198
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Risk assessment of renewable energy projects using a novel hybrid fuzzy approach

, &
Pages 1597-1611 | Received 18 Oct 2022, Accepted 29 Dec 2022, Published online: 22 Jan 2023

References

  • Ahmad, S., A. Nadeem, G. Akhanova, T. Houghton, and F. Muhammad-Sukki. 2017. Multi-criteria evaluation of renewable and nuclear resources for electricity generation in Kazakhstan. Energy 141:1880–91. doi:10.1016/j.energy.2017.11.102.
  • Alizadeh, R., L. Soltanisehat, P. D. Lund, and H. Zamanisabzi. 2020. Improving renewable energy policy planning and decision-making through a hybrid MCDM method. Energy Policy 137:111174. doi:10.1016/j.enpol.2019.111174.
  • Alkan, Ö., and Ö. Karadağ Albayrak. 2020. Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA. Renewable Energy 162:712–26. doi:10.1016/j.renene.2020.08.062.
  • Amirhossein, K., W. Desheng, J. H. Lambert, and C. Luo. 2022. Risk assessment of renewable energy projects using uncertain information. International Journal of Energy Research 46 (13):18079–99. n/a (n/a). doi:10.1002/er.8428.
  • Ancot, J. -P., and J. H. P. Paelinck. 1982. Recent experiences with the qualiflex multicriteria method. In Qualitative and quantitative mathematical economics, edited by Badi H. Baltagi,Yongmiao Hong,Gary KoopWalter, Krämer and László Mátyás, 217–66. Dordrecht: Springer.
  • Büyüközkan, G., and S. Güleryüz. 2017. Evaluation of renewable energy resources in Turkey using an integrated MCDM approach with linguistic interval fuzzy preference relations. Energy 123:149–63. doi:10.1016/j.energy.2017.01.137.
  • Büyüközkan, G., Y. Karabulut, and E. Mukul. 2018. A novel renewable energy selection model for United Nations’ sustainable development goals. Energy 165:290–302. doi:10.1016/j.energy.2018.08.215.
  • Chamandoust, H., G. Derakhshan, S. Mehdi Hakimi, and S. Bahramara. 2020. Tri-objective scheduling of residential smart electrical distribution grids with optimal joint of responsive loads with renewable energy sources. Journal of Energy Storage 27:101112. doi:10.1016/j.est.2019.101112.
  • Chang, Y., Z. Fang, and L. Yanfei. 2016. Renewable energy policies in promoting financing and investment among the East Asia summit countries: Quantitative assessment and policy implications. Energy Policy 95:427–36. doi:10.1016/j.enpol.2016.02.017.
  • Chebotareva, G., W. Strielkowski, and D. Streimikiene. 2020. Risk assessment in renewable energy projects: A case of Russia. Journal of Cleaner Production 269:122110. doi:10.1016/j.jclepro.2020.122110.
  • Chen, T.Y., C.H. Chang, and J.F. Rachel Lu. 2013. The extended QUALIFLEX method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making. European Journal of Operational Research 226 (3):615–25. doi:10.1016/j.ejor.2012.11.038.
  • Chen, Y.S., H.M. Chuang, A. Kumar Sangaiah, C.K. Lin, and W.B. Huang. 2019. A study for project risk management using an advanced MCDM-based DEMATEL-ANP approach. Journal of Ambient Intelligence and Humanized Computing 10 (7):2669–81. doi:10.1007/s12652-018-0973-2.
  • Chiu, W.Y., G.H. Tzeng, and L. Han-Lin. 2013. A new hybrid MCDM model combining DANP with VIKOR to improve e-store business. Knowledge-Based Systems 37:48–61. doi:10.1016/j.knosys.2012.06.017.
  • Çolak, M., and İ. Kaya. 2017. Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: A real case application for Turkey. Renewable and Sustainable Energy Reviews 80:840–53. doi:10.1016/j.rser.2017.05.194.
  • Colmenar-Santos, A., A.M. Muñoz-Gómez, E. Rosales-Asensio, and Á. López-Rey. 2019. Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario. Energy 183:61–74. doi:10.1016/j.energy.2019.06.118.
  • Compernolle, T., K. Welkenhuysen, E. Petitclerc, D. Maes, and K. Piessens. 2019. The impact of policy measures on profitability and risk in geothermal energy investments. Energy Economics 84:104524. doi:10.1016/j.eneco.2019.104524.
  • De Luca, G., S. Fabozzi, N. Massarotti, and L. Vanoli. 2018. A renewable energy system for a nearly zero greenhouse city: Case study of a small city in southern Italy. Energy 143:347–62. doi:10.1016/j.energy.2017.07.004.
  • Demir, C., and R. Cergibozan. 2020. Does alternative energy usage converge across Oecd countries? Renewable Energy 146:559–67.
  • Demirel, H., E. Akyuz, E. Celik, and F. Alarcin. 2019. An interval type-2 fuzzy QUALIFLEX approach to measure performance effectiveness of ballast water treatment (BWT) system on-board ship. Ships and Offshore Structures 14 (7):675–83. doi:10.1080/17445302.2018.1551851.
  • Dinçer, H., S. Yüksel, and L. Martinez. 2019. Interval type 2-based hybrid fuzzy evaluation of financial services in E7 economies with DEMATEL-ANP and MOORA methods. Applied Soft Computing 79:186–202. doi:10.1016/j.asoc.2019.03.018.
  • Dong, J.Y., Y. Chen, and S.P. Wan. 2018. A cosine similarity based QUALIFLEX approach with hesitant fuzzy linguistic term sets for financial performance evaluation. Applied Soft Computing 69:316–29. doi:10.1016/j.asoc.2018.04.053.
  • Dong, K., R. Sun, and G. Hochman. 2017. Do natural gas and renewable energy consumption lead to less CO2 emission? Empirical evidence from a panel of BRICS countries. Energy 141:1466–78.
  • Elshkaki, A., and L. Shen. 2019. Energy-material nexus: The impacts of national and international energy scenarios on critical metals use in China up to 2050 and their global implications. Energy 180:903–17. doi:10.1016/j.energy.2019.05.156.
  • Erfani, A., and M. Tavakolan. 2020. Risk evaluation model of wind energy investment projects using modified fuzzy group decision-making and monte carlo simulation. Arthaniti: Journal of Economic Theory and Practice Practice 0976747920963222. doi:10.1177/0976747920963222.
  • Garni, A., A. K. Hassan, A. Awasthi, D. Komljenovic, and K. Al-Haddad. 2016. A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia. Sustainable Energy Technologies and Assessments 16:137–50. doi:10.1016/j.seta.2016.05.006.
  • Ghosh, S., N. Das Chatterjee, and S. Dinda. 2021. Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: A case study on Kolkata Metropolitan Area, India. Sustainable Cities and Society 68:102773. doi:10.1016/j.scs.2021.102773.
  • Heiskanen, E., M. Jalas, J. K. Juntunen, and H. Nissilä. 2017. Small streams, diverse sources: Who invests in renewable energy in Finland during the financial downturn? Energy Policy 106:191–200.
  • Hoang Phong, L., and S. Asumadu Sarkodie. 2020. Dynamic linkage between renewable and conventional energy use, environmental quality and economic growth: Evidence from emerging market and developing economies. Energy Reports 6:965–73. doi:10.1016/j.egyr.2020.04.020.
  • Ilbahar, E., C. Kahraman, and S. Cebi. 2022. Risk assessment of renewable energy investments: A modified failure mode and effect analysis based on prospect theory and intuitionistic fuzzy AHP. Energy 239:121907. doi:10.1016/j.energy.2021.121907.
  • Jing, H., R. Harmsen, W. Crijns-Graus, and E. Worrell. 2018. Barriers to investment in utility-scale variable renewable electricity (VRE) generation projects. Renewable Energy 121:730–44. doi:10.1016/j.renene.2018.01.092.
  • Kahraman, C., B. Öztayşi, İ. Uçal Sarı, and E. Turanoğlu. 2014. Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowledge-Based Systems 59:48–57. doi:10.1016/j.knosys.2014.02.001.
  • Karamoozian, A., and W. Desheng. 2020. A hybrid risk prioritization approach in construction projects using failure mode and effective analysis. Engineering, Construction and Architectural Management 27 (9):2661–86. doi:10.1108/ECAM-10-2019-0535.
  • Karamoozian, A., and D. Wu. 2022. A hybrid approach for the supply chain risk assessment of the construction industry during the COVID-19 pandemic. IEEE Transactions on Engineering Management 1–16. doi:10.1109/TEM.2022.3210083.
  • Karamoozian, A., D. Wu, C. L. P. Chen, and C. Luo. 2019. An approach for risk prioritization in construction projects using analytic network process and decision making trial and evaluation laboratory. IEEE Access 7:159842–54. doi:10.1109/ACCESS.2019.2939067.
  • Kozlova, M., S.E. Fleten, and V. Hagspiel. 2019. Investment timing and capacity choice under rate-of-return regulation for renewable energy support. Energy 174:591–601. doi:10.1016/j.energy.2019.02.175.
  • Krishankumar, R., D. Pamucar, M. Deveci, M. Aggarwal, and K. Soundarapandian Ravichandran. 2022. Assessment of renewable energy sources for smart cities’ demand satisfaction using multi-hesitant fuzzy linguistic based choquet integral approach. Renewable Energy 189:1428–42. doi:10.1016/j.renene.2022.03.081.
  • Kul, C., L. Zhang, and Y. Ahmed Solangi. 2020. Assessing the renewable energy investment risk factors for sustainable development in Turkey. Journal of Cleaner Production 276:124164. doi:10.1016/j.jclepro.2020.124164.
  • LAZAROIU, G., D.A. CIUPAGEANU, and T. VATUIU. 2020. Highlights of renewable energy integration impact: Evolution and perspectives in Romania. Paper presented at the 2020 21st International Symposium on Electrical Apparatus & Technologies (SIELA), Bourgas, Bulgaria.
  • Lee, H.C., and C.T. Chang. 2018. Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renewable and Sustainable Energy Reviews 92:883–96. doi:10.1016/j.rser.2018.05.007.
  • Leng, Z., J. Shuai, H. Sun, Z. Shi, and Z. Wang. 2020. Do China’s wind energy products have potentials for trade with the “Belt and Road” countries?–a gravity model approach. Energy Policy 137:111172.
  • Ligus, M., and P. Peternek. 2018. Determination of most suitable low-emission energy technologies development in Poland using integrated fuzzy AHP-TOPSIS method. Energy Procedia 153:101–06. doi:10.1016/j.egypro.2018.10.046.
  • Liu, Y., J. Carlos R Alcantud, R. M. Rodríguez, K. Qin, and L. Martínez. 2020. Intertemporal hesitant fuzzy soft sets: Application to group decision making. International Journal of Fuzzy Systems 22 (2):619–35. doi:10.1007/s40815-020-00798-w.
  • Liu, X., and M. Zeng. 2017. Renewable energy investment risk evaluation model based on system dynamics. Renewable and Sustainable Energy Reviews 73:782–88. doi:10.1016/j.rser.2017.02.019.
  • Maqbool, R. 2018. Efficiency and effectiveness of factors affecting renewable energy projects; an empirical perspective. Energy 158:944–56. doi:10.1016/j.energy.2018.06.015.
  • McPherson, M., and S. Tahseen. 2018. Deploying storage assets to facilitate variable renewable energy integration: The impacts of grid flexibility, renewable penetration, and market structure. Energy 145:856–70. doi:10.1016/j.energy.2018.01.002.
  • Ming, L., L. Ying, Q. Peng, J. Wang, and Y. Chunxia. 2021. Evaluating community question-answering websites using interval-valued intuitionistic fuzzy DANP and TODIM methods. Applied Soft Computing 99:106918. doi:10.1016/j.asoc.2020.106918.
  • Moorthy, K., N. Patwa, Y. Gupta, Seetharaman, Saravanan. 2019. Breaking barriers in deployment of renewable energy. Heliyon 5 (1):e01166. doi:10.1016/j.heliyon.2019.e01166.
  • Pfeifer, A., V. Dobravec, L. Pavlinek, G. Krajačić, and N. Duić. 2018. Integration of renewable energy and demand response technologies in interconnected energy systems. Energy 161:447–55. doi:10.1016/j.energy.2018.07.134.
  • Qian, C., L. Haoren, L. Chufu, S. Singh, B. Liming, X. Zhao, and A. Y. Ku. 2018. China baseline coal-fired power plant with post-combustion CO2 capture: 1. Definitions and performance. International Journal of Greenhouse Gas Control 78:37–47. doi:10.1016/j.ijggc.2018.07.021.
  • Rani, P., A. Raj Mishra, K. Raj Pardasani, A. Mardani, H. Liao, and D. Streimikiene. 2019. A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India. Journal of Cleaner Production 238:117936. doi:10.1016/j.jclepro.2019.117936.
  • Rodríguez, R. M., A. Labella, and L. Martínez. 2016. An overview on fuzzy modelling of complex linguistic preferences in decision making. International Journal of Computational Intelligence Systems 9 (sup1):81–94. doi:10.1080/18756891.2016.1180821.
  • Sadiqa, A., A. Gulagi, and C. Breyer. 2018. Energy transition roadmap towards 100% renewable energy and role of storage technologies for Pakistan by 2050. Energy 147:518–33. doi:10.1016/j.energy.2018.01.027.
  • Shahnazi, R., and M. Alimohammadlou. 2022. Investigating risks in renewable energy in oil-producing countries through multi-criteria decision-making methods based on interval type-2 fuzzy sets: A case study of Iran. Renewable Energy 191:1009–27. doi:10.1016/j.renene.2022.04.051.
  • Sim, J. 2018. The economic and environmental values of the R&D investment in a renewable energy sector in South Korea. Journal of Cleaner Production 189:297–306. doi:10.1016/j.jclepro.2018.04.074.
  • Šíma, J. 2022. Stronger separation of analog neuron hierarchy by deterministic context-free languages. Neurocomputing 493:605–12. doi:10.1016/j.neucom.2021.12.107.
  • Singh, S., L. Haoren, Q. Cui, L. Chufu, X. Zhao, X. Wenqiang, and A. Y. Ku. 2018. China baseline coal-fired power plant with post-combustion CO2 capture: 2. Techno-economics. International Journal of Greenhouse Gas Control 78:429–36. doi:10.1016/j.ijggc.2018.09.012.
  • Song, S., H. Zhou, and W. Song. 2019. Sustainable shelter-site selection under uncertainty: A rough QUALIFLEX method. Computers & Industrial Engineering 128:371–86. doi:10.1016/j.cie.2018.12.053.
  • Stein, E. W. 2013. A comprehensive multi-criteria model to rank electric energy production technologies. Renewable and Sustainable Energy Reviews 22:640–54. doi:10.1016/j.rser.2013.02.001.
  • Tang, Z., and H. Dinçer. 2019. Selecting the house-of-quality-based energy investment policies for the sustainable emerging economies. Sustainability 11 (13):3514. doi:10.3390/su11133514.
  • Tang, G., J. Long, G. Xiaowei, F. Chiclana, P. Liu, and F. Wang. 2022. Interval type-2 fuzzy programming method for risky multicriteria decision-making with heterogeneous relationship. Information Sciences 584:184–211. doi:10.1016/j.ins.2021.10.044.
  • Tasri, A., and A. Susilawati. 2014. Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia. Sustainable Energy Technologies and Assessments 7:34–44. doi:10.1016/j.seta.2014.02.008.
  • Wang, Y.M. 2009. Centroid defuzzification and the maximizing set and minimizing set ranking based on alpha level sets. Computers & Industrial Engineering 57 (1):228–36. doi:10.1016/j.cie.2008.11.014.
  • Wang, Y., L. Xu, and Y. Ahmed Solangi. 2020. Strategic renewable energy resources selection for Pakistan: Based on SWOT-Fuzzy AHP approach. Sustainable Cities and Society 52:101861. doi:10.1016/j.scs.2019.101861.
  • Xiangrong, L., S. Zhu, S. Yüksel, H. Dinçer, and G. Gülseven Ubay. 2020. Kano-based mapping of innovation strategies for renewable energy alternatives using hybrid interval type-2 fuzzy decision-making approach. Energy 211:118679. doi:10.1016/j.energy.2020.118679.
  • Xie, Q., T. Sunday Adebayo, M. Irfan, and M. Altuntaş. 2022. Race to environmental sustainability: Can renewable energy consumption and technological innovation sustain the strides for China? Renewable Energy. doi:10.1016/j.renene.2022.07.138.
  • Xin-Gang, Z., L. Pei-Ling, and Z. Ying. 2020. Which policy can promote renewable energy to achieve grid parity? Feed-in tariff vs. renewable portfolio standards. Renewable Energy 162:322–33.
  • Xue, Y.X., J.X. You, X. Zhao, and H.C. Liu. 2016. An integrated linguistic MCDM approach for robot evaluation and selection with incomplete weight information. International Journal of Production Research 54 (18):5452–67. doi:10.1080/00207543.2016.1146418.
  • Yang, Y.Y., X.W. Liu, and F. Liu. 2020. Trapezoidal interval type-2 fuzzy TOPSIS using alpha-cuts. International Journal of Fuzzy Systems 22 (1):293–309. doi:10.1007/s40815-019-00777-w.
  • Yunna, W., X. Chuanbo, and T. Zhang. 2018. Evaluation of renewable power sources using a fuzzy MCDM based on cumulative prospect theory: A case in China. Energy 147:1227–39. doi:10.1016/j.energy.2018.01.115.
  • Yunna, W., J. Wang, J. Shaoyu, and Z. Song. 2020. Renewable energy investment risk assessment for nations along China’s Belt & Road Initiative: An ANP-cloud model method. Energy 190:116381. doi:10.1016/j.energy.2019.116381.
  • Yunna, W., B. Zhang, X. Chuanbo, and L. Lingwenying. 2018. Site selection decision framework using fuzzy ANP-VIKOR for large commercial rooftop PV system based on sustainability perspective. Sustainable Cities and Society 40:454–70. doi:10.1016/j.scs.2018.04.024.
  • Zadeh, L. A. 1965. Fuzzy sets. Information and Control 8 (3):338–53. doi:10.1016/S0019-9958(65)90241-X.
  • Zeng, S., C. Jiang, M. Chen, and S. Bin. 2018. Investment efficiency of the new energy industry in China. Energy Economics 70:536–44. doi:10.1016/j.eneco.2017.12.023.
  • Zhou, P., P. Zhou, S. Yüksel, H. Dinçer, and G. Sena Uluer. 2019. Balanced scorecard-based evaluation of sustainable energy investment projects with it2 fuzzy hybrid decision making approach. Energies 13 (1):82. doi:10.3390/en13010082.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.