Identification mechanism and investment economic evaluation of distribution lines with low power supply reliability based on AHP and random forest algorithm

With social and economic development, people’s demand for reliable power is increasing. Improving power supply reliability has become an important means to improve the level of universal power services and support high-quality development of social and economic. This article focuses on how to concentrate resources on weakness of the distribution network and improve the power supply reliability and economic efficiency of distribution lines when investment is limited. Firstly, a reliability characteristic index system is constructed hierarchically according to the reliability characteristic problems of distribution lines. Secondly, based on the relevant index data of City S, AHP and random forest algorithms are used to calculate the weight of the characteristic indexes. Thirdly, the reliability characteristic score of each distribution line is calculated and investment priorities based on the score ranking are formulated. Finally, a comprehensive evaluation model of power supply reliability and economy considering reliability electricity price mechanism is constructed. In the case of the same investment, the reliability costs of investments sorted by characteristic score versus by project amount are evaluated and compared. The results show that by formulating the investment sequence according to the method in this article, power supply reliability is significantly improved with lower reliability costs, which is helpful to guide the investment decisions of power grid based on power supply reliability.

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Published
2024-09-26
DOI
https://doi.org/10.1117/12.3044575
Primary Topic
Smart Grid and Power Systems
Type
article
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Identification mechanism and investment economic evaluation of distribution lines with low power supply reliability based on AHP and random forest algorithm

Jia‐Qi Huang, Lu Guo, Shenli LIN, Yongli LIU et al.
Smart Grid and Power Systems
article

Identification mechanism and investment economic evaluation of distribution lines with low power supply reliability based on AHP and random forest algorithm

Jia‐Qi Huang, Lu Guo, Shenli LIN, Yongli LIU, Shuaibin Shi
article en

Abstract

With social and economic development, people’s demand for reliable power is increasing. Improving power supply reliability has become an important means to improve the level of universal power services and support high-quality development of social and economic. This article focuses on how to concentrate resources on weakness of the distribution network and improve the power supply reliability and economic efficiency of distribution lines when investment is limited. Firstly, a reliability characteristic index system is constructed hierarchically according to the reliability characteristic problems of distribution lines. Secondly, based on the relevant index data of City S, AHP and random forest algorithms are used to calculate the weight of the characteristic indexes. Thirdly, the reliability characteristic score of each distribution line is calculated and investment priorities based on the score ranking are formulated. Finally, a comprehensive evaluation model of power supply reliability and economy considering reliability electricity price mechanism is constructed. In the case of the same investment, the reliability costs of investments sorted by characteristic score versus by project amount are evaluated and compared. The results show that by formulating the investment sequence according to the method in this article, power supply reliability is significantly improved with lower reliability costs, which is helpful to guide the investment decisions of power grid based on power supply reliability.

China Southern Power Grid (China) (CN)
Openalex Percentile: Top 86%
Smart Grid and Power Systems
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Identification mechanism and investment economic evaluation of distribution lines with low power supply reliability based on AHP and random forest algorithm — Jia‐Qi Huang, Lu Guo, et al. · (2024) | TGRS Research Map | TGRS