Skip to main content
SpringerLink
Log in

An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

  • Research Article
  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

Energy issues have always been one of the most significant concerns for scientists worldwide. With the ongoing over exploitation and continued outbreaks of wars, traditional energy sources face the threat of depletion. Wind energy is a readily available and sustainable energy source. Wind farm layout optimization problem, through scientifically arranging wind turbines, significantly enhances the efficiency of harnessing wind energy. Meta-heuristic algorithms have been widely employed in wind farm layout optimization. This paper introduces an Adaptive strategy-incorporated Integer Genetic Algorithm, referred to as AIGA, for optimizing wind farm layout problems. The adaptive strategy dynamically adjusts the placement of wind turbines, leading to a substantial improvement in energy utilization efficiency within the wind farm. In this study, AIGA is tested in four different wind conditions, alongside four other classical algorithms, to assess their energy conversion efficiency within the wind farm. Experimental results demonstrate a notable advantage of AIGA.

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

Similar content being viewed by others

Data Availability

Data sets generated during the current study are available from the corresponding author on reasonable request.

References

  1. Herbert, G. J., Iniyan, S., Sreevalsan, E., & Rajapandian, S. (2007). A review of wind energy technologies. Renewable and Sustainable Energy Reviews, 11(6), 1117–1145.

    Article  Google Scholar 

  2. Roga, S., Bardhan, S., Kumar, Y., & Dubey, S. K. (2022). Recent technology and challenges of wind energy generation: A review. Sustainable Energy Technologies and Assessments, 52, 102239.

    Article  Google Scholar 

  3. Nazir, M. S., Ali, N., Bilal, M., & Iqbal, H. M. (2020). Potential environmental impacts of wind energy development: A global perspective. Current Opinion in Environmental Science & Health, 13, 85–90.

    Article  Google Scholar 

  4. Gao, S., Zhou, M., Wang, Z., Sugiyama, D., Cheng, J., Wang, J., & Todo, Y. (2023). Fully complex-valued dendritic neuron model. IEEE Transactions on Neural Networks and Learning Systems, 34(4), 2105–2118.

    Article  Google Scholar 

  5. Lackner, M. A., & Elkinton, C. N. (2007). An analytical framework for offshore wind farm layout optimization. Wind Engineering, 31(1), 17–31.

    Article  Google Scholar 

  6. Gao, S., Yu, Y., Wang, Y., Wang, J., Cheng, J., & Zhou, M. (2021). Chaotic local search-based differential evolution algorithms for optimization. IEEE Transactions on Systems, Man and Cybernetics: Systems, 51(6), 3954–3967.

    Article  Google Scholar 

  7. Gao, S., Zhou, M., Wang, Y., Cheng, J., Yachi, H., & Wang, J. (2019). Dendritic neural model with effective learning algorithms for classification, approximation, and prediction. IEEE Transactions on Neural Networks and Learning Systems, 30(2), 601–604.

    Article  Google Scholar 

  8. Kim, H., Singh, C., & Sprintson, A. (2012). Simulation and estimation of reliability in a wind farm considering the wake effect. IEEE Transactions on Sustainable Energy, 3(2), 274–282.

    Article  Google Scholar 

  9. Lei, Z., Gao, S., Zhang, Z., Yang, H., & Li, H. (2023). A chaotic local search-based particle swarm optimizer for large-scale complex wind farm layout optimization. IEEE/CAA Journal of Automatica Sinica, 10(5), 1168–1180.

    Article  Google Scholar 

  10. Gao, S., Yu, Y., Wang, Y., Wang, J., Cheng, J., & Zhou, M. (2019). Chaotic local search-based differential evolution algorithms for optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(6), 3954–3967.

    Article  Google Scholar 

  11. Yoo, D. G., & Kim, J. H. (2014). Meta-heuristic algorithms as tools for hydrological science. Geoscience Letters, 1, 1–7.

    Article  Google Scholar 

  12. Tan, S., Zhao, S., & Wu, J. (2023). QL-ADIFA: Hybrid optimization using q-learning and an adaptive logarithmic spiral-levy firefly algorithm. Mathematical, Biosciences and Engineering, 20(8), 13542–13561.

    Article  MathSciNet  Google Scholar 

  13. Gao, X., Yang, H., Lin, L., & Koo, P. (2015). Wind turbine layout optimization using multi-population genetic algorithm and a case study in hong kong offshore. Journal of Wind Engineering and Industrial Aerodynamics, 139, 89–99.

    Article  Google Scholar 

  14. Ju, X., Liu, F., Wang, L., & Lee, W.-J. (2019). Wind farm layout optimization based on support vector regression guided genetic algorithm with consideration of participation among landowners. Energy Conversion and Management, 196, 1267–1281.

    Article  Google Scholar 

  15. Yang, Q., Hu, J., & Law, S. S. (2018). Optimization of wind farm layout with modified genetic algorithm based on boolean code. Journal of Wind Engineering and Industrial Aerodynamics, 181, 61–68.

    Article  Google Scholar 

  16. Shakoor, R., Hassan, M. Y., Raheem, A., Rasheed, N., & Na’im Mohd Nasir, M. (2014). Wind farm layout optimization by using definite point selection and genetic algorithm. In: 2014 IEEE International Conference on Power and Energy (PECON), Kuching, Malaysia, pp. 191–195

  17. Liu, Z., Fan, S., Wang, Y., & Peng, J. (2021). Genetic-algorithm-based layout optimization of an offshore wind farm under real seabed terrain encountering an engineering cost model. Energy Conversion and Management, 245, 114610.

    Article  Google Scholar 

  18. Chen, Y., Li, H., He, B., Wang, P., & Jin, K. (2015). Multi-objective genetic algorithm based innovative wind farm layout optimization method. Energy Conversion and Management, 105, 1318–1327.

    Article  Google Scholar 

  19. Ju, X., & Liu, F. (2019). Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation. Applied Energy, 248, 429–445.

    Article  Google Scholar 

  20. Song, J., Kim, T., & You, D. (2023). Particle swarm optimization of a wind farm layout with active control of turbine yaws. Renewable Energy, 206, 738–747.

    Article  Google Scholar 

  21. Asaah, P., Hao, L., & Ji, J. (2021). Optimal placement of wind turbines in wind farm layout using particle swarm optimization. Journal of Modern Power Systems and Clean Energy, 9(2), 367–375.

    Article  Google Scholar 

  22. Rehman, S., & Ali, S. S. (2015). Wind farm layout design using modified particle swarm optimization algorithm. In IREC2015 The Sixth International Renewable Energy Congress, Sousse, Tunisia, pp. 1–6

  23. Pillai, A. C., Chick, J., Johanning, L., & Khorasanchi, M. (2018). Offshore wind farm layout optimization using particle swarm optimization. Journal of Ocean Engineering and Marine Energy, 4, 73–88.

    Article  Google Scholar 

  24. Hou, P., Hu, W., Soltani, M., & Chen, Z. (2015). Optimized placement of wind turbines in large-scale offshore wind farm using particle swarm optimization algorithm. IEEE Transactions on Sustainable Energy, 6(4), 1272–1282.

    Article  Google Scholar 

  25. Wang, Y., Liu, H., Long, H., Zhang, Z., & Yang, S. (2017). Differential evolution with a new encoding mechanism for optimizing wind farm layout. IEEE Transactions on Industrial Informatics, 14(3), 1040–1054.

    Article  Google Scholar 

  26. Yu, X., & Lu, Y. (2023). Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization. Energy, 284, 129300.

    Article  Google Scholar 

  27. Feng, J., & Shen, W. Z. (2015). Solving the wind farm layout optimization problem using random search algorithm. Renewable Energy, 78, 182–192.

    Article  Google Scholar 

  28. Chen, K., Song, M., Zhang, X., & Wang, S. (2016). Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm. Renewable Energy, 96, 676–686.

    Article  Google Scholar 

  29. Zhao, S., Wu, Y., Tan, S., Wu, J., Cui, Z., & Wang, Y. G. (2023). QQLMPA: A quasi-opposition learning and q-learning based marine predators algorithm. Expert Systems with Applications, 213, 119246.

    Article  Google Scholar 

  30. Cui, Z., Hou, X., Zhou, H., Lian, W., & Wu, J. (2020). Modified slime mould algorithm via levy flight. In 13th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI). Chengdu, China, 1109–1113.

  31. Lei, Z., Gao, S., Wang, Y., Yu, Y., & Guo, L. (2022). An adaptive replacement strategy-incorporated particle swarm optimizer for wind farm layout optimization. Energy Conversion and Management, 269, 116174.

    Article  Google Scholar 

  32. Hwang, C., Jeon, J. H., Kim, G. H., Kim, E., Park, M., & Yu, I. K. (2015). Modelling and simulation of the wake effect in a wind farm. Journal of International Council on Electrical Engineering, 5(1), 74–77.

    Article  Google Scholar 

  33. Gao, J., Wang, Z., Jin, T., Cheng, J., Lei, Z., & Gao, S. (2024). Information gain ratio-based subfeature grouping empowers particle swarm optimization for feature selection. Knowledge-Based Systems, 286, 111380.

    Article  Google Scholar 

  34. Baptista, J., Jesus, B., Cerveira, A., & Pires, E. J. S. (2023). Offshore wind farm layout optimisation considering wake effect and power losses. Sustainability, 15(13), 9893.

    Article  Google Scholar 

  35. González, J. S., Rodriguez, A. G. G., Mora, J. C., Santos, J. R., & Payan, M. B. (2010). Optimization of wind farm turbines layout using an evolutive algorithm. Renewable Energy, 35(8), 1671–1681.

    Article  Google Scholar 

  36. Rashedi, E., Rashedi, E., & Nezamabadi-Pour, H. (2018). A comprehensive survey on gravitational search algorithm. Swarm and Evolutionary Computation, 41, 141–158.

    Article  Google Scholar 

  37. Wang, Z., Gao, S., Lei, Z., & Omura, M. (2024). An information-based elite-guided evolutionary algorithm for multi-objective feature selection. IEEE/CAA Journal of Automatica Sinica, 11(1), 264–266.

    Article  Google Scholar 

  38. Wang, Y., Yu, Y., Gao, S., Pan, H., & Yang, G. (2019). A hierarchical gravitational search algorithm with an effective gravitational constant. Swarm and Evolutionary Computation, 46, 118–139.

    Article  Google Scholar 

  39. Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.

    Article  Google Scholar 

Download references

Acknowledgements

This research was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant JP22H03643, Japan Science and Technology Agency (JST) Support for Pioneering Research Initiated by the Next Generation (SPRING) under Grant JPMJSP2145, and JST through the Establishment of University Fellowships towards the Creation of Science Technology Innovation under Grant JPMJFS2115.

Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Toyama, Toyama, 9300887, Japan

    Tao Zheng, Haotian Li, Houtian He, Zhenyu Lei & Shangce Gao

Authors
  1. Tao Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haotian Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Houtian He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhenyu Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shangce Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shangce Gao.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no Conflict of interest.

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

Zheng, T., Li, H., He, H. et al. An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization. J Bionic Eng (2024). https://doi.org/10.1007/s42235-024-00498-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42235-024-00498-3

Keywords

Search

Navigation