Skip to main content
SpringerLink
Log in

How Efficient are the Brazilian Electricity Distribution Companies?

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

During the last years, the electricity sector has experienced great changes, especially within the economic regulation. After receiving several criticisms, the rate of return regulation has been replaced by incentive regulation. The main objective of this regulation is to stimulate business efficiency. This paper proposes an alternative application of data envelopment analysis to the Brazilian case, characterized by a large territory: the use of Unit Networks in the distribution segment to regionalize the concession area and then to analyse the efficiencies separately. Many regulators use the entire distribution company as a decision-making unit for price regulation when benchmarking is applied. However, in Brazil, quality performance is measured in detail using sets of consuming units, i.e. quality is measured using small parts of the company. Given that efficiency cannot be assessed without considering various aspects of quality performance and characteristics of the underlying environment in the utility’s concession area, this paper tries to find the trade-off between management, quality, environment and costs. Therefore, the main contribution of this paper is twofold: the solution for Brazilian distribution companies’ heterogeneity and the choice of variables that are better measures for an efficiency analysis. Some examples with Brazilian utilities are provided to show the advantages of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Retail Price Index

  2. Controllable costs composed by operational costs, capital remuneration and depreciation.

  3. Available at: www.aneel.gov.br.

Abbreviations

DEA:

Data envelopment analysis

UN:

Unit Network

DMU:

Decision-making unit

RPI:

Retail Price Index

FRM:

Firm reference model

COLS:

Corrected ordinary least square

SAIDI:

System Average Interruption Duration Index

SAIFI:

System Average Interruption Frequency Index

CRS:

Constant Return to Scale

VRS:

Variable Return to Scale

TINT:

Total time lost due to interruptions

GIP:

Gross internal product

ANOVA:

Analysis of variance

\(U_i\) :

Annual outage time (h)

\(N_i\) :

Number of customers at load point i (person)

\(N\) :

Number of companies (unit)

\(\theta \) :

Efficiency score (0–1)

\(\lambda \) :

Vector of weights

E :

Observed inputs

M :

Observed outputs

X :

Input matrix

Y :

Output matrix

\(x_{i}\) :

Input column vector for the \(i\)th company

\(y_{i}\) :

Output column vector for the \(i\)th company

\(z_{i}\) :

Vector of environmental variables

\(\theta _i^*\) :

Latent variable related with the calculated efficiency score

\(\beta \) :

Vector of parameters that represent the impact of environment

References

  • Agência Nacional de Energia Elétrica (ANEEL) (2006). Second price control review of distribution utilities of electricity in Brazil. Technical Note no. 262. [Online]. Available: http://www.aneel.gov.br.

  • Agência Nacional de Energia Elétrica (ANEEL) [Online]. Available in http://www.aneel.gov.br.

  • Averch, H., & Johnson, L. L. (1962). Behavior of the firm under regulatory constraint. American Economic Review, 52(5), 1052–1069.

    Google Scholar 

  • Banker, R. D., Charnes, R. F., & Cooper, W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.

    Article  MATH  Google Scholar 

  • Billinton, R., & Allan, R. N. (1984). Reliability evaluation of power systems. New York.

  • Bogetoft. P. (2014). Comments on the Brazilian benchmarking model for energy distribution regulation fourth cycle of tariff review—NT 192/2014. Available at http://www.aneel.gov.br/.

  • Cambini, C., Fumagalli, E., & Croce, A. (2012). Output-based incentive regulation: Benchmarking with quality of supply in electricity distribution. Energy Economics, 45, 205–216.

    Article  Google Scholar 

  • Çelen, A. (2013). Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage (DEA&Tobit) analysis. Energy Policy, 63, 300–310.

    Article  Google Scholar 

  • Companhia Energética de Minas Gerais (CEMIG). [Online]. Available in http://www.cemig.com.br.

  • Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444.

    Article  MATH  MathSciNet  Google Scholar 

  • Chilingerian, J. A., & Sherman. H. D. (2004). Health care applications: From hospitals to physicians from productive efficiency to quality frontiers. In Handbook on data envelopment analysis. Boston: Kluwer Academic Publishers.

  • Cook, W. D., Harrison, J., Imanirad, R., Rouse, P., & Zhu, J. (2013). Data envelopment analysis with nonhomogeneous DMUs. Operations Research, 61(3), 666–676.

    Article  MATH  MathSciNet  Google Scholar 

  • Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132(2), 245–259.

    Article  MATH  Google Scholar 

  • Ergas, H., & Small, J. (2001). Price caps and rate of return regulation. Network Economics Consulting Group.

  • Estache, A., Rossi, M. A., & Ruzzier, C. A. (2004). The case for international coordination of electricity regulation: Evidence from the measurement of efficiency in South America. Journal of Regulatory Economics, 25(3), 271–295.

    Article  Google Scholar 

  • Fisher, R. A. (1918). The correlation between relatives on the supposition of Mendelian inheritance. Transactions of the Royal Society of Edinburgh, 52, 399–433.

    Article  Google Scholar 

  • Giannakis, D., Jamasb, T., & Pollitt, M. (2005). Benchmarking and incentive regulation of quality of service: An application to the UK electricity distribution networks. Energy Policy, 33(17), 2256–2271.

    Article  Google Scholar 

  • Growitsch, C., Jamasb, T., & Pollitt, M. (2009). Quality of service, efficiency and scale in network industries: An analysis of European electricity distribution. Applied Economics, 41(20), 2256–2570.

    Article  Google Scholar 

  • Haney, A. B., & Pollitt, M. G. (2009). Efficiency analysis of energy networks: An international survey of regulators. Energy Policy, 37(12), 5814–5830.

    Article  Google Scholar 

  • Instituto Brasileiro de Geografia e Estatística (IBGE). [Online]. Available in http://www.ibge.gov.br/.

  • Jamasb, T., Orea, L., & Pollitt, M. G. (2012). Estimating marginal cost of quality improvements: The case of the UK electricity distribution companies. Energy Economics, 34, 1498–1506.

    Article  Google Scholar 

  • Jamasb, T., & Pollitt, M. (2001). Benchmarking and regulation: International electricity experience. Utilities Policy, 9(3), 107–130.

    Article  Google Scholar 

  • Kirschen, Allan, R., & Strbac, G. (1997). Contributions to individual generators to loads and flows. IEEE Transactions on Power Systems, 12(1), 52–60.

    Article  Google Scholar 

  • Lima, L. M. M., Queiroz, A. R., & Lima, J. W. M. (2011). From voltage level to locational pricing of distribution network: The Brazilian experience. IEEE general meeting conference: Power engineering society.

  • Lowry, M. N., & Getachew, L. (2009). Statistical benchmarking in utility regulation: Role, standards and methods. Energy Policy, 37(4), 1323–1330.

    Article  Google Scholar 

  • Matos, G., Lopes, A. L. M., & Costa, M. A. (2012). A critical analysis of the benchmarking model proposed by the Brazilian Electricity Regulator for the 3rd cycle (2011–2015) of the distribution companies tariff revision. In 10th International conference on data envelopment analysis, Natal.

  • Neuberg, L. G. (1977). Two issues in the municipal ownership of electric power distribution. Bell Journal of Economics, 8(1), 303–323.

    Article  Google Scholar 

  • Pombo, C., & Taborda, R. (2006). Performance and efficiency in Colombia’s power distribution system: Effects of the 1994 reforms. Energy Economics, 28(3), 339–369.

    Article  Google Scholar 

  • Ruggiero, J. (2004). Performance evaluation in education: Modeling educational production. In Handbook on data envelopment analysis. Boston: Kluwer Academic Publishers.

  • Sanhueza, R., Rudnick, H., & Lagunas, H. (2004). DEA efficiency for the determination of the electric power distribution added value. IEEE Transactions on Power Systems, 19(2), 919–925.

    Article  Google Scholar 

  • Simar, & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31–64.

    Article  MathSciNet  Google Scholar 

  • Subhash, R. C. (1988). Data envelopment analysis, nondiscretionary inputs and efficiency: An alternative interpretation. Socio-Economic Planning Sciences, 22(4), 167–176.

    Article  Google Scholar 

  • Subhash, R. C. (2004). Data envelopment analysis: Theory and techniques for economics and operations research. Cambridge: Cambridge University Press.

    Google Scholar 

  • Subhash, R. C., & Chen, L. (2010). Data envelopment analysis for performance evaluation: A child’s guide. Indian Economic Review, 45(2), 373–399.

    Google Scholar 

  • Tanure, E. S., Tahan, M. O., & Lima, J. W. M. (2006). Establishing quality performance of distribution companies based on yardstick regulation. IEEE Transaction on Power Systems, 21(3), 1148–1153.

    Article  Google Scholar 

  • Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26, 24–36.

    Article  MATH  MathSciNet  Google Scholar 

  • Yu, W., Jamasb, T., & Pollitt, M. (2009). Does weather explain cost and quality performance? An analysis of UK electricity distribution companies. Energy Policy, 37(11), 4177–4188.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank CAPES, CNPq, FAPEMIG/MG and INERGE for financial support. As its employee, the first author would like to thank Elektro Distribution Company for its support of this research.

Author information

Authors and Affiliations

  1. Regulatory Affairs Department, Elektro Distribution Company, Street Ary Antenor de Souza, 321, Campinas, Brazil

    S. S. Xavier

  2. Electrical Engineering Department, University of Itajubá, Avenue BPS, 1303, Itajubá, Brazil

    J. W. Marangon Lima & L. M. Marangon Lima

  3. University of Minas Gerais, Avenue Antônio Carlos, 6627, Belo Horizonte, Brazil

    A. L. M. Lopes

Authors
  1. S. S. Xavier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. W. Marangon Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. M. Marangon Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. L. M. Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. S. Xavier.

Additional information

This work was supported by Capes, FAPEMIG and INERGE, Brazil. S. S. Xavier is with Elektro Distribution Company.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xavier, S.S., Lima, J.W.M., Lima, L.M.M. et al. How Efficient are the Brazilian Electricity Distribution Companies?. J Control Autom Electr Syst 26, 283–296 (2015). https://doi.org/10.1007/s40313-015-0178-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-015-0178-2

Keywords

Search

Navigation