بررسی و بهینه‌سازی پارامترهای مؤثر بر تولید توان ماکزیمم از مزارع بادی در زمین هموار
محورهای موضوعی : انرژی های تجدید پذیر
ایوب فرجی پور
1
(دانش آموخته کارشناسی ارشد برق قدرت، دانشگاه آزاد اسلامی واحد بندرعباس *(مسوول مکاتبات))
فرامرز فقیهی
2
(استادیار گروه برق، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران)
رضا شریفی
3
(استادیار گروه برق، دانشگاه آزاد اسلامی واحد تهران غرب)
کلید واژه: الگوریتم ژنتیک, اثر سایه, بهینه‌سازی, توربین بادی, مزارع بادی,
چکیده مقاله :
زمینه و هدف: باد منبعی از انرژی پاک، فراوان و به طور کامل تجدید‌پذیر می باشد. مزارع بادی بزرگ در حال احداث در سراسر جهان به‌عنوان یک راه پاک برای تولید برق می باشد، اما اپراتورها هنوز به دنبال موثرترین آرایش توربین های بادی در مزرعه بادی برای به حداکثر رساندن جذب انرژی باد هستند. بهینه‌سازی جانمایی مزارع بادی یکی از راه‌های افزایش توان خروجی مزرعه بادی می‌باشد. روش بررسی: در این مقاله از الگوریتم ژنتیک برای به حداکثر رساندن انرژی خروجی مورد انتظار، استفاده شده است. هدف الگوریتم‌ژنتیک، بهینه‌سازی آرایش مزرعه بادی از نظر مکان، ارتفاع مبتنی بر هاب و قطر روتور توربین‍ها، برای جذب حداکثر انرژی باد و کاهش اثر سایه می باشد. مدل پیشنهادی با دو سناریو از سرعت باد و جهت توزیع آن از سایت بادی در زمین صاف نشان داده شده است. یافته‌ها: نتایج مطالعه حاضر با نتایج مطالعات پیشین مقایسه شده است. نتایج نشان می دهد که با بهینه‌سازی آرایش مزرعه بادی از نظر مکان، ارتفاع مبتنی بر هاب و قطر روتور توربین‍ها به طور هم‌زمان، اجرای بهتری - بر حسب حداکثر مقادیر انرژی خروجی مورد انتظار و کاهش اثر سایه - از استراتژی های موجود که فقط به بهینه‌سازی یک یا دو مورد از پارامترها بطور هم‌زمان می‌پرداختند، را در بر دارد. نتیجه‌گیری: استفاده از توربین‌های بادی با ارتفاع هاب و قطر روتور متفاوت در یک مزرعه بادی در واقع مزایای کاهش اثر سایه و جذب حداکثر انرژی باد را دارد.
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Adaramola, M., Krogstad, P., 2011.Experimental investigation of wake effects on wind turbine performance. Renewable Energ, vol. 36 , pp. 2078-2086
- Samorani, M., 2010.The wind farm layout optimization problem. Research Paper Series. Leeds School of Business, Jan 28,
- Eroglu, Y., Seckiner, S., 2012.Design of wind farm layout using ant colony algorithm. Renew Energy, vol. 44 , pp. 53-62
- Kusiak, A., Song, Z., 2010.Design of wind farm layout for maximum wind energy capture. Renew Energy, vol. 35 , pp. 685-694
- 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
- 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
- Mustakerov, I., Borissova, D., 2010.Wind turbines type and number choice using combinatorial optimization. Renewable Energy, vol. 35 , pp. 1887–1894
- Gu, H., Wang, J., 2013.Irregular-shape wind farm micro-siting optimization. Energy, vol. 57 , pp. 535-544
- 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
- Jensen, NO., A note on wind generator interaction. Roskilde, Denmark, Risø National Laboratory, 1983.
- 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
- 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
- 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.
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- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Adaramola, M., Krogstad, P., 2011.Experimental investigation of wake effects on wind turbine performance. Renewable Energ, vol. 36 , pp. 2078-2086
- Samorani, M., 2010.The wind farm layout optimization problem. Research Paper Series. Leeds School of Business, Jan 28,
- Eroglu, Y., Seckiner, S., 2012.Design of wind farm layout using ant colony algorithm. Renew Energy, vol. 44 , pp. 53-62
- Kusiak, A., Song, Z., 2010.Design of wind farm layout for maximum wind energy capture. Renew Energy, vol. 35 , pp. 685-694
- 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
- 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
- Mustakerov, I., Borissova, D., 2010.Wind turbines type and number choice using combinatorial optimization. Renewable Energy, vol. 35 , pp. 1887–1894
- Gu, H., Wang, J., 2013.Irregular-shape wind farm micro-siting optimization. Energy, vol. 57 , pp. 535-544
- 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
- Jensen, NO., A note on wind generator interaction. Roskilde, Denmark, Risø National Laboratory, 1983.
- 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
- 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
- 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.
