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Influential factor analysis of China's unsustainable electric power system: A case study of Chengdu Electric Bureau

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  • Xu, Jing
  • Ye, Meng
  • Peng, Xianyi
  • Li, Zhi

Abstract

Electric security is closely related to all aspects of the national macro-economy, but when it comes into considering power policies, the subject of exploring the reasons for large area power outages is often considered as a “separate subject”. This paper proposes a novel evaluation method to analyze and determine the main factors that are commonly found within unsustainable electric power system. This method integrates the fuzzy clustering method, fault tree analysis and an analytic hierarchy process. First, it builds a classification for different factors based on Faulty Tree Analysis methods. Then, the logical relationships between each factors are prioritized and determined. Once the logical hierarchies are established, the Entropy-House of Quality for quantitative analysis is employed to support big blackout prevention and decision-making for various influential factors. Finally, Sichuan province are given to verify its effectiveness so that the policy-makers and security regulators in China can follow its recommendations to create a better electricity supply security management and a more sustainable energy reform in the future.

Suggested Citation

  • Xu, Jing & Ye, Meng & Peng, Xianyi & Li, Zhi, 2019. "Influential factor analysis of China's unsustainable electric power system: A case study of Chengdu Electric Bureau," Energy Policy, Elsevier, vol. 129(C), pages 975-984.
    • Handle: RePEc:eee:enepol:v:129:y:2019:i:c:p:975-984
    DOI: 10.1016/j.enpol.2019.03.011
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    References listed on IDEAS

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    Cited by:

    1. Xian, Huafeng & Che, Jinxing, 2022. "Multi-space collaboration framework based optimal model selection for power load forecasting," Applied Energy, Elsevier, vol. 314(C).

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