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Evaluating power system reform proposals based on the evidential reasoning algorithm with hesitant fuzzy information

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Abstract

The reforming and updating of power systems play a key role in promoting the low-carbon technology progress, saving non-renewable energy resources and reducing carbon emissions. The evaluation of power system reform proposals is an uncertain multiple-criteria decision-making problem given that some indicators cannot be represented by real numbers such as the expected effect of the power system upgrade. The hesitant fuzzy set, as a powerful tool to portray uncertain information by several possible membership functions, is introduced to represent the evaluation information for power system reform proposals. First, based on the linear interpolation method and the proposed ignorance degree, we obtain the defuzzification value involving the linguistic term part and corresponding probability part. Next, the water-filling algorithm is utilized to determine the objective weight of the criterion by the optimization model to maximize the total capacity of the criteria. Then, to model uncertainties associated with intricate decision-making situations and conflicting assessments, the evidential reasoning algorithm is extended to the hesitant fuzzy environment. The proposed hesitant fuzzy evidential reasoning method not only manages conflicting evaluation information but also avoids information loss or distortion. The proposed method is applied to rank the proposals after establishing the indicator system for power system reform. Finally, sensitivity and comparative analysis verify the effectiveness and robustness of the proposed method with a series of simulation experiments and nonparametric tests.

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References

  1. Intergovernmental Panel on Climate Change. IPCC special report: Global warming of 1.5ºC. https://www.ipcc.ch/sr15/. Accessed 21 Jan 2023

  2. International Energy Agency. Global energy review: CO2 emissions in 2021. https://www.iea.org/. Accessed 21 Jan 2023

  3. J.P. Xi. Delivers an important speech at the general debate of the 75th session of the United Nations (UN) general assembly. https://www.chinadaily.com.cn/. Accessed 21 Jan 2023

  4. Nocito F, Dibenedetto A (2020) Atmospheric CO2 mitigation technologies: Carbon capture utilization and storage. Curr Opin Green Sustain Chem 21:34–43

    Article  Google Scholar 

  5. Yuan JH, Li Y, Luo XG, Li LF, Zhang ZL, Li CB (2021) Regional integrated energy system schemes selection based on risk expectation and Mahalanobis-Taguchi system. J Intell Fuzzy Syst 40(6):10333–10350

    Article  Google Scholar 

  6. Kamaci H, Petchimuthu S, Akcetin E (2021) Dynamic aggregation operators and Einstein operations based on interval-valued picture hesitant fuzzy information and their applications in multi-period decision making. Comput Appl Math 40(4):10333–10350. https://doi.org/10.1007/s40314-021-01510-w

    Article  MathSciNet  MATH  Google Scholar 

  7. Gou XJ, Xu ZS, Liao HC (2017) Hesitant fuzzy linguistic entropy and cross-entropy measures and alternative queuing method for multiple criteria decision making. Inf Sci 388:225–246

    Article  Google Scholar 

  8. Bhaumik A, Roy SK, Weber GW (2020) Hesitant interval-value dintuitionistic fuzzy-linguist itermsetap proach in Prisoners’ dilemma game theory using TOPSIS: A case study on Human-trafficking. CEJOR 28(2):797–816

    Article  MathSciNet  MATH  Google Scholar 

  9. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 8(3):199–249

    Article  MathSciNet  MATH  Google Scholar 

  10. Li DF (2018) Lexicographic method for matrix games with payoffs of triangular fuzzy numbers. Int J Uncertain Fuzziness Knowl-Based Syst 16(3):371–389

    Article  MathSciNet  MATH  Google Scholar 

  11. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    Article  MathSciNet  MATH  Google Scholar 

  12. Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25(6):529–539

    MATH  Google Scholar 

  13. Xia MM, Xu ZH (2011) Hesitant fuzzy information aggregation in decision making. Int J Approximate Reasoning 52(3):395–407

    Article  MathSciNet  MATH  Google Scholar 

  14. Hussain Z, Yang MS (2018) Entropy for hesitant fuzzy sets based on hausdorff metric with construction of hesitant fuzzy TOPSIS. Int J Fuzzy Syst 20(8):2517–2533

    Article  MathSciNet  Google Scholar 

  15. Zhang XL, Xu ZS (2014) The TODIM analysis approach based on novel measured functions under hesitant fuzzy environment. Knowl-Based Syst 61:48–58

    Article  Google Scholar 

  16. Liao HC, Xu ZS (2013) A VIKOR-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optim Decis Making 12(4):373–392

    Article  MathSciNet  MATH  Google Scholar 

  17. Chen N, Xia MM, Xu ZS (2015) The ELECTRE I multi-criteria decision-making method based on hesitant fuzzy sets. Int J Inf Technol Decis Mak 14(3):621–657

    Article  Google Scholar 

  18. Zhang RC, Xu ZS, Gou XJ (2022) ELECTRE II method based on the cosine similarity to evaluate the performance of financial logistics enterprises under double hierarchy hesitant fuzzy linguistic environment. Fuzzy Optim Decis Making. https://doi.org/10.1007/s10700-022-09382-3

    Article  MATH  Google Scholar 

  19. Samanlioglu F, Kaya BE (2020) Evaluation of the COVID-19 pandemic intervention strategies with hesitant F-AHP. J Halthc Eng 2020:1–11. https://doi.org/10.1155/2020/8835258

    Article  Google Scholar 

  20. Ayhan MB (2020) Supplier evaluation with hesitant fuzzy analytic hierarchy process in bearing sector and consistency analysis. J Test Eval 48(1):624–646

    Article  MathSciNet  Google Scholar 

  21. Tone KR, Chang TS, Wu CH (2020) Handling negative data in slacks-based measure data envelopment analysis models. Eur J Oper Res 282(3):926–935

    Article  MathSciNet  MATH  Google Scholar 

  22. Wei CP, Rodriguez RM, Li P (2020) Note on entropies of hesitant fuzzy linguistic term sets and their applications. Inf Sci 512:352–368

    Article  MathSciNet  MATH  Google Scholar 

  23. Yu YX, Wang G (2012) Research on power allocation scheme based on water-fllling algorithm in cooperative diversity system. J Electron Inf 34(12):2830–2836

    Article  Google Scholar 

  24. Peng XD, Huang HH, Luo ZG (2022) When CCN meets MCGDM: Optimal cache replacement policy achieved by PRSRV with Pythagorean fuzzy set pair analysis. Artif Intell Rev 55:5621–5671. https://doi.org/10.1007/s10462-022-10139-y

    Article  Google Scholar 

  25. Xing CW, Jing YD, Wang S, Ma SD, Poor HV (2020) New viewpoint and algorithms for water-filling solutions in wireless communications. IEEE Trans Signal Process 68:1618–1634

    Article  MathSciNet  MATH  Google Scholar 

  26. Yang Y, Xu DL, Yang JB, Chen YW (2019) An evidential reasoning-based decision support system for handling customer complaints in mobile telecommunications. Knowl-Based Syst 162:202–210

    Article  Google Scholar 

  27. Yang JB (2001) Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties. Eur J Oper Res 131(1):31–61

    Article  MathSciNet  MATH  Google Scholar 

  28. Krishankumaar R, Mishra AR, Ravichandran KS, Gou XJ (2022) New ranking model with evidence theory under probabilistic hesitant fuzzy context and unknown weights. Neural Comput Appl 34(5):3923–3937

    Article  Google Scholar 

  29. Zhou M, Liu XB, Yang JB (2010) Evidential reasoning-based nonlinear programming model for MCDA under fuzzy weights and utilities. Int J Intell Syst 25(1):31–58

    Article  MATH  Google Scholar 

  30. Wang G, Wang HR, Yang Y, Xu DL, Yang JB, Yue F (2021) Group article recommendation based on ER rule in scientific social networks. Appl Soft Comput 110:107631. https://doi.org/10.1016/j.asoc.2021.107631

    Article  Google Scholar 

  31. Wang J, Zhou ZJ, Hu CH, Tang SW, Cao Y (2021) A new evidential reasoning rule with continuous probability distribution of reliability. IEEE Trans Cybern 52(8):8088–8100. https://doi.org/10.1109/TCYB.2021.3051676

    Article  Google Scholar 

  32. S. Sachan, F. Almaghrabi, J.B. Yang and D.L. Xu. Evidential reasoning for preprocessing uncertain categorical data for trustworthy decisions: An application on healthcare and finance. Expert Systems with Applications 185 (2021) doi: https://doi.org/10.1016/j.eswa.2021.115597.

  33. Wei Z, Xu ZS (2015) Optimal discrete fitting aggregation approach with hesitant fuzzy information. Knowl-Based Syst 78:23–33

    Google Scholar 

  34. Meng FY, Chen XH (2015) A hesitant fuzzy linguistic multi-granularity decision making model based on distance measures. Journal of Intelligent & Fuzzy Systems 28(4):1519–1531

    Article  MathSciNet  MATH  Google Scholar 

  35. Wang YM, Yang JB, Xu DL (2006) Environmental impact assessment using the evidential reasoning approach. Eur J Oper Res 174(3):1885–1913

    Article  MATH  Google Scholar 

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Acknowledgements

The work was partly supported by the National Natural Science Foundation of China (No. 71571123), the National Social Science Fund of China (Nos. 21ATJ010, 20CTJ016, 19ZDA122), China Postdoctoral Science Foundation (2020M673195), Zhejiang Gongshang University “Digital +” Disciplinary Construction Management Project (Nos. SZJ2022A007, SZJ2022B002, SZJ2022A001), the Fundamental Research Funds for the Provincial Universities of Zhejiang (No. XT202216).

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Authors and Affiliations

  1. Collaborative Innovation Center of Statistical Data Engineering Technology & Application, Zhejiang Gongshang University, Hangzhou, 310018, China

    Chonghui Zhang, Wuhui Lu & Wenting Xue

  2. Business School, Sichuan University, Chengdu, 610065, Sichuan, China

    Zeshui Xu

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  1. Chonghui Zhang
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  2. Wuhui Lu
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  4. Wenting Xue
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Contributions

Chonghui Zhang – Methodology, conceptualization, writing; Wuhui Lu – Methodology, computing, writing. Zeshui Xu – Review and editing; Wenting Xue – Investigation, original draft and review.

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Correspondence to Wenting Xue.

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Appendix

Appendix

Table 6 Evaluation information of Expert 1
Full size table
Table 7 Evaluation information of Expert 2
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Table 8 Evaluation information of Expert 3
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Table 9 Comprehensive evaluation information of three experts
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Zhang, C., Lu, W., Xu, Z. et al. Evaluating power system reform proposals based on the evidential reasoning algorithm with hesitant fuzzy information. Appl Intell 53, 26079–26097 (2023). https://doi.org/10.1007/s10489-023-04746-7

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