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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Biao Li & Pengfei Wang & Peng Sun & Rui Meng & Jun Zeng & Guanghui Liu, 2023. "A Model for Determining the Optimal Decommissioning Interval of Energy Equipment Based on the Whole Life Cycle Cost," Sustainability, MDPI, vol. 15(6), pages 1-28, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5569-:d:1104042
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    References listed on IDEAS

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    1. Liu, Jinlong & Dumitrescu, Cosmin E., 2018. "Flame development analysis in a diesel optical engine converted to spark ignition natural gas operation," Applied Energy, Elsevier, vol. 230(C), pages 1205-1217.
    2. Tan, Dongli & Wu, Yao & Lv, Junshuai & Li, Jian & Ou, Xiaoyu & Meng, Yujun & Lan, Guanglin & Chen, Yanhui & Zhang, Zhiqing, 2023. "Performance optimization of a diesel engine fueled with hydrogen/biodiesel with water addition based on the response surface methodology," Energy, Elsevier, vol. 263(PC).
    3. Tan, Dongli & Meng, Yujun & Tian, Jie & Zhang, Chengtao & Zhang, Zhiqing & Yang, Guanhua & Cui, Shuwan & Hu, Jingyi & Zhao, Ziheng, 2023. "Utilization of renewable and sustainable diesel/methanol/n-butanol (DMB) blends for reducing the engine emissions in a diesel engine with different pre-injection strategies," Energy, Elsevier, vol. 269(C).
    4. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    5. Liu, Jinlong & Wang, Bosen & Meng, Zhongwei & Liu, Zhentao, 2023. "An examination of performance deterioration indicators of diesel engines on the plateau," Energy, Elsevier, vol. 262(PB).
    6. Fangqin Zhang & Yan Kang & Xiao Cheng & Peiru Chen & Songbai Song, 2022. "A Hybrid Model Integrating Elman Neural Network with Variational Mode Decomposition and Box–Cox Transformation for Monthly Runoff Time Series Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3673-3697, August.
    7. Zhang, Yagang & Zhao, Yunpeng & Shen, Xiaoyu & Zhang, Jinghui, 2022. "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 305(C).
    Full references (including those not matched with items on IDEAS)

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