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

PLoS One. 2017 Sep 27;12(9):e0184103. doi: 10.1371/journal.pone.0184103. eCollection 2017.

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.

MeSH terms

  • Electric Power Supplies*
  • Electricity*
  • Iran
  • Models, Theoretical
  • Uncertainty*

Grant support

The authors received no specific funding for this work.