IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i5p473-d505594.html
   My bibliography  Save this article

Risk Evaluation of Electric Power Grid Investment in China Employing a Hybrid Novel MCDM Method

Author

Listed:
  • Yana Duan

    (National Electric Power Grid Inner Mongolia Eastern Electric Power Co., Ltd., Economic and Technological Research Institute, Hohhot 010000, China)

  • Yang Sun

    (National Electric Power Grid Inner Mongolia Eastern Electric Power Co., Ltd., Economic and Technological Research Institute, Hohhot 010000, China)

  • Yu Zhang

    (National Electric Power Grid Inner Mongolia Eastern Electric Power Co., Ltd., Economic and Technological Research Institute, Hohhot 010000, China)

  • Xiaoqi Fan

    (National Electric Power Grid Inner Mongolia Eastern Electric Power Co., Ltd., Economic and Technological Research Institute, Hohhot 010000, China)

  • Qinghuan Dong

    (National Electric Power Grid Inner Mongolia Eastern Electric Power Co., Ltd., Economic and Technological Research Institute, Hohhot 010000, China)

  • Sen Guo

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

Socio-economic development is undergoing changes in China, such as the recently proposed carbon peak and carbon neutral targets, new infrastructure development strategy and the Coronavirus disease 2019 (COVID-19) pandemic. Meanwhile, the new-round marketization reform of the electricity industry has been ongoing in China since 2015. Therefore, it is urgent to evaluate the risk of electric power grid investment in China under new socio-economic development situation, which can help the investors manage risk and reduce risk loss. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the latest group MCDM method, namely, Bayesian best–worst method (BBWM) and improved matter-element extension model (IMEEM) is proposed for risk evaluation of electric power grid investment in China under new socio-economic development situation. The BBWM is used for the weights’ determination of electric power grid investment risk criteria, and the IMEEM is employed to rank risk grade of electric power grid investment. The risk evaluation index system of electric power grid investment is built, including economic, social, environmental, technical and marketable risks. The risk of electric power grid investment under new socio-economic development situation in Inner Mongolia Autonomous Region of China is empirically evaluated by using the proposed MCDM method, and the results indicate that it belongs to “Medium” grade, but closer to “High” grade. The main contributions of this paper include: (1) it proposes a hybrid novel MCDM method combining the BBWM and IMEEM for risk evaluation of electric power grid investment; and (2) it provides a new view for risk evaluation of electric power grid investment including economic, social, environmental, technical and marketable risks. The proposed hybrid novel MCDM method for the risk evaluation of electric power grid investment is effective and practical.

Suggested Citation

  • Yana Duan & Yang Sun & Yu Zhang & Xiaoqi Fan & Qinghuan Dong & Sen Guo, 2021. "Risk Evaluation of Electric Power Grid Investment in China Employing a Hybrid Novel MCDM Method," Mathematics, MDPI, vol. 9(5), pages 1-22, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:473-:d:505594
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/5/473/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/5/473/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhou, Shan & Yang, Pu, 2020. "Risk management in distributed wind energy implementing Analytic Hierarchy Process," Renewable Energy, Elsevier, vol. 150(C), pages 616-623.
    2. Guo, Sen & Zhao, Huiru, 2015. "Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective," Applied Energy, Elsevier, vol. 158(C), pages 390-402.
    3. Guindon, A.-A. & Wright, D.J., 2020. "Analytical approach to quantitative risk assessment for solar power projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    4. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
    5. Gyanendra Singh Sisodia & Einas Awad & Heba Alkhoja & Bruno S. Sergi, 2020. "Strategic business risk evaluation for sustainable energy investment and stakeholder engagement: A proposal for energy policy development in the Middle East through Khalifa funding and land subsidies," Business Strategy and the Environment, Wiley Blackwell, vol. 29(6), pages 2789-2802, September.
    6. Wanqing Wang & Shuran Lyu & Yudong Zhang & Shuqi Ma, 2019. "A Risk Assessment Model of Coalbed Methane Development Based on the Matter-Element Extension Method," Energies, MDPI, vol. 12(20), pages 1-30, October.
    7. Xu Lei & Tang Shiyun & Deng Yanfei & Yuan Yuan, 2020. "Sustainable operation-oriented investment risk evaluation and optimization for renewable energy project: a case study of wind power in China," Annals of Operations Research, Springer, vol. 290(1), pages 223-241, July.
    8. Liang, Zhihong & Yang, Kun & Sun, Yaowei & Yuan, Jiahai & Zhang, Hongwei & Zhang, Zhizheng, 2006. "Decision support for choice optimal power generation projects: Fuzzy comprehensive evaluation model based on the electricity market," Energy Policy, Elsevier, vol. 34(17), pages 3359-3364, November.
    9. Gregory, Julian & Sovacool, Benjamin K., 2019. "The financial risks and barriers to electricity infrastructure in Kenya, Tanzania, and Mozambique: A critical and systematic review of the academic literature," Energy Policy, Elsevier, vol. 125(C), pages 145-153.
    10. Mohammadi, Majid & Rezaei, Jafar, 2020. "Bayesian best-worst method: A probabilistic group decision making model," Omega, Elsevier, vol. 96(C).
    11. Zeng, Ming & Yang, Yongqi & Wang, Lihua & Sun, Jinghui, 2016. "The power industry reform in China 2015: Policies, evaluations and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 94-110.
    12. Quan Xiao & Shanshan Wan & Fucai Lu & Shun Li, 2019. "Risk Assessment for Engagement in Sharing Economy of Manufacturing Enterprises: A Matter–Element Extension Based Approach," Sustainability, MDPI, vol. 11(17), pages 1-29, September.
    13. Haoran Zhao & Huiru Zhao & Sen Guo, 2018. "Comprehensive Performance Evaluation of Electricity Grid Corporations Employing a Novel MCDM Model," Sustainability, MDPI, vol. 10(7), pages 1-23, June.
    14. Sen Guo & Wenyue Zhang & Xiao Gao, 2020. "Business Risk Evaluation of Electricity Retail Company in China Using a Hybrid MCDM Method," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    15. Egli, Florian, 2020. "Renewable energy investment risk: An investigation of changes over time and the underlying drivers," Energy Policy, Elsevier, vol. 140(C).
    16. Wu, Yunna & Wang, Jing & Ji, Shaoyu & Song, Zixin, 2020. "Renewable energy investment risk assessment for nations along China’s Belt & Road Initiative: An ANP-cloud model method," Energy, Elsevier, vol. 190(C).
    17. Han, Phoumin & Kimura, Fukunari & Sandu, Suwin, 2020. "Household-level analysis of the impacts of electricity consumption on welfare and the environment in Cambodia: Empirical evidence and policy implications," Economic Modelling, Elsevier, vol. 89(C), pages 476-483.
    18. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    19. Yao, Yunting & Gao, Ciwei & Tian, Hongjie & Zhang, Huiling, 2020. "Review of mid-to long-term trading mechanism for renewable electricity consumption in Ningxia, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    20. Caroline Deilen, Tim Felling, Robin Leisen, and Christoph Weber, 2019. "Evaluation of Risks for Electricity Generation Companies through Reconfiguration of Bidding Zones in Extended Central Western Europe," The Energy Journal, International Association for Energy Economics, vol. 0(The New E).
    21. Hong-ze Li & Sen Guo, 2013. "External Economies Evaluation of Wind Power Engineering Project Based on Analytic Hierarchy Process and Matter-Element Extension Model," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, December.
    22. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Comprehensive assessment for battery energy storage systems based on fuzzy-MCDM considering risk preferences," Energy, Elsevier, vol. 168(C), pages 450-461.
    23. Jiahai Yuan & Yurong Zeng & Xiaoxuan Guo & Yu Ai & Minpeng Xiong, 2018. "Electric Power Investment Risk Assessment for Belt and Road Initiative Nations," Sustainability, MDPI, vol. 10(9), pages 1-21, August.
    24. Yuan, Jiahai & Li, Xinying & Xu, Chuanbo & Zhao, Changhong & Liu, Yuanxin, 2019. "Investment risk assessment of coal-fired power plants in countries along the Belt and Road initiative based on ANP-Entropy-TODIM method," Energy, Elsevier, vol. 176(C), pages 623-640.
    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. Lin Wang & Yuping Xing, 2022. "Risk Assessment of a Coupled Natural Gas and Electricity Market Considering Dual Interactions: A System Dynamics Model," Energies, MDPI, vol. 16(1), pages 1-18, December.
    2. Changzheng Gao & Xiuna Wang & Dongwei Li & Chao Han & Weiyang You & Yihang Zhao, 2023. "A Novel Hybrid Power-Grid Investment Optimization Model with Collaborative Consideration of Risk and Benefit," Energies, MDPI, vol. 16(20), pages 1-23, October.
    3. Weijie Wu & Dongwei Li & Hui Sun & Yixin Li & Yining Zhang & Mingrui Zhao, 2024. "Building a Sustainable Future: A Three-Stage Risk Management Model for High-Permeability Power Grid Engineering," Energies, MDPI, vol. 17(14), pages 1-23, July.

    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. Yu, Donghui & Gu, Baihe & Zhu, Kaiwei & Yang, Jiawen & Sheng, Yuhui, 2024. "Risk analysis of China's renewable energy cooperation with belt and road economies," Energy, Elsevier, vol. 293(C).
    2. Shahnazi, Rouhollah & Alimohammadlou, Moslem, 2022. "Investigating risks in renewable energy in oil-producing countries through multi-criteria decision-making methods based on interval type-2 fuzzy sets: A case study of Iran," Renewable Energy, Elsevier, vol. 191(C), pages 1009-1027.
    3. Nan Li & Haining Zhang & Xiangcheng Zhang & Xue Ma & Sen Guo, 2020. "How to Select the Optimal Electrochemical Energy Storage Planning Program? A Hybrid MCDM Method," Energies, MDPI, vol. 13(4), pages 1-20, February.
    4. Gul, Muhammet & Yucesan, Melih, 2022. "Performance evaluation of Turkish Universities by an integrated Bayesian BWM-TOPSIS model," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    5. Ilbahar, Esra & Kahraman, Cengiz & Cebi, Selcuk, 2022. "Risk assessment of renewable energy investments: A modified failure mode and effect analysis based on prospect theory and intuitionistic fuzzy AHP," Energy, Elsevier, vol. 239(PA).
    6. Sen Guo & Wenyue Zhang & Xiao Gao, 2020. "Business Risk Evaluation of Electricity Retail Company in China Using a Hybrid MCDM Method," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    7. Wu, Yunna & Liao, Mingjuan & Hu, Mengyao & Lin, Jiawei & Zhou, Jianli & Zhang, Buyuan & Xu, Chuanbo, 2020. "A decision framework of low-speed wind farm projects in hilly areas based on DEMATEL-entropy-TODIM method from the sustainability perspective: A case in China," Energy, Elsevier, vol. 213(C).
    8. Zhang, Mingming & Pang, Zhichao & Liu, Liyun & Yang, Zikun & Zhou, Dequn, 2024. "Risk assessment of China's overseas energy investments considering the response ability to major risk events: A case study of COVID-19," Energy, Elsevier, vol. 288(C).
    9. Wan-Chi Jackie Hsu & Huai-Wei Lo & Chin-Cheng Yang, 2021. "The Formulation of Epidemic Prevention Work of COVID-19 for Colleges and Universities: Priorities and Recommendations," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    10. Huang, Jianbai & Liu, Jia & Zhang, Hongwei & Guo, Yaoqi, 2020. "Sustainable risk analysis of China's overseas investment in iron ore," Resources Policy, Elsevier, vol. 68(C).
    11. Peipei You & Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "Operation Performance Evaluation of Power Grid Enterprise Using a Hybrid BWM-TOPSIS Method," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    12. Sarfaraz Hashemkhani Zolfani & Ramin Bazrafshan & Fatih Ecer & Çağlar Karamaşa, 2022. "The Suitability-Feasibility-Acceptability Strategy Integrated with Bayesian BWM-MARCOS Methods to Determine the Optimal Lithium Battery Plant Located in South America," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    13. Dilupa Nakandala & Yung Po Tsang & Henry Lau & Carman Ka Man Lee, 2022. "An Industrial Blockchain-Based Multi-Criteria Decision Framework for Global Freight Management in Agricultural Supply Chains," Mathematics, MDPI, vol. 10(19), pages 1-23, September.
    14. Yuanxin Liu & FengYun Li & Yi Wang & Xinhua Yu & Jiahai Yuan & Yuwei Wang, 2018. "Assessing the Environmental Impact Caused by Power Grid Projects in High Altitude Areas Based on BWM and Vague Sets Techniques," Sustainability, MDPI, vol. 10(6), pages 1-20, May.
    15. Corrente, Salvatore & Greco, Salvatore & Rezaei, Jafar, 2024. "Better decisions with less cognitive load: The Parsimonious BWM," Omega, Elsevier, vol. 126(C).
    16. Vieira, Fabiana C. & Ferreira, Fernando A.F. & Govindan, Kannan & Ferreira, Neuza C.M.Q.F. & Banaitis, Audrius, 2022. "Measuring urban digitalization using cognitive mapping and the best worst method (BWM)," Technology in Society, Elsevier, vol. 71(C).
    17. Jun Dong & Dongxue Wang & Dongran Liu & Palidan Ainiwaer & Linpeng Nie, 2019. "Operation Health Assessment of Power Market Based on Improved Matter-Element Extension Cloud Model," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    18. Huibing Cheng & Shanshui Zheng & Jianghong Feng, 2022. "A Fuzzy Multi-Criteria Method for Sustainable Ferry Operator Selection: A Case Study," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    19. Lin, Xueshan & Huang, Tao & Bompard, Ettore & Wang, Beibei & Zheng, Yaxian, 2023. "Ex-ante market power evaluation and mitigation in day-ahead electricity market considering market maturity levels," Energy, Elsevier, vol. 278(C).
    20. Göçmen Polat, Elifcan & Yücesan, Melih & Gül, Muhammet, 2023. "A comparative framework for criticality assessment of strategic raw materials in Turkey," Resources Policy, Elsevier, vol. 82(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:jmathe:v:9:y:2021:i:5:p:473-:d:505594. 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.