1 / 25

DEA and Electricity Distribution Networks in Portugal

DEA and Electricity Distribution Networks in Portugal. Júlia Boucinha*, Célia Godinho, Catarina Féteira Inácio, Tom Weyman-Jones September 2003. Why does EDP Distribuição use benchmarking? for management decisions;

amalia
Download Presentation

DEA and Electricity Distribution Networks in Portugal

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. DEA and Electricity Distribution Networks in Portugal Júlia Boucinha*, Célia Godinho, Catarina Féteira Inácio, Tom Weyman-Jones September 2003

  2. 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. EDP Distribuição Networks 2

  3. Background • Electricity Distribution company covering the whole of the Portuguese Mainland and, hence, the number of clients is related to the whole population • Efficiency studies started to try and evaluate the company’s performance in comparison with other european utilities – beeing the only operator in the country, comparisons have to be made with foreign companies • More recently, DEA method has been applied to measure the efficiency of the different networks areas, within the company • Network areas are regional business units, with some autonomy 3

  4. Why choose DEA? • 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. 6

  5. DEA model • Objective penalises slack variables in measure of % technical • efficiency: q find network we ights to : ( ( ) ) q - e min slacks å such that for each output and input : ( ) ´ network output network we ight å ( ) ³ network output network being measured ( ) ´ network input network we ight å ( ) £ q network input network being measured ³ all network we ights 0 7

  6. 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 Benchmarking data: 1 8

  7. OUTPUTS – anything customers would pay for if necessary: Economic models use “outputs” with market or shadow prices: energy, service, network connection Company data may measure these approximately: kWh, number of customers, network length Benchmarking data: 2 9

  8. OPERATING CHARACTERISTICS:anything that cannot be controlled by the management In the short run, for example: customer density underground/overhead lines market share of high and low voltage demand Benchmarking data: 3 10

  9. Input: OPEX Outputs: energy delivered, customers, lines Try all subsets of outputs Compare variable returns to scale Add non-discretionary variables to measure operating characteristics customer density, low voltage connections, underground networks Add quality of supply if possible Is there still a network with some inefficiency - how much? Modelling strategy: 1 11

  10. Experiment with variable returns to scale Experiment with new variables to represent operating characteristics Each experiment adds 1 or more constraints to envelopment model Therefore cannot make efficiency score of any network lower Modelling strategy: 2 12

  11. Results - 2002 13

  12. Lines (km) per OPEX - 2002 14

  13. Frontiers for one input and one output 15

  14. Frontier for one input and two outputs Input: OPEX Outputs: Lines, Energy 16

  15. DEA - Model Results: Input: OPEX Outputs: Lines, Energy and Customers 17

  16. Non - controllable variables • Outputs • 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 • Inputs • Lost load - measure for the impact of Quality of Sevice in network efficiency, considered as an input, since it reflects a negative output 18

  17. DEA - Model Results: Inputs: OPEX Outputs: Customers, Energy, Lines, Customer density, Underground Lines/Total, LV Energy/Total 18% 19% 1% 20% 19

  18. DEA - Final model results: Inputs: OPEX, Lost Load Outputs: Customers, Energy, Lines, Customer density, Underground Lines/Total, LV Energy/Total 18% 19% 1% 20% 20

  19. Comparison of models 21

  20. Comparison of models 22

  21. DEA (VRS) - 2002 vs. 2001 Inputs: OPEX, Lost Load Outputs: Customers, Energy, Lines, Customer density, Underground Lines/Total, LV Energy/Total 18% 29% 19% 34% 1% 20% 20% 6% 23

  22. Conclusion 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 levels of the less efficient network areas 24

  23. Slacks 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 Directions for future work? 25

  24. OPEX - 2001/2002

More Related