Modelling Efficiency of Electric Utilities Using Three Stage Virtual Frontier Data Envelopment Analysis with Variable Selection by Loads Method

Abstract

Electric utility regulators and policy makers implement incentive-based regulation to improve electric utilities efficiency or to manage the cost of electricity. However, poorly implemented regulation may produce undesired results such as low reliability or poor quality of service. Moreover, the competition within the electricity sector is likely to be low because of the high barriers to entry, vertically integrated electric utilities, and high capital requirements. Therefore, benchmarking exercises allow policy makers and regulators to gauge the relative efficiency of electric utilities and help them to reward or penalize the electric utilities accordingly. In this study, we examined the variables that significantly influence the efficiency of electric utilities and developed an optimum method to measure the efficiency of the electric utilities. The results of the efficiency measurement were then used to rank the electric utilities. The result of this study indicates that there are 13 variables that significantly affect the efficiency score of electric utilities and three stage virtual frontier data envelopment analysis (3S-VF-DEA) is the optimum method to measure the efficiency of the electric utilities.

The spatial-temporal evolution analysis of carbon emission of China's thermal power industry based on the three-stage SBM—DEA model

“…Many scholars also use the three-stage DEA model to measure and evaluate carbon emission efficiency for more accurate results (Surakiat et al , 2018; Zhou and Yu, 2021). Kannan et al (2021) measured the efficiency of electric power enterprises through the three-stage virtual frontier DEA (3S-VF-DEA) and then ranked electric power companies using the efficiency measurement results. Some scholars have combined the three-stage DEA model with other models to study carbon emission efficiency.…”