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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

Service Quality Regulation in Electricity Distribution

Necmiddin BAĞDADİOĞLU

Orçun SENYÜCEL

objectives
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.
turkish electricity reform
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
briefly sfa v dea
Briefly SFA v DEA

Average Cost (all noise)Syrjanen, M., P. Bogetoft, P. Agrell (2006)

briefly sfa v dea1
Briefly SFA v DEA

Deterministic frontier (all ineff  u)Syrjanen, M., P. Bogetoft, P. Agrell (2006)

briefly sfa v dea2
Briefly SFA v DEA

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

briefly sfa
Briefly SFA
  • Two component error terms, first captures statistical noise
  • Second captures effects of TE.
  • Half normal, exponential, truncated dist.
distance functions
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.
distance functions1

y

y0

xo

x/λ

x

L(y)

Distance Functions

Kumbhakar & Lovell (2003)

distance functions2
Distance Functions
  • Deviations from 1 is technical inefficiency
  • h(.) represents deviation exp (-u)
  • exp (-u) one of the component error terms.
distance functions3
Distance Functions
  • Adding random error term, imposing homogeneity rest.
  • We preffered translog input DF.
methodology
Methodology
  • Following Coelli, (M outputs K inputs)
methodology1
Methodology
  • Following Coelli and Battese,
  • Two environmental variables
models
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)
model i1
Model I

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

model i2
Model I

RTS=0.93=

model ii1
Model II

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

model ii2
Model II

RTS=1.06

average efficiency scores
Average efficiency scores

QoS has significant effect: TE decreased by 16.5%

LLR test also states QoS important

conclusion
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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