Wind Speed Clustering Using Linkage-Ward Method: A Case Study of Khaaf, Iran

Year 2019, , 945 - 954, 01.09.2019

Elnaz Azızı Hamed Kharratı-shıshavan Behnam Mohammadı-ıvatloo Amin Mohammadpour Shotorbanı

https://doi.org/10.35378/gujs.459840

Cited By: 10

Abstract

The use of renewable energy for providing electricity is growing rapidly.
Among others, wind power is one of the most appealing energy sources. The wind speed
has direct impact on the generated wind power and this causes the necessity of wind
speed forecasting. For better power system planning and operation, we need to
forecast the available wind power. Wind power is volatile and intermittent over
the year. For getting better insight and a tractable optimization problem for
different decision making problems in presence of wind power generation, it is
required to cluster the possible wind power generation scenarios. This article
presents probabilistic wind speed clustering prototype for wind speed data of
Khaaf, Iran. This region is known as one of the high potential wind sites in
Iran and several wind farm projects is planned in this area. The average speed
of wind for a ten-minute period measured at height of 40m over a year (2008) is
used for clustering. From the result of this research, the most appropriate
probabilistic model for the wind speed can be obtained.

Keywords

Renewable energy, wind power, probabilistic model, clustering

References

• D. B. Richardson, "Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration," Renewable and Sustainable Energy Reviews, vol. 19, pp. 247-254, 2013.
• J. Twidell and T. Weir, Renewable energy resources: Routledge, 2015.
• P. Meibom, K. B. Hilger, H. Madsen, and D. Vinther, "Energy comes together in Denmark: The key to a future fossil-free Danish power system," Power and Energy Magazine, IEEE, vol. 11, pp. 46-55, 2013.
• M. Marinelli, F. Sossan, G. T. Costanzo, and H. W. Bindner, "Testing of a predictive control strategy for balancing renewable sources in a microgrid," Sustainable Energy, IEEE Transactions on, vol. 5, pp. 1426-1433, 2014.
• D. Fadare, "The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria," Applied Energy, vol. 87, pp. 934-942, 2010.
• A. Mellit and S. A. Kalogirou, "Artificial intelligence techniques for photovoltaic applications: A review," Progress in energy and combustion science, vol. 34, pp. 574-632, 2008.
• E. Erdem and J. Shi, "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, vol. 88, pp. 1405-1414, 2011.
• J. M. Morales, R. Minguez, and A. J. Conejo, "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, vol. 87, pp. 843-855, 2010.
• G. Chicco, "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, vol. 42, pp. 68-80, 2012.
• G. Gómez, W. D. Cabos, G. Liguori, D. Sein, S. Lozano‐Galeana, L. Fita, et al., "Characterization of the wind speed variability and future change in the Iberian Peninsula and the Balearic Islands," Wind Energy, 2015.
• L. Carro-Calvo, S. Salcedo-Sanz, L. Prieto, N. Kirchner-Bossi, A. Portilla-Figueras, and S. Jiménez-Fernández, "Wind speed reconstruction from synoptic pressure patterns using an evolutionary algorithm," Applied Energy, vol. 89, pp. 347-354, 2012.
• H. Goh, S. Lee, Q. Chua, K. Goh, and K. Teo, "Wind energy assessment considering wind speed correlation in Malaysia," Renewable and Sustainable Energy Reviews, vol. 54, pp. 1389-1400, 2016.
• A. Mostafaeipour, A. Sedaghat, M. Ghalishooyan, Y. Dinpashoh, M. Mirhosseini, M. Sefid, et al., "Evaluation of wind energy potential as a power generation source for electricity production in Binalood, Iran," Renewable energy, vol. 52, pp. 222-229, 2013.
• J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques: Elsevier, 2011.
• C. C. Aggarwal and C. K. Reddy, Data clustering: algorithms and applications: CRC Press, 2013.
• Khorasan.ir, Razavi Khorasan Province Portal, http://www.khorasan.ir.
• http://www.satba.gov.ir/fa/regions/windatlas.