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Transmission planning with security criteria via enhanced genetic algorithm

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Abstract

This paper proposes an enhanced genetic algorithm model, named EGA-TEP, to solve the transmission expansion planning (TEP) problem of electric power system networks. Heuristic information is integrated into the evolutionary process of metaheuristic to improve the expansion plans (solutions). This heuristic information is translated in the form of sensitivity indices, based on the circuit loading/overloading and observed load shedding, considering both the intact network and the “N-1” contingency operating conditions (security criteria). In addition, an iterative process of evolutionary runs (ERs) is adopted as the basis for designing the EGA-TEP. These contributions make the optimization tool more robust and ready to handle different types of systems. The efficiency of the proposed EGA-TEP tool is consistently evaluated through performance statistical indices. Results obtained with systems with different characteristics and dimensions are presented and widely discussed.

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References

  1. Gomes PV, Saraiva JT (2019) State-of-the-art of transmission expansion planning: a survey from restructuring to renewable and distributed electricity markets. Electr Power Energy Syst 111:411–424

    Article  Google Scholar 

  2. Hemmati R, Hooshmand RA, Khodabakhshian A (2013) State-of-the-art of transmission expansion planning: comprehensive review. Renew Sust Energy Rev 23:312–319

    Article  Google Scholar 

  3. Lumbreras S, Ramos A (2016) Challenges to transmission expansion planning. Survey of recent practice and literature review. Electr Power Syst Res 134:19–29

    Article  Google Scholar 

  4. Li W (2011) Probabilistic transmission system planning. Wiley, New Jersey

    Book  Google Scholar 

  5. Haffner S, Monticelli A, Garcia A, Mantovani J, Romero R (2000) Branch and bound algorithm for transmission system expansion planning using a transportation model. IEE Proc Gen Trans Distrib 147(3):149–156

    Article  Google Scholar 

  6. Zhang X, Conejo AJ (2018) Candidate line selection for transmission expansion planning considering long- and short-term uncertainty. Electr Power Energy Syst 100:320–330

    Article  Google Scholar 

  7. Leite da Silva AM, Rezende LS, Manso LAF, Anders GJ (2010) Transmission expansion planning: a discussion on reliability and N-1 security criteria. In: Proceedings of the 11th PMAPS, Singapore

  8. Faria H, Binato S, Resende MGC, Falcão DM (2005) Power transmission network design by greedy randomized adaptive path relinking. IEEE Trans Power Syst 20(1):43–49

    Article  Google Scholar 

  9. Leite da Silva AM, Rezende LS, Honário LM, Manso LAF (2011) Performance comparison of meta-heuristics to solve the multistage transmission expansion planning problem. IET-GTD 5(3):360–367

    Google Scholar 

  10. Mendonça IM, Silva Júnior IC, Dias BH, Marcato ALM (2016) Identification of relevant routes for static expansion planning of electric power transmission systems. Electr Power Syst Res 140:769–775

    Article  Google Scholar 

  11. Silva IJ, Rider MJ, Romero R, Garcia AV, Murari CA (2005) Transmission network expansion planning with security constraints. IEE Gen Transm Distrib 152(6):828–836

    Article  Google Scholar 

  12. Sousa AS, Asada EN (2015) Long-term transmission system expansion planning with multi-objective evolutionary algorithm. Electr Power Syst Res 119:149–156

    Article  Google Scholar 

  13. Bakirtzis AG, Biskas PN, Zoumas CE, Petridis V (2002) Optimal power flow by enhanced genetic algorithm. IEEE Trans Power Syst 17(2):229–236

    Article  Google Scholar 

  14. Lumbreras S, Ramos A, Sánchez P (2014) Automatic selection of candidate investments for transmission expansion planning. Int J Electr Power Energy Syst 59:130–140

    Article  Google Scholar 

  15. Martí R, Resende MGC, Ribeiro CC (2013) Multi-start methods for combinatorial optimization. Eur J Oper Res 226:1–8

  16. Apm IEEE (1979) IEEE reliability test system. IEEE Trans Power Appl Syst 98(6):2047–2054

    Google Scholar 

  17. Test systems data. https://sites.google.com/view/testsystemdata/data. Accessed Sept 2020

Download references

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Authors and Affiliations

  1. Electrical Engineering Department, Federal University of São João del-Rei, UFSJ, São João del-Rei, MG, Brazil

    Fernando A. Assis & Leonidas C. Resende

  2. Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, PUC–Rio, Rio de Janeiro, RJ, Brazil

    Iamberg S. Silva & Armando M. Leite da Silva

Authors
  1. Fernando A. Assis
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  2. Iamberg S. Silva
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  3. Armando M. Leite da Silva
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  4. Leonidas C. Resende
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Correspondence to Fernando A. Assis.

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Assis, F.A., Silva, I.S., Silva, A.M.L.d. et al. Transmission planning with security criteria via enhanced genetic algorithm. Electr Eng 103, 1977–1987 (2021). https://doi.org/10.1007/s00202-020-01208-y

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  • DOI: https://doi.org/10.1007/s00202-020-01208-y

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