IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i24p16378-d996526.html
   My bibliography  Save this article

A Strategy for Determining the Decommissioning Life of Energy Equipment Based on Economic Factors and Operational Stability

Author

Listed:
  • Biao Li

    (State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China)

  • Tao Wang

    (Department of Mathematics and Physics, Baoding Campus, North China Electric Power University, Baoding 071000, China)

  • Chunxiao Li

    (State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China)

  • Zhen Dong

    (State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China)

  • Hua Yang

    (State Grid Hebei Electric Power Research Institude, Shijiazhuang 050000, China)

  • Yi Sun

    (State Grid Hebei Electric Power Research Institude, Shijiazhuang 050000, China)

  • Pengfei Wang

    (Beijing Sgitg Accenture Information Technology Center Co., Ltd., Beijing 100000, China)

Abstract

LCC and EL models have been widely used in recent years to determine the decommissioning life of equipment in energy companies, with LCC (life-cycle cost) being the total “lifetime” cost of the equipment from the time it is put into operation until the end of its decommissioning and disposal; the average annual cost of the equipment can be calculated based on the LCC. The overall LCC can be calculated as the average annual LCC, while the EL is the age of the equipment at which its average annual LCC is the lowest. It is believed that the decommissioning of the equipment in the EL year will result in the lowest annual average equipment turnover, thus maximizing the economic benefits of the equipment. Recently, LCC and EL research has been gradually introduced to the energy field, but there remains a lack of research depth. In current practice, energy equipment LCCs are mainly determined by selecting a portion of inventoried equipment to serve as a sample record for all costs incurred. The intent is to derive the economic life of the equipment-year by directly seeking its average annual cost, but this method tends to downplay maintenance, overhaul, and other cost events as “random small probability events”. This method is also incomplete for evaluating the decommissioning life of equipment whose average annual cost strictly decreases year-by-year. In this study, we analyzed the use of 75,220 KV transformers that were put into service by an energy company in 1986 as a case study (costs for this type of equipment were first recorded strictly in terms of LCC in 1986), used Isolated Forest (IF) to screen the outliers of various types of data costs, and then probability-corrected the corrected dataset with a Welbull distribution (Welbull). Then, we employed a stochastic simulation (MC) to calculate the LCC of the equipment and determined its economic lifetime (EL) and compared the results of the stochastic simulation method with those of the traditional method to provide a more reasonable explanation for the “small probability” of cost occurrences. Next, we predicted the average cost of the equipment given a use-period of 38-41-years using AHA, Bi-LSTM, and other comparative algorithms, compared the MAE, MAPE, and RMES indexes, selected the most suitable prediction model, and produced a predicted cost under the chosen method to obtain the economic life of the equipment. Finally, we compared our results with the design life of the equipment (design life being the technical life expectancy of a product based on the expectations of the manufacturer), and determined its best retirement age by comprehensively studying and judging the economic and technical benefits. The retirement age analysis was guided by by a comprehensive study of economic and technical benefits. We refer to our decommissioning life determination model as Monte Carlo -artificial hummingbird algorithm–BiLSTM–lifecycle cost model (MC-AHABi-LCC). We found that the decommissioning life obtained by MC-AHABi-LCC is closer to the actual equipment decommissioning life than that given by standard LCC and EL analysis and that our model is more accurate and scientific.

Suggested Citation

  • Biao Li & Tao Wang & Chunxiao Li & Zhen Dong & Hua Yang & Yi Sun & Pengfei Wang, 2022. "A Strategy for Determining the Decommissioning Life of Energy Equipment Based on Economic Factors and Operational Stability," Sustainability, MDPI, vol. 14(24), pages 1-24, December.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16378-:d:996526
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/24/16378/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/24/16378/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Traverso L. & Mazzoli E. & Miller C. & Pulighe G. & Perelli C. & Morese M. M. & Branca G., 2021. "Cost Benefit and Risk Analysis of Low iLUC Bioenergy Production in Europe Using Monte Carlo Simulation," Energies, MDPI, vol. 14(6), pages 1-18, March.
    2. Antonio Dominguez-Delgado & Helena Domínguez-Torres & Carlos-Antonio Domínguez-Torres, 2020. "Energy and Economic Life Cycle Assessment of Cool Roofs Applied to the Refurbishment of Social Housing in Southern Spain," Sustainability, MDPI, vol. 12(14), pages 1-35, July.
    3. Fathy, Ahmed, 2022. "A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems," Applied Energy, Elsevier, vol. 323(C).
    4. Yongli Wang & Shanshan Song & Mingchen Gao & Jingyan Wang & Jinrong Zhu & Zhongfu Tan, 2020. "Accounting for the Life Cycle Cost of Power Grid Projects by Employing a System Dynamics Technique: A Power Reform Perspective," Sustainability, MDPI, vol. 12(8), pages 1-28, April.
    5. Jordehi, A. Rezaee, 2018. "How to deal with uncertainties in electric power systems? A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 145-155.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rongshen Lai & Zhiyong Wu & Xiangui Liu & Nianyin Zeng, 2023. "Fusion Algorithm of the Improved A* Algorithm and Segmented Bézier Curves for the Path Planning of Mobile Robots," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    2. Abdullah Caliskan & Conor O’Brien & Krishna Panduru & Joseph Walsh & Daniel Riordan, 2023. "An Efficient Siamese Network and Transfer Learning-Based Predictive Maintenance System for More Sustainable Manufacturing," Sustainability, MDPI, vol. 15(12), pages 1-23, June.

    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. Emad A. Mohamed & Mokhtar Aly & Masayuki Watanabe, 2022. "New Tilt Fractional-Order Integral Derivative with Fractional Filter (TFOIDFF) Controller with Artificial Hummingbird Optimizer for LFC in Renewable Energy Power Grids," Mathematics, MDPI, vol. 10(16), pages 1-33, August.
    2. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    3. Hongliang Tian & Liang Zhao & Sen Guo, 2023. "Comprehensive Benefit Evaluation of Power Grid Investment Considering Renewable Energy Development from the Perspective of Sustainability," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
    4. Badr Eddine Lebrouhi & Eric Schall & Bilal Lamrani & Yassine Chaibi & Tarik Kousksou, 2022. "Energy Transition in France," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    5. Fabiana Frota de Albuquerque Landi & Claudia Fabiani & Anna Laura Pisello & Alessandro Petrozzi & Daniele Milone & Franco Cotana, 2022. "Environmental Assessment of an Innovative High-Performance Experimental Agriculture Field," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    6. Yao, Xing & Yi, Bowen & Yu, Yang & Fan, Ying & Zhu, Lei, 2020. "Economic analysis of grid integration of variable solar and wind power with conventional power system," Applied Energy, Elsevier, vol. 264(C).
    7. Zhao, Ning & You, Fengqi, 2022. "Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    8. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    9. Khanahmadi, Abbas & Ghaffarpour, Reza, 2022. "A cost-effective and emission-Aware hybrid system considering uncertainty: A case study in a remote area," Renewable Energy, Elsevier, vol. 201(P1), pages 977-992.
    10. Scott, Ian J. & Carvalho, Pedro M.S. & Botterud, Audun & Silva, Carlos A., 2021. "Long-term uncertainties in generation expansion planning: Implications for electricity market modelling and policy," Energy, Elsevier, vol. 227(C).
    11. Manzoor Ellahi & Ghulam Abbas & Irfan Khan & Paul Mario Koola & Mashood Nasir & Ali Raza & Umar Farooq, 2019. "Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review," Energies, MDPI, vol. 12(22), pages 1-30, November.
    12. Àlex Alonso-Travesset & Diederik Coppitters & Helena Martín & Jordi de la Hoz, 2023. "Economic and Regulatory Uncertainty in Renewable Energy System Design: A Review," Energies, MDPI, vol. 16(2), pages 1-30, January.
    13. Elham Mahdavi & Seifollah Asadpour & Leonardo H. Macedo & Rubén Romero, 2023. "Reconfiguration of Distribution Networks with Simultaneous Allocation of Distributed Generation Using the Whale Optimization Algorithm," Energies, MDPI, vol. 16(12), pages 1-19, June.
    14. Jiajia Li & Jinfu Liu & Peigang Yan & Xingshuo Li & Guowen Zhou & Daren Yu, 2021. "Operation Optimization of Integrated Energy System under a Renewable Energy Dominated Future Scene Considering Both Independence and Benefit: A Review," Energies, MDPI, vol. 14(4), pages 1-36, February.
    15. Wen Cao & Lin Yang & Qinyi Zhang & Lihua Chen & Weidong Wu, 2021. "Evaluation of Rural Dwellings’ Energy-Saving Retrofit with Adaptive Thermal Comfort Theory," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    16. Isadora Luiza Climaco Cunha & Fábio Rosa & Luiz Kulay, 2021. "Green Coalescent Synthesis Based on the Design for Environment (DfE) Principles: Brazilian Experience," Sustainability, MDPI, vol. 13(22), pages 1-22, November.
    17. Alfonso Marino & Paolo Pariso & Michele Picariello, 2023. "Energy use and End-use Technologies: Organizational and Energy Analysis in Italian Hospitals," International Journal of Energy Economics and Policy, Econjournals, vol. 13(3), pages 36-45, May.
    18. Rahim, Sahar & Wang, Zhen & Ju, Ping, 2022. "Overview and applications of Robust optimization in the avant-garde energy grid infrastructure: A systematic review," Applied Energy, Elsevier, vol. 319(C).
    19. Jie Zhu & Buxiang Zhou & Yiwei Qiu & Tianlei Zang & Yi Zhou & Shi Chen & Ningyi Dai & Huan Luo, 2023. "Survey on Modeling of Temporally and Spatially Interdependent Uncertainties in Renewable Power Systems," Energies, MDPI, vol. 16(16), pages 1-19, August.
    20. Jung Ho Kim & Young Il Kim, 2021. "Optimal Combination of External Wall Insulation Thickness and Surface Solar Reflectivity of Non-Residential Buildings in the Korean Peninsula," Sustainability, MDPI, vol. 13(6), pages 1-24, March.

    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:14:y:2022:i:24:p:16378-:d:996526. 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.