Performance evaluation of Iranian electricity distribution units by using stochastic data envelopment analysis. (December 2015)

• Record Type: Journal Article
• Authors: Azadeh, A.; Motevali Haghighi, S.; Zarrin, M.; Khaefi, S.
• Is Part Of: International journal of electrical power & energy systems. Volume 73(2015:Dec.)
• Page Start: 919
• Page End: 931
• Publication Date: 2015-12
• Subjects: Electricity distribution units -- Assessment -- Performance measures -- Stochastic data envelopment analysis -- Network length; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2015.06.002
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Evaluation of factors for performance assessment of electricity distribution units. There are usually incomplete and stochastic data or lack of data. Theory of probability is used. Indicators are: Network length, transport capacity, number of employees & customers and sales. Network length is selected as the most influential factor. Abstract: This paper introduces an approach based on stochastic data envelopment analysis (SDEA) for performance assessment of electricity distribution units. A new approach is applied for assessment of Iranian distribution units from 2001 to 2011 in this paper. There are usually incomplete and stochastic data or lack of data with respect to electricity distribution companies. Due to lack of information about some parameters, theory of probability is imported to the model. Different Iranian distribution units are considered as decision making units (DMUs). Network length, transport capacity and the number of employees are chosen as inputs while number of customers and total electricity sales are chosen as stochastic outputs. Then, the best electricity distributions units are selected with respect to efficiency scores in stochastic environment. Also, SDEA model is performed for each input, separately to identify the most important input indicators by comparing the results of associated efficiencies with SDEA model. The empirical results show that network length is the most important and influential input factor in this particular case study. To the best of our knowledge this is the first paper that examines stochastic outputs for assessment of electricity distribution units by SDEA in Iran.