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Design of experiments applied to environmental variables analysis in electricity utilities efficiency: The Brazilian case

Author

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  • Leme, Rafael C.
  • Paiva, Anderson P.
  • Steele Santos, Paulo E.
  • Balestrassi, Pedro P.
  • Galvão, Leandro de Lima

Abstract

Benchmarking plays a central role in the regulatory scene. Regulators set tariffs according to a performance standard and, if the companies can outperform such a standard, they can retain the gains observed by such outperformance. Efficiency performance is usually assessed by comparison (or a benchmark) against either other companies or the company's own historical performance. This paper discusses the impact of environmental variables on the efficiency performance of electricity distribution companies. Indeed, such variables, which are argued to be unmanageable, may affect the electricity utilities' performance. Thus, this paper proposes a simulation methodology based on design of experiment philosophy for statistically testing environmental variables and the interactions among them, enabling regulators to build the best suited semi-parametric two-stage model of electricity utility benchmarking analysis. To demonstrate the power of the proposed approach, experimental simulations are carried out using real data published by Brazil's regulator. The results show that environmental variables may impact efficiency performance linearly and nonlinearly.

Suggested Citation

  • Leme, Rafael C. & Paiva, Anderson P. & Steele Santos, Paulo E. & Balestrassi, Pedro P. & Galvão, Leandro de Lima, 2014. "Design of experiments applied to environmental variables analysis in electricity utilities efficiency: The Brazilian case," Energy Economics, Elsevier, vol. 45(C), pages 111-119.
    • Handle: RePEc:eee:eneeco:v:45:y:2014:i:c:p:111-119
    DOI: 10.1016/j.eneco.2014.06.017
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    References listed on IDEAS

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    Cited by:

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    3. Patyal, Vishal Singh & Kumar, Ravi & Lamba, Kuldeep & Maheshwari, Sunil, 2023. "Performance evaluation of Indian electricity distribution companies: An integrated DEA-IRP-TOPSIS approach," Energy Economics, Elsevier, vol. 124(C).
    4. Brandão, Roberto & Tolmasquim, Maurício T. & Maestrini, Marcelo & Tavares, Arthur Felipe & Castro, Nivalde J. & Ozorio, Luiz & Chaves, Ana Carolina, 2021. "Determinants of the economic performance of Brazilian electricity distributors," Utilities Policy, Elsevier, vol. 68(C).
    5. L sara Fabr cia Rodrigues & Matheus Alves Madeira de Souza & Thamara Paula dos Santos Dias, 2017. "Performance Assessment of Brazilian Power Transmission and Distribution Segments using Data Envelopment Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 7(3), pages 14-23.
    6. 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).
    7. Raúl Pérez-Reyes & Beatriz Tovar, 2021. "Peruvian Electrical Distribution Firms’ Efficiency Revisited: A Two-Stage Data Envelopment Analysis," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    8. Deng, Na-Qian & Liu, Li-Qiu & Deng, Ying-Zhi, 2018. "Estimating the effects of restructuring on the technical and service-quality efficiency of electricity companies in China," Utilities Policy, Elsevier, vol. 50(C), pages 91-100.

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    More about this item

    Keywords

    Efficiency Analysis; Environmental variables; Electricity utility; Data envelopment analysis; Design of experiments;
    All these keywords.

    JEL classification:

    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • L97 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Utilities: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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