Log in

A four-stage strategy for solving AC transmission expansion planning problem in large power system based on differential evolution algorithm and teaching–learning-based optimization algorithm

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

AC transmission expansion planning (ACTEP) is one of the most critical issues in electric power system expansion planning. In existing research on ACTEP, the reduction of power losses is often overlooked due to the significant computational workload associated with ACTEP problem. While in some instances, minimizing power loss is included as an objective function in the TEP problem, this approach may impact the addition of new lines. Consequently, to address this issue, a four-stage strategy is proposed in this paper for resolving ACTEP problem while considering power loss reduction. Specifically, the reduction of power losses is addressed after the deployment of new transmission lines. Moreover, a hybrid approach, referred to as differential evolution (DE) combined with teaching–learning-based optimization (TLBO) algorithms, called (DE-TLBO), is proposed for optimizing reactive power planning and determining the size of thyristor-controlled series compensators (TCSC) to minimize power loss in ACTEP problem. Simulation results conducted on Graver 6 bus, IEEE 24 bus, and modified IEEE 118 bus systems demonstrate the efficacy of the proposed algorithm when compared to conventional methods such as differential evolution (DE), modified artificial bee colony, and real genetic algorithms (RGA). Additionally, the proposed method also illustrates the effectiveness of utilizing TCSC to reduce power loss in the Graver 6 bus and the IEEE 24 bus systems by 3.72% and 11.95%, respectively.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Algorithm 1
Algorithm 2
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. de Araujo RA, Torres SP, Filho JP, Castro CA, Van Hertem D (2023) Unified AC transmission expansion planning formulation incorporating VSC-MTDC, FACTS devices, and reactive power compensation. Electr Power Syst Res 216:109017. https://doi.org/10.1016/j.epsr.2022.109017

    Article  Google Scholar 

  2. Garver L (1970) Transmission network estimation using linear programming. IEEE Trans Power Appar Syst PAS-89(7):1688–1697. https://doi.org/10.1109/TPAS.1970.292825

    Article  Google Scholar 

  3. Vilaça Gomes P, Saraiva JT, Carvalho L, Dias B, Oliveira LW (2019) Impact of decision-making models in transmission expansion planning considering large shares of renewable energy sources. Electr Power Syst Res 174:105852. https://doi.org/10.1016/j.epsr.2019.04.030

    Article  Google Scholar 

  4. De Oliveira EJ, Moraes CA, Oliveira LW, Honório LM, Poubel RPB (2018) Efficient hybrid algorithm for transmission expansion planning. Electr Eng 100(4):2765–2777. https://doi.org/10.1007/s00202-018-0744-2

    Article  Google Scholar 

  5. Assis FA, Silva IS, da Silva AML, Resende LC (2021) Transmission planning with security criteria via enhanced genetic algorithm. Electr Eng 103(4):1977–1987. https://doi.org/10.1007/s00202-020-01208-y

    Article  Google Scholar 

  6. de Paula AN, de Oliveira EJ, Oliveira LW, Moraes CA (2020) Reliability-constrained dynamic transmission expansion planning considering wind power generation. Electr Eng 102(4):2583–2593. https://doi.org/10.1007/s00202-020-01054-y

    Article  Google Scholar 

  7. C. Rathore, R. Roy, S. Raj, and A. K. Sinha (2013) Mosquitoes-behaviour based (MOX) evolutionary algorithm in static transmission network expansion planning. In: 2013 International Conference on Energy Efficient Technologies for Sustainability, IEEE, pp 1006–1011. https://doi.org/10.1109/ICEETS.2013.6533524.

  8. El-bages MS, Elsayed WT (2017) Social spider algorithm for solving the transmission expansion planning problem. Electr Power Syst Res 143:235–243. https://doi.org/10.1016/j.epsr.2016.09.002

    Article  Google Scholar 

  9. Verma S, Mukherjee V (2018) Investigation of static transmission expansion planning using the symbiotic organisms search algorithm. Eng Optim 50(9):1544–1560. https://doi.org/10.1080/0305215X.2017.1408085

    Article  Google Scholar 

  10. Rider MJ, Garcia AV, Romero R (2007) Power system transmission network expansion planning using AC model. IET Gener Transm Distrib 1(5):731. https://doi.org/10.1049/iet-gtd:20060465

    Article  Google Scholar 

  11. Rahmani M, Rashidinejad M, Carreno EM, Romero R (2010) Efficient method for AC transmission network expansion planning. Electr Power Syst Res 80(9):1056–1064. https://doi.org/10.1016/j.epsr.2010.01.012

    Article  Google Scholar 

  12. Hooshmand R-A, Hemmati R, Parastegari M (2012) Combination of AC transmission expansion planning and reactive power planning in the restructured power system. Energy Convers Manag 55:26–35. https://doi.org/10.1016/j.enconman.2011.10.020

    Article  Google Scholar 

  13. Jabr RA (2013) Optimization of AC transmission system planning. IEEE Trans Power Syst 28(3):2779–2787. https://doi.org/10.1109/TPWRS.2012.2228507

    Article  Google Scholar 

  14. Taylor JA, Hover FS (2013) Conic AC transmission system planning. IEEE Trans Power Syst 28(2):952–959. https://doi.org/10.1109/TPWRS.2012.2214490

    Article  Google Scholar 

  15. Mahmoudabadi A, Rashidinejad M (2013) An application of hybrid heuristic method to solve concurrent transmission network expansion and reactive power planning. Int J Electr Power Energy Syst 45(1):71–77. https://doi.org/10.1016/j.ijepes.2012.08.074

    Article  Google Scholar 

  16. Zhang H, Heydt GT, Vittal V, Quintero J (2013) an improved network model for transmission expansion planning considering reactive power and network losses. IEEE Trans Power Syst 28(3):3471–3479. https://doi.org/10.1109/TPWRS.2013.2250318

    Article  Google Scholar 

  17. Torres SP, Castro CA (2014) Expansion planning for smart transmission grids using AC model and shunt compensation. IET Gener Transm Distrib 8(5):966–975. https://doi.org/10.1049/iet-gtd.2013.0231

    Article  Google Scholar 

  18. Das S, Verma A, Bijwe PR (2019) Security constrained AC transmission network expansion planning. Electr Power Syst Res 172:277–289. https://doi.org/10.1016/j.epsr.2019.03.020

    Article  Google Scholar 

  19. Asadamongkol S, Eua-arporn B (2013) Transmission expansion planning with AC model based on generalized benders decomposition. Int J Electr Power Energy Syst 47:402–407. https://doi.org/10.1016/j.ijepes.2012.11.008

    Article  Google Scholar 

  20. Mouwafi MT, El-Ela AAA, El-Sehiemy RA, Al-Zahar WK (2022) Techno-economic based static and dynamic transmission network expansion planning using improved binary bat algorithm. Alex Eng J 61(2):1383–1401. https://doi.org/10.1016/j.aej.2021.06.021

    Article  Google Scholar 

  21. Mahdavi M, Kimiyaghalam A, Alhelou HH, Javadi MS, Ashouri A, Catalao JPS (2021) Transmission expansion planning considering power losses, expansion of substations and uncertainty in fuel price using discrete artificial bee colony algorithm. IEEE Access 9:135983–135995. https://doi.org/10.1109/ACCESS.2021.3116802

    Article  Google Scholar 

  22. Pegado R, Ñaupari Z, Molina Y, Castillo C (2019) Radial distribution network reconfiguration for power losses reduction based on improved selective BPSO. Electr Power Syst Res 169:206–213. https://doi.org/10.1016/j.epsr.2018.12.030

    Article  Google Scholar 

  23. Gbadamosi SL, Nwulu NI (2021) A comparative analysis of generation and transmission expansion planning models for power loss minimization. Sustain Energy, Grids Netw 26:100456. https://doi.org/10.1016/j.segan.2021.100456

    Article  Google Scholar 

  24. Sharma A, Jain SK (2019) Gravitational search assisted algorithm for TCSC placement for congestion control in deregulated power system. Electr Power Syst Res 174:105874. https://doi.org/10.1016/j.epsr.2019.105874

    Article  Google Scholar 

  25. Ziaee O, Choobineh F (2017) Optimal location-allocation of TCSCs and transmission switch placement under high penetration of wind power. IEEE Trans Power Syst 32(4):3006–3014. https://doi.org/10.1109/TPWRS.2016.2628053

    Article  Google Scholar 

  26. Ziaee O, Alizadeh-Mousavi O, Choobineh FF (2018) Co-optimization of transmission expansion planning and TCSC placement considering the correlation between wind and demand scenarios. IEEE Trans Power Syst 33(1):206–215. https://doi.org/10.1109/TPWRS.2017.2690969

    Article  Google Scholar 

  27. Luburic Z, Pandzic H, Carrion M (2020) Transmission expansion planning model considering battery energy storage, TCSC and lines using AC OPF. IEEE Access 8:203429–203439. https://doi.org/10.1109/ACCESS.2020.3036381

    Article  Google Scholar 

  28. Mokhtari MS, Gitizadeh M, Lehtonen M (2021) Optimal coordination of thyristor controlled series compensation and transmission expansion planning: distributionally robust optimization approach. Electr Power Syst Res 196:107189. https://doi.org/10.1016/j.epsr.2021.107189

    Article  Google Scholar 

  29. Raj S, Bhattacharyya B (2018) Optimal placement of TCSC and SVC for reactive power planning using whale optimization algorithm. Swarm Evol Comput 40:131–143. https://doi.org/10.1016/j.swevo.2017.12.008

    Article  Google Scholar 

  30. Morquecho EG, Torres SP, Castro CA (2021) An efficient hybrid metaheuristics optimization technique applied to the AC electric transmission network expansion planning. Swarm Evol Comput 61:100830. https://doi.org/10.1016/j.swevo.2020.100830

    Article  Google Scholar 

  31. Jitesh J, Saxena A, Kumar R, Gupta V (2022) Transmission expansion planning using composite teaching learning based optimisation algorithm. Evol Intell 15(4):2691–2713. https://doi.org/10.1007/s12065-021-00640-8

    Article  Google Scholar 

  32. Sadegh ZA, Abyaneh HA (2017) Transmission expansion planning using TLBO algorithm in the presence of demand response resources. Energies 10(9):1376. https://doi.org/10.3390/en10091376

    Article  Google Scholar 

  33. Bhattacharyya B, Babu R (2016) Teaching learning based optimization algorithm for reactive power planning. Int J Electr Power Energy Syst 81:248–253. https://doi.org/10.1016/j.ijepes.2016.02.042

    Article  Google Scholar 

  34. Jordehi R (2015) Optimal setting of TCSCs in power systems using teaching–learning-based optimisation algorithm. Neural Comput Appl 26(5):1249–1256. https://doi.org/10.1007/s00521-014-1791-x

    Article  Google Scholar 

  35. Duong T, JianGang Y, Truong V (2014) Application of min cut algorithm for optimal location of FACTS devices considering system loadability and cost of installation. Int J Electr Power Energy Syst 63:979–987. https://doi.org/10.1016/j.ijepes.2014.06.072

    Article  Google Scholar 

  36. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359. https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  Google Scholar 

  37. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315. https://doi.org/10.1016/j.cad.2010.12.015

    Article  Google Scholar 

  38. Alhamrouni, Khairuddin A, Ferdavani AK, Salem M (2014) Transmission expansion planning using AC-based differential evolution algorithm. IET Gener, Transm Distrib 8(10):1637–1644. https://doi.org/10.1049/iet-gtd.2014.0001

    Article  Google Scholar 

  39. R. D. Zimmerman and C. E. M. Sanchez (2022). MATPOWER (Version 8.0b1), [Software]. Available: https://matpower.org.

  40. Illinois Institute of Technology, Electrical and Computer Engineering Department–IEEE 118-bus System Data. Available at: http://motor.ece.iit.edu/Data/.

Download references

Acknowledgements

We acknowledge the support of time and facilities from Industrial University of Ho Chi Minh City for this study.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization done by T.L.D; methodology done by T.L.D; software provided by N.D.H.B; validation done by T.L.D and N.D.H.B; formal analysis done by T.L.D; investigation done by N.D.H.B; data curation done by N.D.H.B; writing—original draft preparation done by T.L.D and N.D.H.B; writing—review and editing done by T.L.D; visualization done by N.D.H.B; supervision done by T.L.D.

Corresponding author

Correspondence to Thanh Long Duong.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

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

Duong, T.L., Bui, N.D.H. A four-stage strategy for solving AC transmission expansion planning problem in large power system based on differential evolution algorithm and teaching–learning-based optimization algorithm. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02566-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00202-024-02566-7

Keywords

Navigation