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Managing power supply interruptions: a bottom-up spatial (frontier) model with an application to a Spanish electricity network

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  • Argüelles, Pablo
  • Orea, Luis

Abstract

In December 2013 a new electricity law was approved in Spain as part of an electricity market reform including a new remuneration scheme for distribution companies. This remuneration scheme wasupdated in December 2019 and the new regulatory framework introduceda series of relevant modifications that aim to encourage the regulated firms to reduce their power supply interruptionsusing a benchmarking approach. While some managerial decisions can prevent electricity power supply interruptions,other managerial decisions are more oriented to mitigate the consequences of these interruptions. This paper examines the second type of decisions using a unique dataset on the power supply interruptionsof a Spanish distribution company network between 2013 and 2019. We focus our analysis in the effect of grid automatization on the restoration times, the relative efficiency of the maintenance staff, and the importance of its location. We combinea bottom-up spatial model and a stochastic frontier model to examine respectively external and internal power supply interruptionsat municipal level. This model resembles the conventional spatial autoregressive models but differ from them in several important aspects.

Suggested Citation

  • Argüelles, Pablo & Orea, Luis, 2020. "Managing power supply interruptions: a bottom-up spatial (frontier) model with an application to a Spanish electricity network," Efficiency Series Papers 2020/01, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    • Handle: RePEc:oeg:wpaper:2020/01
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    1. Skevas, Ioannis, 2020. "Inference in the spatial autoregressive efficiency model with an application to Dutch dairy farms," European Journal of Operational Research, Elsevier, vol. 283(1), pages 356-364.
    2. Yu, William & Jamasb, Tooraj & Pollitt, Michael, 2009. "Does weather explain cost and quality performance? An analysis of UK electricity distribution companies," Energy Policy, Elsevier, vol. 37(11), pages 4177-4188, November.
    3. Hung-pin Lai & Cliff Huang, 2010. "Likelihood ratio tests for model selection of stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 34(1), pages 3-13, August.
    4. Giannakis, Dimitrios & Jamasb, Tooraj & Pollitt, Michael, 2005. "Benchmarking and incentive regulation of quality of service: an application to the UK electricity distribution networks," Energy Policy, Elsevier, vol. 33(17), pages 2256-2271, November.
    5. Jamasb, Tooraj & Orea, Luis & Pollitt, Michael, 2012. "Estimating the marginal cost of quality improvements: The case of the UK electricity distribution companies," Energy Economics, Elsevier, vol. 34(5), pages 1498-1506.
    6. 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.
    7. Alberto Gude & Inmaculada Álvarez & Luis Orea, 2018. "Heterogeneous spillovers among Spanish provinces: a generalized spatial stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 50(3), pages 155-173, December.
    8. Kumbhakar, Subal C. & Parmeter, Christopher F. & Tsionas, Efthymios G., 2013. "A zero inefficiency stochastic frontier model," Journal of Econometrics, Elsevier, vol. 172(1), pages 66-76.
    9. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C., 2016. "A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers," Journal of Econometrics, Elsevier, vol. 190(2), pages 289-300.
    10. Antonio Alvarez & Christine Amsler & Luis Orea & Peter Schmidt, 2006. "Interpreting and Testing the Scaling Property in Models where Inefficiency Depends on Firm Characteristics," Journal of Productivity Analysis, Springer, vol. 25(3), pages 201-212, June.
    11. Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-111, January.
    12. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    13. Neumayer, Eric & Plümper, Thomas, 2010. "Spatial Effects in Dyadic Data," International Organization, Cambridge University Press, vol. 64(1), pages 145-166, January.
    14. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    15. Parmeter, Christopher F. & Kumbhakar, Subal C., 2014. "Efficiency Analysis: A Primer on Recent Advances," Foundations and Trends(R) in Econometrics, now publishers, vol. 7(3-4), pages 191-385, December.
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    Cited by:

    1. Subal C. Kumbhakarⓡ & Emir Malikovⓡ & Christopher F. Parmeterⓡ, 2021. "Applications of efficiency and productivity analysis: editors’ introduction," Empirical Economics, Springer, vol. 60(6), pages 2657-2663, June.

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    More about this item

    JEL classification:

    • H54 - Public Economics - - National Government Expenditures and Related Policies - - - Infrastructures
    • L97 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Utilities: General
    • L98 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Government Policy

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