Electricity Markets Regulation - Lesson 6 - Efficiency Assessments
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Transcript of Electricity Markets Regulation - Lesson 6 - Efficiency Assessments
Experience you can trust.http://www.leonardo-energy.org/training-module-electricity-market-regulation-session-6
Training on Regulation
A webinar for the European Copper Institute
Webinar 6: Efficiency Assessments
Dr. Konstantin Petrov / Dr. Daniel Grote
11.1.2009
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Agenda
a) TOTEX versus OPEX benchmarking
3. Application of efficiency results
a) Overview
2. Methods for efficiency assessments
c) Data Envelopment Analysis
b) Performance indicators
b) Efficiency convergence speed
1. Why measure efficiency?
c) Supporting schemes
e) Virtual network models
d) Parametric Approaches
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1. Why measure efficiency?
Regulation is needed in areas where
competition does not work (e.g. natural
monopolies - transmission, distribution
networks) to limit excessive pricing and
to set incentives for efficient
performance
Regulators apply benchmarking to
assess efficiency of regulated
companies for the purposes of
incentive regulation
Major Reasons
Cap regulation
Actual Cost
Current price levelCurrent price + InflationCurrent price + Inflation – productivity growth
Efficiency gains
time
Influenced by company
Influenced by company
Set by regulator
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1. Why measure efficiency?
Definition of efficiency
Efficiency = OutputsInputs
+ “Correction for Environment”
Distribution Company
e.g. # employees, fuel, operational costs,
Input Factors
e.g. # customers, delivered energy (kWh), peak load (kW)
Output Factors
e.g. firm size, network topology, climate, topography, terrain, task complexity
Environmental Factors
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1. Why measure efficiency?
Technological change (frontier shift): change in production technology within the
sector
Efficiency change (catch-up): change in efficiency of production
Change in the scale of production (scale efficiency)
Pure technical efficiency change
Allocative efficiency
Input mix allocative efficiency: producing same outputs with different mix of
inputs
Output mix allocative efficiency: producing different level of outputs with same
mix of inputs
Changes in operating environment
Reasons for efficiency changes
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1. Why measure efficiency?
Efficiency assessment and price control
Efficiency
Assessment
Efficiency
Scores
Efficiency
Improvement
Targets
Integration in
Price
Control
Allowed Revenue (Tariffs)Efficiency
Inte
rfac
e
Benchmarking
– Approach
– Sample
– Model Orientation
– Data Collection
– Data Validation
Conversion
– Convergence Time
– Convergence Profile
– Inefficiency Caps
– Efficiency Bands
Integration
– Chargeable Basis
– Capex Treatment
– Revenue Requirements
– Regulatory Formula
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1. Why measure efficiency?
Practical Relevance of Benchmarking and the X-factor
Reflects the regulatory view for anticipated efficiency improvement
Ensures ex-ante sharing of the anticipated efficiency gains between customers and
regulated companies
The X-factor is not a confirmation but rather indication of the anticipated efficiency
improvement
In some regulatory regimes the X-factor has a dual function:
Efficiency improvement
Revenue profiling
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2. Methods for efficiency assessments
Overview (1)
Benchmarking Methods
Partial Methods Total Methods
Non-parametric Parametric
Reference Networks (Virtual
Networks)
Index Methods
Data Envelopment
Analysis (DEA)
Stochastic Frontier Analysis
(SFA)
Ordinary Least
Squares (OLS)
Corrected Ordinary
Least Squares (COLS)
Total Factor Productivity
(TFP)
Uni-dimensional
ratios
Performance Indicators
Linear programming
Econometrics
Engineering Models
8
Total methods can be based on the average performance or the efficient frontier of comparable companies
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2. Methods for efficiency assessments
Overview (2)
Efficiency performance assessment (benchmarking) applied in various forms
Methods differ in the standard of comparison
No consensus among regulators as to which methodology is best
Sometimes different methods applied simultaneously for cross-checks
Frontier methods preferred by regulators, in particular DEA and SFA
– Parametric (econometric) models (Germany, UK)
– DEA analysis (Norway, the Netherlands, Germany, several countries in CEE)
– Reference network models (Spain, Sweden, Chile, Argentina)
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2. Methods for efficiency assessments
Overview (3)
Efficiency Score
A B C D E
Measures of relative inefficiencies towards
best performance
Conversion (definition of
efficiency increase targets)
Companies
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2. Methods for efficiency assessments
Performance Indicators
Uni-dimensional ratios:
– Comparison of single performance indicators between firms
– Fails to account for the relationships between different input and output factors
Productivity (Managerial) Indicators
– GWh/Employee
– OPEX/GWh
– OPEX/Employee
– GWh/Line Length
Financial indicators
– Debt/Equity Ratio
– Return on Investment (ROI)
– Return on Capital Employed (ROCE)
Partial methods produce simple, easy to calculate straightforward indicators of performance
… but do not recognize trade-offs between different improvement possibilities or areas
Can only be used as a rough indication
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2. Methods for efficiency assessments
Index methods – Total Factor Productivity (TFP)
Total factor productivity (TFP) is a measure of the physical output of a regulated company produced by a given quantity of inputs
With multiple inputs (Y) and outputs (X), outputs are usually weighted by their revenue shares (sR) and inputs are weighted by their cost shares (sC)
Weights can be either static or dynamic (different weights used for each period) Extensively used in the US for utility regulation (both energy and telecoms) Data requirements can be harsh TFP does not provide any information about ‘infra-marginal’ efficiency improvement
possibilities; for this we need more articulated benchmarking techniques (frontier-based methodologies)
More suitable for an assessment of company performance over time than comparisons between regulated companies
n
jjj
C
m
iii
R
Xs
YsTFP
1
1
Input factors
Output factors
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2. Methods for efficiency assessments
Frontier methods
Frontier methods are based on the concept that all companies should be able to operate at
an optimal efficiency level that is determined by other efficient companies in the same
sample
These efficient companies are usually referred to as the “peer firms” and determine the
“efficiency frontier”
The “efficiency frontier” is formed from the observed performance of the companies in the
analyzed sample, as determined by the relationships between the inputs and outputs of the
sampled units
The companies that form the efficiency frontier use the minimum quantity of inputs to
produce the same quantity of outputs (input oriented model)
The “efficiency frontier” is used as a reference against which the comparative performance
of all other companies (that do not lie on the frontier) is measured
The distance to the efficiency frontier provides a measure for the inefficiency
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2. Methods for efficiency assessments
Data Envelopment Analysis (DEA) (1)
Output 1 Input 1
Input 2
Data Envelope
A
B
C
DE
most efficient companies
F
F’
Inefficiency
Input minimisation
Inefficiency
Output 2
Data Envelope
A
B
C
D
E
F
most efficient companies
F’
Output maximisation
G
G’
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2. Methods for efficiency assessments
Data Envelopment Analysis (DEA) (2)
Outputs
Inputs
A
B
C
constant returns to scale frontier
variable returns to scale frontier
F
F’
Variable returns to scale account for short-run scale inefficiencies In the long run, firms should optimally adjust their size so that constant returns to scale are achieved
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2. Methods for efficiency assessments
Data Envelopment Analysis (DEA) (3)
DEA is a non-parametric approach to calculate the relative Input-Output efficiency of a
regulated company
DEA benchmarks an individual company in relation to the best-practice (most efficient)
companies
Companies that are able to produce a given output at minimum cost or a maximum output
with a given input define the best-practice frontier that envelops all data points
Inefficiency is determined by the distance between the observed company and the best-
practice frontier
Calculation of inefficiency is done via a series of linear programming (mathematical
software needed)
The programs will output a series of efficiency scores, which may be normalized, ranked,
and split according to a number of components (scale, purely technical, allocative etc.)
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2. Methods for efficiency assessments
Data Envelopment Analysis (DEA) (4)
Advantages:
– Multi-dimensional method covering multiple inputs and outputs
– Establishes peer companies
– It does not require functional relationships between input and output factors
– Distinguishes between different types of inefficiency (scale, productive, allocative,
purely technical) in the presence of input (or output) price data
Disadvantages:
– The results could be influenced by random errors, measurement error or extreme
events
– Results depend on the selection of input and output factors
– Companies exhibiting “extreme” parameters will be classified as efficient “by default”
– Provides no information about statistical significance of the results
– Small samples and a high number of input or/and output variables can result in an
over-specification of the model and “made-up” results for efficiency scores (number of
efficient firms increases with the number of input and output variables)
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2. Methods for efficiency assessments
Parametric / Econometric approaches – Regression analysis
Corrected OLS (COLS)
Ordinary Least Square (OLS)
Most efficient observation
Input (Costs)
Output
Stochastic Frontier Analysis (SFA)
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2. Methods for efficiency assessments
Ordinary Least Squares (OLS)
Regression analysis: Mathematical relationship (functional form) that describes the
relationship between a dependent variable and one or more independent variables
Used to determine the values of parameters that cause the function to best fit a set of data
observations
The OLS regression line cuts across the observations by minimising the sum of the
squares of the distance (residual) between the line itself and each of the observations
Fit a line so that, at each point, the (regression) line is close to the corresponding observed
values, while minimising the sum of squared deviations from the line over all the
observable values in the sample
Efficiency frontier is based on the average cost function
OLS compares the (in)efficiency of an individual company with the average efficiency level
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2. Methods for efficiency assessments
Corrected Ordinary Least Squares (COLS)
Estimation of production or cost functions via Ordinary Least Squares
Use of regression residuals to characterise relative distances between observations in the
sample
Corrects the regression line by subtracting the largest negative residual (for a cost function)
from the OLS fit (shift the regression line to (unique) best-practice observation)
Measures the relative inefficiency of all other companies (points) from the line passing
through the largest negative residual (the most efficient company)
Allows to assess the significance of each network cost driver
No measurement of stochastic errors
Requires large data volume in order to create a robust regression relationship
Very dependent on data quality and, in particular, sensitive to outliers (the company
defining the frontier could just be an outlier!)
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2. Methods for efficiency assessments
Stochastic Frontier Analysis (SFA)
Uses same premises as COLS, but treats best practice as a “stochastic” process (a mix of
true efficiency and “random noise” effects)
Several statistical assumptions behind the errors
SFA requires a large sample size to be statistically relevant
In the presence of patchy and/or too small samples, COLS is relatively more reliable than
SFA (SFA cannot be drawn as a “frontier” line as COLS)
Less sensitive to inputs and/or outputs as DEA / COLS
Allows to assess the significance of each network cost driver
Considers stochastic errors explicitly
Complex and statistically demanding
Requires large data sets in order to create a robust regression relationship
Genuine inefficiency could be allocated to stochastic elements: scores might be too
generous (too high)
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2. Methods for efficiency assessments
Virtual network models
Artificially construct an efficient (engineering-designed) reference network according to
commonly accepted planning principles and taking into account technical and geographical
constraints
The regulated firm’s relative (in)efficiency is estimated by the firm’s performance in relation
to the virtual network
Virtual network models are not dependent on obtaining and analyzing data of “real”
companies
Does not require a significant set of comparable companies as benchmarks
Very complicated and difficult to specify
Model sensitive to changes in inputs
Reasons for the deviation from reference network might be beyond control of the company
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3. Application of efficiency results
TOTEX versus OPEX benchmarking
Building Block Approach
Implemented as linked (coupled) cap regulation
Explicit projection of capex for the upcoming regulatory period
Separate checks and inclusion of investments
Sometimes formalised efficiency analysis based on controllable opex
• TOTEX Approach
– Implemented as unlinked (decoupled) cap or yardstick regulation
– Inclusion of (historic) capital cost into efficiency assessment modelling (total cost
analysis)
– Standardisation of capital costs for benchmarking purposes
– (Planned) investment for the regulatory period not taken into account for the annual
allowed revenue
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Building blocks (UK, Australia, Central and Eastern Europe) supported by:
– Efficiency carry over schemes
– Sliding scale schemes
Total cost approach (Germany, Norway, the Netherlands, Austria) supported by:
– Quantity terms (pre-specified cost drivers) incorporated in price control formulas
– Explicit investment allowances
– Inefficiency caps
TOTEX versus OPEX benchmarking
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3. Application of efficiency results
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3. Application of efficiency results
Efficiency convergence speed
Allowed revenue
Initial level Initial one-off cut
1 2 3 4 5 Regulatory period
Proportional decrease
The X-factor prescribes the rate of change in the company’s prices or revenues, reflecting
the expected transition from the existing price level towards the efficient price level
Regulator to decide whether existing price level serve as starting point for the regulatory
formula or whether one-off cut of the initial price
Advantage of initial one-off cut,
prices can be brought to more
realistic levels at once
Large one-off adjustments quickly
eliminate inefficiencies at the
beginning, but decrease incentives
for further efficiency improvements
by the company
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3. Application of efficiency results
Supporting Schemes
Inefficiency caps (Austria, Germany)
– Germany: Minimum (individual)
efficiency score – 60 %
– Austria: Max. (individual) efficiency
increase – 5.45 %
Sliding scale (Norway, 1997-2001 and
2002-2006)
– Base return with dead band plus
caps/ collars
Efficiency bands (Norway, 1997-2001)
Germany
(2009-2013)
Norway
(1997-2001)
KA b,0
Year 1 Year 10
KA dnb,t Permanently
Controllable
costs (base year)
KA vnb,0 Temporary non-
controllable costs
Max. 60% of total costs after deducting of
(permanently) non-controllable cost
(proportionally over 10 years)
-
non-controllable costs
-
15%
Profit floor level(tariff increase)
Profit cap level(tariff reduction))
Dead band
8,3%
2 %Profit floor level(tariff increase)
Profit cap level(tariff reduction))
Dead band
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Summary
There are several benchmarking techniques and no consensus amongst regulators as to
which methodology is best
Data quality and model specification are fundamental for successful and defensible
outcomes
Benchmarking is an indication and not a confirmation of efficiency position
Integration of benchmarking results should take into account its imperfections and the
specifics of the price control design
Experience you can trust.http://www.leonardo-energy.org/training-module-electricity-market-regulation-session-6
End of webinar 6
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Kurt-Schumacher-Str. 8, 53113 Bonn
Tel. +49 (228) 44 690 00Fax +49 (228) 44 690 99
Dr. Konstantin Petrov
Managing Consultant
Mobil +49 173 515 1946 E-mail: [email protected]