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Service Quality Regulation in Electricity Distribution

Service Quality Regulation in Electricity Distribution. Necmiddin BAĞDADİOĞLU Orçun SENYÜCEL. Objectives. Incorporate service quality measure into electricity regulation. New in literature : Growitsch et al (2008), Coelli et al (2008-Draft)

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Service Quality Regulation in Electricity Distribution

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  1. Service Quality Regulation in Electricity Distribution Necmiddin BAĞDADİOĞLU Orçun SENYÜCEL

  2. Objectives • Incorporate service quality measure into electricity regulation. New in literature : Growitsch et al (2008), Coelli et al (2008-Draft) Determine technical efficiency of Turkish electricity distribution utilities • Focus on exogeneous determinants of inefficiency • Analyze effects of electricity losses and illegal usage on TE.

  3. Turkish Electricity Reform • Electricity Sector Reform and Privatization Strategy Paper (2004): TEDAS  2012 • Transitory period: 20 utilites through mergers of 79 distribution utilities. • ESRPSP: mergers determined by operational problems, technical & financial features. • Turkey accession country. EU Energy Acquis • EMRA has not announced regulatory framework

  4. Briefly SFA v DEA Average Cost (all noise)Syrjanen, M., P. Bogetoft, P. Agrell (2006)

  5. Briefly SFA v DEA Deterministic frontier (all ineff  u)Syrjanen, M., P. Bogetoft, P. Agrell (2006)

  6. Briefly SFA v DEA Stochastic frontier (both noise v and ineff u) Syrjanen, M., P. Bogetoft, P. Agrell (2006)

  7. Briefly SFA • Two component error terms, first captures statistical noise • Second captures effects of TE. • Half normal, exponential, truncated dist.

  8. Distance Functions • DF: Distance of the prod to PPB • Two different types: input & output DF • Input DF: How much input vector can be contracted (output constant) • Output: Vice versa.

  9. y y0 xo x/λ x L(y) Distance Functions Kumbhakar & Lovell (2003)

  10. Distance Functions • Deviations from 1 is technical inefficiency • h(.) represents deviation exp (-u) • exp (-u) one of the component error terms.

  11. Distance Functions • Adding random error term, imposing homogeneity rest. • We preffered translog input DF.

  12. Methodology • Following Coelli, (M outputs K inputs)

  13. Methodology • Following Coelli and Battese, • Two environmental variables

  14. Models • Model I: Input: TOTEX+L&IEU (TOTEXL) • Model II: Input: + Interruption Time (ITC) • Output: Energy supplied (ENG) and number of customers (CUST) • Environmental factors: • Village Cust Density (VCD) • Geographic Conditions (GEO)

  15. Model I

  16. Model II

  17. Descriptive Statistics

  18. Model I Note: ***, ** and * denotes significance at the 1, 5 and 10 % levels.

  19. Model I RTS=0.93=

  20. Model II Note: ***, ** and * denotes significance at the 1, 5 and 10 % levels.

  21. Model II RTS=1.06

  22. Average efficiency scores QoS has significant effect: TE decreased by 16.5% LLR test also states QoS important

  23. Average efficiency scores

  24. Conclusion • QoS impact on TE. GEO & VCD are crucial environmental variables. • Excl. losses and illegal electricity usage overestimates TE. • Privatization: Eight utilitiesare established far from the optimal size and have low average efficiency scores (0.43). TPA may merge other six utilities.

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