1. 1 FEDERAL UNIVERSITY OF PERNAMBUCO – UFPE COMPANHIA HIDRO ELÉTRICA DO SÃO FRANCISCO – CHESF “A COMPARATIVE STUDY OF THE EFFICIENCY OF THE BRAZILIAN HYDROELECTRIC POWER PLANTS USING DATA ENVELOPMENT ANALYSIS – DEA” PRESENTER: MSc. EDUARDO ARRUDA CÂMARA Co-author: Prof. Dr. Francisco de Sousa Ramos

2. 2 CountryTotal 1United States964,754 2China517,550 3Japan251,318 4Russia218,370 5India143,773 6Canada122,661 7Germany120,833 8France112,022 9Brazil93,158 10United Kingdom78,706 Source: Energy Information Administration. International Energy Annual 2006. Table Posted: December 8, 2008. Electricity Installed Capacity, in Megawatts

3. 3 CountryTotal 1China117,388 2United States77,821 3Canada71,801 4Brazil70,858 5Russia45,835 6India32,326 7Norway26,410 8Japan22,133 9France20,806 10Sweden16,302 Source: Energy Information Administration. International Energy Annual 2006. Table Posted: December 8, 2008. Next Update: August 2009 Hydroelectric Installed Capacity, in Megawatts

4. 4 USA’s Electricity Installed Capacity by Type, January 1, 2006 (Megawatts)

5. 5 Brazil’s Electricity Installed Capacity by Type, January 1, 2006 (Megawatts)

6. 6 Comparative of the Distribution of Electricity Installed Capacity by Type CountryThermalHydroelectricNuclearOthersTotal United States (MW) 761,60377,821100,33424,996964,754 Distribution (%) 78.94%8.07%10.40%2.59%100.00% Brazil (MW) 14,20570,8582,0076,08893,158 Distribution (%) 15.25%76.06%2.15%6.54%100.00%

7. 7 GENERAL OBJECT This study will identify and compare the efficiency of 87 Brazilian hydroelectric generating plants with installed capacity above 50 MW, based on the inputs and output used as variables of the analysis. This study will identify and compare the efficiency of 87 Brazilian hydroelectric generating plants with installed capacity above 50 MW, based on the inputs and output used as variables of the analysis.

8. 8 METHODOLOGY For the data analysis, the DEA methodology was used, under the input orientation, in the traditional models that allow constant returns of scale (CRS), also known as CCR model, and variable returns of scale (VRS) or BCC model.

9. 9 DATA ENTRY → Sample Due to the availability of data, related to the variables used in the study, 87 Brazilian hydroelectric generating units, from 29 different companies were chosen for analysis.

10. 10 In the selection of variables were used and combined three criteria: the availability of data; the research of related literature and the professional opinion of relevant individuals, concerned with the issue that the research is proposed. DATA ENTRY → → Output and Inputs

11. 11 STUDIES ON EFFICIENCY IN POWER GENERATION

12. 12 STUDIES ON EFFICIENCY IN POWER GENERATION

13. 13 DATA ENTRY → → Output and Inputs Output:. Generated Energy in medium MW. Inputs: ; Installed Power in MW; Height of falls in meters; Age of the Generation Plant in months and Assured Energy in medium MW.

14. 14 OBSERVED RESULTS The indices of efficiency of the 87 HPPs in question were obtained using the EMS program, version 1.3.0, Scheel (2000), which employs the DEA methodology and its traditional DEA-CCR and DEA-BCC models.

15. 15 OBSERVED RESULTS Number of efficient HPPs with 5 variables selected (1 OUTPUT e 4 INPUTS) in both models: DEA-CCR: 8 efficient HPPs (100%); DEA-BCC: 27 efficient HPPs (100%). From the results obtained it is possible to observe that all 8 HPPs considered efficient by the DEA-CCR model were also efficient in the DEA-BCC model.

16. 16 OBSERVED RESULTS The Top 5 Efficient HPPs and the Number of Times they were Considered as Benchmark

17. 17 OBSERVED RESULTS

18. 18 Histogram of the Distribution of Indices of Efficiency (DEA-CCR e DEA-BCC) OBSERVED RESULTS

19. 19 OBSERVED RESULTS Statistical Summary of Indices of Efficiency in the DEA-CCR and DEA-BCC Models

20. 20 Indices of Efficiency Analysis by the Time of Operation of the HPP → - Old HPPs → 75.44% Arithmetic average between the efficiency averages from the models by category. - Intermediary HPPs → →→ → 84% - Recent HPPs → 81.30% OBSERVED RESULTS

21. 21 Indices of Efficiency for Installed Power Analysis → - Small HPPs → 77.46% → - Medium HPPs → 74.71% Arithmetic average between the efficiency averages from the models by category. - Large HPPs → 87.76% OBSERVED RESULTS

22. 22 Indices of Efficiency Analysis by the Height of Falls of the HPP - Medium HF HPPs → 74.38% - High HF HPPs → 80.80% Arithmetic average between the efficiency averages from the models by category. - Low HF HPPs → →→ → 83.01% OBSERVED RESULTS

23. 23 Indices of Efficiency Analysis by the HPP Owner Company OBSERVED RESULTS

24. 24 Indices of Efficiency Analysis by the Geographical Location of the HPP OBSERVED RESULTS

25. 25 OBSERVED RESULTS 88% 84% 71% 80% 82% (Indices of Efficiency by the Geographical (Indices of Efficiency by the Geographical Region)

26. 26 CONCLUSION - The DEA-CCR model, which admits constant returns of scale, is more restrictive in the efficiency of the units than the DEA-BCC model; - The efficient units in the DEA-CCR model will also be efficient in the DEA-BCC model; - The efficiency indices obtained for the units in the DEA- CCR model will always be equal to or lower than the indices for the DEA-BCC model.

27. 27 CONCLUSION - This study suffers a great influence of the 87 HPPs included in the sample, of the variables used as inputs and output and of the scope period covered. - And yet, as a suggestion to carry out further work would be to measure the efficiency of national HPPs that were privatized, showing the situation of them before and after privatization. e-mail: ecamara@chesf.gov.brecamara@chesf.gov.br Phone: 55-81-3229.3477/9234.9398