Statistical benchmarking in utility regulation: Role, standards and methods

Lowry, Mark Newton; Getachew, Lullit

doi:10.1016/j.enpol.2008.11.027

Cited by 19 publications

(6 citation statements)

References 32 publications

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“…Regulators have increasingly been using benchmarking approaches to compare regulated utilities for purposes that range from rate setting to informational (Jamasb and Pollitt, 2000;Lowry and Getachew, 2009). Similarly, regional planning entities typically compile forecasts from utilities under their purview to determine aggregate load growth and expansion scenarios.…”

Section: Evolution Of Forecasting Methodologies and Variablesmentioning

confidence: 99%

Load Forecasting in Electric Utility Integrated Resource Planning

Carvallo

Larsen

Sanstad

et al. 2017

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Section: Evolution Of Forecasting Methodologies and Variablesmentioning

confidence: 99%

Load Forecasting in Electric Utility Integrated Resource Planning

Carvallo

Larsen

Sanstad

et al. 2017

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“…The most common benchmarking methods used in the electricity sector are econometric modeling, involving constrained ordinary least squares (COLS) and SFA, indexing (e.g., unit costs and total factor productivity indexes), and mathematical modeling, using DEA (e.g., Lowry and Getachew 2009). More recently, Kuosmanen and Kortelainen (2012) propose a twostage method, called the stochastic non-smooth envelopment of data (StoNED), to estimate a frontier model.…”

Section: A Brief Literature Reviewmentioning

confidence: 99%

Maximum entropy: a stochastic frontier approach for electricity distribution regulation

2019

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The literature on incentive-based regulation in the electricity sector indicates that the size of this sector in a country constrains the choice of frontier methods as well as the model specification itself to measure economic efficiency of regulated firms. The aim of this study is to propose a stochastic frontier approach with maximum entropy estimation, which is designed to extract information from limited and noisy data with minimal statements on the data generation process. Stochastic frontier analysis with generalized maximum entropy and data envelopment analysis-the latter one has been widely used by national regulators-are applied to a crosssection data on thirteen European electricity distribution companies. Technical efficiency scores and rankings of the distribution companies generated by both approaches are sensitive to model specification. Nevertheless, the stochastic frontier analysis with generalized maximum entropy results indicate that technical efficiency scores have similar distributional properties and these scores as well as the rankings of the companies are not very sensitive to the prior information. In general, the same electricity distribution companies are found to be in the highest and lowest efficient groups, reflecting weak sensitivity to the prior information considered in the estimation procedure.

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“…The most common benchmarking methods used in the electricity sector are econometric modeling, involving constrained ordinary least squares (COLS) and stochastic frontier analysis (SFA) with maximum likelihood (ML), indexing (e.g., unit costs and TFP indexes), and mathematical modeling using DEA (e.g., [11]). There are several studies comparing the performance of several benchmarking methods in the context of regulating the electricity sector or/and using a Monte Carlo simulation study.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

An alternative benchmarking approach for electricity utility regulation using maximum entropy

Silva

Macedo

Soares

2016

2016 13th International Conference on the European Energy Market (EEM)

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The main purpose of this study is to present an alternative benchmarking approach that can be used by national regulators of utilities. It is widely known that the lack of sizeable data sets limits the choice of the benchmarking method and the specification of the model to set price controls within incentive-based regulation. Ill-posed frontier models are the problem that some national regulators have been facing. Maximum entropy estimators are useful in the estimation of such ill-posed models, in particular in models exhibiting small sample sizes, collinearity and nonnormal errors, as well as in models where the number of parameters to be estimated exceeds the number of observations available. The empirical study involves a sample data used by the Portuguese regulator of the electricity sector to set the parameters for the electricity distribution companies in the regulatory period of 2012-2014. DEA and maximum entropy methods are applied and the efficiency results are compared.

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Statistical benchmarking in utility regulation: Role, standards and methods

Cited by 19 publications

References 32 publications

Load Forecasting in Electric Utility Integrated Resource Planning

Load Forecasting in Electric Utility Integrated Resource Planning

Maximum entropy: a stochastic frontier approach for electricity distribution regulation

An alternative benchmarking approach for electricity utility regulation using maximum entropy

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