IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p5569-d1104042.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/5569/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/5569/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jie Hu & Wentong Cao & Feng Jiang & Lingling Hu & Qian Chen & Weiguang Zheng & Junming Zhou, 2023. "Study on Multi-Objective Optimization of Power System Parameters of Battery Electric Vehicles," Sustainability, MDPI, vol. 15(10), pages 1-23, May.
    2. Zhang, Zhiqing & Dong, Rui & Tan, Dongli & Duan, Lin & Jiang, Feng & Yao, Xiaoxue & Yang, Dixin & Hu, Jingyi & Zhang, Jian & Zhong, Weihuang & Zhao, Ziheng, 2023. "Effect of structural parameters on diesel particulate filter trapping performance of heavy-duty diesel engines based on grey correlation analysis," Energy, Elsevier, vol. 271(C).
    3. Zhuang Kang & Zhiwei Shi & Jiahao Ye & Xinghua Tian & Zhixin Huang & Hao Wang & Depeng Wei & Qingguo Peng & Yaojie Tu, 2023. "A Review of Micro Power System and Micro Combustion: Present Situation, Techniques and Prospects," Energies, MDPI, vol. 16(7), pages 1-28, April.
    4. Biao Li & Tao Wang & Zhen Dong & Qian Geng & Yi Sun, 2022. "Economic Planning of Energy System Equipment," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
    5. Tang, Shihao & Wei, Jia & Xie, Bo & Shi, Zhiwei & Wang, Hao & Tian, Xinghua & He, Biao & Peng, Qingguo, 2023. "Experimental and numerical investigation on H2-fueled thermophotovoltaic micro tube with multi-cavity," Energy, Elsevier, vol. 274(C).
    6. Dongli Tan & Yao Wu & Zhiqing Zhang & Yue Jiao & Lingchao Zeng & Yujun Meng, 2023. "Assessing the Life Cycle Sustainability of Solar Energy Production Systems: A Toolkit Review in the Context of Ensuring Environmental Performance Improvements," Sustainability, MDPI, vol. 15(15), pages 1-37, July.
    7. Zhang, Zhiqing & Hu, Jingyi & Tan, Dongli & Li, Junming & Jiang, Feng & Yao, Xiaoxue & Yang, Dixin & Ye, Yanshuai & Zhao, Ziheng & Yang, Guanhua, 2023. "Multi-objective optimization of the three-way catalytic converter on the combustion and emission characteristics for a gasoline engine," Energy, Elsevier, vol. 277(C).
    8. Xueyi Li & Tianyu Yu & Daiyou Li & Xiangkai Wang & Cheng Shi & Zhijie Xie & Xiangwei Kong, 2023. "A Migration Learning Method Based on Adaptive Batch Normalization Improved Rotating Machinery Fault Diagnosis," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    9. Ye, Jiahao & Peng, Qingguo, 2023. "Improved emissions conversion of diesel oxidation catalyst using multifactor impact analysis and neural network," Energy, Elsevier, vol. 271(C).
    10. Monika Andrych-Zalewska & Zdzislaw Chlopek & Jacek Pielecha & Jerzy Merkisz, 2023. "Influence of the In-Cylinder Catalyst on the Aftertreatment Efficiency of a Diesel Engine," Energies, MDPI, vol. 16(6), pages 1-21, March.
    11. 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).
    12. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    13. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    14. Tammo Zobel & Andreas Ritter & Christopher H. Onder, 2023. "The Faster the Better? Optimal Warm-Up Strategies for a Micro Combined Heat and Power Plant," Energies, MDPI, vol. 16(10), pages 1-24, May.
    15. Hegazy Rezk & Mohammad Ali Abdelkareem & Samah Ibrahim Alshathri & Enas Taha Sayed & Mohamad Ramadan & Abdul Ghani Olabi, 2023. "Fuel Economy Energy Management of Electric Vehicles Using Harris Hawks Optimization," Sustainability, MDPI, vol. 15(16), pages 1-15, August.
    16. Lu, Kangbo & Shi, Lei & Zhang, Huiyan & Chen, Ziqiang & Deng, Kangyao, 2023. "Theoretical and experimental study on performance improvement of diesel engines at different altitudes by adaptive regulation method of the two-stage turbocharging system," Energy, Elsevier, vol. 281(C).
    17. Lee, Chia-fon & Pang, Yuxin & Wu, Han & Nithyanandan, Karthik & Liu, Fushui, 2020. "An optical investigation of substitution rates on natural gas/diesel dual-fuel combustion in a diesel engine," Applied Energy, Elsevier, vol. 261(C).
    18. Liu, Jinlong & Dumitrescu, Cosmin E., 2019. "Single and double Wiebe function combustion model for a heavy-duty diesel engine retrofitted to natural-gas spark-ignition," Applied Energy, Elsevier, vol. 248(C), pages 95-103.
    19. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
    20. Kuang, Zhonghong & Chen, Qi & Yu, Yang, 2022. "Assessing the CO2-emission risk due to wind-energy uncertainty," Applied Energy, Elsevier, vol. 310(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5569-:d:1104042. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.