Clara B. Vaz and Ângela P. Ferreira
School of Technology and Management
Polytechnic Institute of Bragança
Clara Vaz (clvaz@ipb.pt)
4th Workshop on Efficiency and Productivity Analysis
Efficiency in the Health Sector
October 29, 2012
Clara Vaz [clvaz@ipb.pt], School of Technology and Management, Polytechnic Institute of Bragança




 DEA
 Malmquist index
 Bootstrapping

 DEA model


Motivation Objective Literature Case study Results Conclusions End
2


 In the last decade, the hydropower production reached about 30% of the annual electricity consumption in Portugal
 Only 58% of the Portuguese hydro potential is harnessed
 The target set for 2020 aims to achieve 70% of the utilization of hydro potential nationwide
 EDP plays a major role in the Portuguese Electricity System
 70% share of the installed capacity within the country (excluding wind farms)
Contents Objective Literature Case study Results
Source: EDP Relatório e contas 2011
3
Conclusions End

 Hydropower
 Non-polluting, but has environmental impacts
 Competitive source of renewable energy
 Depends on rainfall, is seasonal and is climate driven
 Hydroelectric power plants
 Run-of-river systems - use the natural drop of the river
 Lower environmental impact
 Control strategy designed to best capture the available flow
 Reservoir Systems - have storage capacity
 Operational flexibility which allows load-following
Crestuma-Lever run-of-river hydroelectric power plant Alto Lindoso reservoir hydroelectric power plant
Contents Objective Literature Case study Results Conclusions End
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
 23 reservoir and 30 run-of-river power plants
 Interests’ company :
 Benchmarking of hydropower plants involves the use of several performance indicators, which makes complex the data analysis
 Some performance indicators can be ratios that compare the scores actually achieved by each power plant with past data
 Decreasing investment costs of hydropower plants in new areas and in repowering processes
 Identifying factors which influence the conversion energy process efficiency
 Regulatory authority interests:
 Define regimes to regulate the hydroelectric sector
 Define price support system
To facilitate and simplify these processes, we intend to develop a methodology to monitor the efficiency and productivity change of hydroelectric power plants
5
Contents Objective Literature Case study Results Conclusions End


 Methodology
 Separate Performance Assessment
– Reservoir
– Run-of-river
 To assess efficiency of hydropower plants in 2010 and 2011 years
– DEA model (Charnes et al, 1978)
– Bootstrapping (Simar and Wilson, 1998)
 To evaluate the productivity change of hydropower plants between the years 2010 and 2011
– Malmquist index (Fare et al, 1994a,b)
– Bootstrapping (Simar and Wilson, 1999)
Literature Case study Results Conclusions End
6
Contents Motivation


(Charnes et al,1978)

, 1, … ,
0
, 1, … ,
1
∗

(Simar and Wilson, 1998)
 For each DMU, we can calculate:
 Confidence intervals for efficiency
 Bias
 Bias-corrected efficiency
The bias-corrected efficiency scores were used to assess the power plants performance
End
7
Contents Motivation Objective Case study Results Conclusions


(Fare et al , 1994a,b )
 For use when we have panel data
 Decomposes productivity change into efficiency change (power plants moving closer to the frontier) and technological change (shifts in the frontier)
 No need for price data, no need for assumptions of cost minimisation or revenue maximisation
 Input-based or output-based Malmquist index
 Based on input or output distance functions
 Calculates Total Factor Productivity (TFP) using DEA models
 Applicable in generic case: the production technology uses multiple inputs to produce multiple outputs
Case study Results Conclusions End
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Contents Motivation Objective

 The productivity change of a DMU between two years is calculated by using the ratios of distances of DMUs in each period for each technology
I



E t ( t
E t

( t )
1 )

E t

1
E
( t

1 t

1 )


1 / 2
( t )
 Outputs oriented: E → efficiency (DEA model)
I >1 → Productivity improvement
I <1 → Productivity deterioration
 Index decomposition (Fare et al.
, 1994a,b): I

IE

SE

IF
Contents
I

E t

1
( t

1 ) v

E t
( t ) v
E t t t

1

1
1
( t
(
( t t 


1
)
)
1 v
) t t
( ( t t ) )
E t
( t ) v


E t
( t
E t

1
( t

1 )

1 )

E
E t
( t ) t

1
( t )


1 / 2
IE: Pure efficiency change
(VRS) efficiency change
Motivation Objective Case study Results Conclusions End
9


(Simar and Wilson, 1999)
 To allow computing confidence intervals for I, IE and IF
 If the interval contains the value “1” we cannot infer that significant changes occurred in those power plants
 Conversely, if the lower and upper bounds are smaller (or greater) than
“1”, this means that there is statistical evidence that there was a decline
(or progress) in those power plants
 This analysis was extended to the ratios of IF to analyse if the frontiers are crossed over
Case study Results Conclusions End
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Contents Motivation Objective

 Performance of hydroelectric power plants of EDP Produção
 23 reservoir power plants (excluding pumped-storage)
 30 run-of-river power plants
 Study based on data from 2010 and 2011 years
HPC*
2010 1.3
wet year
2011 0.92
dry year
* HPC: Hydro Productivity Coefficient
Contents Motivation Objective Literature
Reservoir
Run-of-river
Results Conclusions End
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Performance evaluation of hydropower plants from EDP
 Objective: To evaluate the productivity change of hydropower plants, between the years 2010 and 2011
 Inputs and outputs
 Reservoir activity model [23 units]
Rainfall (mm)
Reservoir capacity (hm 3 )
Installed capacity (MW)
Energy produced (GWh)
 Run-of-river activity model [30 units]
Rainfall (mm)
Rated flow (m 3 /s)
Installed capacity (MW)
Energy produced (GWh)
 Output oriented : To assess the ability of each power plant to maximize the electric energy produced given the internal resources and the environmental conditions
 VRS assessment
: power plants are only compared to others plants of roughly similar size
 Bootstrapping was implemented in R software ( FEAR and Benchmarking packages )
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Contents Motivation Objective Literature Results Conclusions End

Performance evaluation of hydropower plants from EDP
Results
 Benchmarking analysis (Reservoir power plants)
 Hydropower plant R3: Efficiency estimate=68.40%
16%
84%
Benchmarks
Year 2011
Power plant
R6
Power plant
R23
Observed Target 
=0.8450

=0.1550
Installed capacity (MW) 54.0
54.0
Reservoir capacity (hm 3 ) 158.2
130.4
Rainfall (mm) 111.8
(86.9)
Energy produced (GWh) 157.0
229.5
62.0
144.4
94.4
258.2
10.4
54.0
45.5
73.3
Plant   R6
Plant   R3
Energy produced
(GWh)
Installed capacity   (Mw)
2.0
1.5
1.0
0.5
0.0
Reservoir capacity   (hm3)
Plant   R23
Plant   R3
Energy produced
(GWh)
Installed capacity   (Mw)
1.0
0.5
0.0
Reservoir capacity   (hm3)
Contents Motivation
Rainfall   (mm)
Objective Literature Case study
Rainfall   (mm)
Conclusions End
13

Performance evaluation of hydropower plants from EDP
Results
58%  Benchmarking analysis (Run-of-river power plants)
 Hydropower plant F15: Efficiency estimate=75.52%
42%
Benchmarks
Year 2011
Power plant Power plant
F13 F16
Observed Target 
=0.5802

=0.4198
Installed capacity (MW)
Rated flow (m 3 /s)
Rainfall (mm)
117.0
1350.0
89.0
117.0
505.8
(67.3)
Energy produced (GWh) 365.9
484.5
201.0
870.0
56.8
831.7
0.9
2.3
82.0
4.6
Plant   F13
Plant   F15
Energy produced
(GWh)
Installed capacity   (MW)
3.0
2.0
1.0
0.0
Rated   flow
(m3/s)
Plant   F16
Plant   F15
Energy produced
(GWh)
Installed capacity   (MW)
1.00
0.10
0.01
0.00
Rated   flow
(m3/s)
Contents Motivation
Rainfall   (mm)
Objective Literature Case study
Rainfall   (mm)
Conclusions End
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Performance evaluation of hydropower plants from EDP
Results
 Bias-corrected efficiency assessment of reservoir power plants
Supers
Mean
Stand. Deviat.
Bias corrected eff.
Year 2010
Bias Efficiency estimated
Bias corrected eff.
Year 2011
Bias Efficiency estimated
52.42% 28.51% 80.93% 40.99% 35.60% 76.59%
11.91% 18.42% 24.41% 16.17% 26.54% 27.82%
13 benchmarks 11 benchmarks
 There is scope for performance improvements that can be achieved by spreading the best practices observed in efficient power plants
 The power plants observed in 2010 are more homogenous than in 2011 year
 Differences between bias-corrected efficiency scores and estimated efficiency are higher in 2011 year
Contents Motivation Objective Literature Case study Conclusions End
15

Performance evaluation of hydropower plants from EDP
Results
 Bias-corrected efficiency assessment of run-of-river power plants
Supers
Mean
Stand. Deviat.
Bias corrected eff.
Year 2010
Bias Efficiency estimated
Bias corrected eff.
Year 2011
Bias Efficiency estimated
69.16% 14.27% 83.43% 66.17% 15.97% 82.14%
14.15% 11.52% 20.27% 17.42% 13.69% 23.08%
13 benchmarks 14 benchmarks
 There is scope for performance improvements that can be achieved by spreading the best practices observed in efficient power plants
 The power plants observed in 2010 are slightly more homogenous than in
2011 year
 Differences between bias-corrected efficiency scores and efficiency estimates are higher in 2011
Contents Motivation Objective Literature Case study Conclusions End
16

Performance evaluation of hydropower plants from EDP
Results
 Reservoir power plants
Efficiency estimate ( ○ ), Bias corrected efficiency ( ○ ), 95% confidence limits ( ─ )
2011 year
2010 year
11 plants remain efficient
1 plant move closer to the frontier
11 plants move further from the frontier
5 20 10 power plant
15
 Run-of-river power plants
2010 year
5 20 10 power plant
15
2011 year
0 5
Contents
10 15 power plant
20
Motivation
25 30
Objective
0 5
Literature
10 15 power plant
20
Case study
25 30
11 plants remain efficient
11 plants move closer to the frontier
8 plants move further from the frontier
Conclusions End
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Performance evaluation of hydropower plants from EDP
Analysis of productivity changes between 2010 and 2011
23 reservoir plants
IE IF I
2 1
21 7
-
-
8
-
SE
7
Significant
Scores (90%)
Improvement
Deterioration
No change
 21 power plants decreased overall productivity levels, due to deterioration in the productivity levels of the frontier and/or efficiency levels
 2 power plants improved overall productivity level ( due to improvement of efficiency levels)
There is statistically significant evidence that the frontier of 2011 is less productive than the frontier of 2010
Contents Motivation Objective Literature
I
8
20
-
30 run-of-river plants
IE IF SE
3
4
-
1
17
-
1
3
 20 power plants decreased overall productivity levels, due to deterioration in the productivity levels of the frontier and/or efficiency levels
 8 power plants improved overall productivity level (due to improvement of efficiency levels, and/or of productivity level of the frontier)
For the majority of power plants the frontier declined in 2011 although for some plants the frontier progressed
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Case study Conclusions End

 Power plants observed in 2010 are more homogenous than in 2011, although this difference is lower for run-of-river plants. This suggests that this group is less affected by the hydraulicity of a given year.
 The deterministic analysis should support the company in identifying benchmarks, whose best practices should be disseminated by inefficient hydropower plants.
 This analysis allows to characterize the performance of the hydropower plants and determine whether there were changes in practices of the plants. This information is useful to support the process of target setting for each power plant (continuous improvement).
 The majority of the power plants decreased overall productivity levels, mainly due to declining in the productivity levels of the frontier which is related to the decreasing of rainfall availability observed during last year. Although this unfavourable context, there are some power plants that have improved their performance. This means that there were improvements in the practices of these power plants.
 The impact of decreasing of rainfall availability penalises in greater extent the reservoir power plants than the run-of-river power plants
 Reservoir: There is statistically significant evidence that the frontier of the period 2011 declined to the frontier of period 2010.
 Run-of-river: For the majority of power plants the frontier was declined in 2011 while for some plants the frontier progressed (crossed frontiers).
End
19
Contents Motivation Objective Literature Case study Results

 Contacts:
 Clara Vaz ( clvaz@ipb.pt
)
 Ângela Ferreira ( apf@ipb.pt
)
Contents Motivation Objective Literature Case study Results Conclusions
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