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Economic Evaluation Method of Modern Power Transmission System Based on Improved Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Best-Worst Method-Anti-Entropy Weight

Author

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  • Wenhui Zeng

    (State Grid Sichuan Economic Research Institute, Chengdu 610095, China)

  • Jiayuan Fan

    (State Grid Sichuan Information & Communication Company, Chengdu 610299, China)

  • Zhichao Ren

    (State Grid Sichuan Economic Research Institute, Chengdu 610095, China)

  • Xiaoyu Liu

    (State Grid Sichuan Economic Research Institute, Chengdu 610095, China)

  • Shuang Lv

    (State Grid Sichuan Electric Power Company Chengdu Power Supply, Chengdu 610041, China)

  • Yuqian Cao

    (School of Electrical Engineering, Sichuan University, Chengdu 610017, China)

  • Xiao Xu

    (School of Electrical Engineering, Sichuan University, Chengdu 610017, China)

  • Junyong Liu

    (School of Electrical Engineering, Sichuan University, Chengdu 610017, China)

Abstract

As the demand for power supply increases, the investment in the power transmission system constantly increases. An accurate economic evaluation of the power transmission system is essential for future investment decisions and management. Applying a single method in economic evaluation leads to excessive subjective consciousness and unreasonable weight allocation. The Euclidean distance in the traditional TOPSIS method only partially works on the condition that the criteria are linearly correlated. To solve these problems, an economic evaluation method based on improved TOPSIS and BWM-anti-entropy weight is proposed. For the assignment of weights, the method retains the advantages of subjective and objective weighting methods based on the Nash equilibrium, breaks through the limitation of utilizing a single method, which contributes to one-sided results, and enhances the scientific rigor and rationality of the comprehensive weighting process. Furthermore, based on comprehensive weights, the method improves the TOPSIS by introducing the Mahalanobis distance and Pearson correlation coefficients, which can eliminate the influence of linear correlation. Finally, ten 500 kV transmission and transformation projects are analyzed and ranked to verify the method’s feasibility. Empirical analysis shows that the method can effectively evaluate the economic benefits of the power transmission system.

Suggested Citation

  • Wenhui Zeng & Jiayuan Fan & Zhichao Ren & Xiaoyu Liu & Shuang Lv & Yuqian Cao & Xiao Xu & Junyong Liu, 2023. "Economic Evaluation Method of Modern Power Transmission System Based on Improved Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Best-Worst Method-Anti-Entropy Weight," Energies, MDPI, vol. 16(21), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7242-:d:1266889
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    References listed on IDEAS

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    1. Yongxiu He & Wenya Liu & Jie Jiao & Jie Guan, 2018. "Evaluation method of benefits and efficiency of grid investment in China: A case study," The Engineering Economist, Taylor & Francis Journals, vol. 63(1), pages 66-86, January.
    2. Chengguang Lai & Xiaohong Chen & Xiaoyu Chen & Zhaoli Wang & Xushu Wu & Shiwei Zhao, 2015. "A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 1243-1259, June.
    3. Kuo-Ping Chang, 2023. "Corporate Finance: A Systematic Approach," Springer Texts in Business and Economics, Springer, number 978-981-19-9119-6, August.
    4. Ishizaka, Alessio & Siraj, Sajid, 2018. "Are multi-criteria decision-making tools useful? An experimental comparative study of three methods," European Journal of Operational Research, Elsevier, vol. 264(2), pages 462-471.
    5. Madjid Tavana & Mehdi Soltanifar & Francisco J. Santos-Arteaga, 2023. "Analytical hierarchy process: revolution and evolution," Annals of Operations Research, Springer, vol. 326(2), pages 879-907, July.
    6. He, YongXiu & Liu, Yang & Li, MoXing & Zhang, Yan, 2022. "Benefit evaluation and mechanism design of pumped storage plants under the background of power market reform - A case study of China," Renewable Energy, Elsevier, vol. 191(C), pages 796-806.
    7. Pohekar, S. D. & Ramachandran, M., 2004. "Application of multi-criteria decision making to sustainable energy planning--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 8(4), pages 365-381, August.
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