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Robust network data envelopment analysis approach to evaluate the efficiency of regional electricity power networks under uncertainty

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  • Mohsen Fathollah Bayati
  • Seyed Jafar Sadjadi

Abstract

In this paper, new Network Data Envelopment Analysis (NDEA) models are developed to evaluate the efficiency of regional electricity power networks. The primary objective of this paper is to consider perturbation in data and develop new NDEA models based on the adaptation of robust optimization methodology. Furthermore, in this paper, the efficiency of the entire networks of electricity power, involving generation, transmission and distribution stages is measured. While DEA has been widely used to evaluate the efficiency of the components of electricity power networks during the past two decades, there is no study to evaluate the efficiency of the electricity power networks as a whole. The proposed models are applied to evaluate the efficiency of 16 regional electricity power networks in Iran and the effect of data uncertainty is also investigated. The results are compared with the traditional network DEA and parametric SFA methods. Validity and verification of the proposed models are also investigated. The preliminary results indicate that the proposed models were more reliable than the traditional Network DEA model.

Suggested Citation

  • Mohsen Fathollah Bayati & Seyed Jafar Sadjadi, 2017. "Robust network data envelopment analysis approach to evaluate the efficiency of regional electricity power networks under uncertainty," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-20, September.
    • Handle: RePEc:plo:pone00:0184103
    DOI: 10.1371/journal.pone.0184103
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    Cited by:

    1. Pejman Peykani & Ali Emrouznejad & Emran Mohammadi & Jafar Gheidar-Kheljani, 2024. "A novel robust network data envelopment analysis approach for performance assessment of mutual funds under uncertainty," Annals of Operations Research, Springer, vol. 339(3), pages 1149-1175, August.
    2. Pejman Peykani & Jafar Gheidar-Kheljani & Reza Farzipoor Saen & Emran Mohammadi, 2022. "Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data," Operational Research, Springer, vol. 22(5), pages 5529-5567, November.
    3. Mohammad Izadikhah & Elnaz Azadi & Majid Azadi & Reza Farzipoor Saen & Mehdi Toloo, 2022. "Developing a new chance constrained NDEA model to measure performance of sustainable supply chains," Annals of Operations Research, Springer, vol. 316(2), pages 1319-1347, September.

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