A Neutrosophic Decision Analytics Model for the Sustainability Performance Assessment of Hydroelectric Power Plants

Galip Cihan Yalçın; Karahan Kara; Vladimir Simic; Yavuz Balta; Dragan Pamucar

doi:10.31181/sems41202686

Authors

Galip Cihan Yalçın 1) Department of Business, Faculty of Economics and Administrative Sciences, OSTIM Technical University, 06374 Ankara, Türkiye Author https://orcid.org/0000-0001-9348-0709
Karahan Kara 2) Department of Data Science and Analytics, İzmir Katip Çelebi University, 35620 İzmir, Türkiye; 1) Department of Business, Faculty of Economics and Administrative Sciences, OSTIM Technical University, 06374 Ankara, Türkiye; 3) Department of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Chennai, India Author https://orcid.org/0000-0002-1359-0244
Vladimir Simic 4) Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 02841, Republic of Korea; 5) BEU-Scientific Research Center, Baku Engineering University, AZ0101, Baku, Azerbaijan; 6) Faculty of Engineering, Dogus University, 34775 Umraniye, Istanbul, Türkiye Author https://orcid.org/0009-0003-5836-6881
Yavuz Balta 7) Artvin Vocational School, Artvin Çoruh University, 08300 Artvin, Turkey Author https://orcid.org/0000-0003-0671-658X
Dragan Pamucar 8) Industrial Engineering Department, Faculty of Engineering and Natural Sciences, İstinye University, 34396 İstanbul, Türkiye; 9) Applied Artificial Intelligence Research Center, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan; 10) Faculty of Engineering and Technology, Sunway University, Selangor, Malaysia Author https://orcid.org/0009-0004-2488-9403

DOI:

https://doi.org/10.31181/sems41202686

Keywords:

Sustainable Performance, Hydroelectric Power Plants, Engineering Sciences, Type-2 Neutrosophic Numbers, Logarithmic Percentage Change-driven Objective Weighting, Combined Compromise Solution

Abstract

Evaluating the sustainable performance of hydroelectric power plants is of paramount importance, considering their long-term impact on multiple dimensions. This paper proposes a new decision-making model that combines the type-2 neutrosophic logarithmic percentage change-driven objective weighting (LOPCOW) method with the combined compromise solution (CoCoSo) approach to evaluate the sustainable performance of hydroelectric power plants. The methodology incorporates type-2 neutrosophic sets to handle uncertainties efficiently. One of the main contributions of this study is the introduction of two new aggregation methods, which are designed using trigonometric t-norm and t-conorm functions to enhance decision-making under uncertainty. These operators enhance the accuracy of performance evaluation in complex and uncertain decision-making environments. To validate the hybrid model, a case study is conducted on hydroelectric power plants with an installed capacity from 10 to 50 megawatts in the Artvin province of Türkiye. The results of the model application demonstrated its effectiveness in assessing the sustainable performance of hydroelectric power plants. Furthermore, sensitivity analysis scenarios are employed to test the model's robustness, confirming its reliability. The model's ability to consider both qualitative and quantitative criteria, combined with its robustness, renders it a valuable tool for decision-makers in the field of sustainable energy.

Downloads

Download data is not yet available.

References

Rahman, A., Farrok, O., & Haque, M. M. (2022). Environmental impact of renewable energy source based electrical power plants: Solar, wind, hydroelectric, biomass, geothermal, tidal, ocean, and osmotic. Renewable and Sustainable Energy Reviews, 161, 112279. https://doi.org/10.1016/j.rser.2022.112279.

Yildiz, V., & Vrugt, J. A. (2019). A toolbox for the optimal design of run-of-river hydropower plants. Environmental Modelling & Software, 111, 134-152. https://doi.org/10.1016/j.envsoft.2018.08.018.

Jager, H. I., & Bevelhimer, M. S. (2007). How run-of-river operation affects hydropower generation and value. Environmental Management, 40, 1004-1015. https://doi.org/10.1007/s00267-007-9008-z.

Kuriqi, A., Pinheiro, A. N., Sordo-Ward, A., Bejarano, M. D., & Garrote, L. (2021). Ecological impacts of run-of-river hydropower plants—Current status and future prospects on the brink of energy transition. Renewable and Sustainable Energy Reviews, 142, 110833. https://doi.org/10.1016/j.rser.2021.110833.

Singh, V. K., & Singal, S. K. (2017). Operation of hydro power plants-a review. Renewable and Sustainable Energy Reviews, 69, 610-619. https://doi.org/10.1016/j.rser.2016.11.169.

Zahedi, R., Eskandarpanah, R., Akbari, M., Rezaei, N., Mazloumin, P., & Farahani, O. N. (2022). Development of a new simulation model for the reservoir hydropower generation. Water Resources Management, 36(7), 2241-2256. https://doi.org/10.1007/s11269-022-03138-9.

Güney, M. S., & Kaygusuz, K. (2010). Hydrokinetic energy conversion systems: A technology status review. Renewable and Sustainable Energy Reviews, 14(9), 2996-3004. https://doi.org/10.1016/j.rser.2010.06.016.

Yüksel, I. (2010). Hydropower for sustainable water and energy development. Renewable and Sustainable Energy Reviews, 14(1), 462-469. https://doi.org/10.1016/j.rser.2009.07.025.

Tiago Filho, G. L., dos Santos, I. F. S., & Barros, R. M. (2017). Cost estimate of small hydroelectric power plants based on the aspect factor. Renewable and Sustainable Energy Reviews, 77, 229-238. https://doi.org/10.1016/j.rser.2017.03.134.

Loucks, D.P., van Beek, E. (2017). Water Resources Planning and Management: An Overview. In: Water Resource Systems Planning and Management. Springer, Cham. https://doi.org/10.1007/978-3-319-44234-1_1.

Abbasi, S. A., & Abbasi, N. (2000). The likely adverse environmental impacts of renewable energy sources. Applied Energy, 65(1-4), 121-144. https://doi.org/10.1016/S0306-2619(99)00077-X.

Zhang, S., Pang, B., & Zhang, Z. (2015). Carbon footprint analysis of two different types of hydropower schemes: comparing earth-rockfill dams and concrete gravity dams using hybrid life cycle assessment. Journal of Cleaner Production, 103, 854-862. https://doi.org/10.1016/j.jclepro.2014.06.053.

Jawahar, C. P., & Michael, P. A. (2017). A review on turbines for micro hydro power plant. Renewable and Sustainable Energy Reviews, 72, 882-887. https://doi.org/10.1016/j.rser.2017.01.133.

Krishna, V. M., Sandeep, V., Murthy, S. S., & Yadlapati, K. (2022). Experimental investigation on performance comparison of self excited induction generator and permanent magnet synchronous generator for small scale renewable energy applications. Renewable Energy, 195, 431-441. https://doi.org/10.1016/j.renene.2022.06.051.

Kuriqi, A., Pinheiro, A. N., Sordo-Ward, A., & Garrote, L. (2019). Flow regime aspects in determining environmental flows and maximising energy production at run-of-river hydropower plants. Applied Energy, 256, 113980. https://doi.org/10.1016/j.apenergy.2019.113980.

Vinchurkar, S. H., & Samtani, B. K. (2019). Performance evaluation of the hydropower plants using various Multi-Criteria Decision-Making techniques. International Journal of Engineering and Advanced Technology, 8(6), 2131-2138. https://doi.org/10.35940/ijeat.F8490.088619.

Wang, B., Nistor, I., Murty, T., & Wei, Y. M. (2014). Efficiency assessment of hydroelectric power plants in Canada: A multi criteria decision making approach. Energy Economics, 46, 112-121. https://doi.org/10.1016/j.eneco.2014.09.001.

Özcan, E., Danışan, T., Yumuşak, R., & Eren, T. (2020). An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants. Eksploatacja i Niezawodność, 22(3), 400-418. http://dx.doi.org/10.17531/ein.2020.3.3.

Chien, F., Wang, C. N., Nguyen, V. T., Nguyen, V. T., & Chau, K. Y. (2020). An evaluation model of quantitative and qualitative fuzzy multi-criteria decision-making approach for hydroelectric plant location selection. Energies, 13(11), 2783. https://doi.org/10.3390/en13112783.

Temel, P., Kentel, E., & Alp, E. (2023). Development of a site selection methodology for run-of-river hydroelectric power plants within the water-energy-ecosystem nexus. Science of the Total Environment, 856, 159152. https://doi.org/10.1016/j.scitotenv.2022.159152.

Sarkar, A., Senapati, T., Jin, L., Mesiar, R., Biswas, A., & Yager, R. R. (2023). Sugeno–Weber Triangular Norm-Based Aggregation Operators Under T-Spherical Fuzzy Hypersoft Context. Information Sciences, 654, 119305. https://doi.org/10.1016/j.ins.2023.119305.

Haseli, G., Bonab, S. R., Hajiaghaei-Keshteli, M., Ghoushchi, S. J., & Deveci, M. (2024). Fuzzy ZE-numbers Framework in Group Decision-making using the BCM and CoCoSo to Address Sustainable Urban Transportation. Information Sciences, 653, 119809. https://doi.org/10.1016/j.ins.2023.119809.

Fei, W. (2023). Novel score function and standard coefficient-based single-valued neutrosophic MCDM for live streaming sales. Information Sciences, 119836. https://doi.org/10.1016/j.ins.2023.119836.

Mishra, A. R., Krishankumar, R., Cavallaro, F., Lodhi, R. K., & Ravichandran, K. S. (2023). Single-Valued Neutrosophic CRITIC-Based ARAS Method for the Assessment of Sustainable Circular Supplier Selection. In Decision Making Using AI in Energy and Sustainability: Methods and Models for Policy and Practice (pp. 49-73). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-38387-8_3.

Simic, V., Milovanović, B., Pantelić, S., Pamučar, D., & Tirkolaee, E. B. (2023). Sustainable route selection of petroleum transportation using a type-2 neutrosophic number based ITARA-EDAS model. Information Sciences, 622, 732-754. https://doi.org/10.1016/j.ins.2022.11.105.

Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690.

Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501-2519. https://doi.org/10.1108/MD-05-2017-0458.

Abdel-Basset, M., Saleh, M., Gamal, A., & Smarandache, F. (2019). An approach of TOPSIS technique for developing supplier selection with group decision making under type-2 neutrosophic number. Applied Soft Computing, 77, 438-452. https://doi.org/10.1016/j.asoc.2019.01.035.

Ye, J., Du, S., & Yong, R. (2023). MCDM technique using single-valued neutrosophic trigonometric weighted aggregation operators. Journal of Management Analytics, 1-17. https://doi.org/10.1080/23270012.2023.2264294.

Simic, V., Gokasar, I., Deveci, M., & Karakurt, A. (2022). An integrated CRITIC and MABAC based type-2 neutrosophic model for public transportation pricing system selection. Socio-Economic Planning Sciences, 80, 101157. https://doi.org/10.1016/j.seps.2021.101157.

Hydroelectric Power Plants, (2023). Hydroelectric Power Plants. https://www.enerjiatlasi.com/ (Access Date: 23.01.2026).

Manirathinam, T., Narayanamoorthy, S., Geetha, S., Othman, M. F. I., Alotaibi, B. S., Ahmadian, A., & Kang, D. (2023). Sustainable renewable energy system selection for self-sufficient households using integrated fermatean neutrosophic fuzzy stratified AHP-MARCOS approach. Renewable Energy, 218, 119292. https://doi.org/10.1016/j.renene.2023.119292.

Riaz, M., Alcantud, J. C. R., Yasin, Y., & Jameel, T. (2025). Integration of recursive feature elimination in renewable energy sources with evaluation based on relative utility and nonlinear standardization. Renewable Energy, 124538. https://doi.org/10.1016/j.renene.2025.124538.

Ali, T., Aghaloo, K., Chiu, Y. R., & Ahmad, M. (2022). Lessons learned from the COVID-19 pandemic in planning the future energy systems of developing countries using an integrated MCDM approach in the off-grid areas of Bangladesh. Renewable Energy, 189, 25-38. https://doi.org/10.1016/j.renene.2022.02.099.

Aarthi, K., Narayanamoorthy, S., & Suvitha, K. (2026). An Integrated Bipolar pqr-Spherical Fuzzy Decision Framework for the Selection of AI-driven Security System. Applied Research Advances, 2(1), 179-198. https://doi.org/10.65069/ara21202626.

Fujita, T., Mehmood, A., & Ghaib, A. A. (2025). A Reconsideration Note of the Mathematical Frameworks for Fuzzy and Neutrosophic Risk Management. Management Science Advances, 2(1), 223-238. https://doi.org/10.31181/msa21202523.