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Operational efficiency analysis of China's electric power industry using a dynamic network slack-based measure model

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  • Meng, Ming
  • Pang, Tingting

Abstract

Operational efficiency analysis is the foundational work for the management policy adjustment of the electric power industry in China and many other countries, but it is difficult to conduct because of the system complexity. This research uses a network structure to simulate the operating process of this industry. Through introducing the dynamic slack-based measure algorithm into this structure, the operational efficiencies of the entire industry and each stage (power generation and transmission–distribution) in each period are evaluated. On this basis, extended methods are also designed to measure the factor efficiency and the technological gap of each group. The model is applied to China's electric power industry, and the following conclusions are drawn: (I) The operational efficiency levels of provinces and regions are relatively different. (II) Generally, the operational efficiency of the generation stage is significantly lower than that of the transmission–distribution stage. (III) Factors with efficiencies ranked from highest to lowest are capital, labor, and energy. (IV) Major policy implications include breaking down the barriers to prompt interprovincial electricity transmission, developing the energy storage industry, implementing the flexibility transformation of thermal power units, and reducing the cross subsidy to the residual electricity consumption.

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  • Meng, Ming & Pang, Tingting, 2022. "Operational efficiency analysis of China's electric power industry using a dynamic network slack-based measure model," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222008015
    DOI: 10.1016/j.energy.2022.123898
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    Cited by:

    1. Kai He & Nan Zhu & Wu Jiang & Chuanjin Zhu, 2022. "Efficiency Evaluation of Chinese Provincial Industrial System Based on Network DEA Method," Sustainability, MDPI, vol. 14(9), pages 1-24, April.

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