Download ppt - DATA ENVELOPMENT ANALYSIS IN THE BENCHMARKING OF ELECTRICITY DISTRIBUTION COMPANIES

1M.Sc. Jukka Lassila FI Session 5 – Block 2

Barcelona 12-15 May 2003LAPPEENRANTAUNIVERSITY OF TECHNOLOGY

DATA ENVELOPMENT ANALYSIS IN THE BENCHMARKING OF ELECTRICITY

DISTRIBUTION COMPANIES

M.Sc. Jukka Lassila

M.Sc. Satu Viljainen

M.Sc. Samuli Honkapuro

Prof. Jarmo Partanen



Overview

Overview

Introduction

Evaluation of the present DEA-model

Developments of the present DEA-model

Interruption costs

Conclusions



0

10 000

20 000

30 000

40 000

50 000

Dis tribut ion companies (total num ber is 94)

Ne

twork

le

ng

th [

km

]

Finland – Electricity distribution companies

• The number of electricity distribution companies: ~ 100• Average length of the network: 3 700 km (123…49 000 km)

• Average number of customers: 31 000 (766…314 000)

• 3 years experience of efficiency benchmarking (1999, 2000, 2001)



The factors of the efficiency benchmarking by DEA-model

EFFICIENCYSCORE (0…1)

Operational costs

Power quality

(interruption time) Distributed

energy

Number of customers

Length of the network



The efficiency scores of the Finnish distribution companies

0,00

0,20

0,40

0,60

0,80

1,00

1,20

Distribution companies (total number is 94)

Eff

icie

ncy

scor

e

The average is 0.830



The effects of the efficiency benchmarking (1/2)

1) Directing effects companies tend to pay attention to factors that are used in the DEA-model

2) Efficiency score affect directly to the reasonable return on capital



The effects of the efficiency benchmarking (2/2)

Example:

Operational costs of a company are 200 M€/a.

A) Efficiency score is 1.0

Impact on allowed return = (1.0 - 0.9) * 200 M€ = 20 M€/a

B) Efficiency score is 0.72

Impact on allowed return = (0.72 - 0.9) * 200 M€ = -36 M€/a



Problems of efficiency benchmarking with DEA-model

• The directing effects of benchmarking are not equal for all the companies- There are large numbers of companies for which the

efficiency scores do not depend on power quality

- Power quality affects the efficiency scores randomly

• The changes in the directing effects differ from one year to another

• The present efficiency benchmarking method has to be developed



Problems of efficiency benchmarking with DEA-model

0

10

20

30

40

50

60

Operationalcosts

Power quality * Distributedenergy

Network length Customers

Dis

trib

utio

n c

om

pa

nie

s0

100

200

300

400

500

600

Pri

ce o

f ou

tage

[€/

cust

omer

,h]

Price of outage [€/customer,h]

The number of companies that have insignificant factors in the DEA-model



Developing the DEA-model (1/2)

costs lOperationa

c timeonInterruptiCustomersNetworkEnergy hMax

1

23210

v

vuuu

costs) onInterrupticosts lOperationa(

cCustomersNetworkEnergy hMax

1

3210

v

uuu



Developing the DEA-model (2/2)

• Principle changes in the model- power quality can be measured as a interruption costs

- power quality is not a separate factor in the model

- interruption costs are added to operational costs

Power quality becomes meaningful and almost equally important factor for each company



Number of companies having insignificant factors in efficiency benchmarking

0

10

20

30

40

50

60

Operational costs Power quality * Distributed energy Network length Customers

Dis

trib

utio

n c

ompa

nies

Present DEA-modelDeveloped DEA-model



Price of outages in developed DEA-model

0

2

4

6

8

10

12

14

Distribution companies (total number is 94)

Price o

f outa

ge [

€/c

usto

mer,

h]

• For most companies price of outages is between 4…6 €/customer,h

• Corresponding prices of outages in the present DEA-model are 0…500 €/customer,h



Conclusions

• The directing effects of benchmarking have to be predictable and equal for each company

• This presentation introduced a solution to a problem concerning equality - basic idea was change the way in which power quality is handled in the DEA-model

• Future research activities include improving the predictability and taking investment into account in the efficiency benchmarking