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An extended mixed-network DEA method to analyze the power supply system with shared resources

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  • Zhang, Qingyu
  • Zhang, Rui

Abstract

Amidst the global push for decarbonization, nations are actively developing new power systems (NPS) with an emphasis on renewable energy sources. Traditional studies have utilized a two-stage DEA model to assess the power industry, typically partitioning it into power generation system (PGS) and power sales system (PSS). However, this approach lacks granularity in capturing the nuances of various power generation sources. In response, this paper proposes an advanced extended mixed-network structure utilizing a slack-based measure (SBM) model with an integrated parallel-serial structure. This model differentiates between fossil energy power generation (FEPG) and non-fossil energy power generation (NFPG), while concurrently addressing generation diversity, resource sharing, input optimization, and undesirable outputs. Empirical findings reveal that China's NPS efficiency averaged 0.620 between 2015 and 2020. The inefficiencies predominantly stem from the PGS stage, particularly in the NFPG substage. Notably, Eastern China exhibits superior performance across both overall and stage-specific metrics among the four regions studied. Through its comprehensive evaluation of China's power system, the study offers valuable insights for optimizing performance and supporting the transition to a sustainable and low-carbon energy future.

Suggested Citation

  • Zhang, Qingyu & Zhang, Rui, 2025. "An extended mixed-network DEA method to analyze the power supply system with shared resources," Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:energy:v:324:y:2025:i:c:s0360544225017529
    DOI: 10.1016/j.energy.2025.136110
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    1. Qingxian An & Fanyong Meng & Beibei Xiong & Zongrun Wang & Xiaohong Chen, 2020. "Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach," Annals of Operations Research, Springer, vol. 290(1), pages 707-729, July.
    2. Wu, Jie & Zhu, Qingyuan & Ji, Xiang & Chu, Junfei & Liang, Liang, 2016. "Two-stage network processes with shared resources and resources recovered from undesirable outputs," European Journal of Operational Research, Elsevier, vol. 251(1), pages 182-197.
    3. Liu, Yaqin & Zhao, Guohao & Zhao, Yushan, 2016. "An analysis of Chinese provincial carbon dioxide emission efficiencies based on energy consumption structure," Energy Policy, Elsevier, vol. 96(C), pages 524-533.
    4. An, Qingxian & Wu, Qifan & Li, Jinlin & Xiong, Beibei & Chen, Xiaohong, 2019. "Environmental efficiency evaluation for Xiangjiang River basin cities based on an improved SBM model and Global Malmquist index," Energy Economics, Elsevier, vol. 81(C), pages 95-103.
    5. Mahmoudabadi, Mohammad Zarei & Azar, Adel & Emrouznejad, Ali, 2018. "A novel multilevel network slacks-based measure with an application in electric utility companies," Energy, Elsevier, vol. 158(C), pages 1120-1129.
    6. Shu, Tianheng & Liao, Xia & Yang, Shuo & Yu, Taofang, 2024. "Towards sustainability: Evaluating energy efficiency with a super-efficiency SBM-DEA model across 168 economies," Applied Energy, Elsevier, vol. 376(PA).
    7. Iftikhar, Yaser & Wang, Zhaohua & Zhang, Bin & Wang, Bo, 2018. "Energy and CO2 emissions efficiency of major economies: A network DEA approach," Energy, Elsevier, vol. 147(C), pages 197-207.
    8. Zhongbao Zhou & Wenbin Liu, 2015. "DEA Models with Undesirable Inputs, Intermediates, and Outputs," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 15, pages 415-446, Springer.
    9. 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).
    10. Wu, Jie & Li, Mingjun & Zhu, Qingyuan & Zhou, Zhixiang & Liang, Liang, 2019. "Energy and environmental efficiency measurement of China's industrial sectors: A DEA model with non-homogeneous inputs and outputs," Energy Economics, Elsevier, vol. 78(C), pages 468-480.
    11. Fare, R. & Grosskopf, S. & Logan, J., 1985. "The relative performance of publicly-owned and privately-owned electric utilities," Journal of Public Economics, Elsevier, vol. 26(1), pages 89-106, February.
    12. Sadjadi, S.J. & Omrani, H., 2008. "Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies," Energy Policy, Elsevier, vol. 36(11), pages 4247-4254, November.
    13. Chao, Shih-Liang & Yu, Ming-Miin & Hsieh, Wei-Fan, 2018. "Evaluating the efficiency of major container shipping companies: A framework of dynamic network DEA with shared inputs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 44-57.
    14. Chen, Yao & Du, Juan & David Sherman, H. & Zhu, Joe, 2010. "DEA model with shared resources and efficiency decomposition," European Journal of Operational Research, Elsevier, vol. 207(1), pages 339-349, November.
    15. Zhu, Qingyuan & Xu, Shuqi & Sun, Jiasen & Li, Xingchen & Zhou, Dequn, 2022. "Energy efficiency evaluation of power supply system: A data-driven approach based on shared resources," Applied Energy, Elsevier, vol. 312(C).
    16. Ali Azadeh & Somayeh Ahmadi Movaghar, 2010. "An integrated multivariate approach for performance assessment and optimisation of electricity transmission systems," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 5(2), pages 226-248.
    17. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    18. Zhang, Ruchuan & Wei, Qian & Li, Aijun & Ren, LiYing, 2022. "Measuring efficiency and technology inequality of China's electricity generation and transmission system: A new approach of network Data Envelopment Analysis prospect cross-efficiency models," Energy, Elsevier, vol. 246(C).
    19. Zhao, Linlin & Zhu, Qingyuan & Zhang, Lin, 2021. "Regulation adaptive strategy and bank efficiency: A network slacks-based measure with shared resources," European Journal of Operational Research, Elsevier, vol. 295(1), pages 348-362.
    20. Li, Wanying & Ji, Zhengsen & Dong, Fugui & Yang, Yugui, 2024. "Evaluation of provincial renewable energy generation efficiency and spatio-temporal heterogeneity of influencing factors in China," Renewable Energy, Elsevier, vol. 226(C).
    21. An, Qingxian & Wen, Yao & Ding, Tao & Li, Yongli, 2019. "Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method," Omega, Elsevier, vol. 85(C), pages 16-25.
    22. Yang, Lisha & Lin, Boqiang, 2016. "Carbon dioxide-emission in China׳s power industry: Evidence and policy implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 258-267.
    23. Kao, Chiang, 2016. "Efficiency decomposition and aggregation in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 255(3), pages 778-786.
    24. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    25. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    26. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    27. Liu, Wenbin & Zhou, Zhongbao & Ma, Chaoqun & Liu, Debin & Shen, Wanfang, 2015. "Two-stage DEA models with undesirable input-intermediate-outputs," Omega, Elsevier, vol. 56(C), pages 74-87.
    28. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    29. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    30. Shermeh, H. Ebrahimzadeh & Najafi, S.E. & Alavidoost, M.H., 2016. "A novel fuzzy network SBM model for data envelopment analysis: A case study in Iran regional power companies," Energy, Elsevier, vol. 112(C), pages 686-697.
    31. Wang, Chia-Nan & Nguyen, Thi Thuy-Vy & Chiang, Chia-Chin & Le, Hoang-Dang, 2024. "Evaluating renewable energy consumption efficiency and impact factors in Asia-pacific economic cooperation countries: A new approach of DEA with undesirable output model," Renewable Energy, Elsevier, vol. 227(C).
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