Share This Article:

Toward an Evolutionary Multi-Criteria Model for the Analysis and Estimation of Wind Potential

Abstract Full-Text HTML XML Download Download as PDF (Size:496KB) PP. 14-28
DOI: 10.4236/jpee.2015.311002    3,411 Downloads   3,803 Views   Citations

ABSTRACT

The main objective of this paper is to model, analyze and estimate wind energy at East region of Mohammedia and other Moroccan sites. The basic data were taken from meteorological records of each region. In this context, this work is focused on a methodological approach of a decision support system for optimal choice of wind turbine using multi-criteria model that takes into consideration both the accurate Weibull distribution in the area (wind speed-ground roughness) and the technical parameters of the wind turbine. In this approach we realized an adapted modeling of each element of the turbine (rotor-multiplier-generator). This article also offers a way to forecast wind speed in a region where wind data are not accessible using an artificial neural network.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Amri, F. , Bouattane, O. , Khalili, T. , Raihani, A. and Bifadene, A. (2015) Toward an Evolutionary Multi-Criteria Model for the Analysis and Estimation of Wind Potential. Journal of Power and Energy Engineering, 3, 14-28. doi: 10.4236/jpee.2015.311002.

References

[1] Wind Energy Barometer. EUROBSERV'ER, February 2014.
http://www.energies-renouvelables.org/observ-er/stat_baro/observ/baro-jde14-gb.pdf
[2] Enzili, M., Affani, F. and Nayssa, A. (2007) Les ressources éoliennes du Maroc.
[3] Ouammi, A., Ghigliotti, V., Robba, M., et al. (2012) A Decision Support System for the Optimal Exploitation of Wind Energy on Regional Scale. Renewable Energy, 37, 299-309.
http://dx.doi.org/10.1016/j.renene.2011.06.027
[4] Justus, C.G., Hargraves, W.R. and Yalcin, A. (1976) Nationwide Assessment of Potential Output from Wind-Powered Generators. Journal of Applied Meteorology, 15, 673-678.
http://dx.doi.org/10.1175/1520-0450(1976)015%3C0673:naopof%3E2.0.co;2
[5] Gualtieri, G. and Secci, S. (2012) Methods to Extrapolate Wind Resource to the Turbine Hub Height Based on Power Law: A 1-h Wind Speed vs. Weibull Distribution Extrapolation Comparison. Renewable Energy, 43, 183-200.
http://dx.doi.org/10.1016/j.renene.2011.12.022
[6] Diveux, T., Sebastian, P., Bernard, D., et al. (2001) Horizontal Axis Wind Turbine Systems: Optimization Using Genetic Algorithms. Wind Energy, 4, 151-171.
http://dx.doi.org/10.1002/we.51
[7] Ouammi, A., Dagdougui, H., Sacile, R., et al. (2010) Monthly and Seasonal Assessment of Wind Energy Characteristics at Four Monitored Locations in Liguria Region (Italy). Renewable and Sustainable Energy Reviews, 14, 1959-1968.
http://dx.doi.org/10.1016/j.rser.2010.04.015
[8] Kiranoudis, C.T. and Maroulis, Z.B. (1997) Effective Short-Cut Modelling of Wind Park Efficiency. Renewable Energy, 11, 439-457.
http://dx.doi.org/10.1016/s0960-1481 (97)00011-6
[9] Kiranoudis, C.T., Voros, N.G. and Maroulis, Z.B. (2001) Short-Cut Design of Wind Farms. Energy Policy, 29, 567-578.
http://dx.doi.org/10.1016/s0301-4215 (00)00150-6
[10] Arbaoui, A. (2006) Aide à la décision pour la définition d'un système éolien, adéquation au site et à un réseau faible. Thèse de Doctorat, ENSAM, Paris. https://tel.archives-ouvertes.fr/pastel-00002722/document
[11] Harrison, R. and Jenkins, G. (1994) Cost Modelling of Horizontal Axis Wind Turbines (Phase 2). ETSU W/34/00170/ REP, University of Sunderland, Sunderland.
http://www.opengrey.eu/item/display/10068/633220
[12] Harrison, R., Jenkins, G. and Taylor, R.J. (1989) Cost Modelling of Horizontal Axis Wind Turbines—Results and Conclusions. Wind Engineering, 13, 315-323.
http://www.opengrey.eu/item/display/10068/633220
[13] Ouammi, A., Zejli, D., Dagdougui, H. and Benchrifa, R. (2012) Artificial Neural Network Analysis of Moroccan Solar Potential. Renewable and Sustainable Energy Reviews, 16, 4876-4889.
http://dx.doi.org/10.1016/j.rser.2012.03.071
[14] Ata, R. (2015) Artificial Neural Networks Applications in Wind Energy Systems: A Review. Renewable and Sustainable Energy Reviews, 49, 534-562.
http://dx.doi.org/10.1016/j.rser.2015.04.166
[15] Gardner, M.W. and Dorling, S.R. (1998) Artificial Neural Networks (the Multilayer Perceptron)—A Review of Applications in the Atmospheric Sciences. Atmospheric Environment, 32, 2627-2636.
http://dx.doi.org/10.1016/s1352-2310(97)00447-0
[16] Velo, R., López, P. and Maseda, F. (2014) Wind Speed Estimation Using Multilayer Perceptron. Energy Conversion and Management, 81, 1-9.
http://dx.doi.org/10.1016/j.enconman.2014.02.017
[17] NASA Atmospheric Science Data Center.
http://eosweb.larc.nasa.gov
[18] Chaturvedi, D.K. (2008) Soft Computing. Studies in Computational Intelligence, 103.
http://dx.doi.org/10.1007/978-3-540-77481-5

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.