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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 M.Sc. Jukka Lassila M.Sc. Satu Viljainen M.Sc. Samuli Honkapuro Prof. Jarmo Partanen

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 2 Overview Introduction Evaluation of the present DEA-model Developments of the present DEA-model Interruption costs Conclusions

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 3 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)

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 4 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

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 5 The efficiency scores of the Finnish distribution companies The average is 0.830

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 6 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

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 7 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

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 8 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

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 9 Problems of efficiency benchmarking with DEA-model Price of outage [€/customer,h] The number of companies that have insignificant factors in the DEA-model

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 10 Developing the DEA-model (1/2)

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 11 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

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 12 Number of companies having insignificant factors in efficiency benchmarking

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 13 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

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M.Sc. Jukka Lassila FI Session 5 – Block 2 Barcelona 12-15 May 2003 14 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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