] A. Faramarzi, M. Heidarinejad, B. Stephens, S. Mirjalili. "Equilibrium optimizer: A novel optimization algorithm." Knowledge-based systems, vol. 191, 2020, 105190