Top

Neural Computing and Applications

Published in:

22-06-2019 | Original Article

Solution of the optimal power flow problem considering security constraints using an improved chaotic electromagnetic field optimization algorithm

Author: Houssem Bouchekara

Published in: Neural Computing and Applications | Issue 7/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The main objective of this paper is to solve different configurations of the optimal power flow (OPF) problem efficiently using an improved version of the newly proposed electromagnetic field optimization (EFO) algorithm. The developed and improved new version of EFO is based on chaotic maps and on a new mechanism. This improved version is called improved chaotic electromagnetic field optimization (ICEFO) algorithm. The performances of the ICEFO algorithm are evaluated on a large set of cases using: tow formulations, three objective functions (cost minimization, cost minimization and voltage profile improvement and cost minimization and voltage stability enhancement) and three test systems (the IEEE 30-bus, the IEEE 57-bus and the IEEE 118-bus test systems). The obtained results of the developed algorithm are compared with other well-known algorithms. These results demonstrate that the developed algorithm is able to solve efficiently different configurations of the OPF problem and for different test systems.

previous article Estimation of missing prices in real-estate market agent-based simulations with machine learning and dimensionality reduction methods

next article Compressive sensing MRI reconstruction using empirical wavelet transform and grey wolf optimizer

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Roy PK, Ghoshal SP, Thakur SS (2010) Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function. Expert Syst Appl 37:8221–8228. https://doi.org/10.1016/j.eswa.2010.05.064 CrossRef

Ghasemi M, Ghavidel S, Ghanbarian MM, Gharibzadeh M, Azizi Vahed A (2014) Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm. Energy 78:276–289. https://doi.org/10.1016/j.energy.2014.10.007 CrossRef

Hazra J, Sinha AK (2010) A multi-objective optimal power flow using particle swarm optimization. https://doi.org/10.1002/etep

Fonoberova M (2010) Handbook of power systems II, networks. https://doi.org/10.1007/978-3-642-12686-4

Niu M, Wan C, Xu Z (2014) A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems. J Mod Power Syst Clean Energy 2:289–297. https://doi.org/10.1007/s40565-014-0089-4 CrossRef

Frank S, Steponavice I (2012) Optimal power flow: a bibliographic survey I. Formulations and deterministic methods. Energy Syst 3:221–258. https://doi.org/10.1007/s12667-012-0056-y CrossRef

Frank S, Steponavice I, Rebennack S (2012) Optimal power flow: a bibliographic survey II. Non-deterministic and hybrid methods. Energy Syst 3:259–289. https://doi.org/10.1007/s12667-012-0057-x CrossRef

AlRashidi MR, El-Hawary ME (2009) Applications of computational intelligence techniques for solving the revived optimal power flow problem. Electr Power Syst Res 79:694–702. https://doi.org/10.1016/j.epsr.2008.10.004 CrossRef

Yuryevich J (1999) Evolutionary programming based optimal power flow algorithm. IEEE Trans Power Syst 14:1245–1250. https://doi.org/10.1109/59.801880 CrossRef

10.

Bakirtzis AG, Biskas PN, Zoumas CE, Petridis V (2002) Optimal power flow by enhanced genetic algorithm. IEEE Trans Power Syst 17:229–236. https://doi.org/10.1109/TPWRS.2002.1007886 CrossRef

11.

Lai LL, Ma JT, Yokoyama R, Zhao M (1997) Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Int J Electr Power Energy Syst 19:287–292. https://doi.org/10.1016/S0142-0615(96)00051-8 CrossRef

12.

Paranjothi SR, Anburaja K (2002) Optimal power flow using refined genetic algorithm. Electr Power Compon Syst 30:1055–1063. https://doi.org/10.1080/15325000290085343 CrossRef

13.

Younes M, Rahli M, Abdelhakem-Koridak L (2007) Optimal power flow based on hybrid genetic algorithm. J Inf Sci Eng 23:1801–1816

14.

Todorovski M, Rajičić D (2006) An initialization procedure in solving optimal power flow by genetic algorithm. IEEE Trans Power Syst 21:480–487. https://doi.org/10.1109/TPWRS.2006.873120 CrossRef

15.

Bouchekara HREH, Chaib AE, Abido MA (2016) Optimal power flow using GA with a new multi-parent crossover considering: prohibited zones, valve-point effect, multi-fuels and emission. Electr Eng. https://doi.org/10.1007/s00202-016-0488-9 CrossRef

16.

Abido MA (2002) Optimal power flow using particle swarm optimization. Int J Electr Power Energy Syst 24:563–571. https://doi.org/10.1016/S0142-0615(01)00067-9 CrossRef

17.

Hinojosa VH, Araya R (2013) Modeling a mixed-integer-binary small-population evolutionary particle swarm algorithm for solving the optimal power flow problem in electric power systems. Appl Soft Comput 13:3839–3852. https://doi.org/10.1016/j.asoc.2013.05.005 CrossRef

18.

Kang Q, Zhou M, Xu C (2012) Solving optimal power flow problems subject to distributed generator failures via particle swarm intelligence. In: Adv. Mechatron. Syst. (ICAMechS), 2012 Int. Conf., pp 418–423

19.

Umapathy P, Venkataseshaiah C, Arumugam MS (2010) Particle swarm optimization with various inertia weight variants for optimal power flow solution. Discrete Dyn Nat Soc. https://doi.org/10.1155/2010/462145 MathSciNetCrossRefMATH

20.

Liang R-H, Tsai S-R, Chen Y-T, Tseng W-T (2011) Optimal power flow by a fuzzy based hybrid particle swarm optimization approach. Electr Power Syst Res 81:1466–1474. https://doi.org/10.1016/j.epsr.2011.02.011 CrossRef

21.

Tehzeeb-Ul-Hassan H, Zafar R, Mohsin SA, Lateef O (2012) Reduction in power transmission loss using fully informed particle swarm optimization. Int J Electr Power Energy Syst 43:364–368. https://doi.org/10.1016/j.ijepes.2012.05.028 CrossRef

22.

Abido MA (2002) Optimal power flow using tabu search algorithm. Electr Power Compon Syst 30:469–483. https://doi.org/10.1080/15325000252888425 CrossRef

23.

Roa-Sepulveda CA, Pavez-Lazo BJ (2003) A solution to the optimal power flow using simulated annealing. Int J Electr Power Energy Syst 25:47–57. https://doi.org/10.1016/S0142-0615(02)00020-0 CrossRef

24.

Abou El Ela AA, Abido MA, Spea SR (2010) Optimal power flow using differential evolution algorithm. Electr Power Syst Res 80:878–885. https://doi.org/10.1016/j.epsr.2009.12.018 CrossRef

25.

Chung CY, Liang CH, Wong KP, Duan XZ (2010) Hybrid algorithm of differential evolution and evolutionary programming for optimal reactive power flow. IET Gener Transm Distrib 4(1):84–93. https://doi.org/10.1049/iet-gtd.2009.0007 CrossRef

26.

Sivasubramani S, Swarup KS (2011) Sequential quadratic programming based differential evolution algorithm for optimal power flow problem. IET Gener Transm Distrib 5:1149. https://doi.org/10.1049/iet-gtd.2011.0046 CrossRef

27.

Sayah S, Zehar K (2008) Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers Manag 49:3036–3042. https://doi.org/10.1016/j.enconman.2008.06.014 CrossRef

28.

Slimani L, Bouktir T (2012) Optimal power flow solution of the Algerian electrical network using differential evolution algorithm. TELKOMNIKA 10:199–210CrossRef

29.

Amjady N, Sharifzadeh H (2011) Security constrained optimal power flow considering detailed generator model by a new robust differential evolution algorithm. Electr Power Syst Res 81:740–749. https://doi.org/10.1016/j.epsr.2010.11.005 CrossRef

30.

Bhattacharya A, Chattopadhyay PK (2011) Application of biogeography-based optimisation to solve different optimal power flow problems. IET Gener Transm Distrib 5:70. https://doi.org/10.1049/iet-gtd.2010.0237 CrossRef

31.

Ghanizadeh AJ, Mokhtari G, Abedi M, Gharehpetian GB (2011) Optimal power flow based on imperialist competitive algorithm. Int Rev Electr Eng 6:1847–1852

32.

Bouchekara HREH, Abido MA, Chaib AE, Mehasni R (2014) Optimal power flow using the league championship algorithm: a case study of the Algerian power system. Energy Convers Manag 87:58–70. https://doi.org/10.1016/j.enconman.2014.06.088 CrossRef

33.

Bouchekara HREH (2014) Optimal power flow using black-hole-based optimization approach. Appl Soft Comput J 24:879–888. https://doi.org/10.1016/j.asoc.2014.08.056 CrossRef

34.

Bouchekara HREH, Abido MA, Boucherma M (2014) Optimal power flow using teaching–learning-based optimization technique. Electr Power Syst Res 114:49–59. https://doi.org/10.1016/j.epsr.2014.03.032 CrossRef

35.

Ghasemi M, Ghavidel S, Gitizadeh M, Akbari E (2015) An improved teaching-learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow. Int J Electr Power Energy Syst 65:375–384. https://doi.org/10.1016/j.ijepes.2014.10.027 CrossRef

36.

Bouchekara HRE-H, Abido MA (2014) Optimal power flow using differential search algorithm. Electr Power Compon Syst 42:1683–1699. https://doi.org/10.1080/15325008.2014.949912 CrossRef

37.

Bouchekara HREH, Chaib AE, Abido MA, El-Sehiemy RA (2016) Optimal power flow using an improved colliding bodies optimization algorithm. Appl Soft Comput J 42:119–131. https://doi.org/10.1016/j.asoc.2016.01.041 CrossRef

38.

Bouchekara HREH, Abido MA, Chaib AE (2016) Optimal power flow using an improved electromagnetism-like mechanism method. Electr Power Compon Syst 44:434–449. https://doi.org/10.1080/15325008.2015.1115919 CrossRef

39.

Chaib AE, Bouchekara HREH, Mehasni R, Abido MA (2016) Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm. Int J Electr Power Energy Syst 81:64–77. https://doi.org/10.1016/j.ijepes.2016.02.004 CrossRef

40.

Mohamed A-AA, Mohamed YS, El-Gaafary AAM, Hemeida AM (2017) Optimal power flow using moth swarm algorithm. Electr Power Syst Res 142:190–206. https://doi.org/10.1016/j.epsr.2016.09.025 CrossRef

41.

Daryani N, Hagh MT, Teimourzadeh S (2016) Adaptive group search optimization algorithm for multi-objective optimal power flow problem. Appl Soft Comput 38:1012–1024. https://doi.org/10.1016/j.asoc.2015.10.057 CrossRef

42.

Mahdad B, Srairi K (2016) Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm. Appl Soft Comput 46:501–522. https://doi.org/10.1016/j.asoc.2016.05.027 CrossRef

43.

Panda A, Tripathy M (2015) Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm. Energy 93:816–827. https://doi.org/10.1016/j.energy.2015.09.083 CrossRef

44.

Warid W, Hizam H, Mariun N, Abdul-Wahab N (2016) Optimal power flow using the Jaya algorithm. Energies 9:678. https://doi.org/10.3390/en9090678 CrossRef

45.

Berrouk F, Chaib AE, Abido MA, Bounaya K, Javaid MS (2018) A new multi-objective Jaya algorithm for solving the optimal power flow problem. J Electr Syst 14(3):165–181

46.

Kaveh A (2014) Advances in metaheuristic algorithms for optimal design of structures. https://doi.org/10.1007/978-3-319-05549-7

47.

Mukherjee A, Mukherjee V (2015) Solution of optimal power flow using chaotic krill herd algorithm. Chaos Solitons Fractals 78:10–21. https://doi.org/10.1016/j.ijepes.2015.12.001 MathSciNetCrossRef

48.

Yuan X, Wang P, Yuan Y, Huang Y, Zhang X (2015) A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem. Energy Convers Manag 100:1–9. https://doi.org/10.1016/j.enconman.2015.04.051 CrossRef

49.

Polprasert J, Ongsakul W (2014) Chaotic based PSO with time-varying acceleration coefficients for security constrained optimal power flow problem, pp 19–21

50.

Ayan K, Kılıç U, Baraklı B (2015) Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow. Int J Electr Power Energy Syst 64:136–147. https://doi.org/10.1016/j.ijepes.2014.07.018 CrossRef

51.

Ghasemi M, Ghavidel S, Akbari E, Vahed AA (2014) Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos. Energy 73:340–353. https://doi.org/10.1016/j.energy.2014.06.026 CrossRef

52.

Arul R, Ravi G, Velusami S (2013) Solving optimal power flow problems using chaotic self-adaptive differential harmony search algorithm. Electr Power Compon Syst 41:782–805. https://doi.org/10.1080/15325008.2013.769033 CrossRef

53.

Abedinpourshotorban H, Mariyam Shamsuddin S, Beheshti Z, Jawawi DNA (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8–22. https://doi.org/10.1016/j.swevo.2015.07.002 CrossRef

54.

Kessel P, Glavitsch H (1986) Estimating the voltage stability of a power system. IEEE Trans Power Deliv 1:346–354. https://doi.org/10.1109/TPWRD.1986.4308013 CrossRef

55.

Stehlík M, Dušek J, Kiseľák J (2016) Missing chaos in global climate change data interpreting? Ecol Complex 25:53–59. https://doi.org/10.1016/j.ecocom.2015.12.003 CrossRef

56.

Lu H, Wang X, Fei Z, Qiu M (2014) The effects of using chaotic map on improving the performance of multiobjective evolutionary algorithms. Math Probl Eng. https://doi.org/10.1155/2014/924652 MathSciNetCrossRefMATH

57.

Naanaa A (2015) Fast chaotic optimization algorithm based on spatiotemporal maps for global optimization. Appl Math Comput 269:402–411. https://doi.org/10.1016/j.amc.2015.07.111 MathSciNetCrossRefMATH

58.

Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18:89–98. https://doi.org/10.1016/j.cnsns.2012.06.009 MathSciNetCrossRefMATH

59.

OPF PROBLEMS https://www.uni-due.de/ieee-wgmho/competition2014 (n.d.)

60.

Zimmerman RD, Murillo-Sánchez CE (2016) MATPOWER. http://www.pserc.cornell.edu/matpower/#docs

Title: Solution of the optimal power flow problem considering security constraints using an improved chaotic electromagnetic field optimization algorithm
Author: Houssem Bouchekara
Publication date: 22-06-2019
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 7/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04298-3

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 7/2020

Single-column CNN for crowd counting with pixel-wise attention mechanism

Auto-MeDiSine: an auto-tunable medical decision support engine using an automated class outlier detection method and AutoMLP

Robust nonlinear fractional order fuzzy PD plus fuzzy I controller applied to robotic manipulator

DC–DC converters design using a type-2 wavelet fuzzy cerebellar model articulation controller

Applications of deep convolutional neural networks in prospecting prediction based on two-dimensional geological big data

Genetic algorithm combined with BP neural network in hospital drug inventory management system

Premium Partner