INVESTIGATION OF WIND FARM BORDER SHAPE EFFECT ON WİND FARM LAYOUT PROBLEM

Year 2019, Issue: 042, 55 - 69, 27.06.2019

Onur Koşar M. Arif Özgür

Abstract

Interest in renewable energy sources has been increasing due to the fossil-based sources being exhaustible and the harmful effects to the environment. Wind energy technology is spreading rapidly all over the world because it offers lower unit energy costs compared to other renewable energy technologies. Increasing wind energy has triggered the demand for more efficient wind farm layouts. A great number of research has been published for the solution of the complex optimization problem which has many limitations. However, the effect of the wind farm boundary shape on the results of optimization is rarely mentioned. For this reason, in this study, optimization calculations were carried out for a wind model based on long-term Kütahya wind data and four different boundaries (square, rectangular, circle and irregular). The rate of change of important design parameters depending on the wind farm boundary shape has been investigated. It was observed that the most important design parameters such as the wake efficiency, energy cost, total cost and annual energy production are less than 10% against to the sensitivities to the change of the wind farm border shape.

Keywords

Variable turbine model, irregular boundary, wind energy, wind farm layout optimization

SANTRAL SINIR ŞEKLİNİN RÜZGÂR SANTRALİ KONUMLANDIRMA PROBLEMİNE OLAN ETKİSİNİN ARAŞTIRILMASI

Abstract

Fosil kökenli yakıtların tükenebilir olması ve çevreye olan zararlı etkileri neticesinde yenilenebilir enerji kaynaklarına olan ilgi artış göstermektedir. Rüzgâr enerjisi teknolojisi, diğer yenilenebilir enerji teknolojilerine kıyasla daha düşük birim enerji maliyetleri sunması sebebi ile tüm dünyada hızla yaygınlaşmaktadır. Artan rüzgâr enerjisi talebi daha verimli santral düzenlerinin elde edilmesini tetiklemiştir. Birçok kısıtlama içeren bu karmaşık problemin çözümü üzerine birçok araştırma yapılmıştır. Fakat santral sınır şeklinin optimizasyon sonuçlarına olan etkisine nadiren değinilmiştir. Bu sebeple, bu çalışmada uzun dönemli Kütahya rüzgar verisi temel alınarak oluşturulmuş bir rüzgar modeli için dört farklı sınır şeklinde (kare, dikdörtgen, daire ve düzensiz) optimizasyon hesaplamaları gerçekleştirilmiştir. Önemli tasarım parametrelerinin santral sınır şekline bağlı değişim oranları araştırılmıştır. En önemli tasarım parametreleri olan iz bölgesi verimi, enerji maliyeti, toplam maliyet ve yıllık enerji üretiminin santral sınır şeklinin değişimine olan hassasiyetlerinin %10’un altında olduğu gözlenmiştir.

Keywords

Değişken türbin modeli, düzensiz sınır, rüzgâr enerjisi, rüzgâr santrali konumlandırma optimizasyonu

References

• [1] British Petroleum Company (BP), (2018), BP statistical review of world energy. British Petroleum Company.
• [2] REN21. (2017), Renewables 2017 Global Status Report (Paris: REN21 Secretariat).
• [3] REN21. (2018), Renewables 2018 Global Status Report (Paris: REN21 Secretariat).
• [4] Mosetti, G. P. C. D. B., Poloni, C., Diviacco, B. (1994), Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. Journal of Wind Engineering and Industrial Aerodynamics, 51(1), 105-116.
• [5] Grady, S. A., Hussaini, M. Y., Abdullah, M. M. (2005), Placement of wind turbines using genetic algorithms. Renewable energy, 30(2), 259-270.
• [6] Emami, A., Noghreh, P. (2010), New approach on optimization in placement of wind turbines within wind farm by genetic algorithms. Renewable Energy, 35(7), 1559-1564.
• [7] Eroğlu, Y., Seçkiner, S. U. (2012), Design of wind farm layout using ant colony algorithm. Renewable Energy, 44, 53-62.
• [8] Kusiak, A., Song, Z. (2010), Design of wind farm layout for maximum wind energy capture. Renewable energy, 35(3), 685-694.
• [9] Pookpunt, S., Ongsakul, W. (2013), Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients. Renewable Energy, 55, 266-276.
• [10] Montoya, F. G., Manzano-Agugliaro, F., López-Márquez, S., Hernández-Escobedo, Q., Gil, C. (2014), Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms. Expert Systems with Applications, 41(15), 6585-6595.
• [11] 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.
• [12] Wang, L., Tan, A. C., Gu, Y. (2015), Comparative study on optimizing the wind farm layout using different design methods and cost models. Journal of Wind Engineering and Industrial Aerodynamics, 146, 1-10.
• [13] Pillai, A. C., Chick, J., Khorasanchi, M., Barbouchi, S., Johanning, L. (2017), Application of an offshore wind farm layout optimization methodology at Middelgrunden wind farm. Ocean Engineering, 139, 287-297.
• [14] Jensen, N. O. (1983), A note on wind generator interaction. Risø-M-2411 Risø Natl Lab Roskilde.
• [15] Katic, I., Højstrup, J., Jensen, N. O. (1986), A simple model for cluster efficiency. In European wind energy association conference and exhibition, 407-410.
• [16] Frandsen, S., Barthelmie, R., Pryor, S., Rathmann, O., Larsen, S., Højstrup, J., Thøgersen, M. (2006), Analytical modelling of wind speed deficit in large offshore wind farms. Wind energy, 9(1‐2), 39-53.
• [17] Gu, H., Wang, J. (2013), Irregular-shape wind farm micro-siting optimization. Energy, 57, 535-544.
• [18] Canny, J. (1987), A computational approach to edge detection. In Readings in Computer Vision 184-203.
• [19] Kolesnikov, A. (2012), ISE-bounded polygonal approximation of digital curves. Pattern Recognition Letters, 33(10), 1329-1337.
• [20] Huang, C. W., Shih, T. Y. (1997), On the complexity of point-in-polygon algorithms. Computers & Geosciences, 23(1), 109-118.
• [21] Liu F, Wang Z. (2014), Offshore Wind Farm Layout Optimization Using Adapted Genetic Algorithm: A different perspective.
• [22] Koşar O. (2018), Rüzgâr Güç Santralleri Konumlandirmasinin Sayisal Olarak İncelenmesi Ve Optimizasyonu. Doktora Tezi. Kütahya Dumlupınar Üniversitesi Fen Bilimleri Enstitüsü.
• [23] Abdulrahman, M., Wood, D. (2017), Investigating the Power-COE trade-off for wind farm layout optimization considering commercial turbine selection and hub height variation. Renewable energy, 102, 267-278.
• [24] Kruskal, J. B. (1956), On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical society, 7(1), 48-50.