Clean Energy Regulators Initiative - Role of Efficiency Analysis

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Regulatory authorities use different efficiency assessment methods to support the setting of efficiency increase targets for the regulated service providers. Session 2 describes the principles of this regulatory benchmarking. Within this session the various mathematical techniques to measure efficiency and their characteristics are presented:

· Uni-dimensional ratio analysis

· Statistical and econometric methods

· Linear programming methods

· Virtual network models

Furthermore, it is discussed why efficiency should be measured, what role efficiency assessment plays and how the efficiency results are applied and incorporated in the price control. The status quo of efficiency analysis in the EU is presented in a short synopsis.

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Clean Energy Regulators Initiative - Role of Efficiency Analysis

  1. 1. Introduction to Network Regulation Module 2: Role of Efficiency Analysis Dr. Konstantin Petrov, DNV KEMA 4 November 2013
  2. 2. Agenda 1. Introduction to Efficiency Analysis 2. Methods for Efficiency Assessment 3. Application of Efficiency Results 4. International Examples Introduction to Network Regulation 4 November 2013
  3. 3. Introduction to Efficiency Analysis Why measure efficiency?  Usually, competition forces companies to operate in an efficient way Cap Regulation  But, in areas where competition does not work (e.g. natural monopolies - transmission, distribution networks) regulation is needed to limit excessive pricing and to set incentives for efficient performance  In cost-based regulatory schemes, a fixed rate of return compensates the companies and little incentives to minimise costs are provided  Incentive regulatory schemes are explicitly designed to provide incentives for cost-efficiency  Incentive regulation is based on benchmarking which regulators use to assess efficiency of regulated companies and to set targets Introduction to Network Regulation 4 November 2013 3 Current price level Current price + Inflation Current price + Inflation – productivity growth Actual Cost Efficiency gains Set by regulator Influenced by company Base price for next regulatory period Influenced by company time
  4. 4. Introduction to Efficiency Analysis What is efficiency? EfficiencyA = OutputsA InputsA + “Correction for Environment” Input Factors Output Factors Distribution Company A e.g. # customers, delivered energy (kWh), peak load (kW) e.g. # employees, fuel, operational costs, Environmental Factors e.g. firm size, network topology, climate, topography, terrain, task complexity  Efficiency characterises the productivity of a company compared with the productivity of other companies. Introduction to Network Regulation 4 November 2013 4
  5. 5. Introduction to Efficiency Analysis Why are companies inefficient?  Inefficiency is a deviation from the optimal point on the production or cost frontier.  Two main sources for this deviation: technological deficits and problems due to a non-optimal allocation of resources into production  Sources for efficiency changes: - 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 Introduction to Network Regulation 4 November 2013 5
  6. 6. Agenda 1. Introduction to Efficiency Analysis 2. Methods for Efficiency Assessment 3. Application of Efficiency Results 4. International Examples Introduction to Network Regulation 4 November 2013 6
  7. 7. Methods for Efficiency Assessment Decision Sequence Benchmarking Process Steps in Benchmarking Analysis Step 1 Step 2 Step 3 Model Specification Choice of Approach for Efficiency Measurement 1) DEA 2) SFA 3) OLS 4) COLS 5) Partial Choice of Model Parameters 1) Model Orientation 2) Constant or Variable Returns to Scale 3) Definition of Inputs/ Outputs Step 4 Step 5 Model Application Choice of Sample Size and Data Collection 1) National versus International 2) Comparison Criteria 3) Data Validation Introduction to Network Regulation 4 November 2013 7 Model Run Result Validation 1) Application of Alternative Approaches 2) Sensitivity Analysis Related to Input and Output Parameters 3) Check for Outliers
  8. 8. Methods for Efficiency Assessment Choice of Benchmarking Method  Benchmarking (efficiency performance assessment) is applied based on a variety of methods ranging from basic indicators to more complex measures  Methods differ in the standard of comparison  Benchmarking influences the allowed revenue of companies and the price level reliability of inefficiency scores and the method chosen is crucial for the regulator  There is no consensus among regulators at to which methodology is the best Benchmarking should not be applied mechanically Sometimes different methods are applied simultaneously  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) Introduction to Network Regulation 4 November 2013 8
  9. 9. Methods for Efficiency Assessment Overview of Benchmarking Methods Benchmarking Methods Partial Methods Performance Indicators Total Methods Index Methods NonParametric Linear Programming UniDimensional Ratios Total Factor Productivity (TFP) Engineering Models Parametric Econometrics Data Envelopment Analysis (DEA) Ordinary Least Squares (OLS) Introduction to Network Regulation 4 November 2013 9 Corrected Ordinary Least Squares (COLS) Stochastic Frontier Analysis (SFA) Reference Networks (Virtual Networks)
  10. 10. Methods for Efficiency Assessment Partial vs. Total Methods  Partial methods use uni-dimensional ratios; comparison of single performance indicators between firms: Productivity Indicators: Financial Indicators: • GWh/Employee • Debt/Equity Ratio • OPEX/GWh • Return on Investment (ROI) • OPEX/Employee • GWh/Line Length • Return on Capital Employed (ROCE)  Partial methods produce simple, easy to calculate and straightforward indicators of performance  But: they fail to account for the relationships between different input and output factors and do not recognise trade-offs between different improvement possibilities  Total methods can capture this trade-off …at the expense of higher computational complexity Introduction to Network Regulation 4 November 2013 10
  11. 11. Methods for Efficiency Assessment Index- vs. Frontier-based Methods  Index method – Total Factor Productivity (TFP): - Measure of physical output of a regulated company produced by a given TFP  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) - Usually used for assessments of company performance over time m s R i 1 n s j 1 C j Y i i Xj  Frontier-based methods: - based on the concept that all companies should be able to operate at an optimal efficiency level/ “frontier” that is determined by other efficient “peer” companies in the same sample - 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 Introduction to Network Regulation 4 November 2013 11
  12. 12. Methods for Efficiency Assessment Non-parametric vs. Parametric Models (Frontier-based Methods) Input X (Costs) 𝑋2 𝑌 Parametric Methods Ordinary Least Squares (OLS) Stochastic Frontier Approach (SFA) Corrected Ordinary Least Squares (COLS) Most Efficient Observation Non-Parametric Methods Efficiency Frontier Most Efficient Companies A E B E’ Inefficiency C Output Y  Econometric methods use cost or production functions and regression analysis. SFA accounts for stochastic noise in the data sample 𝑋1 𝑌 D E  DEA uses multi-input / output analysis based on linear programming x1 /y Introduction to Network Regulation 4 November 2013 12
  13. 13. Agenda 1. Introduction to Efficiency Analysis 2. Methods for Efficiency Assessment 3. Application of Efficiency Results 4. International Examples Introduction to Network Regulation 4 November 2013 13
  14. 14. Application of Efficiency Results Efficiency Assessment and Price Control Efficiency Interface Allowed Revenue (Tariffs) Efficiency Benchmarking Price Targets Scores Integration in Improvement Efficiency Assessment Efficiency Control Conversion Integration  Approach  Convergence Time  Chargeable Basis  Sample  Convergence Profile  Capex Treatment  Model Orientation  Inefficiency Caps  Revenue Requirements  Data Collection  Efficiency Bands  Regulatory Formula  Data Validation Introduction to Network Regulation 4 November 2013 14
  15. 15. Application of Efficiency Results Defining Efficiency Increase Targets  Once calculated efficiency scores should be converted into efficiency increase requirements (X-factors).  X-Factor ensures ex-ante Measures of relative sharing of anticipated efficiency inefficiencies towards best gains Efficiency performance  X-Factor can be calculated: Score Conversion (definition of efficiency increase targets) - Indirectly as a difference between the level of actual costs and target (efficient) costs - Directly without reference to target costs using just past performance  In some regulatory regimes the X-factor has a dual function: - Efficiency improvement - Revenue profiling A B Introduction to Network Regulation 4 November 2013 15 C D E Companies
  16. 16. Application of Efficiency Results Efficiency Convergence Speed  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  The efficiency convergence may be based on an initial one-off cut or gradual adjustment path during the regulatory period  Advantage of initial one-off cut, prices can be brought to more realistic levels at once Allowed Revenue  Large one-off adjustments quickly eliminate inefficiencies at the beginning, but decrease incentives for further efficiency improvements by the company Initial one-off cut Initial Level Proportional decrease 1 2 3 4 5  Incentives for efficiency increase can be further supported by efficiency carry-over schemes: companies are allowed to continue keeping part of the efficiency gains of the previous period Regulatory Period Introduction to Network Regulation 4 November 2013 16
  17. 17. Agenda 1. Introduction to Efficiency Analysis 2. Methods for Efficiency Assessment 3. Application of Efficiency Results 4. International Examples Introduction to Network Regulation 4 November 2013 17
  18. 18. International Examples Country Benchmarking Methods Benchmarking Sample United Kingdom COLS until 2009; DEA and OLS (OPEX) 14 electricity distribution companies 8 gas network distribution companies The Netherlands DEA (total controllable costs) 19 Dutch utilities (electricity) Germany DEA, SFA (total controllable costs) 198 electricity distribution companies 188 gas distribution companies Austria DEA, MOLS (total controllable costs) 20 electricity distribution companies 20 gas distribution companies Finland DEA, SFA (OPEX) 88 electricity distribution companies Norway DEA 150 national distribution utilities (electricity) Sweden Reference network model until 2007;SFA, DEA 170 electricity distribution companies Spain Network reference model 5 large and 320 smaller electricity distribution companies Portugal DEA 11 gas distribution companies Poland OLS; COLS & DEA Introduction to Network Regulation 4 November 2013 18
  19. 19. End of Session 2. Dr. Konstantin Petrov Service Line Leader Markets & Regulation / Business Line Director Gas Consulting Services DNV KEMA Energy & Sustainability KEMA Consulting GmbH Kurt-Schumacher-Str. 8 53113 Bonn Tel: +49 228 44690 56 Fax: +49 228 4469099 Mobile: +49 173 515 1946 E-mail: konstantin.petrov@dnvkema.com Introduction to Network Regulation 4 November 2013 19
  20. 20. www.dnvkema.com Introduction to Network Regulation 4 November 2013 20
  21. 21. Appendix: Efficiency Assessment Models Introduction to Network Regulation 4 November 2013
  22. 22. Methods for Efficiency Assessment Non-parametric Model: Data Envelopment Analysis (DEA)  Calculates the relative Input-Output efficiency of a regulated company by benchmarking 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 bestpractice frontier  Calculation of inefficiency is conducted via a series of linear programming (mathematical software needed)  The programs will output a series of efficiency scores, which may be normalised, ranked, and split according to a number of components (scale, purely technical, allocative etc.)  Advantages: multi-dimensional method; functional relationships between input and output factors not required; distinguishes between different types of inefficiency  Disadvantages: results may be influenced by random errors; no information about statistical significance of the results; danger of over-specification of model and “made-up” results for efficiency scores; “extreme” parameters regarded as efficient “by default” Introduction to Network Regulation 4 November 2013 22
  23. 23. Methods for Efficiency Assessment Non-parametric Model: Data Envelopment Analysis (DEA) Output Maximisation Input Minimisation Output 1 Input 1 G’ Data Envelope Data Envelope A most efficient companies G B most efficient companies A F’ F Inefficiency C F Inefficiency B D F’ C E D Output 2 Introduction to Network Regulation 4 November 2013 23 E Input 2
  24. 24. Methods for Efficiency Assessment Parametric Models  Regression analysis: Mathematical relationship (functional form) that describes the relationship between a dependent variable and one or more independent variables  Use of regression residuals to characterise relative distances between observations in the sample  Treats best practice as a “stochastic” process (a mix of true efficiency and “random noise” effects, SFA)  Advantages: ability to control for unobserved heterogeneity among companies; less sensitive to inputs and/or outputs than other parametric models; allows to assess the significance of each network cost driver; considers stochastic errors explicitly  Disadvantages: requires assumptions of functional form; requires large data sets in order to create a robust regression relationship; complex and statistically demanding Introduction to Network Regulation 4 November 2013 24
  25. 25. Methods for Efficiency Assessment Parametric Models Input (Costs) Ordinary Least Square (OLS) Stochastic Frontier Analysis (SFA) Corrected OLS (COLS) Most efficient observation Output Introduction to Network Regulation 4 November 2013 25
  26. 26. Methods for Efficiency Assessment Virtual Network Models  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  Advantages: Virtual network models are not dependent on obtaining and analysing data of “real” companies; does not require a significant set of comparable companies as benchmarks  Disadvantages: It might be 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 Introduction to Network Regulation 4 November 2013 26

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