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What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods

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  • Kuosmanen, Timo
  • Saastamoinen, Antti
  • Sipiläinen, Timo

Abstract

Electricity distribution is a natural local monopoly. In many countries, the regulators of this sector apply frontier methods such as data envelopment analysis (DEA) or stochastic frontier analysis (SFA) to estimate the efficient cost of operation. In Finland, a new StoNED method was adopted in 2012. This paper compares DEA, SFA and StoNED in the context of regulating electricity distribution. Using data from Finland, we compare the impacts of methodological choices on cost efficiency estimates and acceptable cost. While the efficiency estimates are highly correlated, the cost targets reveal major differences. In addition, we examine performance of the methods by Monte Carlo simulations. We calibrate the data generation process (DGP) to closely match the empirical data and the model specification of the regulator. We find that the StoNED estimator yields a root mean squared error (RMSE) of 4% with the sample size 100. Precision improves as the sample size increases. The DEA estimator yields an RMSE of approximately 10%, but performance deteriorates as the sample size increases. The SFA estimator has an RMSE of 144%. The poor performance of SFA is due to the wrong functional form and multicollinearity.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:enepol:v:61:y:2013:i:c:p:740-750
    DOI: 10.1016/j.enpol.2013.05.091
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    2. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    3. Zakaria, Muhammad & Noureen, Rabia, 2016. "Benchmarking and regulation of power distribution companies in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1095-1099.
    4. repec:eee:ejores:v:262:y:2017:i:2:p:792-801 is not listed on IDEAS
    5. Saastamoinen, Antti & Bjørndal, Endre & Bjørndal, Mette, 2017. "Specification of merger gains in the Norwegian electricity distribution industry," Energy Policy, Elsevier, vol. 102(C), pages 96-107.
    6. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2014. "Using the latent class approach to cluster firms in benchmarking: An application to the US electricity transmission industry," Operations Research Perspectives, Elsevier, vol. 1(1), pages 6-17.
    7. repec:eee:energy:v:178:y:2019:i:c:p:832-852 is not listed on IDEAS
    8. Agrell, Per J. & Brea-Solís, Humberto, 2017. "Capturing heterogeneity in electricity distribution operations: A critical review of latent class modelling," Energy Policy, Elsevier, vol. 104(C), pages 361-372.
    9. repec:eee:ejores:v:273:y:2019:i:1:p:278-287 is not listed on IDEAS
    10. repec:eee:ejores:v:265:y:2018:i:1:p:133-148 is not listed on IDEAS
    11. Chen, Zhongfei & Barros, Carlos Pestana & Borges, Maria Rosa, 2015. "A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies," Energy Economics, Elsevier, vol. 48(C), pages 136-144.
    12. repec:eee:chieco:v:55:y:2019:i:c:p:99-110 is not listed on IDEAS
    13. repec:eee:enepol:v:118:y:2018:i:c:p:573-583 is not listed on IDEAS
    14. Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, vol. 34(6), pages 2189-2199.
    15. Janda, Karel & Krska, Stepan, 2014. "Benchmarking Methods in the Regulation of Electricity Distribution System Operators," MPRA Paper 59442, University Library of Munich, Germany.
    16. Ferrara, Giancarlo & Vidoli, Francesco, 2017. "Semiparametric stochastic frontier models: A generalized additive model approach," European Journal of Operational Research, Elsevier, vol. 258(2), pages 761-777.
    17. Li, Hong-Zhou & Tian, Xian-Liang & Zou, Tao, 2015. "Impact analysis of coal-electricity pricing linkage scheme in China based on stochastic frontier cost function," Applied Energy, Elsevier, vol. 151(C), pages 296-305.
    18. repec:eee:eneeco:v:74:y:2018:i:c:p:802-812 is not listed on IDEAS
    19. Olesen, Ole B. & Petersen, Niels Christian, 2016. "Stochastic Data Envelopment Analysis—A review," European Journal of Operational Research, Elsevier, vol. 251(1), pages 2-21.
    20. Saastamoinen, Antti & Kuosmanen, Timo, 2016. "Quality frontier of electricity distribution: Supply security, best practices, and underground cabling in Finland," Energy Economics, Elsevier, vol. 53(C), pages 281-292.

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