Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, , 143 - 161, 31.08.2022
https://doi.org/10.54187/jnrs.1159263

Öz

Kaynakça

  • E. B. Agyekum, Energy poverty in energy rich Ghana: A SWOT analytical approach for the development of Ghana’s renewable energy, Sustainable Energy Technologies and Assessments, 40, (2020) 1–9.
  • F. Adusah-Poku, K. Takeuchi, Energy poverty in Ghana: any progress so far? Renewable Sustainable Energy Reviews, 112, (2019) 853–64.
  • C. Emeksiz, B. Demirci, The determination of offshore wind energy potential of Turkey by using novelty hybrid site selection method, Sustainable Energy Technologies and Assessments, 36, (2019) 1–21.
  • Renewables Global Status Report-REN 21, https://www.ren21.net/reports/global-status-report/, 2021 (accessed 15 May 2022)
  • M. Lydia, S. S. Kumar, A. I. Selvakumar, G. E. P. Kumar, A comprehensive review on wind turbine power curve modeling techniques, Renewable and Sustainable Energy Reviews, 30, (2014) 452–460.
  • C. Carrillo, A. O. Montaño, J. Cidrás, E. Díaz-Dorado, Review of power curve modelling for wind turbines, Renewable and Sustainable Energy Reviews, 21, (2013) 572–581.
  • I. Tiseo, Global market share of wind turbine OEMs 2019. https://www.statista.com/statistics/554377/wind-turbine-suppliers-globally-based-on-market-share/, 2021 (accessed 20 November 2021).
  • F. A. Jowder, Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain, Applied Energy, 86(4), (2009) 538–545.
  • S. E. Alimi, T. Maatallah, A. W. Dahmouni, S. B. Nasrallah, Modeling and investigation of the wind resource in the gulf of Tunis, Tunisia, Renewable and Sustainable Energy Reviews, 16(8), (2012) 5466–5478.
  • L. De Araujo Lima, C. R. B. Filho, Wind resource evaluation in são joão do cariri (sjc) – Paraiba, Brazil, Renewable and Sustainable Energy Reviews, 16(1), (2012) 474–480.
  • A. H. I. Lee, H. H. Chen, H. Y. Kang, Multi-criteria decision making on strategic selection of wind farms, Renewable Energy, 34(1), (2009)120–126.
  • M. S. Adaramola, M. Agelin-Chaab, S. S. Paul, Assessment of wind power generation along the coast of Ghana, Energy Conversion and Management, 77, (2014) 61–69.
  • M. S. Adaramola, O. M. Oyewola, O. S. Ohunakin, O. O. Akinnawonu, Performance evaluation of wind turbines for energy generation in niger delta, Nigeria, Sustainable Energy Technologies and Assessments, 6, (2014) 75–85.
  • A. Kolios, M. Collu, A. Chahardehi, F. P. Brennan, M. H. Patel, A multi-criteria decision-making method to compare support structures for offshore wind turbines, in: N. Ladefoged, T. L. Destaintot, J. Mroczek, U. Nuscheler (Eds.), European Wind Energy Conference 2010, Warsaw, Poland, 2010, pp. 21–23.
  • F. G. Montoya, F. Manzano-Agugliaro, S. Lopez-Marquez, Q. Hernandez-Escobedo, Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms, Expert Systems with Applications, 41(15), (2014) 6585–6595.
  • S. Nahi, S. M. H. Nabavi, Choose suitable wind turbines for manjil wind power plant using Monte Carlo simulation, International Journal of Computer Applications, 15(1), (2011) 26–34.
  • S. Shokrzadeh, Wind turbine power curve modeling using advanced parametric and nonparametric methods, IEEE Transactions on Sustainable Energy, 5(4), (2014) 1262–1269.
  • M. Di Somma, B. Yan, N. Bianco, G. Graditi, P. B. Luh, L. Mongibello, V. Naso, Operation optimisation of a distributed energy system considering energy costs and exergy efficiency, Energy Conversion and Management, 103, (2015) 739–751.
  • B. Yan, M. Somma, N. Bianco, G. Graditi, P. B. Luh, L. Mongibello, V. Naso, Exergy-based operation optimisation of a distributed energy system through the energy-supply chain, Applied Thermal Engineering, 101, (2016) 741–751.
  • R. Haaren Van, F. Vasilis, GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): Evaluating the case for New York State, Renewable and Sustainable Energy Reviews, 15(7), (2011) 3332–3340.
  • S. K. Kim, O. Song, A MAUT approach for selecting a dismantling scenario for the thermal column in KRR-1, Annals of Nuclear Energy, 36(2), (2009) 145–150.
  • R. Keeney, F. P. Seven, Independence concepts and continuous multi-attribute utility functions, Journal of Mathematical Psychology, 11(3), (1974) 294–327.
  • E. Loken, Use of multi-criteria decision analysis methods for energy planning problems, Renewable and Sustainable Energy Reviews, 11(7), (2007) 1584–95.
  • P. K. Dean Ting, C. Zhang, B. Wang, A. Deshmukh, B. Dubrosky, Product and Process Cost Estimation with Fuzzy Multi-Attribute Utility Theory, The Engineering Economist, 44(4), (1999) 303–331.
  • D. Winterfeldt, W. Von Edwards, Decision analysis and behavioral research, Cambridge University Press, 1986.
  • A. Ishızaka, P. Nemery, Multi-criteria decision analysis: Methods and software, John Wiley & Sons Ltd. Published, Chichester/UK, 2013.
  • H. Zhang, C. L. Gu, L. W. Gu, Y. Zhang, The Evaluation of Tourism Destination Competitiveness by TOPSIS & Information Entropy–A Case in The Yangtze River Delta of China, Tourism Management, 32(2), (2011) 443–451.
  • J. Wu, J. Sun, L. Liang, Y. Zha, Determination of Weights for Ultimate Cross Efficiency Using Shannon Entropy, Expert Systems with Applications, 38(5), (2011) 5162–5165.
  • A. Karami, R. Johansson, Utilisation of multi attribute decision making techniques to integrate automatic and manual ranking of options, Journal of Information Science and Engineering, 30(2), (2014) 519–534.
  • R. W. Saaty, The analytic hierarchy process—what it is and how it is used, Mathematical Modelling, 9(3-5), (1987) 161–76.
  • L. Mikhailov, P. Tsventinov, Evaluation of services using a fuzzy analytic hierarchy process, Applied Soft Computing, 5(1), (2004) 23–33.
  • T. L. Saaty, The analytical hierarchy process: Planning, priority setting, resource allocation, New York: McGraw-Hill, 1980.
  • J. A. Alonso, M. T. Lamata, Consistency in the analytic hierarchy process: A new approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14(4), (2006) 445−459.
  • E. H. Forman, Random indices for incomplete pairwise comparison matrices, European Journal of Operational Research, 48(1), (1990) 153−155.
  • H. Martin, G. Spano, J. F. Küster, M. Collu, A. J. Kolios, Application and extension of the TOPSIS method for the assessment of floating offshore wind turbine support structures, Ships and Offshore Structures, 8(5), (2013) 477−487.
  • Wind turbine models, https://en.wind-turbine-models.com/turbines, 2022, (accessed 18 January 2022).

A suitable wind turbine selection for achieving maximum efficiency from wind energy by an adaptive hybrid multi-criteria decision-making approach

Yıl 2022, , 143 - 161, 31.08.2022
https://doi.org/10.54187/jnrs.1159263

Öz

Wind energy is rapidly developing and gaining great importance among renewable energy sources. Moreover, wind energy is an important renewable energy option that is clean and environmentally friendly but has comparatively high costs. Wind turbines especially play an essential role in increasing wind energy conversion systems costs. For this reason, choosing the most suitable turbine in planning wind energy systems is very valuable for investors. The approaches used in literature studies have a limited perspective. Therefore, this study presented an adaptive hybrid multi-criteria decision-making approach for the first time in the appropriate wind turbine selection. Expert interviews and literature reviews were considered in the application phase of the model. Four mains (technical, economic, environmental, and customer service criteria) and seventeen sub-criteria were applied for the thirty-five wind turbine brands selected in the suggested adaptive hybrid assessment model. Additionally, the consistency analysis performed to test the consistency of comparisons shows that the analyses and choices have high consistency. The adaptive hybrid model suggested in this study can also be easily used to select a suitable wind turbine for onshore and offshore wind farm planning.

Kaynakça

  • E. B. Agyekum, Energy poverty in energy rich Ghana: A SWOT analytical approach for the development of Ghana’s renewable energy, Sustainable Energy Technologies and Assessments, 40, (2020) 1–9.
  • F. Adusah-Poku, K. Takeuchi, Energy poverty in Ghana: any progress so far? Renewable Sustainable Energy Reviews, 112, (2019) 853–64.
  • C. Emeksiz, B. Demirci, The determination of offshore wind energy potential of Turkey by using novelty hybrid site selection method, Sustainable Energy Technologies and Assessments, 36, (2019) 1–21.
  • Renewables Global Status Report-REN 21, https://www.ren21.net/reports/global-status-report/, 2021 (accessed 15 May 2022)
  • M. Lydia, S. S. Kumar, A. I. Selvakumar, G. E. P. Kumar, A comprehensive review on wind turbine power curve modeling techniques, Renewable and Sustainable Energy Reviews, 30, (2014) 452–460.
  • C. Carrillo, A. O. Montaño, J. Cidrás, E. Díaz-Dorado, Review of power curve modelling for wind turbines, Renewable and Sustainable Energy Reviews, 21, (2013) 572–581.
  • I. Tiseo, Global market share of wind turbine OEMs 2019. https://www.statista.com/statistics/554377/wind-turbine-suppliers-globally-based-on-market-share/, 2021 (accessed 20 November 2021).
  • F. A. Jowder, Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain, Applied Energy, 86(4), (2009) 538–545.
  • S. E. Alimi, T. Maatallah, A. W. Dahmouni, S. B. Nasrallah, Modeling and investigation of the wind resource in the gulf of Tunis, Tunisia, Renewable and Sustainable Energy Reviews, 16(8), (2012) 5466–5478.
  • L. De Araujo Lima, C. R. B. Filho, Wind resource evaluation in são joão do cariri (sjc) – Paraiba, Brazil, Renewable and Sustainable Energy Reviews, 16(1), (2012) 474–480.
  • A. H. I. Lee, H. H. Chen, H. Y. Kang, Multi-criteria decision making on strategic selection of wind farms, Renewable Energy, 34(1), (2009)120–126.
  • M. S. Adaramola, M. Agelin-Chaab, S. S. Paul, Assessment of wind power generation along the coast of Ghana, Energy Conversion and Management, 77, (2014) 61–69.
  • M. S. Adaramola, O. M. Oyewola, O. S. Ohunakin, O. O. Akinnawonu, Performance evaluation of wind turbines for energy generation in niger delta, Nigeria, Sustainable Energy Technologies and Assessments, 6, (2014) 75–85.
  • A. Kolios, M. Collu, A. Chahardehi, F. P. Brennan, M. H. Patel, A multi-criteria decision-making method to compare support structures for offshore wind turbines, in: N. Ladefoged, T. L. Destaintot, J. Mroczek, U. Nuscheler (Eds.), European Wind Energy Conference 2010, Warsaw, Poland, 2010, pp. 21–23.
  • F. G. Montoya, F. Manzano-Agugliaro, S. Lopez-Marquez, Q. Hernandez-Escobedo, Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms, Expert Systems with Applications, 41(15), (2014) 6585–6595.
  • S. Nahi, S. M. H. Nabavi, Choose suitable wind turbines for manjil wind power plant using Monte Carlo simulation, International Journal of Computer Applications, 15(1), (2011) 26–34.
  • S. Shokrzadeh, Wind turbine power curve modeling using advanced parametric and nonparametric methods, IEEE Transactions on Sustainable Energy, 5(4), (2014) 1262–1269.
  • M. Di Somma, B. Yan, N. Bianco, G. Graditi, P. B. Luh, L. Mongibello, V. Naso, Operation optimisation of a distributed energy system considering energy costs and exergy efficiency, Energy Conversion and Management, 103, (2015) 739–751.
  • B. Yan, M. Somma, N. Bianco, G. Graditi, P. B. Luh, L. Mongibello, V. Naso, Exergy-based operation optimisation of a distributed energy system through the energy-supply chain, Applied Thermal Engineering, 101, (2016) 741–751.
  • R. Haaren Van, F. Vasilis, GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): Evaluating the case for New York State, Renewable and Sustainable Energy Reviews, 15(7), (2011) 3332–3340.
  • S. K. Kim, O. Song, A MAUT approach for selecting a dismantling scenario for the thermal column in KRR-1, Annals of Nuclear Energy, 36(2), (2009) 145–150.
  • R. Keeney, F. P. Seven, Independence concepts and continuous multi-attribute utility functions, Journal of Mathematical Psychology, 11(3), (1974) 294–327.
  • E. Loken, Use of multi-criteria decision analysis methods for energy planning problems, Renewable and Sustainable Energy Reviews, 11(7), (2007) 1584–95.
  • P. K. Dean Ting, C. Zhang, B. Wang, A. Deshmukh, B. Dubrosky, Product and Process Cost Estimation with Fuzzy Multi-Attribute Utility Theory, The Engineering Economist, 44(4), (1999) 303–331.
  • D. Winterfeldt, W. Von Edwards, Decision analysis and behavioral research, Cambridge University Press, 1986.
  • A. Ishızaka, P. Nemery, Multi-criteria decision analysis: Methods and software, John Wiley & Sons Ltd. Published, Chichester/UK, 2013.
  • H. Zhang, C. L. Gu, L. W. Gu, Y. Zhang, The Evaluation of Tourism Destination Competitiveness by TOPSIS & Information Entropy–A Case in The Yangtze River Delta of China, Tourism Management, 32(2), (2011) 443–451.
  • J. Wu, J. Sun, L. Liang, Y. Zha, Determination of Weights for Ultimate Cross Efficiency Using Shannon Entropy, Expert Systems with Applications, 38(5), (2011) 5162–5165.
  • A. Karami, R. Johansson, Utilisation of multi attribute decision making techniques to integrate automatic and manual ranking of options, Journal of Information Science and Engineering, 30(2), (2014) 519–534.
  • R. W. Saaty, The analytic hierarchy process—what it is and how it is used, Mathematical Modelling, 9(3-5), (1987) 161–76.
  • L. Mikhailov, P. Tsventinov, Evaluation of services using a fuzzy analytic hierarchy process, Applied Soft Computing, 5(1), (2004) 23–33.
  • T. L. Saaty, The analytical hierarchy process: Planning, priority setting, resource allocation, New York: McGraw-Hill, 1980.
  • J. A. Alonso, M. T. Lamata, Consistency in the analytic hierarchy process: A new approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14(4), (2006) 445−459.
  • E. H. Forman, Random indices for incomplete pairwise comparison matrices, European Journal of Operational Research, 48(1), (1990) 153−155.
  • H. Martin, G. Spano, J. F. Küster, M. Collu, A. J. Kolios, Application and extension of the TOPSIS method for the assessment of floating offshore wind turbine support structures, Ships and Offshore Structures, 8(5), (2013) 477−487.
  • Wind turbine models, https://en.wind-turbine-models.com/turbines, 2022, (accessed 18 January 2022).
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Articles
Yazarlar

Cem Emeksiz 0000-0002-4817-9607

Abdullah Yüksel 0000-0001-6638-7867

Yayımlanma Tarihi 31 Ağustos 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Emeksiz, C., & Yüksel, A. (2022). A suitable wind turbine selection for achieving maximum efficiency from wind energy by an adaptive hybrid multi-criteria decision-making approach. Journal of New Results in Science, 11(2), 143-161. https://doi.org/10.54187/jnrs.1159263
AMA Emeksiz C, Yüksel A. A suitable wind turbine selection for achieving maximum efficiency from wind energy by an adaptive hybrid multi-criteria decision-making approach. JNRS. Ağustos 2022;11(2):143-161. doi:10.54187/jnrs.1159263
Chicago Emeksiz, Cem, ve Abdullah Yüksel. “A Suitable Wind Turbine Selection for Achieving Maximum Efficiency from Wind Energy by an Adaptive Hybrid Multi-Criteria Decision-Making Approach”. Journal of New Results in Science 11, sy. 2 (Ağustos 2022): 143-61. https://doi.org/10.54187/jnrs.1159263.
EndNote Emeksiz C, Yüksel A (01 Ağustos 2022) A suitable wind turbine selection for achieving maximum efficiency from wind energy by an adaptive hybrid multi-criteria decision-making approach. Journal of New Results in Science 11 2 143–161.
IEEE C. Emeksiz ve A. Yüksel, “A suitable wind turbine selection for achieving maximum efficiency from wind energy by an adaptive hybrid multi-criteria decision-making approach”, JNRS, c. 11, sy. 2, ss. 143–161, 2022, doi: 10.54187/jnrs.1159263.
ISNAD Emeksiz, Cem - Yüksel, Abdullah. “A Suitable Wind Turbine Selection for Achieving Maximum Efficiency from Wind Energy by an Adaptive Hybrid Multi-Criteria Decision-Making Approach”. Journal of New Results in Science 11/2 (Ağustos 2022), 143-161. https://doi.org/10.54187/jnrs.1159263.
JAMA Emeksiz C, Yüksel A. A suitable wind turbine selection for achieving maximum efficiency from wind energy by an adaptive hybrid multi-criteria decision-making approach. JNRS. 2022;11:143–161.
MLA Emeksiz, Cem ve Abdullah Yüksel. “A Suitable Wind Turbine Selection for Achieving Maximum Efficiency from Wind Energy by an Adaptive Hybrid Multi-Criteria Decision-Making Approach”. Journal of New Results in Science, c. 11, sy. 2, 2022, ss. 143-61, doi:10.54187/jnrs.1159263.
Vancouver Emeksiz C, Yüksel A. A suitable wind turbine selection for achieving maximum efficiency from wind energy by an adaptive hybrid multi-criteria decision-making approach. JNRS. 2022;11(2):143-61.


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