1 federal university of pernambuco – ufpe companhia hidro elÉtrica do sÃo francisco – chesf...

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FEDERAL UNIVERSITY OF PERNAMBUCO – UFPE FEDERAL UNIVERSITY OF PERNAMBUCO – UFPE COMPANHIA HIDRO ELÉTRICA DO SÃO FRANCISCO – COMPANHIA HIDRO ELÉTRICA DO SÃO FRANCISCO –

CHESFCHESF

““A A COMPARATIVE STUDY OF THE COMPARATIVE STUDY OF THE EFFICIENCY OF THE BRAZILIAN EFFICIENCY OF THE BRAZILIAN

HYDROELECTRIC POWER PLANTS HYDROELECTRIC POWER PLANTS USING DATA ENVELOPMENT USING DATA ENVELOPMENT

ANALYSIS – DEA”ANALYSIS – DEA”

PRESENTER: MSc. EDUARDO ARRUDA PRESENTER: MSc. EDUARDO ARRUDA CÂMARACÂMARA

Co-authorCo-author: Prof. Dr. Francisco de : Prof. Dr. Francisco de Sousa RamosSousa Ramos

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Country Total1 United States 964,754

2 China 517,550

3 Japan 251,318

4 Russia 218,370

5 India 143,773

6 Canada 122,661

7 Germany 120,833

8 France 112,022

9 Brazil 93,158

10 United Kingdom 78,706Source: Energy Information Administration. International Energy Annual 2006. Table Posted: December 8, 2008.

Electricity Installed Capacity, in Megawatts

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This slide shows the 10 countries that had the biggest electricity installed capacity in the world, considering the data of 2006.

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Country Total1 China 117,388

2 United States 77,821

3 Canada 71,801

4 Brazil 70,858

5 Russia 45,835

6 India 32,326

7 Norway 26,410

8 Japan 22,133

9 France 20,806

10 Sweden 16,302Source: Energy Information Administration. International

Energy Annual 2006.

Table Posted: December 8, 2008. Next Update: August 2009

Hydroelectric Installed Capacity, in Megawatts

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This slide shows the 10 countries that had the biggest hydroelectric installed capacity in the world, considering the data of 2006.

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USA’s Electricity Installed USA’s Electricity Installed Capacity by Type, January 1, 2006 Capacity by Type, January 1, 2006

(Megawatts)(Megawatts)

77,821

100,33424,996

761,603

ConventionalThermal

Hydroelectric

Nuclear

Geothermal, Solar,Wind,and Woodand Waste

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This slide shows how the USA's electricity installed capacity is distributed, considering the data of 2006. It is possible to observe that its majority comes from the conventional thermal units.

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Brazil’s Electricity Installed Brazil’s Electricity Installed Capacity by Type, January 1, 2006 Capacity by Type, January 1, 2006

(Megawatts)(Megawatts)

14,205

6,088

2,007

70,858

ConventionalThermal

Hydroelectric

Nuclear

Geothermal, Solar,Wind,and Woodand Waste

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This slide shows how Brazil's electricity installed capacity is distributed, considering the data of 2006. It is possible to observe that its majority comes from the hydroelectric units.

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Comparative of the Distribution Comparative of the Distribution of of Electricity Installed Capacity Electricity Installed Capacity

by Typeby Type

CountryTherm

alHydroelect

ricNucle

arOther

sTotal

United States (MW)

761,603 77,821

100,334

24,996

964,754

Distribution (%)

78.94% 8.07%

10.40%

2.59%

100.00%

Brazil (MW) 14,205 70,858 2,007 6,088 93,158Distribution

(%)15.25

% 76.06% 2.15%6.54

%100.00

%

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The table shows a comparative of the Distribution of Electricity Installed Capacity by Type, considering the United States and Brazil. Here we can observe that in Brazil around 76% of electricity installed capacity comes from the hydroelectric units, while in the USA it accounts for around 8% only.

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GENERAL OBJECTGENERAL OBJECT

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

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METHODOLOGYMETHODOLOGY

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.

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DATA ENTRYDATA ENTRY→ → SampleSample

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.

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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 ENTRYDATA ENTRY→→ Output and Inputs

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STUDIES ON EFFICIENCY IN POWER STUDIES ON EFFICIENCY IN POWER

GENERATIONGENERATION Author/Country Method/Model Sample Input Output

Pollitt DEA Model 78 nuclear i) labor; ii) capital; Energy (1996) (CCR and power stations iii) fuel; iv) price produced in USA, UK, statistical tests) of labor; v) price of kWh Canada, capital; vi) price of Japan and fuel; vii) age and type South Africa of reactor. Arocena and DEA Model 28 generating i) average number of Annual Waddams (hyperbolic graph energy units employees; power Price Malmquist index) (1984 to 1997) ii) fuel; produced (2002) iii) capital as a proxy (MWh) Spain of the average capacity (MW). Raczka DEA Model 41 heat plants i) labor; Heating (2001) (allocative and (1997) ii) fuel; production Poland regressive) iii) pollution. Alemán and DEA Model 19 and 23 i) installed power (MW); i) generated Cáceres thermoelectric ii) fuel price; energy (MWh) (2002) and hydroelectric iii) age of plant; ii) use factor Colombia plants (1999 and 2000) iv) number of employees (%) or number of accidents at iii) plant work. availability (MWh) Yunos and DEA Model 27 electricity i) installed power (MW); generated Hawdon (Malmquist producers in ii) number of employees; energy (1997) index) several countries iii) loss of electricity (%); (GWh) Malaysia iv) technical efficiency (%)

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Here it is illustrated the research of related literature. These studies used DEA and its models that had like sample generating energy units in different countries.

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STUDIES ON EFFICIENCY IN POWER STUDIES ON EFFICIENCY IN POWER

GENERATIONGENERATION Author/Country Method/Model Sample Input Output

Vaninsky DEA Model Generation of electric i) operational costs; Use of the (2006) energy in the USA ii) loss of energy net capacity USA (1991-2004) (%). (%) Ilhan Or DEA Model 65 thermal, hydro i) investment cost; i) installed (2005) and wind ii) time of energy Turkey generating units construction. capacity (MW); ii) availability or average use. Barros DEA Model 25 hydroelectric i) number of employees; i) production (2006) (Malmquist generating ii) operational costs; (MWh); Portugal index) units. iii) investments; ii) capacity iv) proxy of capital by utilization as % physical assets. of total. Sueyoshi DEA Model 25 generating i) generation Total energy and Goto (slack-adjusted) units (1984-1993) capacity (MW); generation (2001) ii) fuel; (GWh) Japan iii) number of employees. Sant’Ana DEA Model Hydroelectric i) construction cost; i e ii) firm and and Estellita generating ii) representative fall; average (1998) units iii) maximum volume; energies; Brazil without reservoir iv) measure of flow. iii) guaranteed power. Sampaio DEA Model 71 hydroelectric i) installed power i) generated Ramos and (CCR and BCC) generating (MW); energy (annual Sampaio units ii) height of falls (m); average MW) (2005) iii) labor force. Brazil

Source: own elaboration (2007). The first 3 studies were obtained in Barros (2006).

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DATA ENTRYDATA ENTRY→→ Output and Inputs

Output:

Generated Energy in medium MW . .

Inputs:

Installed Power in MW;;Height of falls in metersHeight of falls in meters;;Age of the Generation Plant in monthsAge of the Generation Plant in months andandAssured Energy in medium MWAssured Energy in medium MW..

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Here we can see the output and inputs chosen. As an output the generated energy in medium MW was chosen and as inputs, the installed power in MW, the height of falls in meters, the age of the generation plant in months and the assured energy in medium MW were chosen.

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OBSERVED RESULTSOBSERVED 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.

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OBSERVED RESULTSOBSERVED RESULTSNumber 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.

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In the CCR model, 8 HPPs were proved to be efficient, that is with 100%. While in the BCC model, 27 HPPs were considered efficient.

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OBSERVED RESULTSOBSERVED RESULTSThe Top 5 Efficient HPPs and the

Number of Times they were Considered as Benchmark

DEA-CCR (Times)

DEA-BCC (Times)

31 ITAPEBI 70 3870 SALTO CAXIAS 24 2565 PORTO PRIMAVERA 16 2115 DONA FRANCISCA 16 1587 XINGÓ 14 6

DMU HPPBENCHMARK

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This table highlights the top 5 DMUs that were considered as benchmark to the others. I would like to call your attention to the fact that all the top 5 belong to the 27 HPPs that obtained 100% of efficiency, in the two models (DEA-CCR and DEA-BCC).

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Stratification of the Indices of Efficiency in the DEA-CCR and DEA-BCC Models _________________________________________________________________

Quartile of HPPs HPPs Efficiency DEA-CCR (%) DEA-BCC (%)

_______________________________________________________________________

1° Quartile (up to 25%) 1 1.15 0 0.00 2° Quartile (up to 50%) 10 11.50 4 4.60 3° Quartile (up to 75%) 31 35.63 19 21.84 4° Quartile (up to 100%) 45 51.72 64 73.56

_________________________________________________________________ Total 87 100.0 87 100.0

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In this table the indices of efficiency obtained in both models were stratified by quartile of efficiency. Here it is possible to observe that the DEA-BCC model is more restrictive in the effciency than the DEA-CCR model, because the majority (73%) of the units were concentrated in the 4th quartile, while in the DEA-CCR model only 51% were in this quartile.

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Histogram of the Distribution of Indices of Efficiency (DEA-CCR e DEA-BCC)


1

10

45

04

64

31

19

0

10

20

30

40

50

60

70

80

1 2 3 4

Quartile

Nu

mb

er

of

HP

Ps

DEA-CCR

DEA-BCC

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Here it is easier to observe in the DEA-BCC model that the majority 64 units or 73% of the units were concentrated in the 4th quartile, while in the DEA-CCR model only 45 units or 51% of the units were in this quartile.

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Statistical Summary of Indices of Efficiency in the DEA-CCR and DEA-BCC Models

Measures DEA-CCR DEA-BCC

Average 0.7398 0.8486 Minimum 0.2159 0.2699 Maximum 1.0000 1.0000 Standard Deviation 0.1816 0.1681

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This table helps understanding the distribution of the indices of efficiency. Through some statistics measures, as the standard deviation, we can see that the indices of effciency in the DEA-CCR model are more sprayed and also that in the DEA-BCC model there is a greater concentration near the average, so that the standard deviation in the DEA-BCC model is lower than in the DEA-CCR model.

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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%


DEA-CCR DEA-BCCRecent (AP <= 180) 32 73.87% 88.74%Intermediary (180 < AP <= 360) 19 80.00% 88.00%Old (AP > 360) 36 70.92% 79.75%

HPP by the Time of Operation in Months

Number of HPPs HPPs’ Efficiency Average

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In the efficiency analysis by time of operation of the HPPs, it was observed that, in general, the units considered as intermediaries stood out from the other categories because, in whole, that is using the arithmetic mean obtained by each category in both models, the intermediary HPPs are in advantage with 84% of efficiency, while the recent HPPs, second best category showed an average of efficiency of 81.30%.

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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%


DEA-CCR DEA-BCCSmall (IP <= 300 MW) 40 68.66% 86.26%Medium (300 MW < IP <= 1000 MW) 24 71.22% 78.20%Large (IP > 1000 MW) 23 86.14% 89.37%

HPP by Installed Power in MW Number of HPPs HPPs’ Efficiency Average

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In the efficiency analysis considering the size of the HPP by installed Power in MW, the HPPs that stood out most were the ones considered as part of the large-sized category, because they obtained higher average indices of efficiency in both models.

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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%


DEA-CCR DEA-BCCLow (HF <= 45m) 30 75.21% 90.81%Medium (45m < HF <= 90m) 29 70.74% 78.02%High (HF > 90m) 28 76.05% 85.56%

HPPs’ Height of Falls in Meters Number of HPPsHPPs’ Efficiency Average

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Looking at this table it can be seen that the best performances are from the high altitude HPPs for the DEA-CCR model, and low altitude for the DEA-BCC model. And considering the arithmetic average between the efficiency averages from the models by category the best were the units belonging to the low category.

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Indices of Efficiency Analysis by the HPP Owner Company


Subgroup Average IndexCompanies with 5 or + HPPs 76.30%Companies with 2 to 4 HPPs 87.76%Companies with 1 HPP 82.36%

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By subgroup, considering both models, the DEA-CCR and DEA-BCC, it can be observed that the companies owning 2-4 HPPs belonging to the sample had a higher average rate, reaching 87.76 % of efficiency. While the worst performance is in the first subgroup, companies owning 5 or more HPPs, with an average index of efficiency of 76.30%.

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Indices of Efficiency Analysis by the Indices of Efficiency Analysis by the Geographical Location of the HPPGeographical Location of the HPP


Region AverageNumber of

HPPsNorth 84.39% 2

Northeast 71.17% 11

Southeast 79.64% 54

South 88.39% 20

Central West 81.97% 14

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By geographical region it was observed that the HPPs from the South region were the ones that showed better performance with an average index of efficiency of 88.39%, while the HPPs located in the Northeast region were the ones with the worst performance with 71.17% of efficiency.

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88%

84%71%

80%

82%

((Indices of Efficiency by the Geographical Indices of Efficiency by the Geographical Region)

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This slide shows a map of Brazil by geographical region. Here it is possible to observe the location of the South Region, that had the best performance in the average efficiency indices (88%), in both models.

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CONCLUSIONCONCLUSION

- 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.

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CONCLUSIONCONCLUSION

- 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: [email protected]: 55-81-3229.3477/9234.9398

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In this study, data from 22 privatized HPPs was provided. But it should be noted that the efficiency will be best measured and compared if the variables used in the model are the same used in the two periods - before and after the privatization.

1 federal university of pernambuco – ufpe companhia hidro elÉtrica do sÃo francisco – chesf...

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