Study and Optimization of Parameters Affecting the Maximum Power Output of Wind Farms on Flat Ground

Subject Areas : Renewable Energy

Ayyub Farajipoor ¹ , Faramarz Faghihi ² , Reza Sharifi ³

1 - Department of Electrical and Computer Engineering, Science and Research branch, Islamic Azad University, Hormozgan, Iran. *(Corresponding Auothers)
2 - - Department of Electrical and Computer Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran.
3 - Department of Electrical and Computer Engineering, West Tehran branch, Islamic Azad University, Tehran, Iran.

Received: 2016-04-10 Accepted : 2016-07-16 Published : 2018-03-21

Keywords: Genetic algorithm, wake effect, Optimization, wind turbines, wind far,

Abstract :

Background and Objective: Wind is a clean and abundant of source of energy which is completely renewable. Large wind farms are being built around the world as a way to generate electricity, but operators still seeking the most effective arrangement of wind turbines in the wind farm to maximize absorption of wind energy. Wind farm layout optimization is one the way to increase the output of the wind farm. Method: In this paper, a genetic algorithm to maximize the expected energy output was used. The purpose of the genetic algorithm optimization of wind farm was arranged in terms of location, hub height and rotor diameter of the turbines to capture maximum wind energy and reduce the wake effect. The proposed model with two scenarios of wind speed and direction distribution of wind sites are shown on the flat ground. Results: The results of the present study were compared with the previous studies. The results showed by wind farm layout optimization of the place, the hub height and rotor diameter of the turbines, at the same time, has a better performance - in terms of the maximum value of the expected energy output and reduces the wake effect with strategies which optimize with one or two parameters simultaneously. Discussion and Conclusion: The use of wind turbines with a hub height and rotor diameter varies in a wind farm and has the benefits of reducing the wake effect and captures maximum wind energy.  

References:

1. K, Chen., M.X, Song., X, Zhang., S.F, Wang., 2016. Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm. Renewable Energy, vol. 96 , pp. 676-686


2. Bryony, DuPont., Jonathan, Cagan., Patrick, Moriarty., 2016. An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm. Energy, vol. 106 , pp. 802-814


3. Chen, Y., Li, H., Jin, K., Song, Q., 2013.Wind farm layout optimization using genetic algorithm with different hub height wind turbines. Energy Conversion and Management, vol. 70 , pp. 56–65


4. Yeh, T-H., Wang, L., 2008.A Study on Generator Capacity for Wind Turbines Under Various Tower Heights and Rated Wind Speeds Using Weibull Distribution. IEEE Transactions. Energy Conversion, vol. 23, pp. 592-602


5. Chowdhury, S., Zhang, J., Messac, A., Castillo, L., 2012.Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation. Renewable Energy, vol. 38 , pp.16-30


6. Husien, W., El-Osta, W., Dekam, E., 2013.Effect of the wake behind wind rotor on optimum energy output of wind farms. Renewable Energ, vol. 49 , pp. 128-132


7. Son, E., Lee, S., Hwang, B., Lee, S., 2013.Characteristics of turbine spacing in a wind farm using an optimal design process. Renewable Energy, vol. 65 , pp. 1-5


8. Adaramola, M., Krogstad, P., 2011.Experimental investigation of wake effects on wind turbine performance. Renewable Energ, vol. 36 , pp. 2078-2086


9. Samorani, M., 2010.The wind farm layout optimization problem. Research Paper Series. Leeds School of Business, Jan 28,


10. Eroglu, Y., Seckiner, S., 2012.Design of wind farm layout using ant colony algorithm. Renew Energy, vol. 44 , pp. 53-62


11. Kusiak, A., Song, Z., 2010.Design of wind farm layout for maximum wind energy capture. Renew Energy, vol. 35 , pp. 685-694


12. Wagner, M., Day, J., Neumann, F., 2013.A fast and effective local search algorithm for optimizing the placement of wind turbines. Renewable Energy, vol. 51 , pp. 64-70


13. Katsigiannis, Y., Stavrakakis, G., 2013.Estimation of wind energy production in various sites in Australia for different wind turbine classes: A comparative technical and economic assessment. Renewable Energy, vol. 67 , pp. 1-7


14. Mustakerov, I., Borissova, D., 2010.Wind turbines type and number choice using combinatorial optimization. Renewable Energy, vol.  35 , pp. 1887–1894


15. Gu, H., Wang, J., 2013.Irregular-shape wind farm micro-siting optimization. Energy, vol. 57 , pp. 535-544


16. Sorensen, P., Nielsen, T., Recalibrating wind turbine wake model parameters- validating the wake model performance for large offshore wind farms, European wind energy conference and exhibition, 2006, Athens: Greece


17. Jensen, NO., A note on wind generator interaction. Roskilde, Denmark, Risø National Laboratory, 1983.


18. Katic, I., Hojstrub, J., Jensen, ON. A simple model for cluster efficiency. European wind energy Association Conferance and Exhibition, 7-9 October 1986, Rome, Italy,  pp. 407-410


19. Frandsen, S., 1992.On the wind speed reduction in the center of large clusters of wind turbines. J Wind Eng Ind Aerodyn, vol. 39(1–3) , pp. 251–65


20. 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 Aerodynamics, vol. 51 , pp. 105-116.