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Electricity-Related Water Network Analysis in China Based on Multi-Regional Input–Output Analysis and Complex Network Analysis

Author

Listed:
  • Yiyi Zhang

    (Guangxi Power Transmission and Distribution Network Lightning Protection Engineering Technology Research Center, Guangxi University, Nanning 530004, China)

  • Huanzhi Fu

    (Guangxi Power Transmission and Distribution Network Lightning Protection Engineering Technology Research Center, Guangxi University, Nanning 530004, China)

  • Xinghua He

    (SPIC Guangxi Electric Power Co., Ltd., Nanning 530004, China)

  • Zhen Shi

    (Guangxi Power Transmission and Distribution Network Lightning Protection Engineering Technology Research Center, Guangxi University, Nanning 530004, China)

  • Tao Hai

    (Guangxi Power Transmission and Distribution Network Lightning Protection Engineering Technology Research Center, Guangxi University, Nanning 530004, China)

  • Peng Liu

    (Guangxi Electric Power Grid Co., Ltd., Nanning 530004, China)

  • Shan Xi

    (Guangxi Power Transmission and Distribution Network Lightning Protection Engineering Technology Research Center, Guangxi University, Nanning 530004, China)

  • Kai Zhang

    (Guangxi Power Transmission and Distribution Network Lightning Protection Engineering Technology Research Center, Guangxi University, Nanning 530004, China)

Abstract

The transfer of electricity-related water across regions and sectors provides an opportunity to alleviate water stress and make the development of the power system sustainable. Yet, the key node identification and properties of the electricity-related water network have not been studied. In this study, the properties and key nodes of the regional sectoral electricity-related water network in China were analyzed based on a multi-regional input–output model and complex network analysis. An iterative method was proposed to calculate the water consumption index inventory. The results showed electricity transmission can affect the regional water consumption index. Degree, intensity, betweenness centrality, and closeness centrality indicators of nodes were used to identify the key nodes. Sector 24 in Shandong was the key node with the largest closeness centrality. Sector 9 in Xinjiang was the key node with the largest betweenness centrality. They were the best choice for establishing points to observe and control flows, respectively. The transfer network did not have the small-world nature with the average clustering coefficient being 0.478 and the average path length being 2.327. It is less likely to cause large-scale clustering change in the network. This study can provide references for the common sustainable development of power systems and water resources.

Suggested Citation

  • Yiyi Zhang & Huanzhi Fu & Xinghua He & Zhen Shi & Tao Hai & Peng Liu & Shan Xi & Kai Zhang, 2023. "Electricity-Related Water Network Analysis in China Based on Multi-Regional Input–Output Analysis and Complex Network Analysis," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5360-:d:1100243
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    References listed on IDEAS

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