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

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

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)

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

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

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

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

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

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

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

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

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

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

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