Abstract
This paper introduces the application of a new planning strategy based on a stochastic optimization method namely Moth flame optimization (MFO) to solve the optimal power flow management (OPFM) of the Algerian electric transmission system 114-Bus. Three objective functions have been optimized, the total fuel cost, the total power losses and total voltage deviation. The particularity and efficiency of the proposed MFO algorithm in terms of solution quality and convergence characteristics have been validated on the practical Algerian 114-bus test system. Obtained results have been compared to the standard genetic algorithm (GA) and to the particle swarm optimization (PSO) which demonstrates the efficacy of the MFA in solving large scale OPFM.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lawan, S.M., Abidin, W.A.W.Z.: Chapter a review of hybrid renewable energy systems based on wind and solar energy: modeling, design and optimization. Publisher of Open Access Books Built by Scientists, Science™ Core Collection (BKCI) (2020). https://doi.org/10.5772/intechopen.85838
El-Hawary, M.E., Christensen, G.S.: Optimal Economic Operation of Electric Power Systems. A Series of Monographs and Textbooks Ed. by Richard, University of Southern California. Publishers Bellman, PP (4, 5), vol. 142 (1979)
Bouchekara, H.: Solution of the optimal power flow problem considering security constraints using an improved chaotic electromagnetic field optimization algorithm. Neural Comput. Appl., April 2020. https://doi.org/10.1007/s00521-019-04298-3
Held, L., Mueller, F., Steinle, S., Barakat, M., Suriyah, M.R., Leibfried, T.: An optimal power flow algorithm for the simulation of energy storage systems in unbalanced three-phase distribution grids. In: Conference (UPEC 2020), Torino, Italy, pp. 1–4, September 2020
Daqaq, F., Ouassaid, M., Ellaia, R.: A new meta-heuristic programming for multi-objective optimal power flow. Electr. Eng. 103(2), 1217–1237 (2021). https://doi.org/10.1007/s00202-020-01173-6
Ma, L., Wang, C., Xie, N.-G., Shi, M., Ye, Y., Wang, L.: Moth-flame optimization algorithm based on diversity and mutation strategy. Appl. Intell. 51(8), 5836–5872 (2021). https://doi.org/10.1007/s10489-020-02081-9
Kaymaz, E., Duman, S., Guvenc, U.: Optimal power flow solution with stochastic wind power using the Levy coyote optimization algorithm. Neural Comput. Appl. 33, 6775–6804 (2021). https://doi.org/10.1007/s00521-020-05455-9
Senthilkumar, R., Karimulla, P.S.K., Subrahmanyam, K.B.V.S.R., Deshmukh, R.: Solution for optimal power flow problem using WDO algorithm. India Article 2021 Turk. J. Comput. Math. Educ. 12(2), 889–895 (2021)
Nguyen, T.T.: A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization. Energy 171, 218–240 (2019)
Sayed, F., Kamel, S., Ahmed Taher, M., Jurado, F.: Enhancing power system loadability and optimal load shedding based on TCSC allocation using improved moth flame optimization algorithm. Electr. Eng. 103, 205–225 (2021). https://doi.org/10.1007/s00202-020-01072
Bahrami, M., Bozorg-Haddad, O., Chu, X.: Advanced Optimization by Nature-Inspired Algorithms. Studies in Computational Intelligence, vol. 720. Ed. by O. Bozorg-Haddad (2018). ISBN 978-981-10-5220-0. ISBN 978-981-10-5221-7 (eBook). https://doi.org/10.1007/978-981-10-5221-7
Shehab, M., Abualigah, L., Al Hamad, H., Alabool, H., Alshinwan, M., Khasawneh, A.M.: Moth–flame optimization algorithm: variants and applications. Neural Comput. Appl. 32(14), 9859–9884 (2019). https://doi.org/10.1007/s00521-019-04570-6
Li, Y., Zhu, X., Liu, J.: An improved moth-flame optimization algorithm for engineering problems. Open Access Article 2020, China, July 2020. https://doi.org/10.3390/sym12081234
Tan, Z., Zeng, M., Sun, L.: Optimal placement and sizing of distributed generators based on swarm moth flame optimization, April 2021. https://doi.org/10.3389/fenrg.2021.676305
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zahia, D., Belkacem, M. (2022). Optimal Power Flow Management of the Algerian Electric Transmission System Using Moth Flame Optimizer Algorithm. In: Hatti, M. (eds) Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities. IC-AIRES 2021. Lecture Notes in Networks and Systems, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-030-92038-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-92038-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92037-1
Online ISBN: 978-3-030-92038-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)