Abstract
Purpose - This study aims to determine the optimal renewable energy investment project providing a guideline to the investors in decision
making process.
Methodology - This study presents a comprehensive and solid mathematical approach considering the assessment of the ambiguities in
the preferences of the decision maker for selection of the optimal renewable energy investment project via fuzzy analytic network process
(FANP). FANP captures vagueness along with uncertainties in the evaluation.
Findings - After FANP method had been implemented for the considered problem, Hydropower with 31% of importance is selected as
optimum renewable energy investment project for the firm.
Conclusion- This study provides a realistic assessment of energy resources and the consideration of the ambiguities presented in the
preferences of the decision maker
Keywords
Multi Criteria Decision Analysis (MCDA) Fuzzy Logic Fuzzy Analytic Network Process (FANP) Triangular Fuzzy Number Renewable Energy (RE)
References
- Beccali M. et al(2003). Decision-making in energy planning. Application of the Electre method at regional level for the diffusion of renewable energy technology. Renewable Energy 28 2063–2087.
- Benli H. (2013) ''Potential of renewable energy in electrical energy production and sustainable energy development of Turkey: Performance and policies'' Renewable Energy 50: 33-46.
- Boran, S; Goztepe K. (2010) Development of a Fuzzy Decision Support System for Commodity Acquisition Using Fuzzy Analytic Network Process. Expert Systems with Applications, 37 1939–1945.
- Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal Of Operational Research, 95(3), 649-655.
- Dong, M. G., & Li, S. Y. (2016). Project investment decision making with fuzzy information: A literature review of methodologies based on taxonomy. Journal of Intelligent & Fuzzy Systems, 30(6), 3239-3252.
- Haddad B. et al.(2017) A multi-criteria approach to rank renewables for the Algerian electricity system. Renewable Energy,107, 462-472.
- Jefferson, M. (2006). Sustainable energy development: performance and prospects. Renewable Energy, 31(5), 571-582.
- Kahraman C et al (2009) A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy 34:1603–1616.
- Kahraman C, Kaya I (2010) A fuzzy multicriteria methodology for selection among energy alternatives. Expert Systems with Applications 37:6270–6281.
- Kaya I, Kahraman C (2010) Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy 35: 2517-2527.
- Kaya I, Kahraman C (2011) Multicriteria decision making in energy planning using a modified fuzzy TOPSIS Methodology. Expert Systems with Applications 38:6577–6585.
- Lin ,C.T.; Lee, C. and Wu, C.S (2009) Optimizing a Marketing Expert Decision Process for the Private Hotel. Expert Systems with Applications ,36 5613–5619.
- Mohanty, R. P., Agarwal, R., Choudhury, A. K., & Tiwari, M. K. (2005). A fuzzy ANP-based approach to R&D project selection: a case study. International Journal of Production Research, 43(24), 5199-5216.
- Noorollahi Y., Yousefi H. and Mohammadi M.,''Multi-criteria decision support system for wind farm site selection using GIS'' Sustainable Energy Technologies and Assessments 13 (2016), 38–50.
- Promentilla, M. A. B., Furuichi, T., Ishii, K., & Tanikawa, N. (2008). A fuzzy analytic network process for multi-criteria evaluation of contaminated site remedial countermeasures. Journal of Environmental Management, 88(3), 479-495.
- Saaty, T. L. (2005) Theory and applications of the analytic network process RWS Publications, Pittsburgh.
- Saaty, T. L., & Vargas, L. G. (2006). Decision making with the analytic network process. Springer Science Business Media, LLC.
- Stein Eric W. (2013) A comprehensive multi-criteria model to rank electric energy production technologies. Renewable and Sustainable Energy Reviews, 22 (2013).640–654.
- Taha, R. A., & Daim, T. (2013). Multi-criteria applications in renewable energy analysis, a literature review. In Research and Technology Management in the Electricity Industry (pp. 17-30). Springer London.
- Tasri A. and Susilawati A.(2014). Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia. Sustainable Energy Technologies and Assessments 7 (2014), 34–44.
- Troldborg M. et al. (2014) Assessing the sustainability of renewable energy technologies using multi-criteria analysis: Suitability of approach for national-scale assessments and associated uncertainties. Renewable and Sustainable Energy Reviews 39 (2014)1173–1184.
- Zhang et al (2015) Evaluating clean energy alternatives for Jiangsu, China: An improved multi-criteria decision making method. Energy 90: 953–964.
- Zhou, X. (2012). Fuzzy analytical network process implementation with matlab. MATLAB–A fundamental tool for scientific computing and engineering applications, 3, 133-160.
- Zhu, K. J., Jing, Y., & Chang, D. Y. (1999). A discussion on extent analysis method and applications of fuzzy AHP. European Journal Of Operational Research, 116(2), 450-456.
Abstract
References
- Beccali M. et al(2003). Decision-making in energy planning. Application of the Electre method at regional level for the diffusion of renewable energy technology. Renewable Energy 28 2063–2087.
- Benli H. (2013) ''Potential of renewable energy in electrical energy production and sustainable energy development of Turkey: Performance and policies'' Renewable Energy 50: 33-46.
- Boran, S; Goztepe K. (2010) Development of a Fuzzy Decision Support System for Commodity Acquisition Using Fuzzy Analytic Network Process. Expert Systems with Applications, 37 1939–1945.
- Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal Of Operational Research, 95(3), 649-655.
- Dong, M. G., & Li, S. Y. (2016). Project investment decision making with fuzzy information: A literature review of methodologies based on taxonomy. Journal of Intelligent & Fuzzy Systems, 30(6), 3239-3252.
- Haddad B. et al.(2017) A multi-criteria approach to rank renewables for the Algerian electricity system. Renewable Energy,107, 462-472.
- Jefferson, M. (2006). Sustainable energy development: performance and prospects. Renewable Energy, 31(5), 571-582.
- Kahraman C et al (2009) A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy 34:1603–1616.
- Kahraman C, Kaya I (2010) A fuzzy multicriteria methodology for selection among energy alternatives. Expert Systems with Applications 37:6270–6281.
- Kaya I, Kahraman C (2010) Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy 35: 2517-2527.
- Kaya I, Kahraman C (2011) Multicriteria decision making in energy planning using a modified fuzzy TOPSIS Methodology. Expert Systems with Applications 38:6577–6585.
- Lin ,C.T.; Lee, C. and Wu, C.S (2009) Optimizing a Marketing Expert Decision Process for the Private Hotel. Expert Systems with Applications ,36 5613–5619.
- Mohanty, R. P., Agarwal, R., Choudhury, A. K., & Tiwari, M. K. (2005). A fuzzy ANP-based approach to R&D project selection: a case study. International Journal of Production Research, 43(24), 5199-5216.
- Noorollahi Y., Yousefi H. and Mohammadi M.,''Multi-criteria decision support system for wind farm site selection using GIS'' Sustainable Energy Technologies and Assessments 13 (2016), 38–50.
- Promentilla, M. A. B., Furuichi, T., Ishii, K., & Tanikawa, N. (2008). A fuzzy analytic network process for multi-criteria evaluation of contaminated site remedial countermeasures. Journal of Environmental Management, 88(3), 479-495.
- Saaty, T. L. (2005) Theory and applications of the analytic network process RWS Publications, Pittsburgh.
- Saaty, T. L., & Vargas, L. G. (2006). Decision making with the analytic network process. Springer Science Business Media, LLC.
- Stein Eric W. (2013) A comprehensive multi-criteria model to rank electric energy production technologies. Renewable and Sustainable Energy Reviews, 22 (2013).640–654.
- Taha, R. A., & Daim, T. (2013). Multi-criteria applications in renewable energy analysis, a literature review. In Research and Technology Management in the Electricity Industry (pp. 17-30). Springer London.
- Tasri A. and Susilawati A.(2014). Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia. Sustainable Energy Technologies and Assessments 7 (2014), 34–44.
- Troldborg M. et al. (2014) Assessing the sustainability of renewable energy technologies using multi-criteria analysis: Suitability of approach for national-scale assessments and associated uncertainties. Renewable and Sustainable Energy Reviews 39 (2014)1173–1184.
- Zhang et al (2015) Evaluating clean energy alternatives for Jiangsu, China: An improved multi-criteria decision making method. Energy 90: 953–964.
- Zhou, X. (2012). Fuzzy analytical network process implementation with matlab. MATLAB–A fundamental tool for scientific computing and engineering applications, 3, 133-160.
- Zhu, K. J., Jing, Y., & Chang, D. Y. (1999). A discussion on extent analysis method and applications of fuzzy AHP. European Journal Of Operational Research, 116(2), 450-456.
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | July 13, 2018 |
| Published in Issue | Year 2018 Volume: 5 Issue: 2 |
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