A Model for Determining the Optimal Decommissioning Interval of Energy Equipment Based on the Whole Life Cycle Cost

Biao Li, Pengfei Wang, Peng Sun, Rui Meng (), Jun Zeng and Guanghui Liu
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Biao Li: State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050022, China
Pengfei Wang: Beijing Sgitg Accenture Information Technology Center Co., Ltd., Beijing 100000, China
Peng Sun: Department of Mathematics and Science, North China Electric Power University, Beijing 102206, China
Rui Meng: Department of Mathematics and Science, North China Electric Power University, Beijing 102206, China
Jun Zeng: State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050022, China
Guanghui Liu: State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050022, China

Sustainability, 2023, vol. 15, issue 6, 1-28

Abstract: An appropriate technical overhaul strategy is very important for the development of enterprises. Most enterprises pay attention to the design life of the equipment, that is, the point when the equipment can no longer be used as stipulated by the manufacturer. However, in the later stage of the equipment, the operation and maintenance costs may be higher than the benefit of the equipment. Therefore, only the design life of the equipment may cause a waste of funds, so as to avoid the waste of funds, the enterprise’s strategy of technical reform and overhaul are optimized. This paper studies the optimal decommissioning life of the equipment (taking into account both the safety and economic life of the equipment), and selects the data of a 35 kV voltage transformer in a powerful enterprise. The enterprise may have problems with the data due to recording errors or loose classification. In order to analyze the decommissioning life of the equipment more accurately, it is necessary to first use t-distributed stochastic neighbor embedding (t-SNE) to reduce the data dimension and judge the data distribution. Then, density-based spatial clustering of applications with noise (DBSCAND) is used to screen the outliers of the data and mark the filtered abnormal data as a vacancy value. Then, random forest is used to fill the vacancy values of the data. Then, an Elman neural network is used for random simulation, and finally, the Fisher orderly segmentation is used to obtain the optimal retirement life interval of the equipment. The overall results show that the optimal decommissioning life range of the 35 kV voltage transformer of the enterprise is 31 to 41 years. In this paper, the decommissioning life range of equipment is scientifically calculated for enterprises, which makes up for the shortage of economic life. Moreover, considering the “economy” and “safety” of equipment comprehensively will be conducive to the formulation of technical reform and overhaul strategy.

Keywords: life-cycle cost; density-based spatial clustering of applications with noise; random forest; Elman neural network; Fisher ordered segmentation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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