Fahrettin Fırat ÖZDEMİR, Abdulla SAKALLI

Türkiye’nin Rüzgar Enerji Potansiyelinin Sayısal Hava Tahmin Sistemi ile Simülasyonu ve Analizi

Dünya genelinde enerji ihtiyacı giderek artmaktadır. Kullanılan fosil temelli yakıtlar, dünya üzerinde yeryüzü sıcaklığındaki yükselmelere, ozon tabakasında oluşan yıkımlara, iklim değişikliklerine sebep olduğu bilinmektedir ve sonucunda geri dönülmesi zor hasarlar meydana getirmektedir. Rüzgâr enerjisi gibi çevreci ve yenilebilir enerji kaynakları her yıl daha da gelişmekte, kara ve su üzerinde kullanılarak, etkileyici bir potansiyele sahiptir. Bununla birlikte atmosferin stokastik ve tahmin edilmesi zor yapısı, rüzgâr hızında rastgeleliklere ve kesintilere ve sonucunda rüzgâr gücündeki dalgalanmalara sebep olmaktadır. Bu nedenle enerji piyasalarında, rüzgâr gücünün etkili, güvenilir ve kararlı bir yapıda kullanılabilmesi için kısa vadede yapılan tahminler büyük önem arz etmektedir. Atmosferin yapısının sayısal denklemlerle ve WRF-ARW (Weather Research and Forecasting Model) modellemesiyle iyi temsil edilmesi ile bu sorun günümüzde daha kolay bir hale gelmiştir. Yine de model parametreleri, başlangıç koşulları doğru bir şekilde seçilmelidir. Türkiye’de altı farklı bölgede yaptığımız ayrıca tüm Türkiye’yi kapsayan sıcaklık, yağış ve rüzgâr hızı tahminleri ve eğri eşitleme metodu ile Türkiye’nin 2,3 MW ve 3 MW’ lık rüzgâr gücü üretim potansiyeli tahminlerimiz, orta ve uzun vadede uygulanabilir enerji yatırımları için uygun bir alternatif kaynak sağlayabilir. Bu çalışmada Çanakkale bölgesinde yıllık rüzgâr hızı tahminlerinde sırasıyla 1,35 MAE (Mean Absolute Error) ve d (0,87), IOA (Index of Agreement) değerlerine ulaşılmıştır.

Anahtar Kelimeler:

Rüzgâr enerjisi, Sistem simülasyonu, WRF, Matematiksel modelleme, Yöneylem araştırması.

Simulation and Analysis of Turkey's Wind Energy Potential with Numerical Weather Forecasting System

Energy requirements are increasing all over the world. The fossil-based fuels used are known to cause rises in the earth's temperature, destruction of the ozone layer, and climate changes, and as a result, they cause irreversible damage. Environmental and renewable energy sources such as wind energy are developing more and more each year and have an impressive potential by being used on onshore and offshore. However, the stochastic and difficult-to-predict structure of the atmosphere causes randomness and interruptions in wind speed and consequently fluctuations in wind power. For this reason, short-term forecasts have a great value in order to use wind power in an effective, reliable and stable structure in energy markets. This problem has become easier today, as the structure of the atmosphere is well represented by numerical equations and WRF-ARW (Weather Research and Forecasting Model) modelling. However, the model parameters, initial conditions must be chosen correctly. With the temperature, precipitation and wind speed forecasts we made in six different locations in Turkey, as well as our 2.3 MW and 3 MW wind power generation potential forecasts covering the whole country using the curve equalization method, we can provide a suitable alternative source in medium and long term for feasible energy investments. In this study, the annual wind speed forecasting in Çanakkale was 1.35 MAE (Mean Absolute Error) and d (0.87), IOA (Index of Agreement) values, respectively.

Keywords:

Wind energy, System simulation, WRF, Mathematical modeling, Operational research,

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Akdağ, O., & Yeroğlu, C. (2019). Offshore/Onshore Rüzgâr Santralinin Modellenmesi ve Şebekeye Bağlantısı. Avrupa Bilim ve Teknoloji Dergisi, (16), 505-520.
Altan, A., & Karasu, S. (2020). Ayrıştırma yöntemlerinin derin öğrenme algoritması ile tanımlanan rüzgâr hızı tahmin modeli başarımına etkisinin incelenmesi. Avrupa Bilim ve Teknoloji Dergisi, (20), 844-853.
Bilal, M., Solbakken, K., Birkelund, Y. (2016). Wind speed and direction predictions by WRF and WindSim coupling over Nygårdsfjell. Journal of Physics: Conference Series 753 (8): 082018.
Bodini, N., Hu, W., Optis, M., Cervone, G., Alessandrini, S. (2021). Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble. Wind Energy Science, 6(6): 1363-1377.
Carvalho, D., Rocha, A., Gómez-Gesteira, M., Santos, C. (2012). A sensitivity study of the WRF model in wind simulation for an area of high wind energy. Environmental Modelling & Software, 33: 23-34.
Carvalho, D., Rocha, A. M. A. C., Gómez-Gesteira, M., Santos, C. S. (2014). Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula. Applied Energy, 135: 234-246.
Christoforou, E., Emiris, I. Z., Florakis, A., Rizou, D., Zaharia, S. (2021). Spatio-temporal deep learning for day-ahead wind speed forecasting relying on WRF predictions. Energy Systems, 1-21.
Devrim, M. A., Sakalli, A. (2021). Estimation of wind speed and energy potential by atmospheric model for day-ahead market and wind power plants in Turkey. In IOP Conference Series: Materials Science and Engineering 1032 (1) 012042.
Di, Z., Ao, J., Duan, Q., Wang, J., Gong, W., Shen, C., Liu, Z. (2019). Improving WRF model turbine-height wind-speed forecasting using a surrogate-based automatic optimization method. Atmospheric Research, 226: 1-16.
Doğanşahin, K., Uslu, A. F., & Kekezoğlu, B. (2019). İki Bileşenli Weibull Dağılımı ile Rüzgâr Hızı Olasılık Dağılımlarının Modellenmesi. Avrupa Bilim ve Teknoloji Dergisi, (15), 315-326.
Dupuy, F., Duine, G. J., Durand, P., Hedde, T., Pardyjak, E., Roubin, P. (2021). Valley winds at the local scale: Correcting routine weather forecast using artificial neural networks. Atmosphere, 12(2): 128.
Emeksiz, C., & Tan, M., (2021). Geliştirilmiş EEMD-EWT Tabanlı Yapay Sinir Ağı Modeli Kullanarak Çok Adımlı Rüzgâr Hızı Tahmini. Avrupa Bilim ve Teknoloji Dergisi, (26), 165-173.
Erduman, A., Kekezoğlu, B., & Durusu, A. (2018). Küçük Güçlü Rüzgâr Santrallerinin Kurulumu ve Şebekeye Etkilerinin Teknik ve Ekonomik Açıdan Değerlendirilmesi: Uygulama Çalışması. Avrupa Bilim ve Teknoloji Dergisi, (13), 112-117.
Feroz, R.M.A., Javed, A., Syed, A.H., Kazmi, S.A.A., Uddin, E. (2020). Wind speed and power forecasting of a utility-scale wind farm with inter-farm wake interference and seasonal variation. Sustainable Energy Technologies and Assessments, 42: 100882.
Giannakopoulou, E. M., Nhili, R. (2014). WRF model methodology for offshore wind energy applications. Advances in Meteorology. Global Wind Energy Council, G. W. E. C. (2021). Global wind report 2021.
Groch, M., Vermeulen, H. J. (2019). Wind speed event forecasting using a Hybrid WRF and ANN model. In 2019 9th International Conference on Power and Energy Systems (ICPES) 1-6.
Groch, M., Vermeulen, J. (2019). Short-term ensemble nwp wind speed forecasts using mean-variance portfolio optimization and neural networks. In 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) 1-6.
Guo, Z., Xiao, X. (2014). Wind power assessment based on a WRF wind simulation with developed power curve modeling methods. Hindawi Publishing Corporation Abstract and Applied Analysis, http://dx.doi.org/10.1155/2014/941648.
Holley, J.W., Guilford, J.P. (1964). A note on the G index of agreement. Educational and psychological measurement, 24(4): 749-753. Jacondino, W.D., da Silva Nascimento, A.L., Calvetti, L., Fisch, G., Beneti, C.A.A., da Paz, S.R. (2021). Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model. Energy, 230: 120841.
Jiang, P., Liu, Z., Niu, X., Zhang, L. (2021). A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting. Energy, 217: 119361.
Li, F., Ren, G., Lee, J. (2019). Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks. Energy conversion and management, 186: 306-322.
Liu, X., Zhang, L., Zhang, Z., Zhao, T., Zou, L. (2021). Ultra Short Term Wind Power Prediction Model Based on WRF Wind Speed Prediction and CatBoost. In IOP Conference Series: Earth and Environmental Science 838 (1): 012001.
Martínez-Arellano, G., Nolle, L. (2013). Genetic programming for wind power forecasting and ramp detection. In International Conference on Innovative Techniques and Applications of Artificial Intelligence 403-417.
Men, Z., Yee, E., Lien, F. S., Wen, D., Chen, Y. (2016). Short-term wind speed and power forecasting using an ensemble of mixture density neural networks. Renewable Energy, 87: 203-211.
Mentes, S., Tan, E., Ozdemir, T., Unal, E., Unal, Y., Efe, B., Borhan, Y. (2021.) Short term wınd power forecast ın Manisa, Turkey by usıng the wrf model coupled to a cfd model.
Niu, D., Pu, D., Dai, S. (2018). Ultra-short-term wind-power forecasting based on the weighted random forest optimized by the niche immune lion algorithm. Energies, 11(5): 1098.
Oettl, D., Veratti, G. (2021). A comparative study of mesoscale flow-field modelling in an Eastern Alpine region using WRF and GRAMM-SCI. Atmospheric Research, 249: 105288.
Özen, C., Dinç, U., Deniz, A., Karan, H. (2021). Wind power generation forecast by coupling numerical weather prediction model and gradient boosting machines in Yahyalı wind power plant. Wind Engineering, 45(5): 1256-1272.
Prieto-Herráez, D., Frías-Paredes, L., Cascón, J. M., Lagüela-López, S., Gastón-Romeo, M., Asensio-Sevilla, M. I., González-Aguilera, D. (2021). Local wind speed forecasting based on WRF-HDWind coupling. Atmospheric Research, 248: 105219.
Salamanca, F., Zhang, Y., Barlage, M., Chen, F., Mahalov, A., Miao, S. (2018). Evaluation of the WRF‐urban modeling system coupled to Noah and Noah‐MP land surface models over a semiarid urban environment. Journal of Geophysical Research: Atmospheres, 123(5): 2387-2408.
Salazar, A. A., Che, Y., Zheng, J., Xiao, F. (2021). Multivariable neural network to postprocess short‐term, hub‐height wind forecasts. Energy Science & Engineering, 1‒15. doi:10.1002/ese3.928.
Salfate, I., Marin, J. C., Cuevas, O., Montecinos, S. (2020). Improving wind speed forecasts from the Weather Research and Forecasting model at a wind farm in the semiarid Coquimbo region in central Chile. Wind Energy, 23(10): 1939-1954.
Sayeed, A., Choi, Y., Jung, J., Lops, Y., Eslami, E., & Salman, A. K. (2020). A deep convolutional neural network model for improving WRF forecasts. arXiv preprint arXiv: 2008.06489.
Şahin, B., Bilgili, M., Akıllı, H. (2005). The wind power potential of the eastern Mediterranean region of Turkey. Journal of Wind Engineering and Industrial Aerodynamics, 93(2): 171-183.
Tan, E., Mentes, S. S., Unal, E., Unal, Y., Efe, B., Barutcu, B., & Incecik, S. (2021). Short term wind energy resource prediction using WRF model for a location in western part of Turkey. Journal of Renewable and Sustainable Energy, 13 (1): https://doi.org/10.1063/5.0026391.
Teixeira, R. S., Santos Conterato, F., Maria, P., Dias, A., Kaore, Y., Kitagawa, L. (2020). Hybrid model of wınd speed prediction in short time range using wrf and artificial neural networks. VI Internatıonal Symposıum on Innovation and Technology (SIINTEC).
Thompson, R.D. (2002). Atmospheric processes and systems. Routledge. Turkish Wind Energy Association. (2021). Turkish Wind Energy Statistics Report.
Wei, C.C. (2020). Development of stacked long short-term memory neural networks with numerical solutions for wind velocity predictions. Advances in Meteorology, 2020.
Xu, W., Liu, P., Cheng, L., Zhou, Y., Xia, Q., Gong, Y., & Liu, Y. (2021). Multi-step wind speed prediction by combining a WRF simulation and an error correction strategy. Renewable Energy, 163: 772-782.
Zhao, J., Guo, Z. H., Su, Z. Y., Zhao, Z. Y., Xiao, X., Liu, F. (2016). An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. Applied Energy, 162: 808-826.
Zhao, J., Guo, Y., Xiao, X., Wang, J., Chi, D., Guo, Z. (2017). Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method. Applied Energy, 197: 183-202.