Journal of Energy

Volume 2016, Article ID 2486319, 10 pages

http://dx.doi.org/10.1155/2016/2486319

## Benchmarking of Electricity Distribution Licensees Operating in Sri Lanka

Department of Electrical Engineering, University of Moratuwa, 10400 Moratuwa, Sri Lanka

Received 3 October 2015; Revised 4 January 2016; Accepted 20 January 2016

Academic Editor: Jin-Li Hu

Copyright © 2016 K. T. M. U. Hemapala and Lilantha Neelawala. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Electricity sector regulators are practicing benchmarking of distribution companies to regulate the allowed revenue. Mainly this is carried out based on the relative efficiency scores produced by frontier benchmarking techniques. Some of these techniques, for example, Corrected Ordinary Least Squares method and Stochastic Frontier Analysis, use econometric approach to estimate efficiency scores, while a method like Data Envelopment Analysis uses linear programming. Those relative efficiency scores are later used to calculate the efficiency factor (X-factor) which is a component of the revenue control formula. In electricity distribution industry in Sri Lanka, the allowed revenue for a particular distribution licensee is calculated according to the allowed revenue control formula as specified in the tariff methodology of Public Utilities Commission of Sri Lanka. This control formula contains the X-factor as well, but its effect has not been considered yet; it just kept it zero, since there were no relative benchmarking studies carried out by the utility regulators to decide the actual value of X-factor. This paper focuses on producing a suitable benchmarking methodology by studying prominent benchmarking techniques used in international regulatory regime and by analyzing the applicability of them to Sri Lankan context, where only five Distribution Licensees are operating at present.

#### 1. Introduction

The regulators in distribution sector in the world expect to increase investments for increasing electrification with reductions of losses, reduction of the number of employees, and so forth. In Sri Lankan context the regulator is looking to reduce the tariff also as distribution sector is running with government funds. Therefore the main target of the Sri Lankan regulator is to provide the utility with incentives to improve their operating efficiency to ensure that customer will get quality electricity with low price. There are five electricity Distribution Licensees operating in Sri Lanka. In Sri Lanka the allowed revenue for a particular distribution licensee (DL) is calculated according to the allowed revenue control formula as specified in the tariff methodology of Public Utilities Commission of Sri Lanka (PUCSL). The Operational Expenditures (OPEX) component of the base allowed revenue needs to be adjusted at a rate defined by an efficiency factor per year. In successive tariff periods, the regulator (PUCSL) can revise the methodology for computing the efficient OPEX to be included in the distribution allowed revenue. A relative OPEX efficiency score obtained from a benchmarking study is an input to formulate this efficient factor (X-factor). PUCSL can decide on X-factor using the result of a benchmarking study. At present PUCSL does not take into account the X-factor when deciding the allowed revenue for each DL. The reason for not considering the X-factor is that there are no benchmarking studies that have been done on DLs to obtain relative OPEX efficiency scores. Without these relative efficiency scores (percentage values like 100% for one DL, 60% for another, etc.) X-factor cannot be obtained. Therefore PUCSL requires a suitable methodology to benchmark Distribution Licensees in Sri Lanka.

This paper describes a methodology to benchmark five Distribution Licensees in Sri Lanka, which facilitate PUCSL to regulate allowed revenue for each DL according to the relative OPEX efficiencies. The regulator can set differentiated price limits based on the companies’ efficiency performance estimated from a benchmarking analysis [1]. And also it can decide which companies deserve closer examination, so that scarce investigative resources are allocated efficiently [2].

There are different benchmarking techniques used by international regulators [2–5]. Selecting the most appropriate benchmarking methodology is done after considering the principles discussed by CEPA’s reports on benchmarking [3, 6]. The rest of the paper is organized as follows. A brief overview of different benchmarking techniques is presented in Section 2. Data Envelopment Analysis with methodologies is discussed in Section 3. Section 4 is presented results and discussion and finally the conclusions are given in Section 5.

#### 2. Benchmarking Techniques

The following were identified as prominent techniques from the literature review for considerations against the principles discussed by CEPA’s report [7]:(i)Partial Performance Indicators (PPIs),(ii)Ordinary Least Square (OLS),(iii)Data Envelopment Analysis (DEA),(iv)Stochastic Frontier Analysis (SFA).

##### 2.1. Partial Performance Indicators (PPIs)

These indicators are used to compare the ratios of single output to a single input of firms (e.g., energy sold per OPEX). They are often significantly affected by the capital substitution effects [7]. PPIs used in isolation are not possible to use the differences in the energy sector that directly impact on the market. For example, a utility may experience a relatively high or low unit cost simply because of the customer category. Therefore PPIs may not provide a meaningful comparison across different DLs as they are operating at different conditions [8].

##### 2.2. Data Envelopment Analysis (DEA)

Data Envelopment Analysis (DEA) is the prominent technique used by the researchers for benchmarking in the literature [9–13]. Thakur has used DEA and Malmquist Productivity Index to find the rate at which the efficiency frontier has moved over recent years after implementing the reforms process in India [14]. Cui et al. applied DEA and Malmquist Productivity Index to calculate the energy efficiencies of nine countries during 2008–2012 and explained the reasons for energy efficiency changing with respect to technical and management factors [15]. Javier Ramos-Real et al. have estimated the changes in the productivity of the Brazilian electricity distribution sector using Data Envelopment Analysis in terms of productivity change [16]. Chien et al. applied Malmquist Productivity Index comparing the performance of different thermal power plants in Taiwan [17].

DEA involves linear programming to determine the efficient firm(s) from a sample relative to the other firms in the sample [18–20] while the Malmquist Productivity Index evaluates the efficiency change over time [21]. In this nonparametric technique, the ratio of weighted outputs to the weighted input is maximized subjected to constraints (required to solve individual linear programming problems for each firm in the sample). The efficient firm is the one where no other firm or linear combination of other firms can produce more of all the outputs using less input [6]. It is important to select input output variables reflecting the use of resources and misspecification of variables can lead to wrong results [6]. DEA can also accommodate environmental variables that are beyond the control of the firms but can affect their performance (e.g., population density of a particular area of operation). This method is a multidimensional method and inefficient firms are compared to actual firms (or linear combinations of these) rather than to some statistical measure. This does not require specifying a cost or production function. Importantly DEA can be implemented on a small dataset, where regression analysis tends to require larger minimum sample size, but in case of small samples and high number of input or/and output variables there is a danger of overspecification of model and eventually “made-up” results for efficiency scores [22]. As more variables are included in the model, the number of firms on the efficient frontier increases.

##### 2.3. Corrected Ordinary Least Squares (COLS)

With this regression technique the most efficient firm or the frontier is estimated. This “corrected” form of ordinary least square has assumed that all deviations from the frontier are due to inefficiency [6]. This method requires the details of the cost or production function and assumptions about technological properties. COLS method is easy to implement and allow statistical inference about which parameters to include in the frontier estimation [6]. This method requires large data volume in order to create robust regression relationship and is sensitive to data quality.

##### 2.4. Stochastic Frontier Analysis (SFA)

Similar to COLS, SFA requires the specification of a production function based on input variables. But in this model the errors in parameters are incorporated into the model and do not assume that all errors are due to inefficiency [23]. A model of the form described under COLS is estimated with two error functions. The first of these will be assumed to have a one-sided distribution. The second error term has a symmetric distribution with mean zero. The Cobb-Douglas stochastic frontier model takes the form [24]where is an output, is an input, and , are error terms. SFA is theoretically the most appealing technique but the hardest to apply. Since it is difficult to implement in small samples, regulators traditionally have been reluctant to use SFA techniques in setting X-factors [6].

Further it is important to note that the reliable panel data of OPEX was not available. Unavailability of this published/audited historical OPEX data was mainly due to the fact that major 4 DLs are from the same legal entity having no separate audited accounts till the year 2010 (OPEX for year 2011 and 2010 was the only available data). This results in avoiding techniques that rely on panel data for this study.

#### 3. Data Envelopment Analysis

There are a number of variables that can be considered when implementing any benchmarking technique as described in Section 2. In regulators’ point of view factors such as quality of the data, availability, ease of collection, relevance to the business, international practices/reviews, use of statistical indicators (such as correlation), nonredundancy to minimize overlapping, high discriminating power, and reflection of the scale of operation and cost drivers have to be considered when selecting variables.

Therefore the regulator must take care to keep the number of variables to minimum while those variables are strong cost drivers (i.e., OPEX). Relevant data should be accurate and importantly be practical to collect from the DLs timely. In order to find quality and feasible data several reports were analyzed. These include published reports by PUCSL [25–28] and Licensees [29–40]. After studying the above reports the following set of variables were collected:(i)energy sold,(ii)total number of consumers—this is the number of consumer accounts or the number of consumer connection points,(iii)number of new connections provided,(iv)number of employees,(v)total distribution of lines’ length—this includes MV and LV network length,(vi)number of substations,(vii)authorized operation area—this is a constant for each licensee,(viii)operational expenditure.

Note that, in international benchmarking practices, the use of supply/service quality as a variable is rare. Most of the countries reviewed separately run a quality-of-service reward/penalty regime [23]. In Sri Lanka, the supply/service quality is to be determined according to the drafted electricity distribution performance regulations, where penalties have been introduced for underperformance [41].

##### 3.1. Justification of Selected Variables

###### 3.1.1. Cost Drivers

Cost is clearly depending on scale of the operation. Accurate data on energy distributed, production of the distribution business, the number of consumer accounts, network length (MV and LV line lengths), and the number of distribution substations can be timely obtained from DLs in Sri Lanka. Since data on the above-mentioned variables can be timely obtained, regulator can timely perform benchmarking exercise to figure out allowed revenue for each year.

###### 3.1.2. Dispersion of Consumers

Distribution line length per consumer can be taken as indication of what extent the consumer concentration is. It is also an indication of the extent of rural electrification efforts taken by the DLs. For each DL this value is different. For example, DL5 is having a lower value indicating higher concentration of consumers, whereas DL4 is having a larger value as indicated in Table 1.