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)

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

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

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

(2006)

Briefly SFA v DEA

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

Briefly SFA v DEA

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

Briefly SFA

Two component error terms, first captures statistical noise

Second captures effects of TE.

Half normal, exponential, truncated dist.

2,0~ vNv

0u

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 Functions

x

y

L(y)

xo

x/λ

y0

Kumbhakar & Lovell (2003)

Distance Functions

Deviations from 1 is technical inefficiency

h(.) represents deviation exp (-u) exp (-u) one of the component error terms.

yLxyxDI /:max,

.,1 hyxDI

Distance Functions

Adding random error term, imposing homogeneity rest.

We preffered translog input DF.

uvDx IK lnln

Methodology

Following Coelli, (M outputs K inputs)

ititmitkit

K

k

M

mkmlitkit

K

k

K

lkl

M

m

M

m

N

n

K

kkitknitmitmnmitmKit

uvyxxx

xyyyx

lnln2

1ln

2

1

lnlnln2

1lnln

*1

1 1

**1

1

1

1

1 1 1

1

1

*0

Kitkitkit xxx /* Kitlitlit xxx /*

Methodology

Following Coelli and Battese,

Two environmental variables

),( 2~ uitNu

it

itit z 0

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 I

ititititit

itititit

uvCUSTENGCUST

ENGCUSTENGTOTEXL

lnlnln2

1

ln2

1lnln

1ln

122

22

211210

iitit GEOVCD 210

Model II

itititit

it

itit

it

it

it

it

itititit

itititit

uvCUSTTOTEXL

ITC

ENGTOTEXL

ITC

TOTEXL

ITC

TOTEXL

ITCCUSTENGCUST

ENGCUSTENGTOTEXL

ln

lnlnln2

1

lnlnlnln2

1

ln2

1lnln

1ln

2

1

2

11

1122

22

211210

iitit GEOVCD 210

Descriptive Statistics

Variable Obs Mean Std. Dev. Min Max

TOTEXL (million TL) 120 160 159 30 891

Energy Supplied (GWh) 120 4744 3596 628 17987

Number of customers (000) 120 1376 812 323 3719

ITC 120 2.60 2.14 0.07 11.88

VCD 120 0.216 0.106 0 0.419

Model I

2

Variables Coefficient t-ratio

Constant 0.5223 0.7

ln ENG (y1) -0.3429*** -2.8

ln CUST (y2) -0.7294** -4.8

y12 -0.1342 -0.3

y22 -1.0085 -1.5

y1y2 0.3645 0.7

VCD 3.8507** 2.0

GEO 6.4172* 1.7

0.6373*** 2.8

0.9023*** 23.5

LLF -36.0925Note: ***, ** and * denotes significance at the 1, 5 and 10 % levels.

)/( 222vuu

Model IRelationship between efficiency and firm size

0.00

0.20

0.40

0.60

0.80

1.00

0.00 1.00 2.00 3.00 4.00

Number of customers (million)

Te

chn

ica

l effi

cie

ncy

1

1

M

mmeyRTS=0.93=

Model II

2

Variables Coefficient t-ratio

Constant 0.6918*** 13.2

ln ENG (y1) -0.3988*** -3.1

ln CUST (y2) -0.5408*** -3.7

y12 -0.2412 -0.4

y22 -1.6727** -2.3

y1y2 0.7813 1.3

ln (ITC/TOTEXL) 0.2745*** 6.4

ln(ITC/TOTEXL)2 0.1682*** 2.7

ln (ITC/TOTEXL)*y1 -0.1434 -1.0

ln (ITC/TOTEXL)*y2 0.3120*** 2.9

VCD 5.8604*** 4.4

GEO 1.5384*** 6.3

0.2293*** 3.3

0.9129*** 19.8

LLF 12.7273Note: ***, ** and * denotes significance at the 1, 5 and 10 % levels.

Model II

Relationship between efficiency and firm size

0.00

0.20

0.40

0.60

0.80

1.00

0.00 1.00 2.00 3.00 4.00

Number of customers (million)

Te

chn

ica

l effi

cie

ncy

RTS=1.06

Average efficiency scores

Quality excluded model Quality included model

Small utilities 0.60 0.43

Medium utilities 0.91 0.77

Large utilities 0.91 0.84

Total 0.79 0.65

QoS has significant effect: TE decreased by 16.5%

LLR test also states QoS important

Utility Quality excluded Quality includedQuality included(Cost of Losses

Excluded)

Losses and Illegal

Usage (%)

Dicle Edas 0.11 0.10 0.50 62.04

Vangolu Edas 0.16 0.12 0.47 61.95

Aras Edas 0.33 0.19 0.48 31.71

Coruh Edas 0.87 0.40 0.75 13.70

Firat Edas 0.64 0.47 0.52 14.67

Camlibel Edas 0.89 0.67 0.81 10.52

Toroslar Edas 0.90 0.79 0.95 15.38

Meram Edas 0.90 0.71 0.89 8.58

Baskent Edas 0.91 0.77 0.95 11.26

Akdeniz Edas 0.89 0.78 0.92 11.21

Gediz Edas 0.93 0.89 0.98 7.62

Uludag Edas 0.93 0.91 0.93 9.09

Trakya Edas 0.89 0.72 0.89 10.52

AYEDAS 0.93 0.93 1.00 10.80

Sakarya Edas 0.92 0.84 0.94 12.33

Osmangazi Edas 0.93 0.87 0.94 7.42

Bogazici Edas 0.90 0.87 1.00 17.32

Menderes Edas 0.91 0.78 0.87 9.66

Goksu Edas 0.92 0.80 0.83 10.78

Yesilirmak Edas 0.89 0.48 0.81 12.28

Average efficiency scores

Conclusion

QoS impact on TE. GEO & VCD are crucial environmental variables.

Excl. losses and illegal electricity usage overestimates TE.

Privatization: Eight utilities are established far from the optimal size and have low average efficiency scores (0.43). TPA may merge other six utilities.