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Application of Recent Metaheuristic Techniques for Optimizing Power Generation Plants with Wind Energy

  • F. F. Panoeiro
  • G. Rebello
  • V. A. Cabral
  • C. A. Moraes
  • I. C. da Silva Junior
  • L. W. Oliveira
  • B. H. DiasEmail author
Chapter
  • 18 Downloads
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)

Abstract

The wind farm layout optimization problem consists of determining the optimal configuration with the objectives of maximizing the extracted power while minimizing the costs related to the project. The present work aims at comparing the performance of the computational intelligence techniques named bat algorithm, grey wolf optimizer, and sine cosine algorithm, considering different wind direction scenarios and the probabilities of occurrence of such scenarios under analysis. The methodologies employed consider the wind weakening effect to determine the number and positions of the wind turbines in an offshore wind farm. A case study from the literature is used to evaluate the methodologies employed with the representation of different wind direction scenarios.

Keywords

Wind farms Optimum layout Bat algorithm Grey wolf optimizer Sine cosine algorithm 

Notes

Acknowledgements

The authors acknowledge the Brazilian National Research Council (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES), the Foundation for Supporting Research in Minas Gerais, and Electric Power National Institute (INERGE) for their great support.

References

  1. 1.
    Global Wind Energy Council GWEC (2019) Global wind report forecasts over 300 GW capacity to be added in next 5 years—growth to come from emerging markets and offshore wind, 3 Apr 2019. Available https://gwec.net/. Accessed 10 May 2019
  2. 2.
    Hou P (2017) Optimization of large-scale offshore wind farm. Ph.D Dissertation, Aalborg UniversitetsforlagGoogle Scholar
  3. 3.
    Kerkvliet H, Polatidis H (2016) Offshore wind farms decommissioning: a semi quantitative multi-criteria decision aid framework. Sustain Energy Technol Assess 18:69–79 (Elsevier)Google Scholar
  4. 4.
    Han X, Guo J, Wang P, Jia Y (2011) Adequacy study of wind farms considering reliability and wake effect of WTGs. In: Power and energy society general meeting, IEEE, pp 1–7Google Scholar
  5. 5.
    Jensen NO, Katic I, Hojstrup C (1986) A simple model for cluster efficiency. In: European wind energy association conference and exhibition, pp 407–410Google Scholar
  6. 6.
    Kusiak A, Song Z (2010) Design of wind farm layout for maximum wind energy capture. Renew Energy 35(3):685–694CrossRefGoogle Scholar
  7. 7.
    González JS, Rodriguez AGG, Mora JC, Santos JR, Payan MB (2010) Optimization of wind farm turbines layout using an evolutive algorithm. Renew Energy 35(8):1671–1681Google Scholar
  8. 8.
    Gao X, Yang H, Lin L, Koo P (2015) Wind turbine layout optimization using multipopulation genetic algorithm and a case study in Hong Kong offshore. J Wind Eng Indus Aerodyn, 139Google Scholar
  9. 9.
    Wu YK et al (2014) Optimization of the wind turbine layout and transmission system planning for a large-scale offshore windfarm by ai technology. IEEE Trans Indus Appl 50(3):2071–2080 (IEEE)CrossRefGoogle Scholar
  10. 10.
    Changshui Z, Guangdong H, Jun W (2011) A fast algorithm based on the submodular property for optimization of wind turbine positioning. Renew Energy 36(11):2951–2958CrossRefGoogle Scholar
  11. 11.
    Duan B, Wang J, Gu H (2014) Modified genetic algorithm for layout optimization of multi-type wind turbines. In: IEEE, American control conference (ACC), pp 3633–3638Google Scholar
  12. 12.
    Shakoor R et al (2014) Wind farm layout optimization by using definite point selection and genetic algorithm. In: 2014 IEEE international conference on power and energy (PECon), IEEE, pp 191–195Google Scholar
  13. 13.
    Mosetti G, Poloni C, Diviacco B (1994) Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. J Wind Eng Ind Aerodyn 51(1):105–116CrossRefGoogle Scholar
  14. 14.
    Jiang D et al (2013) Modified binary differential evolution for solving wind farm layout optimization problems. In: 2013 IEEE symposium on computational intelligence for engineering solutions (CIES), IEEE, pp 23–28Google Scholar
  15. 15.
    Gomes LL, Oliveira LW, Silva IC Jr, Passos Filho JA (2017) Optimization of wind farms layout through artificial immune system. In: Latin—American congress on electricity generation and transmission, GLACTEE, vol 12Google Scholar
  16. 16.
    Pookpunt S, Ongsakul W (2013) Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients. Renew Energy 55:266–276 (Elsevier)CrossRefGoogle Scholar
  17. 17.
    Hou P et al (2015) Optimized placement of wind turbines in large-scale offshore wind farm using particle swarm optimization algorithm. IEEE Trans Sustain Energy 6(4):1272–1282 (IEEE)Google Scholar
  18. 18.
    Yang H et al (2016) Wind farm layout optimization and its application to power system reliability analysis. IEEE Trans Power Syst 31(3):2135–2143 (IEEE)CrossRefGoogle Scholar
  19. 19.
    Dey N (ed) (2017) Advancements in applied metaheuristic computing. IGI GlobalGoogle Scholar
  20. 20.
    Dey N (2018) Advancements in applied metaheuristic computing. IGI Global, Hershey, PA, pp 1–978Google Scholar
  21. 21.
    Gupta N, Patel N, Tiwari BN, Khosravy M (2018) Genetic algorithm based on enhanced selection and log-scaled mutation technique. In: Proceedings of the future technologies conference, Springer, pp 730–748Google Scholar
  22. 22.
    Singh G, Gupta N, Khosravy M (2015) New crossover operators for real coded genetic algorithm (RCGA). In: 2015 international conference on intelligent informatics and biomedical sciences (ICIIBMS), IEEE, pp 135–140Google Scholar
  23. 23.
    Gupta N, Khosravy M, Patel N, Sethi IK (2018) Evolutionary optimization based on biological evolution in plants. Proc Comput Sci 126:146–155 (Elsevier)CrossRefGoogle Scholar
  24. 24.
    Gupta N, Khosravy M, Patel N, Senjyu T (2018) A bi-level evolutionary optimization for coordinated transmission expansion planning. IEEE Access 6:48455–48477CrossRefGoogle Scholar
  25. 25.
    Moraes CA, De Oliveira EJ, Khosravy M, Oliveira LW, Honrio LM, Pinto MF, A hybrid bat-inspired algorithm for power transmission expansion planning on a practical Brazilian network. In: Applied nature-inspired computing: algorithms and case studies, from springer tracts in nature inspired computing (STNIC), Springer International Publishing, will be appeared in 2019Google Scholar
  26. 26.
    Khosravy M, Gupta N, Patel N, Senjyu T, Duque CA (2019) Particle swarm optimization of morphological filters for electrocardiogram baseline drift estimation. In: Applied nature-inspired computing: algorithms and case studies, from springer tracts in nature-inspired computing (STNIC),Springer International Publishing (in press)Google Scholar
  27. 27.
    Jagatheesan K, Anand B, Samanta S, Dey N, Ashour AS, Balas VE (2017) Particle swarm optimisation-based parameters optimisation of PID controller for load frequency control of multi-area reheat thermal power systems. Int J Adv Intell Paradig 9(5–6):464–489CrossRefGoogle Scholar
  28. 28.
    Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2017) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl 28(8):2005–2016CrossRefGoogle Scholar
  29. 29.
    Satapathy SC, Raja NSM, Rajinikanth V, Ashour AS, Dey N (2018) Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput Appl 29(12):1285–1307CrossRefGoogle Scholar
  30. 30.
    Mirjalili S (2016) Sca: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133 (Elsevier)Google Scholar
  31. 31.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf optimizer. Adv Eng softw 69:46–61 (Elsevier)CrossRefGoogle Scholar
  32. 32.
    Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization. Springer, pp 65–74Google Scholar
  33. 33.
    Tavozoei MS, Haeri M (2007) Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms. Appl Math Comput 187(2):1076–1085 (Elsevier)MathSciNetzbMATHGoogle Scholar
  34. 34.
    Mendel E, Krohling RA, Campos M (2011) Swarm algorithms with chaotic jumps applied to noisy optimization problem. Inform Sci 181(20):4494–4514 (Elsevier)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Gandomi AH, Yang XS (2014) Chaotic bat algorithm. J Comput Sci 5(2):224–232 (Elsevier)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • F. F. Panoeiro
    • 1
  • G. Rebello
    • 1
  • V. A. Cabral
    • 1
  • C. A. Moraes
    • 1
  • I. C. da Silva Junior
    • 1
  • L. W. Oliveira
    • 1
  • B. H. Dias
    • 1
    Email author
  1. 1.Department of Electrical EnergyFederal University of Juiz de Fora (UFJF)Juiz de ForaBrazil

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