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A Hybrid MCDM Model for Evaluating the Market-Oriented Business Regulatory Risk of Power Grid Enterprises Based on the Bayesian Best-Worst Method and MARCOS Approach

Author

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  • Zhuola Zhang

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

  • Shiyuan Lin

    (State Grid Fujian Electric Power Economic and Technology Research Institute, Fuzhou 350012, China)

  • Yingjin Ye

    (State Grid Fujian Electric Power Economic and Technology Research Institute, Fuzhou 350012, China)

  • Zhao Xu

    (State Grid Energy Research Institute Co., Ltd., Beijing 102209, China)

  • Yihang Zhao

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

  • Huiru Zhao

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

  • Jingqi Sun

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

Abstract

As a result of the deepening of China’s power system reform, the market-oriented business development of power grid enterprises is in full swing. However, most of the existing research has focused on the regulatory risks of the regulatory business for power grid enterprises, while ignoring the regulatory risks faced by market-oriented businesses. In order to promote the sustainable development of market-oriented business, a comprehensive regulatory risk assessment framework was constructed for the market business of power grid enterprises. First, the risk assessment index system was constructed from the perspectives of policy risk, business isolation risk, market risk, and safety risk. Then, a novel hybrid multi-criteria decision-making (MCDM) model based on the Bayesian best-worst method and the measurement alternatives and ranking according to the compromise solution approach, was adopted. Finally, eight market-oriented businesses were selected as case studies. The result indicates that organizational isolation risk and operational security risk are the key regulatory risks of the market-oriented business for power grid enterprises. Compared with two other MCDM models, the proposed hybrid MCDM model has good applicability and effectiveness for risk evaluation of the regulatory business. The results of this research can provide support for power enterprises to deal with market-oriented business supervision, and can also provide a reference for power industry regulators.

Suggested Citation

  • Zhuola Zhang & Shiyuan Lin & Yingjin Ye & Zhao Xu & Yihang Zhao & Huiru Zhao & Jingqi Sun, 2022. "A Hybrid MCDM Model for Evaluating the Market-Oriented Business Regulatory Risk of Power Grid Enterprises Based on the Bayesian Best-Worst Method and MARCOS Approach," Energies, MDPI, vol. 15(9), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:2978-:d:796958
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. Himanshu Gupta & Manjeet Kharub & Kumar Shreshth & Ashwani Kumar & Donald Huisingh & Anil Kumar, 2023. "Evaluation of strategies to manage risks in smart, sustainable agri‐logistics sector: A Bayesian‐based group decision‐making approach," Business Strategy and the Environment, Wiley Blackwell, vol. 32(7), pages 4335-4359, November.
    4. 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.
    5. Xin Zhao & Qiushuang Li & Wanlei Xue & Yihang Zhao & Huiru Zhao & Sen Guo, 2022. "Research on Ultra-Short-Term Load Forecasting Based on Real-Time Electricity Price and Window-Based XGBoost Model," Energies, MDPI, vol. 15(19), pages 1-11, October.
    6. Lingwen Meng & Jingliang Zhong & Shasha Luo & Xinshan Zhu & Yulin Wang & Shumei Zhang, 2024. "Multimodal Operation Data Mining for Grid Operation Violation Risk Prediction," Energies, MDPI, vol. 17(21), pages 1-16, October.

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