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.

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) xoxo x/λ y0y0 Kumbhakar & Lovell (2003)

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

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

Methodology Following Coelli, (M outputs K inputs)

Methodology Following Coelli and Battese, Two environmental variables

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

Model II

Descriptive Statistics VariableObsMeanStd. Dev.MinMax TOTEXL (million TL) Energy Supplied (GWh) Number of customers (000) ITC VCD

Model I VariablesCoefficientt-ratio Constant ln ENG (y 1 ) ***-2.8 ln CUST (y 2 ) **-4.8 y12y y22y y1y2y1y VCD3.8507**2.0 GEO6.4172* *** ***23.5 LLF Note: ***, ** and * denotes significance at the 1, 5 and 10 % levels.

Model I RTS=0.93=

Model II VariablesCoefficientt-ratio Constant0.6918***13.2 ln ENG (y 1 ) ***-3.1 ln CUST (y 2 ) ***-3.7 y12y y22y **-2.3 y1y2y1y ln (ITC/TOTEXL)0.2745***6.4 ln(ITC/TOTEXL) ***2.7 ln (ITC/TOTEXL)*y ln (ITC/TOTEXL)*y ***2.9 VCD5.8604***4.4 GEO1.5384*** *** ***19.8 LLF Note: ***, ** and * denotes significance at the 1, 5 and 10 % levels.

Model II RTS=1.06

Average efficiency scores Quality excluded modelQuality included model Small utilities Medium utilities Large utilities Total QoS has significant effect: TE decreased by 16.5% LLR test also states QoS important

UtilityQuality excludedQuality included (Cost of Losses Excluded) Losses and Illegal Usage (%) Dicle Edas Vangolu Edas Aras Edas Coruh Edas Firat Edas Camlibel Edas Toroslar Edas Meram Edas Baskent Edas Akdeniz Edas Gediz Edas Uludag Edas Trakya Edas AYEDAS Sakarya Edas Osmangazi Edas Bogazici Edas Menderes Edas Goksu Edas Yesilirmak Edas 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.