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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 1 DATA ENVELOPMENT ANALYSIS IN THE BENCHMARKING OF ELECTRICITY DISTRIBUTION COMPANIES.

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Presentation on theme: "M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 1 DATA ENVELOPMENT ANALYSIS IN THE BENCHMARKING OF ELECTRICITY DISTRIBUTION COMPANIES."— Presentation transcript:

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

2 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May Overview Introduction Evaluation of the present DEA-model Developments of the present DEA-model Interruption costs Conclusions

3 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May Finland – Electricity distribution companies The number of electricity distribution companies: ~ 100 Average length of the network: km (123 … km) Average number of customers: (766 … ) 3 years experience of efficiency benchmarking (1999, 2000, 2001)

4 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May The factors of the efficiency benchmarking by DEA-model EFFICIENCY SCORE (0…1) Operational costs Power quality (interruption time) Distributed energy Number of customers Length of the network

5 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May The efficiency scores of the Finnish distribution companies The average is 0.830

6 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May 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

7 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May 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 = ( ) * 200 M€ = 20 M€/a B)Efficiency score is 0.72  Impact on allowed return = ( ) * 200 M€ = -36 M€/a

8 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May 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

9 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May Problems of efficiency benchmarking with DEA-model Price of outage [€/customer,h] The number of companies that have insignificant factors in the DEA-model

10 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May Developing the DEA-model (1/2)

11 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May 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

12 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May Number of companies having insignificant factors in efficiency benchmarking

13 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May Price of outages in developed DEA-model 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

14 M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona May 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


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