DEA and Electricity Distribution Networks in Portugal

Júlia Boucinha*,Júlia Boucinha*,

Célia Godinho,Célia Godinho,

Catarina Féteira Inácio, Catarina Féteira Inácio,

Tom Weyman-JonesTom Weyman-Jones

September 2003September 2003

EDP Distribuição Networks

Why does EDP Distribuição use benchmarking?Why does EDP Distribuição use benchmarking?

for management decisions;

to provide better customer service since EDP Distribuição is public utility;

to respond to incentive regulations based on price capping.

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Electricity Distribution company covering the whole of the Electricity Distribution company covering the whole of the Portuguese Mainland and, hence, the number of clients is Portuguese Mainland and, hence, the number of clients is related to the whole populationrelated to the whole population

Efficiency studies started to try and evaluate the company’s Efficiency studies started to try and evaluate the company’s performance in comparison with other european utilities – performance in comparison with other european utilities – beeing the only operator in the country, comparisons have to beeing the only operator in the country, comparisons have to be made with foreign companiesbe made with foreign companies

More recently, DEA method has been applied to measure the More recently, DEA method has been applied to measure the efficiency of the different networks areas, within the efficiency of the different networks areas, within the companycompany

Network areas are regional business units, with some Network areas are regional business units, with some autonomyautonomy

Background

Why choose DEA?

DEA (data envelopment analysis)DEA (data envelopment analysis)

many models (including returns to scale) without mathematical specification of technology;

clear interpretation of results: % efficiency;

ability to penalise networks with slack in input use and output production;

but data must be accurate, without serious measurement error: consistently monitored by EDP Distribuição;

must allow for uncontrollable factors, characteristics of operating environment.

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DEA model

Objective penalises slack variables in measure of % technicalObjective penalises slack variables in measure of % technical

efficiency:efficiency:

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0ightsnetwork we all

inputnetwork

ightnetwork weinputnetwork

outputnetwork

ightnetwork weoutputnetwork

:input andoutput each for such that

slacksmin

: toightsnetwork we find

measured beingnetwork

measured beingnetwork

Benchmarking data: 1

INPUTS - anything on which money is spent:INPUTS - anything on which money is spent:

Economic models use “inputs” in physical sense: labour, capital

Company data uses financial equivalents: OPEX, CAPEX, …

This study concentrates on OPEX only

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Benchmarking data: 2

OUTPUTS – anything customers would pay for if OUTPUTS – anything customers would pay for if necessary:necessary:

Economic models use “outputs” with market or shadow prices:

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energy, service, network connection

Company data may measure these approximately:

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kWh, number of customers, network length

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Benchmarking data: 3

OPERATING CHARACTERISTICS:OPERATING CHARACTERISTICS: anything that anything that cannot be controlled by the managementcannot be controlled by the management

In the short run, for example:In the short run, for example:

customer density

underground/overhead lines

market share of high and low voltage demand

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Modelling strategy: 1

Input: OPEXInput: OPEX

Outputs: energy delivered, customers, linesOutputs: energy delivered, customers, lines

Try all subsets of outputsTry all subsets of outputs

Compare variable returns to scaleCompare variable returns to scale

Add non-discretionary variables to measure operating characteristicsAdd non-discretionary variables to measure operating characteristics

– customer density, low voltage connections, underground networks

Add quality of supply if possibleAdd quality of supply if possible

Is there still a network with some inefficiency - how much?Is there still a network with some inefficiency - how much?

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Modelling strategy: 2

Experiment with variable returns to scaleExperiment with variable returns to scale

Experiment with new variables to represent operating Experiment with new variables to represent operating characteristicscharacteristics

Each experiment adds 1 or more constraints to Each experiment adds 1 or more constraints to envelopment modelenvelopment model

Therefore cannot make efficiency score of any Therefore cannot make efficiency score of any network lowernetwork lower

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Results - 2002

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Lines (km) per OPEX - 2002

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0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

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Frontiers for one input and one output

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2 000

4 000

6 000

8 000

10 000

12 000

14 000

16 000

18 000

5 000 10 000 15 000 20 000 25 000

OPEX (103 EUROS)

Lin

es (

km)

Input: OPEXOutput: Lines

CRS

VRS

Frontier for one input and two outputsInput: OPEX Outputs: Lines, Energy

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0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

1,60

1,80

2,00

0,00 0,05 0,10 0,15 0,20 0,25 0,30

Energy/ OPEX (GWh/ 103 EUROS)

Lin

es/ O

PE

X (

km/ 1

03 E

UR

OS

)

CRS

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

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DEA - Model Results:Input: OPEX Outputs: Lines, Energy and Customers

Nº Eff. Mean

CRS 3 0.90

VRS 7 0.93

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Non - controllable variables

OutputsOutputs

Customer density (area per client) - reflects the difficulty in reaching clients, in some networks areas

Share of underground lines – network areas with a bigger share of underground lines bear higher costs

Percentage of LV energy on the total - reflects different cost levels

InputsInputs

Lost load - measure for the impact of Quality of Sevice in network efficiency, considered as an input, since it reflects a negative output

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

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DEA - Model Results: Inputs: OPEX Outputs: Customers, Energy, Lines, Customer density, Underground Lines/Total, LV Energy/Total

20%

18%

1%

19%

Nº Eff. Mean

CRS 6 0.92

VRS 10 0.96

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

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DEA - Final model results: Inputs: OPEX, Lost Load

Outputs: Customers, Energy, Lines, Customer density, Underground Lines/Total, LV Energy/Total

20%

18%

19%

1%

Nº Eff. Mean

CRS 8 0.93

VRS 10 0.96

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Comparison of models

INPUTS: OPEX OUTPUTS: Customers, Energy, LinesINPUTS: OPEX OUTPUTS: Customers, Energy, Lines, Customer density, Underground Lines/Total, LV Energy/TotalINPUTS: OPEX, Lost Load OUTPUTS: CCustomers, Energy, Lines, Customer density, Underground Lines/Total, LV Energy/Total

DEA - CRS

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

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Comparison of models

INPUTS: OPEX OUTPUTS: Customers, Energy, Lines

INPUTS: OPEX OUTPUTS: Customers, Energy, Lines, Customer density, Underground Lines/Total, LV Energy/Total

INPUTS: OPEX, Lost Load OUTPUTS: Customers, Energy, Lines, Customer density, Underground Lines/Total, LV Energy/Total

DEA - VRS

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

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DEA (VRS) - 2002 vs. 2001Inputs: OPEX, Lost Load

Outputs: Customers, Energy, Lines, Customer density, Underground Lines/Total, LV Energy/Total

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

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2002 2001

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20%20%

18%

19%

1%

29%

34%

6%

Conclusion

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Network areas efficiency analysis

Shows the priority areas for management improvement

Leading to measures to reduce innefficiencies

In general, we have an improvement in the efficiency In general, we have an improvement in the efficiency levels of the less efficient network areaslevels of the less efficient network areas

Directions for future work?

Slacks based measurementSlacks based measurement

recent development,Tone (2001) computes new efficiency measure based on slack variables:

% efficiency = [reduction in inputs relative to sample]/[expansion of outputs relative to sample]

gives more discrimination amongst small sample of networks

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OPEX - 2001/2002

6%

0%

-10%

1%

-10%

-16%

-9%

15%

-4%

0%

-18%

-6%

3%

-1%

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Mean -3%