IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3227-d374678.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Daniel Leite & José Pessanha & Paulo Simões & Rodrigo Calili & Reinaldo Souza, 2020. "A Stochastic Frontier Model for Definition of Non-Technical Loss Targets," Energies, MDPI, vol. 13(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3227-:d:374678
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3227/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3227/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christian von Hirschhausen & Astrid Cullmann & Andreas Kappeler, 2006. "Efficiency analysis of German electricity distribution utilities - non-parametric and parametric tests," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2553-2566.
    2. Depuru, Soma Shekara Sreenadh Reddy & Wang, Lingfeng & Devabhaktuni, Vijay, 2011. "Electricity theft: Overview, issues, prevention and a smart meter based approach to control theft," Energy Policy, Elsevier, vol. 39(2), pages 1007-1015, February.
    3. Yurtseven, Çağlar, 2015. "The causes of electricity theft: An econometric analysis of the case of Turkey," Utilities Policy, Elsevier, vol. 37(C), pages 70-78.
    4. Simões, Paulo Fernando Mahaz & Souza, Reinaldo Castro & Calili, Rodrigo Flora & Pessanha, José Francisco Moreira, 2020. "Analysis and short-term predictions of non-technical loss of electric power based on mixed effects models," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    5. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    6. Kuosmanen, Timo & Saastamoinen, Antti & Sipiläinen, Timo, 2013. "What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods," Energy Policy, Elsevier, vol. 61(C), pages 740-750.
    7. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    8. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    9. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    10. Jamil, Faisal & Ahmad, Eatzaz, 2019. "Policy considerations for limiting electricity theft in the developing countries," Energy Policy, Elsevier, vol. 129(C), pages 452-458.
    11. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107029514, September.
    12. Yakubu, Osman & Babu C., Narendra & Adjei, Osei, 2018. "Electricity theft: Analysis of the underlying contributory factors in Ghana," Energy Policy, Elsevier, vol. 123(C), pages 611-618.
    13. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
    14. Jamasb, T. & Pollitt, M., 2000. "Benchmarking and regulation: international electricity experience," Utilities Policy, Elsevier, vol. 9(3), pages 107-130, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    2. Marco Toledo-Orozco & Carlos Arias-Marin & Carlos Álvarez-Bel & Diego Morales-Jadan & Javier Rodríguez-García & Eddy Bravo-Padilla, 2021. "Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities," Energies, MDPI, vol. 14(4), pages 1-23, February.
    3. Cardoso de Mendonça, Mário Jorge & Pereira, Amaro Olimpio & Medrano, Luis Alberto & Pessanha, José Francisco M., 2021. "Analysis of electric distribution utilities efficiency levels by stochastic frontier in Brazilian power sector," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).
    4. Gautier, Axel & Nsabimana, René & Walheer, Barnabé, 2023. "Quality performance gaps and minimal electricity losses in East Africa," Utilities Policy, Elsevier, vol. 82(C).
    5. de Mendonça, Mário Jorge Cardoso & Pereira, Amaro Olimpio & Bellido, Marlon Max H. & Medrano, Luis Alberto & Pessanha, José Francisco Moreira, 2023. "Service quality performance indicators for electricity distribution in Brazil," Utilities Policy, Elsevier, vol. 80(C).
    6. Eduardo Correia & Rodrigo Calili & José Francisco Pessanha & Maria Fatima Almeida, 2023. "Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions," Energies, MDPI, vol. 16(6), pages 1-22, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    2. Babar, Zainab & Jamil, Faisal & Haq, Wajiha, 2022. "Consumer's perception towards electricity theft: A case study of Islamabad and Rawalpindi using a path analysis," Energy Policy, Elsevier, vol. 169(C).
    3. Hugo Brise o & Omar Rojas, 2020. "Factors Associated with Electricity Losses: A Panel Data Perspective," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 281-286.
    4. Wabukala, Benard M. & Mukisa, Nicholas & Watundu, Susan & Bergland, Olvar & Rudaheranwa, Nichodemus & Adaramola, Muyiwa S., 2023. "Impact of household electricity theft and unaffordability on electricity security: A case of Uganda," Energy Policy, Elsevier, vol. 173(C).
    5. Hugo Brise o & Omar Rojas, 2020. "Factors Associated with Electricity Theft in Mexico," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 250-254.
    6. Hugo Brise o & Jessica Rubiano & Rodolfo Garc a & Omar Rojas, 2021. "Factors Associated with Electricity Losses in Colombia," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 465-470.
    7. Gautier, Axel & Nsabimana, René & Walheer, Barnabé, 2023. "Quality performance gaps and minimal electricity losses in East Africa," Utilities Policy, Elsevier, vol. 82(C).
    8. Stracqualursi, Erika & Rosato, Antonello & Di Lorenzo, Gianfranco & Panella, Massimo & Araneo, Rodolfo, 2023. "Systematic review of energy theft practices and autonomous detection through artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    9. Subal C. Kumbhakar & Gudbrand Lien, 2017. "Yardstick Regulation of Electricity Distribution Disentangling Short-run and Long-run Inefficiencies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    10. Pontus Mattsson & Jonas Mansson & William H. Greene, 2018. "TFP Change and its Components for Swedish Manufacturing Firms During the 2008-2009 Financial Crisis," Working Papers 18-27, New York University, Leonard N. Stern School of Business, Department of Economics.
    11. Habtamu ALEM, 2018. "Effects of model specification, short-run, and long-run inefficiency: an empirical analysis of stochastic frontier models," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 64(11), pages 508-516.
    12. Xiaoyan Zheng & Almas Heshmati, 2020. "An Analysis of Energy Use Efficiency in China by Applying Stochastic Frontier Panel Data Models," Energies, MDPI, vol. 13(8), pages 1-17, April.
    13. Fernando de Souza Savian & Julio Cezar Mairesse Siluk & Tai s Bisognin Garlet & Felipe Moraes do Nascimento & Jose Renes Pinheiro & Zita Vale, 2022. "Non-technical Losses in Brazil: Overview, Challenges, and Directions for Identification and Mitigation," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 93-107, May.
    14. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    15. Karlaftis, Matthew G. & Tsamboulas, Dimitrios, 2012. "Efficiency measurement in public transport: Are findings specification sensitive?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 392-402.
    16. Eduardo Correia & Rodrigo Calili & José Francisco Pessanha & Maria Fatima Almeida, 2023. "Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions," Energies, MDPI, vol. 16(6), pages 1-22, March.
    17. MAIMOUNA DIAKITE & Jean-François BRUN, 2016. "Tax Potential and Tax Effort: An Empirical Estimation for Non-Resource Tax Revenue and VAT’s Revenue," EcoMod2016 9537, EcoMod.
    18. Farsi, Mehdi & Filippini, Massimo, 2009. "An analysis of cost efficiency in Swiss multi-utilities," Energy Economics, Elsevier, vol. 31(2), pages 306-315, March.
    19. Dorgyles C.M. Kouakou, 2022. "Separating innovation short-run and long-run technical efficiencies: Evidence from the Economic Community of West African States (ECOWAS)," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 19(1), pages 103-141, June.
    20. Ablam Estel APETI & Bao-We-Wal BAMBE & Jean Louis COMBES, 2022. "On the Macroeconomic Effects of Fiscal Reforms : Fiscal Rules and Public Expenditure Efficiency," LEO Working Papers / DR LEO 2985, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3227-:d:374678. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.