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A Stochastic Frontier Model for Definition of Non-Technical Loss Targets

Daniel Leite (), José Pessanha (), Paulo Simões (), Rodrigo Calili () and Reinaldo Souza ()
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Daniel Leite: Enel Brasil, Niterói 24020-005, Brazil
José Pessanha: Institute of Mathematics and Statistics, Rio de Janeiro State University, Rio de Janeiro 20550-000, Brazil
Paulo Simões: Brazilian Institute of Geography and Statistics, Rio de Janeiro 20021-120, Brazil
Rodrigo Calili: Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil
Reinaldo Souza: Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil

Energies, 2020, vol. 13, issue 12, 1-20

Abstract: The theft of electrical energy is one of the main problems faced by electricity distribution utilities, especially in developing countries. Aware of the difficulties in combating non-technical losses (NTLs) in Brazil, the National Electric Energy Agency (ANEEL) established tolerable limits for the percentage of non-technical losses to each Brazilian distribution utility. Despite the notable progress made by ANEEL, when comparing public utility NTLs and their regulatory targets in the last decade, it was observed that the goals defined by this agency were not able to lead to a general reduction in NTLs in the country. Thus, the search for alternative methodologies to deal with the topic is necessary. A more attractive alternative to the ANEEL’s model is an efficient frontier model. This paper describes a stochastic frontier cost model for panel data whose equation is specified to provide the tolerable limits for the percentage of NTLs. The proposed model was applied to a panel of data containing annual observations, over 10 years, of 41 distribution utilities in the Brazilian electrical system.

Keywords: non-technical losses; distribution; stochastic frontier analysis; panel data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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