Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes

Abstract

In this paper, a customized network data envelopment analysis model is developed to evaluate the efficiency of electric power production and distribution processes. In the production phase, power plants consume fuels such as oil and gas to generate the electricity. In the distribution phase, regional electricity companies transmit and distribute the electricity to the customers in houses, industries, and agriculture. Although, the electricity is assumed to be a clean type of energy, several types of emissions and pollutions are produced during electricity generation. The generated emissions are considered as an undesirable output. A customized network data envelopment analysis (NDEA) approach is proposed to evaluate the efficiency of these processes Each decision making unit (DMU) includes two serially connected sub-DMUs, i.e., production and distribution stages. The models are extended using interval data to address the considerable uncertainty in the problem. The efficiency scores of main process, and each sub-process are determined. The final ranking of DMUs and sub-DMUs are achieved using a multi-attribute decision making (MADM) method. The whole approach is applied in a real case study in electrical power production and distribution network with 14 DMUs. The proposed approach has the following innovations in comparison with existing methods: (1) Both production and distribution process are evaluated in a unique model; (2) Undesirable outputs and uncertainty of data are considered in proposed approach; (3) Properties of proposed models are discussed through several theorems; (4) The efficiencies of production and distribution phases are determined distinctively; (5) A full ranking approach is proposed; (6) A real case study of electrical power production and distribution network is surveyed. The results of proposed approach are adequate and interesting. This approach can be customized for application in similar systems such as water production-supply management, Oil and fuel production–distribution systems, and supply chains.