Comparison Study On Economic Load Dispatch Using Metaheuristic Algorithm

Year 2022, Volume: 35 Issue: 1, 26 - 40, 01.03.2022

Widi Aribowo

https://doi.org/10.35378/gujs.820805

Cited By: 6

Abstract

This paper presents an approach to overcome economic load dispatch (ELD) using a metaheuristic algorithm. Economic load dispatch (ELD) is one of the most important problems in a power system, and solving it quickly is extremely important. The main problem that will be addressed in this paper is how to optimize the economy of the power grid with various operational limitations, the loss in transmission line power, and consider minimizing the fuel costs produced. In this study, some of the newest metaheuristics inspired by nature will be explored, namely Seagull Optimization Algorithm (SOA), Marine Predator Algorithm (MPA), Sine Tree-Seed Algorithm (STSA), Chimp Optimization Algorithm (ChOA), Equilibrium Optimizer (EO), and Giza Pyramids Construction (GPC). The performance appraisal of the method applied in this study was tested using 2 case studies, namely a system with 3 and 6 power system units. The results are presented by comparing between metaheuristic and mathematical methods. The experimental results is showed that the Sine Tree-Seed Algorithm (STSA) is presented the best performance with various case studies with constraints.

Keywords

Economic load dispatch, Metaheuristic, Optimization, Nature inspired algorithm

References

• [1] Aribowo, W., “Tuning for Power System Stabilizer Using Distributed Time-Delay Neural Network,” SINERGI, 22(3): 205-210, (2017).
• [2] Khan, I., Li, Z., Gu, W. and Xu, Y., “Distributed control algorithm for optimal reactive power control in power grids”, International Journal of Electrical Power and Energy Systems, 83: 505–513, (2016).
• [3] Liang, H., Liu, Y., Shen, Y., Li, F. and Man, Y., “A Hybrid Bat Algorithm for Economic Dispatch With Random Wind Power,” in IEEE Transactions on Power Systems, 33(5): 5052-5061, (2018).
• [4] Priyanto, T. K., Maulana M. F. and Giyantara, A., “Dynamic economic dispatch using chaotic bat algorithm on 150kV Mahakam power system”, 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, 116-121, (2017).
• [5] Liang, H., Liu, Y., Shen Y. and Li, F. "A multiobjective chaotic bat algorithm for economic and emission dispatch," 2017 Chinese Automation Congress (CAC), Jinan, 4684-4689, (2017).
• [6] Adarsh, B. R., Raghunathan, T., Jayabarathi, T. and Yang, X. S. “Economic dispatch using chaotic bat algorithm”, Energy, 96: 666-675, (2016).
• [7] Rabiee, A., Jamadi, M., Mohammadi-Ivatloo, B., Ahmadian, A., “Optimal Non-Convex Combined Heat and Power Economic Dispatch via Improved Artificial Bee Colony Algorithm”, Processes, 8, (2020).
• [8] Sharifi, S., Sedaghat, M., Farhadi, P., Ghadimi, N., and Taheri, B., “Environmental economic dispatch using improved artificial bee colony algorithm”, Evolving Systems, 8: 233–242, (2017).
• [9] Rao, R. K., Srinivas, P., Divakar, M. S. M. and Venkatesh, G. S. N. M., "Artificial bee colony optimization for multi objective economic load dispatch of a modern power system”, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, 4097-4100, (2016).
• [10] Jayabarathi, T., Raghunathan, T., Adarsh, B. R. and Suganthan, P. N., “Economic dispatch using hybrid grey wolf optimizer”, Energy, 111: 630-641, (2016).
• [11] Hardiansyah, H., “Grey Wolf Optimizer Applied to Dynamic Economic Dispatch Incorporating Wind Power”, Global Journal of Research in Engineering, 20(4-F), (2020).
• [12] Mostafa, E., Abdel-Nasser M. and Mahmoud, K. “Application of mutation operators to grey wolf optimizer for solving emission-economic dispatch problem”, 2018 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, 278-282, (2018).
• [13] Pradhan, M., Roy, P. K. and Pal, T. “Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system”, Ain Shams Engineering Journal, 9(4): 2015-2025, (2018).
• [14] Zhao, J., Liu, S., Zhou, M., Guo, X. and Qi, L., “Modified cuckoo search algorithm to solve economic power dispatch optimization problems”, in IEEE/CAA Journal of Automatica Sinica, 5(4): 794-806, (2018).
• [15] Nguyen, T.T., Nguyen, T.T. and Vo, D. N., “An effective cuckoo search algorithm for large-scale combined heat and power economic dispatch problem”, Neural Comput & Applic, 30: 3545–3564, (2018).
• [16] Nguyen, T. T., Vo, D. N. and Dinh, B. H., “Cuckoo search algorithm for combined heat and power economic dispatch”, International Journal of Electrical Power & Energy Systems, 81: 204-214, (2016).
• [17] Chellappan, R. and Kavitha, D. “Economic and emission load dispatch using Cuckoo search algorithm”, 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, 1-7, (2017).
• [18] Dhiman, G. and Kumar, V. “Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems”, Knowledge-Based Systems, 165: 169-196, (2019).
• [19] Faramarzi, A., Heidarinejad, M., Mirjalili, S. and Gandomi, A. H., “Marine Predators Algorithm: A nature-inspired metaheuristic”, Expert Systems with Applications, 152, (2020).
• [20] Jiang, J., Xu, M., Meng, X. and Li, K., “STSA: A sine Tree-Seed Algorithm for complex continuous optimization problems”, Physica A: Statistical Mechanics and its Applications, 537, (2020).
• [21] Khishe M. and Mosavi, M.R., “Chimp optimization algorithm”, Expert Systems with Applications, 149, (2020).
• [22] Faramarzi, A., Heidarinejad, M., Stephens, B. and Mirjalili, S., “Equilibrium optimizer: A novel optimization algorithm”, Knowledge-Based Systems, 191, (2020).
• [23] Harifi, S., Mohammadzadeh, J., Khalilian, M. and Ebrahimnejad, S., “Giza Pyramids Construc-tion: an ancient-inspired metaheuristic algorithm for optimization”, Evol. Intel, (2020).
• [24] Wood, A. J. and Wollenberg, B. F, “Power generation operation and control (2nd ed.)”, (2010)
• [25] Saadat. H., “Power system analysis”, McGraw-Hill Companies, (2008).