Skip to main content
SpringerLink
Log in

Hybrid Approach with Combining Cuckoo-Search and Grey-Wolf Optimizer for Solving Optimal Power Flow Problems

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

An electrical power system is a vast interlocked network that needs a vigilant design to sustain the system with an uninterrupted power flow operation without any restrictions, called optimal power flow (OPF). OPF problem requires robust and fast optimization techniques to volve it due to its complexity. Cuckoo Search (CS) is one method that is being applied in OPF problems, and it has many advantages, e.g., ease use and littler tuning parameters. But it is not good enough, falling into local optimal resolutions and slow converges. Therefore, recently developed Grey Wolf Optimization (GWO) algorithm is used to solve OPF, but it has low accuracy and inadequate local searching ability. To overcome these problems, this paper proposed to combine CS with GWO to create a novel the Hybrid algorithm, called here HCSGWO. The main objective is to deduce the emission, true power generation cost, true power losses, and voltage stability, being a multi-objective problem. THCSGWO are validated by solving the OPF problem considering the calssic IEEE57 bus system. The results are compared with GWO and other algorithms employed in the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Abbreviations

ABC:

Artificial bee colony algorithm

AI:

Artificial intelligence

ANN:

Artificial neural networks

BSA:

Backtracking search algorithm

GA:

Genetic algorithms

GWO:

Grey wolf optimization

HCSGWO:

Hybrid cuckoo search and grey wolf optimizer

IEEE:

Institute of electrical and electronics engineers

OPF:

Optimal power flow optimal scheduling

PSO:

Particle swarm optimization

F1:

Total fuel cost function

F2:

Emission of the gases function

F3:

Power loss function

F4:

L-index function

W1, W2, W3:

Weight factors

X:

Site vector of the wolf

T:

Number of iterations

Xp:

Site vector of prey

r1 and r2:

Control restrictions of GWO

k:

Scale value

Pa:

Possibility of nest reconstruction

α, β, γ:

Cost coefficients

a,b,c,d,e:

Emission coefficients

PTG2, PTG3, PTG6, PTG8, PTG9, PTG12 :

Thermal real power generation

VTG1, VTG2, VTG3, VTG6, VTG8, VTG9, VTG12 :

Voltages at PV buses

QC18, QC25, QC53 :

Shunt capacitors

T19, T20, T31, T35, T36, T37, T41, T46, T54, T58, T59, T65, T66, T71, T73, T76, T80 :

Tap changing transformers

References

  1. Ding C, Yan W, Ren Z, Zhao R, Lee W, Tang X (2019) Continuation power flow model for interconnected systems considering the electricity market influence and its corresponding distributed algorithm. IEEE Access 7:75910–75924. https://doi.org/10.1109/ACCESS.2019.2922173

    Article  Google Scholar 

  2. Meyer-Huebner N, Suriyah M, Leibfried T (2019) Distributed optimal power flow in hybrid AC–DC grids. IEEE Trans Power Syst 34(4):2937–2946. https://doi.org/10.1109/TPWRS.2019.2892240

    Article  Google Scholar 

  3. Xue A et al (2020) A new quantitative analysis method for overvoltage in sending end electric power system with UHVDC. IEEE Access 8:145898–145908. https://doi.org/10.1109/ACCESS.2020.3015267

    Article  Google Scholar 

  4. Chen G, Qian J, Zhang Z, Sun Z (2019) Multi-objective optimal power flow based on hybrid firefly-bat algorithm and constraints-prior object-fuzzy sorting strategy. IEEE Access 7:139726–139745. https://doi.org/10.1109/ACCESS.2019.2943480

    Article  Google Scholar 

  5. Birogul S (2019) Hybrid Harris Hawk optimization based on differential evolution (HHODE) algorithm for optimal power flow problem. IEEE Access 7:184468–184488. https://doi.org/10.1109/ACCESS.2019.2958279

    Article  Google Scholar 

  6. Suvorov AA et al (2020) Comprehensive validation of transient stability calculations in electric power systems and hardware-software tool for its implementation. IEEE Access 8:136071–136091. https://doi.org/10.1109/ACCESS.2020.3011207

    Article  Google Scholar 

  7. Khokhlov V, Meyer J, Grevener A, Busatto T, Rönnberg S (2020) Comparison of measurement methods for the frequency range 2–150 kHz (Supraharmonics) based on the present standards framework. IEEE Access 8:77618–77630. https://doi.org/10.1109/ACCESS.2020.2987996

    Article  Google Scholar 

  8. Luo J, Shi L, Ni Y (2018) A solution of optimal power flow incorporating wind generation and power grid uncertainties. IEEE Access 6:19681–19690. https://doi.org/10.1109/ACCESS.2018.2823982

    Article  Google Scholar 

  9. Khan IU, Javaid N, Gamage KAA, Taylor CJ, Baig S, Ma X (2020) Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources. IEEE Access 8:148622–148643. https://doi.org/10.1109/ACCESS.2020.3015473

    Article  Google Scholar 

  10. Ullah Z, Wang S, Radosavljević J, Lai J (2019) A solution to the optimal power flow problem considering WT and PV generation. IEEE Access 7:46763–46772. https://doi.org/10.1109/ACCESS.2019.2909561

    Article  Google Scholar 

  11. Mugemanyi S, Qu Z, Rugema FX, Dong Y, Bananeza C, Wang L (2020) Optimal reactive power dispatch using chaotic bat algorithm. IEEE Access 8:65830–65867. https://doi.org/10.1109/ACCESS.2020.2982988

    Article  Google Scholar 

  12. Oyewole PA, Jayaweera D (2020) Power system security with cyber-physical power system operation. IEEE Access 8:179970–179982. https://doi.org/10.1109/ACCESS.2020.3028222

    Article  Google Scholar 

  13. Chen G, Qian J, Zhang Z, Sun Z (2019) Applications of novel hybrid bat algorithm with constrained pareto fuzzy dominant rule on multi-objective optimal power flow problems. IEEE Access 7:52060–52084. https://doi.org/10.1109/ACCESS.2019.2912643

    Article  Google Scholar 

  14. Khan NH, Wang Y, Tian D, Raja MAZ, Jamal R, Muhammad Y (2020) Design of fractional particle swarm optimization gravitational search algorithm for optimal reactive power dispatch problems. IEEE Access 8:146785–146806. https://doi.org/10.1109/ACCESS.2020.3014211

    Article  Google Scholar 

  15. Ilyas MA, Abbas G, Alquthami T, Awais M, Rasheed MB (2020) Multi-objective optimal power flow with integration of renewable energy sources using fuzzy membership function. IEEE Access 8:143185–143200. https://doi.org/10.1109/ACCESS.2020.3014046

    Article  Google Scholar 

  16. Elattar EE (2019) Optimal power flow of a power system incorporating stochastic wind power based on modified moth swarm algorithm. IEEE Access 7:89581–89593. https://doi.org/10.1109/ACCESS.2019.2927193

    Article  Google Scholar 

  17. Ebeed M, Kamel S, Jurado F (2018) Chapter 7—Optimal power flow using recent optimization techniques. In: Zobaa AF, Abdel Aleem SHE, Abdelaziz AY (eds) Classical and recent aspects of power system optimization. Academic Press, New York, pp. 157–183. https://doi.org/10.1016/B978-0-12-812441-3.00007-0.

  18. Shaheen MAM, Hasanien HM, Mekhamer SF, Talaat HEA (2019) Optimal power flow of power systems including distributed generation units using sunflower optimization algorithm. IEEE Access 7:109289–109300. https://doi.org/10.1109/ACCESS.2019.2933489

    Article  Google Scholar 

  19. Jamal R, Men B, Khan NH (2020) A novel nature inspired meta-heuristic optimization approach of GWO optimizer for optimal reactive power dispatch problems. IEEE Access 8:202596–202610. https://doi.org/10.1109/ACCESS.2020.3031640

    Article  Google Scholar 

  20. Alhejji A, Ebeed Hussein M, Kamel S, Alyami S (2020) Optimal power flow solution with an embedded center-node unified power flow controller using an adaptive grasshopper optimization algorithm. IEEE Access 8:119020–119037. https://doi.org/10.1109/ACCESS.2020.2993762.

  21. 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Ramesh Kumar A, Premalatha L (2015) Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization. Int J Electrical Power Energy Syst 73:393–399. https://doi.org/10.1016/j.ijepes.2015.05.011.

  24. Lakshmi P, Rao BV, Devarapalli R, Rai P (2020) Optimal power flow with BAT algorithm for a power system to reduce transmission line losses using SVC. In: 2020 International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), Jul. 2020, pp. 1–5. https://doi.org/10.1109/ICEFEET49149.2020.9186964.

  25. Pravallika DL, Rao BV (2016) Flower pollination algorithm based optimal setting of TCSC to minimize the transmission line losses in the power system. Proc Comp Sci 92:30–35. https://doi.org/10.1016/j.procs.2016.07.319

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Khorsandi A, Hosseinian SH, Ghazanfari A (2013) Modified artificial bee colony algorithm based on fuzzy multi-objective technique for optimal power flow problem. Electric Power Syst Res 95:206–213. https://doi.org/10.1016/j.epsr.2012.09.002

    Article  Google Scholar 

  29. Swarnkar Kk (2012) Economic load dispatch problem with reduce power losses using firefly algorithm. J Adv Comp Sci Technol 1(2):42–56. https://doi.org/10.14419/jacst.v1i2.21.

  30. Taher MA, Kamel S, Jurado F, Mohamed Ebeed (2019) Modified grasshopper optimization framework for optimal power flow solution. Electrical Eng 101:1–11.

  31. Abdo, Kamel S, Mohamed Ebeed, Yu J, Jurado F (1692) Solving non-smooth optimal power flow problems using a developed grey wolf optimizer. Energies 11(7):1692.

  32. Taher MA et al (2019) An improved moth‐flame optimization algorithm for solving optimal power flow problem. Int Trans Electrical Energy Syst 29(3):e2743.

  33. Venkateswara Rao B, Devarapalli R, Malik H, Bali SK, Márquez FPG, Chiranjeevi T (2021) Wind integrated power system to reduce emission: an application of Bat algorithm. J Intell Fuzzy Syst, pp. 1–9. https://doi.org/10.3233/JIFS-189770.

  34. Devarapalli R, Venkateswara Rao B, Dey B, Vinod Kumar K, Malik H, Márquez FPG (2021) An approach to solve OPF problems using—novel hybrid whale and sine cosine optimization algorithm. J Intell Fuzzy Syst, pp. 1–11. https://doi.org/10.3233/JIFS-189763.

  35. Zhao X et al (2019) Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Comput Biol Chem 78:481–490. https://doi.org/10.1016/j.compbiolchem.2018.11.017

    Article  Google Scholar 

  36. Zhao D et al (2021) Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy. Knowl-Based Syst 216:106510. https://doi.org/10.1016/j.knosys.2020.106510

    Article  Google Scholar 

  37. Shan W, Qiao Z, Heidari AA, Chen H, Turabieh H, Teng Y (2021) Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis. Knowl-Based Syst 214:106728. https://doi.org/10.1016/j.knosys.2020.106728

    Article  Google Scholar 

  38. Tu J et al (2021) Evolutionary biogeography-based whale optimization methods with communication structure: towards measuring the balance. Knowl-Based Syst 212:106642. https://doi.org/10.1016/j.knosys.2020.106642

    Article  Google Scholar 

  39. Camacho Villalón CL, Stützle T, Dorigo M (2020) Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any Novelty. In: Dorigo M, Stützle T, Blesa MJ, Blum J, Hamann H, Heinrich MK, Strobel V (eds) Swarm Intelligence, vol. 12421. Springer International Publishing, Cham, pp. 121–133. https://doi.org/10.1007/978-3-030-60376-2_10.

  40. Dorigo M et al (eds) (2020) Swarm Intelligence: 12th International Conference, ANTS 2020, Barcelona, Spain, October 26–28, 2020; Proceedings, vol. 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2.

  41. Hu J et al (2021) Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection. Knowl-Based Syst 213:106684. https://doi.org/10.1016/j.knosys.2020.106684

    Article  Google Scholar 

  42. Daniel E, Anitha J, Gnanaraj J (2017) Optimum laplacian wavelet mask based medical image using hybrid cuckoo search—grey wolf optimization algorithm. Knowl-Based Syst 131:58–69. https://doi.org/10.1016/j.knosys.2017.05.017

    Article  Google Scholar 

  43. Abhishek G (2021) Hybrid Grey Wolf and Cuckoo Search Optimization Algorithm. https://in.mathworks.com/matlabcentral/fileexchange/69392-hybrid-grey-wolf-and-cuckoo-search-optimization-algorithm. Accessed April 07, 2021.

  44. Biswas PP, Suganthan PN, Mallipeddi R, Amaratunga GAJ (2018) Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Eng Appl Artif Intell 68:81–100. https://doi.org/10.1016/j.engappai.2017.10.019

    Article  Google Scholar 

  45. Vaisakh K, Srinivas LR (2011) Evolving ant direction differential evolution for OPF with non-smooth cost functions. Eng Appl Artif Intell 24(3):426–436. https://doi.org/10.1016/j.engappai.2010.10.019

    Article  Google Scholar 

  46. Duman S, Güvenç U, Sönmez Y, Yörükeren N (2012) Optimal power flow using gravitational search algorithm. Energy Convers Manage 59:86–95. https://doi.org/10.1016/j.enconman.2012.02.024

    Article  Google Scholar 

  47. Abaci K, Yamacli V (2016) Differential search algorithm for solving multi-objective optimal power flow problem. Int J Electr Power Energy Syst 79:1–10. https://doi.org/10.1016/j.ijepes.2015.12.021

    Article  Google Scholar 

  48. Rezaei Adaryani M, Karami A (2013) Artificial bee colony algorithm for solving multi-objective optimal power flow problem. Int J Electrical Power Energy Syst 53:219–230. https://doi.org/10.1016/j.ijepes.2013.04.021.

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

    Article  Google Scholar 

  50. Pulluri H, Naresh R, Sharma V (2017) An enhanced self-adaptive differential evolution based solution methodology for multi-objective optimal power flow. Appl Soft Comput 54:229–245. https://doi.org/10.1016/j.asoc.2017.01.030

    Article  Google Scholar 

  51. Ghasemi M, Ghavidel S, Ghanbarian MM, Gitizadeh M (2015) Multi-objective optimal electric power planning in the power system using Gaussian bare-bones imperialist competitive algorithm. Inf Sci 294:286–304. https://doi.org/10.1016/j.ins.2014.09.051

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The work reported herein was supported financially by the Ministerio de Ciencia e Innovación (Spain) and the European Regional Development Fund, under Research Grant WindSound project (Ref.: PID2021-125278OB-I00).

Author information

Authors and Affiliations

  1. Department of EEE, V R Siddhartha Engineering College, Kanuru, 520007, Andhra Pradesh, India

    Venkateswararao Bathina

  2. Department of EEE, Lendi Institute of Engineering and Technology, Denkada, 535003, Andhra Pradesh, India

    Ramesh Devarapalli

  3. Ingenium Research Group, Department, Correspondence Mail:, Universidad Castilla-La Mancha, 13071, Ciudad Real, Spain

    Fausto Pedro García Márquez

Authors
  1. Venkateswararao Bathina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramesh Devarapalli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fausto Pedro García Márquez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fausto Pedro García Márquez.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bathina, V., Devarapalli, R. & García Márquez, F. Hybrid Approach with Combining Cuckoo-Search and Grey-Wolf Optimizer for Solving Optimal Power Flow Problems. J. Electr. Eng. Technol. 18, 1637–1653 (2023). https://doi.org/10.1007/s42835-022-01301-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-022-01301-1

Keywords

Search

Navigation