Wind Farm Layout Design using Cuckoo Search Algorithm

Shafiqur Rehman, Syed S. Ali, Syed H. Adil

Abstract

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms demands an efficient layout of the wind farms. This layout determines the location of each turbine in the wind farm. Due to its sheer complexity, the wind farm layout design problem is considered a complex optimization problem. In recent years, several attempts have been made to develop techniques and algorithms for optimization of wind farms. This paper proposes yet another optimization algorithm based on the cuckoo search (CS), which is a recent optimization method. The proposed cuckoo search algorithm is compared with genetic algorithm which is by far the highest utilized algorithm for wind farm layout design. Empirical results indicate that the proposed cuckoo search algorithm outperformed the genetic algorithm for the given test scenarios in terms of yearly power output and efficiency.

References

  1. Chowdhury, S. and Zhang, J., 2010 Exploring key factors influencing optimal farm design using mixed-discrete particle swarm optimization. In Proceedings of 13th AIAA/ISSMO multidisciplinary analysis and optimization conference, 1-16.
  2. Chowdhury S., Zhang, J., Messac, A., and Castillo, L., 2012. Unrestricted wind farm layout optimization UWFLO: investigating key factors influencing the maximum power generation. Renew. Energ. 38, 16- 30.
  3. Emami, A., and Noghreh, P., 2010. New approach on optimization in placement of wind turbines within wind farm by genetic algorithms. Renew. Energ. 25. 1559-1564.
  4. Ettoumi, F. Y., Adane, A., Benzaoui, M. L., and Bouzergui, N., 2008. Comparative simulation of wind park design and siting in Algeria. Renew. Energ. 33, 2333-2338.
  5. Goldberg, D. E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company.
  6. Gonzalez, J., Santos, J., and Payan, M., 2010. Wind farm optimal design including risk. In Proceedings of IEEE international symposium modelling electric power systems, 1-6.
  7. Grady, S., Hussaini, M. , and Abdullah, M., 2005. Placement of wind turbines using genetic algorithms. Renew. Energ. 30, 2, 259-270.
  8. Huang, H., 2007. Distributed genetic algorithm for optimization of wind farm annual profits. In Proceedings of the IEEE international conference intelligence system applied to power systems, 1-6.
  9. Huang, H.,2009. Efficient hybrid distributed genetic algorithms for wind turbine positioning in large wind farms. In Proceedings of the IEEE international symposium industrial electronics, 2196-2201.
  10. Khan, S. A., and Rehman, S., 2013. Iterative nondeterministic algorithms in on-shore wind farm design: A brief survey. Renew. Sust. Energ. Rev. 19, 3, 370-384.
  11. Mittal, A., 2010. Optimization of the layout of large wind farms using a genetic algorithm. MS thesis, Case Western Reserve University.
  12. Mosetti, G., Poloni, C., Diviacco, B.,1994. Optimization of wind turbine positioning in large wind farms by means of a genetic algorithm, J. Wind. Eng. Ind. Aero., 51, 105-116.
  13. Mustakerov, I. and Borissova, D., 2010. Wind turbines type and number choice using combinatorial optimization. Renew. Energ. 35, 1887-1894.
  14. Rahmani, R., Khairuddin, A., Cherati, S.M., and Pesaran, H.A., 2010. A novel method for optimal placing wind turbines in a wind farm using particle swarm optimization (PSO). In Proceedings of IEEE international conference on power engineering, 134- 139.
  15. Wan, C., Wang, J., Yang, G., and Zhang, X., 2010. Optimal micro-siting of wind farms by particle swarm optimization. In Proceedings of International conference on swarm intelligence, LNCS, 198-205.
  16. Wang, F., Liu, D., and Zeng, L., 2009. Modeling and simulation of optimal wind turbine configurations in wind farms. In Proceedings of the IEEE world nongrid connected wind power energy conference, p. 1-5.
  17. Wang, F., Liu, D., and Zeng, L., 2009. Study on computational grids in placement of wind turbines using genetic algorithm. In Proceedings of the IEEE world non-grid connected wind power energy conference, 1-4.
  18. Yang, X., and Deb, S., 2009. Cuckoo search via levy fights. In Proceedings of IEEE Conference on Nature & Biologically Inspired Computing, 210-214.
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Paper Citation


in Harvard Style

Rehman S., Ali S. and Adil S. (2016). Wind Farm Layout Design using Cuckoo Search Algorithm . In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-184-7, pages 257-262. DOI: 10.5220/0005733002570262


in Bibtex Style

@conference{smartgreens16,
author={Shafiqur Rehman and Syed S. Ali and Syed H. Adil},
title={Wind Farm Layout Design using Cuckoo Search Algorithm},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={257-262},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005733002570262},
isbn={978-989-758-184-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Wind Farm Layout Design using Cuckoo Search Algorithm
SN - 978-989-758-184-7
AU - Rehman S.
AU - Ali S.
AU - Adil S.
PY - 2016
SP - 257
EP - 262
DO - 10.5220/0005733002570262