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EUROPEAN COMISSION – ERAMUS MUNDUS PROGRAM (EMIN) Master Thesis on Economics and Management of Network Industries

Universidad Pontificia Comillas de Madrid – Université Paris XI

An Approach to set Incentives for Quality

of Electricity Supply

CAMILA FORMOZO FERNANDES

Under the Supervision of:

Antonio Candela Martínez (Comisión Nacional de Energía – España) Prof. Tomás Gómez San Román (Universidad Pontificia Comillas)

Madrid, July 2008

An Approach to set Incentives for Quality of Electricity Supply

Camila Formozo Fernandes 2

ACKNOWLEDGEMENT

First and foremost, I would like to express my deep gratitude to Antonio

Candela Martínez, my main supervisor, from the National Energy Commission (CNE)

in Spain, for sharing his knowledge not only of regulation but also of economics and

engineering. I would especially like to thank him for his trust, patience, encouragement

and kindness. I hope that this work is worthy of his careful supervision.

I am also very much indebted to Professor Tomás Gómez San Román, my thesis

coordinator and tutor from Comillas University. Since I was in Paris XI University he

helped me with my shift to Madrid in what regards my internship and my thesis. I am

also very grateful for his valuable suggestions, attention and disposal to clarify my

doubts.

I want to thank Professor Ignacio Pérez Arriaga for his marvellous classes of

regulation, which deeply inspired me to work in this field. Discussing with him is

always an interesting experience by which I have broadened my knowledge to new

horizons. I would also like to thank him for believing in me and for helping me to

obtain this internship.

I would like to thank the European Commission and the Master program EMIN

for giving me this great opportunity of studying in excellent European universities. I

want also to thank all the professors of the master and, especially, my EMIN

coordinators, Javier García González, from Comillas University, and Yannick Perez,

from Paris XI University, who are always trying to do their best for the master students.

I can not forget my first supervisor, Professor Helder Queiroz Pinto Jr., from the

Federal University of Rio de Janeiro. He was the one who “introduced” me to the

energy sector. I want to thank him for sharing his knowledge for more than three years,

for his advices, for supporting me to come to Europe for this master and, especially, for

his friendship during this whole time.

Last but not least, my deepest thanks are devoted to my beloved family and

friends who are always supporting me, listening to my complaints in bad times, making

me feel proud and, especially, trying to make the distance shorter as they can.



ABSTRACT

In this study an approach to set incentives for quality of service in electricity

distribution activity is proposed. The approach consists on the estimation of the impact

of quality increments on distribution network costs. For this purpose, a network

reference model was used in order to calculate distribution costs resultant from different

quality requirements. After that, a cost-function that relates quality of supply and

distribution costs was derived.

This methodology was applied to three areas of service in Spain – one urban,

one semi-urban and one rural – in such a way differences in distribution costs caused by

zonal characteristics could be taken into account.

The analysis demonstrated that the unitary incentive given to distribution

companies serving rural zones should not be the same as the one given to distribution

companies supplying urban zones. It was also observed that the incentive for reducing

number of service interruptions should be higher than the one given for reducing time of

service interruptions.

Finally, the current unitary incentives for continuity of supply in Spain were

analyzed under the perspective of the approach proposed in this thesis.

Key words: Quality, regulation, incentives, electricity distribution

JEL: L94, R32, L43, D24



TABLE OF CONTENTS

LIST OF ILUSTRATIONS ........................................................................................................ 6

LIST OF EQUATIONS............................................................................................................... 8

INTRODUCTION ...................................................................................................................... 9

I – QUALITY OF ELECTRICITY SUPPLY ......................................................................... 11

I.1 – Definition of Quality of Supply .............................................................................................. 11

I.1.1 – Continuity of Supply ................................................................................... 12

I.1.1.1 –Indicators of Continuity of Supply............................................................ 12

I.2 – Provision of Quality under Monopoly ................................................................................ 14

I.3 – Quality Regulation................................................................................................................... 18

I.3.1 – Optimum Quality Level .............................................................................. 18

I.3.2 – Traditional Regulation versus Incentive Regulation................................... 20

I.3.2.1 – Types of Quality Control ......................................................................... 22

I.3.2.1.1 – Reward/Penalty Schemes ...................................................................... 24

II – METHODOLOGY .............................................................................................................26

II.1 – Network Reference Model ...................................................................................................27

II.2 – Econometric Model................................................................................................................ 31

II.2.1 – Including Quality in the Electricity Distribution Cost Function................ 31

II.2.1.1 –Specification of the cost function ............................................................ 32

III – SPANISH ELECTRICITY DISTRIBUTION REGULATION...............................35

III.1 – Characterization of the Spanish Electricity Distribution Activity...........................35

III.2 – Regulation of the Distribution Activity ..........................................................................36

III.2.1 – Incentives to Improve Quality of Supply ................................................. 40

III.2.1.1 – New Incentive Scheme to Improve Quality of Supply.......................... 41

IV – APPLICATION OF THE METHODOLOGY TO THE SPANISH CASE.......... 44

IV.1 – NRM Applied to the Spanish Case .................................................................................. 44

IV.2 – Econometric Model Applied to the Spanish Case ........................................................ 51

IV.3 – Setting Incentives for Continuity of Supply ..................................................................55

CONCLUSION ..........................................................................................................................62



REFERENCES............................................................................................................................64

APPENDIX A: Network Reference Model.........................................................................68

APPENDIX B: Regressions’ Statistics..................................................................................70

APPENDIX C: Setting Incentives for Continuity of Supply.......................................... 72



LIST OF ILUSTRATIONS FIGURES Figure 1 – Probabilistic Distribution of Individual Indicators Depending on Individual,

System or Both Indicators Control...............................................................................................23

Figure 2 – Scope of Network Reference Models.......................................................................27

Figure 3 – Customers’ Characteristics........................................................................................ 29

Figure 4 – Main Distribution Companies in Spain ..................................................................35

Figure 5 – Small Distribution Companies in Spain ..................................................................35

Figure 6 – NRM Results..................................................................................................................47

GRAPHS Graph 1 – Optimum Quality Level ................................................................................................ 19

Graph 2 – Reward/Penalty Scheme..............................................................................................25

Graph 3 – Distribution Network Costs/Consumed Energy X TIEPI ................................. 49

Graph 4 – Distribution Network Costs/Consumed Energy X TIEPI ................................. 50

Graph 5 – Marginal Cost Curve and Incentive for Total TIEPI ............................................56

Graph 6 – Marginal Cost Curve and Incentive for Total NIEPI............................................58

Graph 7 – Marginal Cost Curve and Incentive for TIEPI in the Representative Zone

Type of each Pair “firm, province”................................................................................................ 60

TABLES Table 1 – Summary of Customers’ Data ...................................................................................... 29

Table 2 – Summary of Distribution Network Costs’ Results ................................................30

Table 3 – Summary of Quality Indicators ................................................................................... 31

Table 4 – Individual Limits for Continuity of Supply............................................................. 40

Table 5 – Zone Limits for Continuity of Supply ...................................................................... 40

Table 6 – Example: Quality Constraints when K = 0,20......................................................... 46

Table 7 – Example Pair (1, U): Urban Constraints when K = 0,20....................................... 46

Table 8 – Estimation Results for Pair (1, U) Considering Q as Total TIEPI/NIEPI .........52

Table 9 – Estimation Results for Pair (3, S) Considering Q as Total TIEPI/NIEPI..........52

Table 10 – Estimation Results for Pair (4, R) Considering Q as Total TIEPI/NIEPI.......53



Table 11 – Estimation Results for Pair (1, U) Considering Q as TIEPI/NIEPI in the

Urban Zone........................................................................................................................................ 54

Table 12 – Estimation Results for Pair (3, S) Considering Q as TIEPI/NIEPI in the Semi-

urban Zone......................................................................................................................................... 54

Table 13 – Estimation Results for Pair (4, R) Considering Q as TIEPI/NIEPI in the Rural

Concentrated Zone...........................................................................................................................55

Table 14 – Regression Pair (1, U), Q = Total TIEPI/NIEPI ......................................................70

Table 15 – Regression Pair (3, S), Q = Total TIEPI/NIEPI .......................................................70

Table 16 – Regression Pair (4, R), Q = Total TIEPI/NIEPI ......................................................70

Table 17 – Regression Pair (1, U), Q = Urban TIEPI/NIEPI.....................................................70

Table 18 – Regression Pair (3, S), Q = Semi-urban TIEPI/NIEPI ........................................... 71

Table 19 – Regression Pair (4, R), Q = Rural Concentrated TIEPI/NIEPI .......................... 71

Table 20 – Parameters of the Cost function ...............................................................................72

Table 21 – Parameters of the Cost function................................................................................73



LIST OF EQUATIONS Equation 1: SAIFI (System Average Interruption Frequency Index)................................... 13

Equation 2: SAIDI (System Average Interruption Duration Index) .................................... 13

Equation 3: ENS (Energy Not-Supplied) .................................................................................... 13

Equation 4: NIEPI (Average System Interruption Frequency Index) ................................. 14

Equation 5: TIEPI (Average System Interruption Duration Index)..................................... 14

Equation 6: Distribution Network Business Total Cost function........................................33

Equation 7: Distribution Network Business Cost function Considering Quality as an

Explanatory Variable .......................................................................................................................33

Equation 8: Revenue Cap Formula for the 5 Main Distribution Companies in Spain

According to the RD 2819/1998 .....................................................................................................37

Equation 9: Base Remuneration for Distribution Companies in Spain According to RD

222/2008..............................................................................................................................................38

Equation 10: Annual Remuneration for Distribution Companies in Spain According to

RD 222/2008.......................................................................................................................................39

Equation 11: Formula for Remuneration Updating According to RD 222/2008 ...............39

Equation 12: Incentive Scheme for Quality of Supply According to RD 222/2008........... 41

Equation 13 : New Incentive Scheme for Quality of Supply .................................................. 42

Equation 14: Formulas for Target TIEPI and NIEPI................................................................ 43

Equation 15: Formulas for Observed TIEPI and NIEPI........................................................... 43



INTRODUCTION

At different speeds, and starting at different times in the past 20 years, many

countries have been reforming their network industries, such as telecommunications,

transport and energy. All these sectors are characterized by the presence of an

infrastructure with natural monopoly characteristics. During the reforms, these

industries, which were traditionally stated-owned, were privatized and divided into

competitive and non-competitive segments (Martin et Al., 2005). In the case of the

electricity sector, competition was introduced in generation and retailing, while

transmission and distribution remained as natural monopolies.

The role of the State changed from owner to regulator. In this context, rate of

return regulation (ROR) was the traditional approach adopted to regulate privately

owned monopolies and an alternative to public owned utilities. The main drawback of

this approach is that it does not provide incentives for cost savings and efficiency

improvements but rewards overinvestment. According to Jamasb & Pollitt (2000),

within the framework of the principal-agent theory, ROR regulation causes a managerial

slack or X-inefficiency due to the absence of competition.

As a consequence, regulators around the world have been replacing the

traditional rate-of-return regulation of natural monopolies with performance-based

regulation, such as price cap and revenue cap. The objective of this regulatory approach

is to provide incentives to the companies responsible for the activities of the chain that

remained as natural monopolies to improve efficiency.

On the other hand, regulators have been concerned that with this regulatory

change utilities might be tempted to reduce quality of supply in order to achieve costs

savings. For this reason, in many countries quality regulation was introduced to

counterbalance the negative effect of incentive regulation over quality of service. One

regulatory tool commonly adopted in Europe was the reward/penalty schemes1. Under

these schemes, companies are given incentives if they supply quality above minimum

required levels and are penalized if quality is below those levels.

In this context, this thesis proposes a new approach on how incentives for

quality of electricity supply improvement should be calibrated so that distribution

1 See Jamasb & Pollitt (2000) and CEER (2005).



companies are motivated to increase quality of service. For this purpose, distribution

costs under different quality requirements will be calculated by a network reference

model. Besides, a cost function for distribution network business will be estimated

taking into account continuity of supply as an explanatory variable.

The main objective of this study is to evaluate the sensibility of distribution

network costs in relation to quality of electricity supply in different zone types in such a

way that incentives for quality of electricity supply can be properly calibrated.

For this reason, this approach will be applied to three Spanish areas of service,

which means the area of province “y” supplied by a distribution company “x”. Three

provinces were chosen according to their regional differences: the first one was

characterized as an urban province, the second one as a semi-urban province and the

third one as a rural province.

In order to meet the proposed objective, this thesis was divided into four

chapters besides this introduction and the conclusion.

Chapter I defines the different aspects of quality of service. Besides, it is shown

that quality provision under monopoly tends to deviate from the social optimum level.

Finally, the remedy for this problem is discussed.

Chapter II covers the methodology and it is divided into two sections. Section

II.1 introduces network reference models and Section II.2 derives the distribution

network cost-function.

Chapter III reviews the Spanish regulation of distribution activity remuneration,

especially on what concerns incentives to improve quality of electricity supply.

Finally, Chapter IV contains the results of the methodology described in Chapter

II applied to the Spanish case. Besides, the current unitary incentives for continuity of

supply in Spain are analyzed under the proposed approach’s perspective.



I – QUALITY OF ELECTRICITY SUPPLY

Demand for quality of electricity supply has been increasing in recent years, not

only by the industry, which is greatly affected by service interruptions, but also by

residential customers.

This Chapter defines the different aspects of quality of service. Furthermore, it is

demonstrated that quality provision under monopoly tends to deviate from the social

optimum level. Finally, the remedy for this problem is discussed.

I.1 – Definition of Quality of Supply

Quality of electricity supply in distribution and retail activities generally

comprises three aspects: commercial quality (non-technical aspect), power quality and

reliability (technical aspects).

Commercial quality refers to a non-technical relationship between distribution

companies and customers which involves certain services as provision of new

connections, metering, billing, etc. Therefore, commercial quality is not related to the

planning and maintenance of the distribution network infrastructure.

Power quality or voltage quality is related to disturbances on the voltage

waveform. Some examples of these disturbances are network losses and frequency

variations; voltage magnitudes variations (flickers); and waveform distortions

(harmonics). Many of power quality disturbances are caused by clients or are originated

in the transmission network.

Finally, reliability refers to the capacity of distribution operators to continuously

meet demand. This aspect of quality of service can be divided into two main

dimensions: adequacy and security. Adequacy is related to the availability of sufficient

network capacity to serve demand in the long run. It means that, in normal operational

and demand conditions, no supply interruptions will happen. On the other hand, security

means the capacity of distribution operators (given that the network is adequately

designed) to control disturbances so that consumers are not interrupted (López et Al.,

2008).



The capacity of distribution companies to continuously meet demand is

commonly known as continuity of supply. This aspect of quality of service is extremely

related to network investments and practices of operation and maintenance by

distribution operators and it will be the focus of this study.

I.1.1 – Continuity of Supply

Continuity of supply is determined by number and duration of supply

interruptions. Interruptions can be divided into planned and unplanned. Planned

interruptions generally occur due to network maintenance or new installations. In this

case, clients are notified in advance. Unplanned interruptions occur due to unexpected

reasons, as network components failures, force majeure, external agents, climate

conditions, etc.

Regarding duration, interruptions can be classified as short or long. Short

interruptions are the ones which last less than three minutes and long interruptions are

the ones that last more than three minutes. Continuity of supply regulation deals only

with long interruptions.

I.1.1.1 –Indicators of Continuity of Supply

There are individual and system indicators of continuity of supply. Individual

indicators reflect the quality that one particular customer receives, for instance number

of interruptions, duration of interruptions, energy not supplied. System indicators refer

to the average quality in a specific region or service area. These two kinds of indicators

are not the same and play different roles in quality regulation, as it will be seen in

Section I.3.

Regarding system control of quality of supply, both duration and frequency of

interruptions are controlled by indicators. The most frequently used are average number

of interruptions per consumer year; average interruption duration per consumer per year

and energy not supplied. These indicators are described in Fumagalli et al. (2007) and

are presented below:



Equation 1: SAIFI (System Average Interruption Frequency Index)

T

n

i

N

Ni∑=SAIFI

It indicates the average number of interruptions per customer per year, where i

refers to one interruption; n is the total number of interruptions in a year; Ni is the

number of customers affected by interruption i; and Nt is the total number of customers

in the system for which the indicator is calculated.

Equation 2: SAIDI (System Average Interruption Duration Index)

T

n

i

N

DiNi∑=

.SAIDI

It indicates the average duration of interruptions in minutes per year, where Di is

the duration of interruption i.

Equation 3: ENS (Energy Not-Supplied)

∑=n

iDiPiENS .

It indicates the total disconnected capacity per year in KWh where Pi is capacity

disconnected due to interruption i.

In Spain, the system indicators used to control quality of supply are different

from SAIDI and SAIFI in the sense that the average frequency and duration of

interruptions are weighted by installed capacity instead of customer. These indicators

are shown below:



Equation 4: NIEPI (Average System Interruption Frequency Index)

T

n

i

P

PiNIEPI

∑=

It gives the average number of interruptions per installed capacity per year,

where Pi is the installed capacity disconnected by interruption i and Pt is the total

installed capacity in the system.

Equation 5: TIEPI (Average System Interruption Duration Index)

T

n

i

P

DiPiTIEPI

∑=

.

It indicates the average duration of interruptions in hours per year, where Di is

the duration of interruption i.

I.2 – Provision of Quality under Monopoly

Distribution companies are the main agents of the electricity industry

responsible for quality of supply since approximately 95% of supply interruptions have

their origin in distribution networks (Rivier, 1999).

Distribution networks are characterized by strong economies of scale and,

therefore, are organized as monopolies. Natural monopoly is the market structure

resultant from the situation when producing with more than one firm is uneconomic.

However, this kind of market organization gives the monopolist market power, which

leads to allocative inefficiency and productive inefficiency as well as suboptimal

provision of quality.

Monopoly results in allocative inefficiency since social surplus – which is the

sum of consumer and producer surpluses – is not maximized under this market

structure. The reason for this is that consumer surplus is maximized when the price paid

for the product is equal to the marginal cost of this product. However, as monopolists



have market power, the charged price will be equal to marginal cost plus a mark-up. In

that way, prices under monopoly are higher than under perfect competition and

produced quantities are lower in the former case.

Monopolies also create productive inefficiency. Under competition, firms have

incentives to produce at the lowest cost possible so that they can sell their products at a

lower price and therefore gain market share. A monopolist has no such incentives since

he is the only firm producing in the market.

According to Sheshinski (1976), monopolist’ maximizing decisions always

deviate from the social optimum due to the divergence between consumer average and

consumer marginal values.

Standard theory shows that monopolists will produce the social optimum output

only if they are able to perfectly price-discriminate, in other words, when they sell each

unit of production to the consumer at its demand price. If perfect price-discrimination is

not feasible (which is the most common case), a smaller amount of output will be

produced since monopolists take into account the reduction in price due to an increase

in output on all the units sold.

Similar reasoning applies to decisions regarding quality level: due to the fact that

monopolists are not able to extract the value of quality increments of each consumer, he

assesses all consumers equally by the consumer marginal valuation of quality

increments.

Spence (1975) in his study about firm’s quality choice under monopoly states

that the monopolist makes his decisions based on the marginal consumer, whilst, when

pursuing social welfare, the average consumer is the one that matters. As the marginal

consumer is unlike to represent the average consumer, the monopolist’s choice of

quality level is unlikely to coincide with the social optimum level.

Following Spence’s model, assume that a monopolist provider produces x units

of a product, with a price p and a quality level q. Inverse demand is P(x,q) and costs of

producing x units of quality q is c(x,q). Consumer surplus, revenues, profits, and total

surplus are denoted as respectively S, R, Π and W, and defined as follows:

)(.),(0

xqPxduquPSx

−= ∫



),(. qxPxR =

),( qxcR −=π

π+= SW

When quality is a decision variable, social welfare is maximized if:

∫ =−=x

qq cduPdqdW

0

0

But firm’s profits are maximized if:

0. =−= qq cPxdqdπ

Therefore, the firm quality choice will coincide with the social optimum only if:

q

x

q PxduP .0

=∫ q

x

q PduPx

=∫0

1

The above equation shows that monopolists will provide the social optimum

level of quality only if the average and marginal valuations of quality are equal. A

monopolist firm decision is based on profit maximization, therefore this firm responds

to the marginal consumer. Since it is unlikely that the marginal consumer represents the

average, the firm’s quality choice will not be the optimal one.

Spence also tries to answer how monopolists deviates from the social optimal

quality level; in other words, if firms will undersupply or oversupply quality. According

to this author, when average consumer valuation of quality increments exceeds marginal

valuation, then firms set the level of quality too low; conversely, when average

valuation of quality increments is lower than marginal valuation, the quality level is set

too high.

From another perspective, Swan (1970, 1971) demonstrates there is no implied

relationship between monopoly power and quality. A simple illustration of his



conclusions, described by Shy (1996), will be exposed here as they apply, with some

modifications, to the case of electricity distribution companies under an incentive

scheme for quality of supply:

Considering a consumer deciding about light services for two periods and that

this consumer is willing to pay an amount of x > 0 for each period of service. On the

supply side, it will be assumed a firm that provides light bulbs can produce a short- -

durability (low quality) light bulb, which have duration of one period, or a long-

durability (high quality) light bulb, which have duration of two periods. The cost of

producing the low quality bulb is Cl and the cost of producing the high quality bulb is

Ch, with 0 < Cl < x, 0 < Ch < 2x and Cl < Ch.

Firstly it will be assumed that the monopolist firm sells low quality light bulbs.

Since the consumer is willing to pay x/period of light services, this firm will charge Pl =

x per period and will sell two units (one per period). Therefore, his profits will be:

Πl = 2(x – Cl)

Secondly it will be assumed that the monopoly firm sells high quality light

bulbs. Since high quality bulbs lasts for two periods, then the monopolist will charge Ph

= 2x. The monopolist’s profit in this case will be:

Πh = 2x – Ch

Therefore, in order to maximize profits, the firm will produce low quality light

bulbs if 2Cl < Ch and will produce high quality light bulbs if 2Cl > Ch.

It is possible to apply Swan’s example to quality provided by distribution

companies considering that:

▪ Increasing quality of electricity supply implies increasing distribution costs: Ch

> Cl;

▪ Consumers are willing to pay more for higher levels of quality: xh > xl;

therefore, incentives are provided to companies that supply a higher level of quality;

▪ Quality decision by companies will depend on the difference between profits if

they choose to supply low quality level (xl - Cl) and profits if they choose to supply high

quality level (xh – Ch).



I.3 – Quality Regulation

Regulation is used as a remedy for the natural monopoly problem. Regulatory

mechanisms are created with the aim of moving the monopoly outcome closer to social

welfare optimum. In this sense, two regulatory objectives will be pursued: to maximize

economic efficiency and to achieve optimal quality levels.

I.3.1 – Optimum Quality Level

Regulators face the pressure of different interest groups, who have different

needs, and therefore value quality in a different way. In this sense, they have been trying

to develop regulatory frameworks that meet, at certain point, these distinct interests

(Tahvanainen et Al., 2004). The main groups of interest are:

▪ Customers: from this point of view, the regulatory framework should ensure

that consumers are not overcharged by the monopolist and sufficient quality levels are

delivered. According to these principles, reasonable prices and appropriate quality

levels should be explicitly determined.

▪ Companies: from this point of view, the regulatory framework should ensure

the companies’ financial viability. Besides, incentives should be provided for optimal

capacity expansion, capacity utilizations and compliance with quality levels.

▪ Society: from this point of view, costs of regulation should not be greater than

costs savings.

In this context, some of the regulators tasks are:

▪ To get reliable information from distributors concerning the quality of service

they are providing to their consumers;

▪ To make companies provide an efficient level of quality of service to the whole

system;

▪ To protect “worst served” consumers;

▪ To pay distribution companies the costs of delivering the quality level required.



In order to maximize social welfare, regulators should provide incentives to

distribution companies to achieve the optimal quality level. Increasing quality of supply

implies costs to distribution companies – for instance, they have to invest in lines,

increase expenses with operation and maintenance, increase the number of protective

devices, number of technicians in the grid, etc. On the other hand, interruptions impose

costs to customers – for example, industries have to stop producing, domestic

consumers may lose works on computer or have electrical appliances damaged, etc.

Following a pure economic point of view, the optimal quality level should at the

point where marginal costs and marginal benefits of changes in quality are equal. For

levels of quality below the optimal level, an extra unit of quality generates more

benefits to customers than it costs; for levels above the optimal one, the costs of an extra

unity of quality is greater than the benefits it provides to customers. From another

perspective, optimal quality level should be the one at which total costs incurred by

consumers and companies are minimized.

Graph 1 – Optimum Quality Level

In order to determine the optimal quality level, regulators must know

consumers’ willingness to pay for each unity of quality and each individual company’s

costs to provide different levels of quality.

Ajodhia (2006) points out some indirect methods and direct methods (surveys)

to evaluate interruption costs incurred by customers. Some of these methods are:

Source: Rivier & Gómez (2000)



▪ Consumer surplus method (indirect): this method derives interruption costs

information from electricity demand curves. The idea is that the willingness-to-pay for

electricity depends on the degree to which the consumption of each unit can be deferred

to another hour. When elasticity is low, the consumer surplus losses – which are

equivalent to the households’ willingness-to-pay to avoid a total interruption in that

hour – are larger. The consumer surplus losses minus the bill savings provide a measure

of the interruption costs.

▪ Cost of backup power (indirect): some consumers, in order to avoid

interruption costs, install backup power. The expenses with backup power maybe a

proxy for the interruption costs suffered by a customer.

▪ Direct costs (ex-ante surveys): in this kind of method, consumers are directly

asked about interruption costs. Firstly, consumers are requested to identify the different

costs categories in case of an interruption. In case of industrial and commercial

consumers these may be lost sales or production, damage, etc. Secondly, an economic

value is attached to each cost category. Optionally, a list of possible measures and

associated costs can be provided and consumers are asked to indicate which measure

they would employ for different interruption scenarios.

▪ Econometric (ex-ante surveys): there are two main econometric methods. One

is the contingency method, in which customers are requested to value reliability as if

there was a market for it. So, a hypothetical market is created where consumers must

indicate their willingness to pay (WTP) for higher levels of reliability and their

willingness to accept (WTA) lower level of reliability.

I.3.2 – Traditional Regulation versus Incentive Regulation

Traditionally, regulators have tended to use rate of return regulation to set prices

in infrastructure industries. Under this type of regulation, prices are set in such a way

that firms are able to recover their estimated costs, including a return on investments.

Averch & Johnson (1962) developed a model to demonstrate that rate of return

regulation creates incentives to firms overinvest in capital. The main conclusion from

their analysis is that when the rate of return is set higher than capital costs, the company

will employ too much capital. If the rate of return is set equal to costs of capital, then



the firm will be indifferent among many levels of output and input combinations,

including the option of closing down. Finally, if the rate of return is set lower than

capital costs, the firm will stop operating (Ajodhia, 2006).

Therefore, regulators will always need to set the rate of return higher than the

capital costs. As a result, the firm will overinvest. Since the firm overspends on capital

and quality in electricity distribution is closely related to investments made, the

consequence is that quality level under this regulatory approach will be probably high.

On the other hand, this type of regulation leads to cost inefficiencies and for this

reason it has been replaced by a new regulatory regime, the performance-based

regulation, such as revenue and price caps.

According to Sappington & Bernstein (1999), a primary appeal for this kind of

regulation is that it provides stronger incentives for cost reduction and technological

innovation than rate of return regulation does. Price cap or revenue cap regulation

typically specifies the rate at which prices that a regulated company charges for its

services or revenues that this company is allowed to receive must decline, on average,

after adjusting for inflation. This rate at which prices/revenues must decline is

commonly known as the “X factor”.

The purpose of incentive regulation is, according to these authors, “to replicate

the discipline that market forces would impose on the regulated firm if they were

present” (Sappington & Bernstein, 1999, p. 5). It means that, primarily, it should limit

the profits’ growth of a firm. If prices/revenues received by firms are capped,

companies will have to improve productivity so as to keep their costs down.

In order to provide incentives for productivity gains, the regulator should allow

firms’ prices/revenues to vary according expected, and not actual, changes in the firms’

productivity and input prices. In such a way, firms will financially benefit if

productivity growth exceeds expectations and will be financially penalized if

productivity is lower than expectations. Consequently, firms will have strong incentives

to assure productivity gains since a profit opportunity is created if they are able to

reduce costs. Such incentives are not present under rate of return regulation since this

regulatory approach links prices firms are allowed to charge to incurred costs.

Although incentive regulation has been widely assumed as an effective means to

induce regulated firms to reduce their operation costs and operate efficiently, the effects



of mechanisms like price cap and revenue cap over quality may be negative since the

firm may reduce its quality of service with the intention of achieving cost savings (Ai et

al., 2004).

Ter-Martirosyan (2003) studies the impact of incentive regulation on two quality

dimensions – average duration and frequency of electric outages – in the American

electricity industry between 1993 and 1999. This study is based on a panel data set for

78 major utilities from 23 states of the U.S. and her main conclusion is that incentive

regulation is associated with deterioration of quality of service if it is not accompanied

with strict quality benchmarks.

Therefore, many countries which have implemented this regulatory regime to

remunerate electricity distribution utilities imposed explicit quality standards, subjecting

firms to financial penalties if standards are not met as a means in order to avoid quality

deterioration. Besides that, most European countries adopted reward/penalty schemes to

motivate distribution companies to improve quality beyond reference levels.

I.3.2.1 – Types of Quality Control

Regarding quality control, Rivier (1999) points out that both individual and

system indicators should be submitted to quality control since these two types of

indicators plays different rolls in quality regulation.

In order to better understand this difference is important to keep in mind that

individual quality level is a random variable which follows a probabilistic distribution.

System indicators are the mean of this distribution and individual indicators are the

variance of this distribution.

In this case, controlling system indicators is the same as controlling the mean of

this probabilistic distribution. The control of system indicators implies that distribution

companies are submitted to quality regulation by the average quality they provide.

However, if only the mean of the probabilistic distribution is controlled, the variance of

this distribution may be high. In other words, if only system quality indicators are

controlled, some customers may receive a quality service below a minimum desirable

level. On the other hand, if only individual indicators are taken into account, the average

quality may be below its optimal social level.



Regulating both system and individual indicators will ensure the control of the

mean and the variance of this probabilistic distribution. That is why these indicators are

rather complementary than substitutes. This can be better observed in the Figure 1

below.

Figure 1 – Probabilistic Distribution of Individual Indicators Depending on Individual, System or Both Indicators Control

Regarding types of quality control, different approaches are used. These

approaches can be applied separately or jointly. One widely applied type of quality

control is performance publication. Under this type of quality control regulators require

the regulated firms to make public information about their quality performance. This

can be done through the companies’ annual reports and websites or through dedicated

regulatory publications. Moreover, regulators can oblige companies to take into account

consumer views and concerns regarding quality of service. It can be noticed that the

incentive here to control quality is rather reputational than financial.

Source: Rivier (1999)



Another common approach is the establishment of minimum quality standards

(MQS). In this type of quality control, regulators require the firms to comply with

certain quality limits. If companies do not comply, they can be financially penalized.

There are, though, cases in which these standards are only indicative, therefore they do

not lead to any penalty.

Reward and penalty schemes can be considered as a step forward MQS. Here,

there is a direct relation between companies’ revenues and their quality performances.

Companies’ performance levels imply a financial incentive, which will depend on the

difference between the actual quality performance and the minimum required level

established by the regulator. If the former is greater than the latter, firms will have their

revenues increased, and if the latter is greater than the former, firms will have their

revenues reduced.

I.3.2.1.1 – Reward/Penalty Schemes

According to Fumagalli et al. (2007), reward and penalty schemes try to answer

to the objective of ensuring desirable quality levels to customers. This regulatory

instrument is often used to counterbalance the potential negative effect of performance-

based regulation on quality of supply.

The decision of which quality indicators are subject to rewards/penalties are

crucial since offering incentives to improve some indicators and not to others may

induce firms to neglect indicators that are not subject these incentives (Williamson,

2001). In many countries, regulation is applied on frequency and/or duration indexes of

continuity of supply. Alternatively, some countries have chosen the ENS (Energy-Not-

Supplied) indicator.

Another important issue that regulators must deal with when applying

reward/penalty schemes is the establishment of the minimum reference quality level.

According to CEER (2005), factors that regulators should take into account when

defining quality standards can be divided into three groups: inherent factors, such as

weather conditions, geography and population density of an area; inherited factors, the

network design at the time the regulatory instrument takes place; and incurred factors,

such as managerial performance, maintenance of assets and effective use of resources.



Two ways of setting the baseline while taking into account these factors are

either to consider the company’s performance over time or to use a mathematical or

engineering model.

Regarding the incentive level, the reward/penalty should be set at point where

the marginal cost of quality equals to marginal consumers’ willingness to pay,

according to Graph 2 below.

Graph 2 – Reward/Penalty Scheme

Source: Williamson (2001)

Firms take their decisions with a profit maximizing objective. When quality is a

decision variable, distribution companies’ optimum choice will depend on costs to

supply quality and incentives received for producing such an amount of quality.

Regardless the amount of incentives and the level of costs, the firm will keep increasing

quality as long as the marginal cost of a quality increment is lower than the incentive

this company gets for that marginal increment. Until the point where the marginal cost

of a quality increment is equal to the marginal reward the firm receives for that

increment, the distribution company is increasing its revenues and, therefore, its profits.

In the absence of proper incentives, it is not likely that regulated firms will provide

the optimal quality level. Either the incentives to improve quality will be too low and there

will be under-performance or the incentives will be set too high which will lead the

company to provide quality levels above the optimal one (Jamasb & Pollitt, 2007).



II – METHODOLOGY

As previously mentioned, this thesis aims to estimate the impact of quality of

service improvements on distribution network costs so that incentives for quality of

electricity supply can be calibrated. However, real costs of quality increments in real

networks are unknown2.

For this reason, a network reference model (hereinafter NRM) will be used in

order to estimate distribution costs resultant from different quality requirements. These

costs will be used as proxies for real distribution network costs. In this sense, the

objective here is not to compare the model’s results with real distribution costs but

rather to analyze these results under a ceteris paribus assumption.

Network reference models characterize distribution areas of service and design

distribution networks that connects final customers of electricity, which are represented

by their geographic locations, load peaks load and voltage levels. The NRM used in this

work is the one currently used by the Spanish energy regulator (Comisión Nacional de

Energía – CNE), which is based on the model developed by Jesús Peco in his PhD

thesis “Modelo de Cobertura Geográfica de una Red de Distribución de Energía

Eléctrica” (2001) with certain adjustments.

The methodology to determine the reference network in the model used in this

thesis considers criteria of optimal and feasible network planning, taking into account

not only technical and quality constraints but also geographic constraints, investment,

network losses and also operation and maintenance costs. Therefore, it can be used as an

accurate benchmark of real networks (Peco, 2004).

NRM results will be applied in a cost function that relates distribution network

costs and quality of service in such a way the sensibility of distribution network costs in

relation to quality improvements can be estimated.

This methodology will be explained in Sections II.1 and II.2 below.

2 In this sense, it is important to keep in mind that even distribution operators, when taking decisions regarding investments in quality of service, are not able to exactly predict the costs they will incur due to a quality improvement. Therefore, their decisions are based on estimated costs.



II.1 – Network Reference Model

The NRM is used to design an optimal and feasible network minimizing

investment, operational and maintenance expenditures and losses, subject to quality

constraints, taking into account the localization of the customers served by the

distribution company. Two types of NRM can be described:

(1) From scratch: a whole distribution network that connects customers of

electricity to the transmission substations is designed without taking into account the

actual network, but considering the same technical constraints and planning principles.

(2) Expansion planning: a distribution network expansion is designed in order to

supply both a horizontal and vertical demand increase optimally, given the actual

network as well as considering the same technical constraints and planning principles.

Figure 2 – Scope of Network Reference Models

Source: Peco (2008)

These models are very flexible tools that can be used by regulators for achieving

different goals. For instance, they reduce information asymmetry between the regulator

and the DISCO. These models may also be used to determine the curve that relates

quality of service and distribution investment costs. In this sense, the regulator is able to

Network Structure Type and number of facilities

Input data: HV, MV and LV customers, transmission substations Results of the model: LV, MV and HV network, HV/LV and MV/LV substations



determine the level of quality attainable with a specific network or the network needed

to provide a specific level of quality.

In an attempt to analyze the impact of quality of supply increments on

distribution network costs, a NRM from scratch3 will be used to establish distribution

costs resultant from different quality requirements. This will be done for three different

areas of service in Spain so that differences in the magnitude of the impact due to zonal

characteristics can be estimated.

Firstly, the model is run, for each area of service, taking into account as quality

constraints the minimum required levels for continuity of supply established by the

Spanish law (SEE Chapter III, Tables 4 and 5). This is the base case. Other 32 cases

will be run for each area of service varying the indicators of frequency and duration of

interruptions by a specific K factor for each execution of the model. The results of this

analysis will be presented in Chapter IV.

The input data used in the NRM are number and localization of customers and

transmission substations; contracted power and billed energy; standardized equipment –

substations, transformers, lines, cables, capacitors, maintenances crews, protective

devices, etc; technical parameters – low voltage, medium voltage and high voltage

network parameters (for instance, minimum and maximum voltage, loss factor, load

factor, simultaneity coefficients); economic parameters – rate of return, demand growth

rate, etc; geographic data and constraints (orography, forbidden paths, street maps, etc).

In Spain, the energy regulator established by the Circular 1/2006 (updated by

Circular 1/2007, Circular 2/2008 and Circular 3/2009) minimum information

requirements from distribution companies. In this document, CNE defines the

procedures for data collection and the homogeneity codes which distribution companies

must respect when submitting the information to the regulator.

These information requirements regard not only the characterization of

distribution companies’ markets and employed infrastructure to supply their markets but

also their forecasted demand and financial and accounting information. Figure 3 below

shows an example of how information must be submitted by companies to the regulator.

3 The network reference model used in this study is the one currently used by the Spanish Energy Regulator.



Figure 3 – Customers’ Characteristics

Source: Circular 1/2006, CNE.

Figure 3 above presents some of the information required by CNE about

customers’ characterization: node; coordinates X, Y, Z; type of customer (low, medium

or high voltage); tariff code; energy delivering point code; distribution company code;

city; region; type of connection (overhead or underground); voltage level; power

contracted and energy consumed. The information regarding demand and network

infrastructure’ characterization will be used as input data to the NRM.

Numerical results from the NRM – such as summary of customers’ data,

kilometres of network (low, medium and high voltage), number of transformer centres,

number of substations, levels of quality indicators attainable with the designed network,

etc – are resumed in an html file. Table 1 below presents the summary of customers’

data taken from this html file for a random pair “firm, province” in Spain4.

Table 1 – Summary of Customers’ Data

Number of

clients

Number of energy

delivering points

Power contracted

(MW)

Peak demand

(MW)

Consumed energy year 0 (MWh)

Average power factor

LV 3.258.567 1.010.212 15.106 4.497 11.818.482 0,96 MV 5.341 7.858 3.042 2.434 6.396.443 0,96 HV 42 35 144 144 379.335 0,96

TOTAL 3.263.950 1.018.105 18.293 7.075 18.594.259 0,96 Source: html file of the NRM from scratch.

The NRM from scratch, as mentioned previously, designs the whole distribution

network that connects consumers to transmission. In this case, the model gives the total

cost of the network, which includes the cost of fixed asset, the annual costs of

preventive and corrective maintenance and the annual costs of losses. Table 2 below

4 For reasons of confidentiality, the real values were multiplied by an “x” factor.



presents the summary of distribution network costs’ results taken from the html file of

one execution of the NRM from scratch for a random pair “firm, province” in Spain5.

Table 2 – Summary of Distribution Network Costs’ Results

Fixed asset

Preventive Maintenance

(annual)

Corrective Maintenance

(annual) Losses year 0LV Network 635.055.161 205.922 4.702.919 10.389.867

Transformer centers 399.028.342 20.591.950 857.304 6.698.575MV Network 542.482.944 9.953.115 12.083.759 5.487.799

AT/MT Substations 623.698.915 10.090.099 5.045 6.862.436HV Network 377.941.903 4.327.431 1.355.115 1.438.754

Total 2.578.207.265 45.168.517 19.004.142 30.877.432 Source: html file of the NRM from scratch.

It is important to clarify that in order to take into account these results in the

econometric model, firstly, fixed asset costs must be annualized. Total annual

distribution network costs will be the sum of the capital costs (fixed asset annualized)

plus the preventive and corrective maintenance costs (SEE Appendix A).

Besides costs of the network, the model also gives the levels of continuity of

supply resultant from the network designed. These quality results will depend on the

constraints set for continuity of supply before the model is run. In this sense, it

important to point out that the constraints set for quality of supply before the model is

run are not equal to the results for quality indicators after the model is run. The NRM,

when designing the network, takes into account these quality constraints and the

optimization will depend on costs of non-supplied energy (which must be established

before the model is run) and on costs to improve continuity of supply (as quality

constraints increases, the network elements and operation and maintenance expenses

also increases).

Table 3 below presents the model results for continuity of supply indicators for a

random pair “firm, province” in Spain6.

5 For reasons of confidentiality, the real values were multiplied by an “x” factor. 6 For reasons of confidentiality, the real values were multiplied by an “x” factor.



Table 3 – Summary of Quality Indicators Zone TIEPI NIEPIUrban 1,26 0,828

Semi-Urban 3,12 1,62Rural Concentrated 4,824 3,204

Rural Dispersed 6,012 3,036 Source: html file of the NRM from scratch.

The data resulting from the network reference model executions regarding

quality indicators and distribution costs will be applied in the econometric model

presented in the following section.

II.2 – Econometric Model

As previously mentioned, the econometric model used here aims to estimate the

sensibility of distribution network costs in relation to quality of service. This will be

done by the estimation of a cost function that relates distribution costs to its outputs.

Quality of supply will be considered in this analysis as service that distribution

companies provide to their clients; therefore, in the cost function, it will be considered

an output.

This study will be based on a cross-sectional data set. The main data source is

the network reference model results as it was presented in Section II.1 (SEE Tables 2

and 3). Finally, Stata is the econometrics package used in order to estimate the

parameters of the cost function. This cost function will be presented below and results

will be analyzed in Chapter IV.

II.2.1 – Including Quality in the Electricity Distribution Cost Function

The estimation of cost functions of electricity distribution is well documented in

empirical research. Some references are Neuberg (1977), Burns & Weyman-Jones

(1996) and Filippini & Wild (1998). However, until now, no attempts were made to

specify the costs of quality of supply in these cost functions. Even though these authors

do not directly relate quality to cost functions, their works will serve as a basis for this

study.



According to Burns & Weyman (1996), the actual distribution system costs are

the costs of building and maintaining mains, lines and transformers. According to these

authors these costs of distribution may depend on:

• Maximum demand on the system;

• The total KWh sold;

• Number of customers;

• Area of service;

• The dispersion of customers across the distribution region;

• Type of consumer;

• Length of distribution line;

• Transformer capacity.

Maximum demand and total KWh sold are output characteristics of the

distribution function. As these two variables are correlated one of them must be chosen

to represent the main output of the cost function so as to avoid multicollinearity

problems. Lengths of distribution line and transformer capacity are rather inputs than

outputs; therefore they should not be included in the cost function.

Filippini & Wild (1998) estimate an average cost function splitting the electricity

sales function of utilities from the network operation function. In the model for the

network operation function, which is the one of interest for this work, maximum

demand, measured in KW, is the main output variable. They consider in the cost

function as output characteristics as well number of customers and area of service of the

distribution utility in square kilometres.

II.2.1.1 –Specification of the cost function

This section presents a general cost function in terms of output as a

simplification of the model described by Filippini &Wild (1998). A more specific

model will then be derived in order to estimate the impact of quality of supply on

distribution costs. The latter model is a cost function which takes into account quality as

an as an explanatory variable for distribution network costs.



Equation 6: Distribution Network Business Total Cost function

(a)σφδα

jijijijiji ODCSAxY ,,,., =

where:

Yi,j = total annual distribution network costs of company j in province i. It is

calculated as the annual investment costs plus annual operation and maintenance

expenses;

Xi,j = maximum demand on the system in province i and firm j in MW;

Si,j = area of service in squared kilometres province i and company j;

Ci,j = number of customers in province i and firm j;

ODi,j = proxy for the intensiveness use of the market served. It is calculated as

the power contracted divided by the number of customers in province i and company j;

This is a general cost function describing output characteristics of the

distribution network business. Even though quality in this study is considered a service

that distribution companies provide to their customers and, therefore, it is an output

characteristic, it will be not included in Equation 6.

In order to estimate the impact of quality in different zones, one regression must

be made for each area of service. For one single area, the data regarding number of

customers, contracted power, peak demand and area of service do not change in the

different executions. Therefore, the cost function that includes quality as a regressor will

not include the explanatory variables presented in Equation 6.

For this reason, a more specific cost function will derived, considering quality as

an explanatory variable for distribution costs. Quality will be measured here as an

indicator of continuity of supply. The cost function is presented below.

Equation 7: Distribution Network Business Cost function Considering Quality as an Explanatory Variable

(a) β

jiji AQY ,, =

Where:

Yi,j = total annual distribution network costs of firm j in province i



Qi,j = quality indicator in the region i and firm j;

Equation (a) is written at nominal levels. For estimating purposes, it will be

derived in logarithmic terms, according to the following:

(b) jiji QconsY ,, lnln β+=

Where:

lnYi,j = log(Yi,j)

lnQi,j = log(Qi,j)

The explanatory variable Q will assume values of a continuity of supply

indicator (explained in Chapter I). Therefore, it is expected an inverse relation between

Y and Q (or lnY and lnQ), since, as the indicator decreases quality increases and

distribution costs are higher. In this sense, β should be negative.

As mentioned before, this methodology will be applied to three Spanish areas of

service and its results will be presented in Chapter IV.



III – SPANISH ELECTRICITY DISTRIBUTION REGULATION

The methodology explained in Chapter II will be applied in this work to the

Spanish case. Therefore, the objective of this Chapter is to present the Spanish

regulation of distribution activity remuneration’s evolution, especially on what concerns

incentives to improve quality of electricity supply.

III.1 – Characterization of the Spanish Electricity Distribution Activity

Electricity distribution networks connect transmission to final customers. In

other words, distribution operators transport electricity from the transmission or sub-

transmission networks to the consumption points with certain quality conditions. They

are responsible for the network operation and maintenance – voltage control, network

reconfiguration (in case of failures, outages), predictive, preventive and corrective

maintenance; network planning; and for providing clients with commercial and

technical services.

Distribution networks are integrated by electrical lines and all installations

below 220kV. The main numbers of the Spanish distribution network are:

▪ Substations HV/MV (MVA): 90.840

▪ Transformers MV/LV (MVA): 49.866

▪ Lines (km): LV = 281.678; MV = 219.617; HV = 60.396 Source: CNE

Figure 4 – Main Distribution Companies in Spain

Figure 5 – Small Distribution Companies in Spain

-

Hidrocantábrico

GrupoEndesa

GrupoEndesa

GrupoEndesa

GrupoEndesa

IberdrolaIberdrola

Iberdrola

Iberdrola

Iberdrola

Iberdrola

Iberdrola

Iberdrola

Iberdrola

Unión Fenosa

Unión Fenosa

Unión Fenosa

Unión Fenosa

Viesgo

Viesgo

ViesgoViesgo

GrupoEndesa

5 biggest distribution companies: Iberdrola Endesa Unión Fenosa Hidrocantábrico Viesgo

Source: CNE



The remuneration of this activity is set by Administration in order to avoid any

abuse of dominant position that may result from the existence of a single network

(regional monopolies).

III.2 – Regulation of the Distribution Activity

The process of reforms of the sectors that were traditionally regulated and

vertically integrated began in the decade of 1980 in Spain and culminated in the

electricity industry in 1987 with the approval of a regulatory framework called “Marco

Legal Estable” (MLE). MLE defined the electricity sector’s regulation in the decade of

1988-1997, particularly on what concerns aspects that influenced electric companies’

remuneration.

A key element of MLE was the concept of standard costs, which meant the

determination of target costs by the Administration that could eliminate superfluous

expenses and which was the basis for the companies’ remuneration (Gómez & Grifell-

Tatjé, 2004).

The objective of this kind of regulation is to pay the distribution company its

cost of service; it means that companies would be paid the efficient costs of providing

the service plus a rate of return on capital. The strong point of this approach is that it is

very efficient when the problem faced by the system is a lack of investments. The

Around 320 small distributors = 2,5% of energy

Source: CNE



drawback is that as companies tend to overinvest, it is difficult to increase productivity

and reduce costs under this approach.

Therefore, in many countries this kind of regulatory approach was substituted by

an approach that provided incentives to efficiency improvements – the incentive-based

regulation.

In Spain it was not different. The regulatory framework established by MLE was

substituted by an incentive regulation, introduced by Law 54/1997 of November 27th of

the Electricity Sector – a new technical and economic regulation system based on a

market of electricity generation and liberalization of electricity supply was crated. This

law established the separation between regulated activities – transmission and

distribution – from non regulated activities – generation and retail.

Regarding distribution companies’ remuneration, Law 54/1997 established that

it should be based on objective, non-discriminatory and transparent criteria and it should

take into account market characteristics and geographic differences of the served zones.

In 1998, Royal Decree 2819 of December 23rd established the economic regime

for distribution activity. It set a revenue cap formula for the all the five biggest

distribution companies (Endesa, Iberdrola, Unión Fenosa, Hidrocantábrico and Viesgo).

This formula was given by:

Equation 8: Revenue Cap Formula for the 5 Main Distribution Companies in Spain According to the RD 2819/1998

Rin = Rin-1 (1+ (IPC-1)/100) * (1+ (∆D * Fe))

where:

Rin-1: distribution costs recognized in the previous year;

IPC: consumer price index;

∆D: demand variation;

Fe: efficiency factor.

Small companies’ revenues were based on tariffs’ differences. These companies

had a special tariff for buying energy and they sold their energy at a price higher than

what they had previously paid for it.



It is important to point out that RD 2819/1998 had some important flaws. First,

the annual increments of distribution companies revenues were made trough a global

remuneration that was shared between these companies by applying sharing

coefficients. Therefore, the specificities of each company – as geographic zone, demand

variations – were not taken into account. Consequently, this regime did not compensate

the investments made by the companies. Moreover, it did not provide incentives to

quality improvement or losses reduction.

In this context, RD 222/2008 replaced RD 2819/1998 and established a new

remuneration model. Through this normative it was established a revenue cap for each

distribution company. The goal of the remuneration criteria in this Royal Decree was to

provide incentives for the improvement of management, technical and economical

efficiency; the improvement of quality of supply; and the reduction of losses in the

distribution network.

The base remuneration is updated taking into account actual investment and

OPEX, but using as a benchmark a network reference model and increment of revenues

is a function of the annual market increments. Besides, incentives for quality

improvements and losses reduction are provided.

The base remuneration for each company will be calculated every four years

(regulatory period) according to the following formula:

Equation 9: Base Remuneration for Distribution Companies in Spain According to RD 222/2008

Ribase = CIi

base + COMibase + OCDi

base,

where:

Ribase is the reference remuneration for the distribution company i;

CIibase is the remuneration of capital costs calculated as the annual depreciation

of assets and the rate of return on investment (WACC);

COMibase is remuneration of operation and maintenance costs. They are

calculated applying standard efficient costs to existing installations;



OCDibase is the remuneration of other distribution costs: customer services

(metering, billing, etc), network planning and energy management.

The annual remuneration for each company during the four years of the

regulatory period will be calculated as follows:

Equation 10: Annual Remuneration for Distribution Companies in Spain According to RD 222/2008

Ri0 = Ri

base * (1 + IA0)

Ri1 = Ri

0 * (1 + IA1) + Yi0 + Qi

0 + Pi0

Ri2 = (Ri1 - Qi

0 - Pi0) * (1 + IA2) + Yi

1 + Qi1 + Pi

1

Ri3 = (Ri2 - Qi

1 - Pi1) * (1 + IA3) + Yi

2 + Qi2 + Pi

2

Ri4 = (Ri3 - Qi

2 - Pi2) * (1 + IA4) + Yi

3 + Qi3 + Pi

3

where:

i is the distribution company i;

n is the year n;

Rin is the remuneration in the year n of the regulatory period;

Y is the revenue increment due to a demand increase;

Q refers to rewards/penalties due to continuity of supply results;

P refers to rewards/penalties due to losses reduction/increments;

IAn is the remuneration updating (price index less the efficiency factor) and it is

calculated according to the following formula:

Equation 11: Formula for Remuneration Updating According to RD 222/2008

IAN = 0,2 * (IPCn-1 – x) + 0,8 * (IPRIn-1 – y)

where:

IPC is the consumer price index;

IPRI is the industrial prices index;

x and y are the efficiency factors7.

7 For the regulatory period of 2009-2012, these factors will assume the values of x = 80 and y = 40 points



III.2.1 – Incentives to Improve Quality of Supply

As costs of distribution varies according to regional differences – for instance,

supplying a KWh in an rural area does not cost the same as supplying a KWh in an

urban area – different levels of quality of supply will be required for different

geographic zones. According to the Spanish Regulation, Order ECO/797/2002 of 22 of

March establishes that for quality of supply zones will be classified as follows:

Urban (U): group of municipalities of a province with more than 20.000

clients, including the capitals of provinces;

Semi-urban (SU): group of municipalities of a province with more than 2.000

and less than 20.000 clients;

Rural concentrated (RC): group of municipalities of a province with more

than 200 and less than 2.000 clients;

Rural dispersed (RD): group of municipalities of a province with less than 200

clients and supply points located outside industrial or residential sites.

Royal Decree 1955/2000 of December 1st established the limit levels for

individual and zone quality indicators that were modified by RD 1634/2006 of

December 29th. The current reference values for continuity of supply are indicated in

Tables 4 and 5 below:

Table 4 – Individual Limits for Continuity of Supply

Source: RD 1634/2006 Table 5 – Zone Limits for Continuity of Supply

Source: RD 1634/2006



Distribution companies are responsible for keeping these indicators within the

above mentioned limits. In case of non-compliance, companies must compensate their

customers paying until five times the energy price related to the corresponding

deviation from the established limits.

The incentive (or penalty) to quality improvement defined by RD 222/2008 is a

percentage of the total distributor’s remuneration varying from 3% to -3% and it is

calculated according to the following formula:

Equation 12: Incentive Scheme for Quality of Supply According to RD 222/2008

Qin-1 = 0,03 * Ri

n-1 (βiU * Xi

U,n-1 + βiSU * Xi

SU,n-1 + βiRC * Xi

RC,n-1 + βiRD * Xi

RD,n-1),

Where:

Qin-1 is the incentive or penalty to given to the distribution company i in the year

n due to the deviation in the year n-1 from the quality indicators limits established;

XiU,n-1 is the quality compliance indicator in the urban zone covered by company

i in the year n-1 and it is calculated as follows:

⎥⎥⎦

⎤

⎢⎢⎣

⎡−+

⎥⎥⎦

⎤

⎢⎢⎣

⎡−=

−−

−−

−−

−−−

1,

1,

1,

1,1, 11

nREFERENCEU

nOBSERVEDU

NREFERENCEU

nOBSERVEDUinU NIEPI

NIEPITIEPITIEPI

X

βiU is the weight factor for the urban zone used for the calculation of the quality

incentive given to company i.

The values of TIEPI and NIEPI observed correspond to the ones actually

observed for the company i during the year n-1. The values of TIEPI and NIEPI

reference are the limits for continuity of supply for each zone established by law.

III.2.1.1 – New Incentive Scheme to Improve Quality of Supply

In December 26th, Order ITC/3801/2008 was published, modifying the

incentives received by distribution companies for quality improvement defined by the



Royal Decree 222/2008. According to this new normative, for each company, quality

incentives will be calculated as follows:

Equation 13 : New Incentive Scheme for Quality of Supply

Qin-1 = QTIEPIi

n-1 + QNIEPIin-1

Where:

QTIEPIin-1 = PTIEPI x Σtz [Poti

tz,n-1 x (TIEPIitz-TARGET,n-1 - TIEPIi

tz-OBSERVED,n-1)]

QNIEPIin-1 = PNIEPI x Σtz [Clii

tz,n-1 x (NIEPIitz-TARGET,n-1 - NIEPIi

tz-OBSERVED,n-1)]

And:

QTIEPIin-1: reward or penalty given to the distribution company i in the year n,

associated to the compliance with the TIEPI reference levels;

QNIEPIin-1: reward or penalty given to the distribution company i in the year n,

associated to the compliance with the NIEPI reference levels;

PTIEPI: unitary incentive associated to the TIEPI and it will take the value of

100c€/Kwh

PNIEPI: unitary incentive associated to the NIEPI and it will take the value of

150c€/client and interruption.

Potitz,n-1: installed power of distribution company i in zone type “tz” (according

to the Order ECO 792/2002, described previously) in the year n-1;

Cliitz,n-1: number of customers of distribution company i in each zone type “tz” in

the year n-1;

TIEPIitz-TARGET,n-1 and NIEPIi

tz-TARGET,n-1: target indicators for each zone “tz” in

force in the year n-1;

TIEPIitz-OBSERVED,n-1 and NIEPIi

tz-OBSERVED,n-1: indicators of the degree of

compliance with the target values;

The reward/penalty received/paid is limited by 3% and - 3% of the distribution

company’s total remuneration. Target indicators for each company are calculated as

follows:



Equation 14: Formulas for Target TIEPI and NIEPI

[ ]2

*31

4

6,,

1,

∑−

−=−−

+=

n

nk

eragenationalavktz

iktz

inTARGETtz

TIEPITIEPITIEPI

[ ]2

*31

4

6,,

1,

∑−

−=−−

+=

n

nk

eragenationalavktz

iktz

inTARGETtz

NIEPINIEPINIEPI

According to the above formulas, the target TIEPI/NIEPI of year “n-1” for

distribution company “i” in the zone type “tz” is an average of past performance of this

company in zone type “tz” and the national average TIEPI/NIEPI for the same zone

type.

The values of TIEPIitz-OBSERVED,n-1 and NIEPIi

tz-OBSERVED,n-1 for each company in

each zone type “tz” are calculated as follows:

Equation 15: Formulas for Observed TIEPI and NIEPI

∑−

−=−− =

1

3,1, *

31 n

nk

iktz

inOBSERVEDtz TIEPITIEPI

∑−

−=−− =

1

3,1, *

31 n

nk

iktz

inOBSERVEDtz NIEPINIEPI

According to the formulas, observed TIEPI/NIEPI in the year “n-1” for zone

type “tz” is an average of the performance of company “i” in the year “n-1” and the

previous two years.

In Chapter IV, an analysis of the incentives provided by this new scheme will be

made.



IV – APPLICATION OF THE METHODOLOGY TO THE SPANISH CASE

This Chapter is divided into three sections. Section IV.1 explains how the

network reference model was applied to the Spanish case; Section IV.2 analyses the

results of the econometric model described in Chapter II for the Spanish case; finally, in

Section III, unitary incentives for continuity of supply (frequency and duration of

interruptions) guaranteed by the current Spanish legislation are analyzed under the

perspective of the approach adopted in this thesis.

IV.1 – NRM Applied to the Spanish Case

As mentioned in Chapter II, a network reference model will be used in this work

in order to estimate distribution network costs for three Spanish areas of service under

different quality requirements and for reasons of confidentiality, provinces’ names will

not be mentioned here. Instead, each region will be called by a letter – U, S and R. Each

letter represents the type of province: U is an urban province, S is a semi-urban province

and R is a rural province.

The analysis will be done for each pair “firm, province”; it means that both

models (network reference model and econometric model) will be run for the area of

service of a specific distribution company in a specific province. As the provinces’

names, for reasons of confidentiality, companies’ names will not be mentioned in this

work.

▪ Province U is mainly served by firms 1 and 2 (1, U) & (2, U);

▪ Province S is mainly served by firm 3 (3, S);

▪ Province R is mainly served by firms 4 and 5 (4, R) & (5, R).

The pairs “firm, province” that will be analyzed are (1, U), (3, S) and (4, R).

The motivation for choosing different types of province is that distribution costs

of delivering one KWh, for instance, in an urban area is lower than delivering one KWh

in a rural area. Therefore it is expected that supplying better levels of quality in an urban

area be cheaper than increasing quality in rural areas.



Area of service (1, U) has approximately 97% of its installed power concentrated

in the urban zone; area of service (3, S) has 20% of its installed power located in the

semi-urban zone; finally, area of service (4, R) has 42% of its installed power located in

the rural zone8. Representative zones types were chosen for each one of the areas of

service (1, U), (3, S) and (4, R) which are, respectively, urban zone, semi-urban zone

and rural concentrated zone.

As explained in Chapter II, there are two types of Network Reference Model:

NRM from scratch and expansion planning. In this study, the type of model used was

the NRM from scratch, which is the one currently used by the Spanish regulator. It is

important to keep in mind that the results from this model are the distribution costs of

building a whole new network; therefore these costs must be annualized before any

calculation is done (SEE Appendix A).

One of the constraints of this model is the requirements of continuity of supply,

which are the minimum values for individual and system TIEPI and NIEPI established

by the Spanish law (SEE Chapter III, Tables 4 and 5). This means the model designs the

network taking into account the minimum quality requirements for each zone type.

For the purpose of costs estimation under different quality requirements, quality

constraints will be multiplied by a different K factor in each model’s execution. K

varies between 0,20 and 1,80, each K increasing by 0,05 – which results in 33 Ks;

therefore, there are 33 different levels of quality constraints for each pair “firm,

province”.

In order to analyze distribution costs resultant from improvements on quality

levels in all zone types at the same time and costs resultant from improvements on

quality levels of only a specific zone type, K factors modify quality constraints in two

ways:

● Firstly, quality constraints of all zones types were multiplied by each K factor

for each pair “firm, province”, according to Table 6 below (case when K = 0,20).

8 28% of the total installed power of this area of service is installed in the rural concentrated zone and 14% in the rural dispersed zone, which amounts 42% of the installed power located in the rural zone.



Table 6 – Example: Quality Constraints when K = 0,20

Source: Own elaboration

● Secondly, only quality constraints related to the representative zone type of

each pair “firm, province” were multiplied by K factors. Table 7 below shows the case

of pair (1, U) where only quality constraints for the urban zone are modified. Quality

constraints for the other zones remain as the minimum reference level established by

RD 1634/2006.

Table 7 – Example Pair (1, U): Urban Constraints when K = 0,20


The NRM will be run for each pair “firm, province” taking into account both

types of quality constraints, as presented in Table 6 and 7 above.

It can be seen in Tables 6 and 7 above that when K=0,20, indicators are

reduced by 80% (for instance, the reference level of TIEPI in urban zones, which is

equal to 1,5 hour, is reduced to 0,30 hour), therefore quality constraints are more strict.

When K=0,25, quality constraints are less strict if compared to the case when K=0,20.

When K=1,00 quality constraints are equal to the minimum reference values established

by law. For Ks greater than 1,00, quality constraints are below the minimum values

established by law.



The input data used in all executions of the model for each pair “firm, province”

were the same, except for the quality constraints, which were modified according to

Tables 6 and 7.

For each value of K, NRM results changes. Marginal changes in quality

constraints may imply only a relatively small increase of costs. For instance, quality

indicators of continuity of supply can be reduced by an increase in protective devices,

better operation and maintenance practices, higher number of technicians on the grid,

etc. On the other hand, if quality requirements increase considerably, network’s design

may change in order to supply that higher level of quality.

Figure 6 below shows the results of the model which modifies all quality

constraints for K=1, K=0,60 and K=0,20 for a random pair “firm, province”.

Figure 6 – NRM Results

K=1 Transformers: 33 Substations.: 4.158



Source: CNE It can be seen in Figure 6 above that as quality constraints get stricter (lower K

factors), network elements’ requirements increases. For instance, comparing k=1 and

k=0,20, the number of substations in the latter case is more than three times higher than

K=0,60 Transformers: 43 Substations.: 4.158

K=0,20 Transformers: 109 Substations.: 4.158



in the first case. Therefore, as higher levels of quality demand more investments,

distribution costs resultant from the model are, generally, higher9.

Graph 3 – Distribution Network Costs/Consumed Energy X TIEPI

Graph 3 above shows total annual distribution network costs divided by energy

consumed for the three pairs “firm, province” and total TIEPI10. It is important to keep

in mind that as K increases continuity of supply decreases. For this reason, it is

observed an inverse relation between K and distribution costs.

9 Sometimes, from one case to the other (from one k factor to the other), the costs resultant from the model may reduce due to other effects as, for instance, economies of scale. 10 These results were obtained by the NRM where all quality constraints were modified by K factors. Total TIEPI is the average of the zonal TIEPI indicators, resultant from the NRM, weighted by the installed power of each zone type for each province.

Pair (4, R)

Pair (1, U) Pair (3, S)

Source: Own elaboration from the results of the NRM from scratch.



By looking at the graph, it can be noticed that for the same K factor, the ratio

distribution costs/energy consumed in the three provinces are different: the less urban is

the area of service the higher is the ratio correspondent to a specific K factor, meaning

that distribution costs are more sensitive to variations in quality of service in rural

regions. Besides, comparing TIEPI for the same K factor in the three areas of service

analyzed, the rural province presents higher levels of TIEPI.

Graph 4 – Distribution Network Costs/Consumed Energy X TIEPI

Graph 4 above presents the relation between continuity of supply and

distribution network costs resultant from the network reference model for each area of

service. It can be seen that, from the rural province to the urban province, the curves

become flatter (which can be confirmed by the coefficient of Ln(x)). In other words, the

sensibility of costs in relation to quality of service decreases from the rural region to the

Pair (4, R)


Source: Own elaboration from the results of the NRM from scratch.



urban area, meaning that it is more costly to improve quality in rural areas than in urban

areas.

IV.2 – Econometric Model Applied to the Spanish Case

As previously mentioned, this study is based on a cross-sectional data set. The

primary data source for number of customers, contracted power, peak demand and

number of energy delivering points is the Spanish energy regulator (CNE). Distribution

costs and quality indicators resultant from the NRM from scratch will be applied in the

cost function. Stata is the econometrics package used in order to estimate parameters of

this cost function.

The estimation of parameters in this model was done separately for each one the

three pairs “province, firm” (1, U), (3, S) and (4, R), taking into account 33 K factors –

33 observations – for each pair.

Two types of regressions were made for each pair “firm, province”: firstly,

regressions were run, for each pair, considering costs and quality indicators results from

the NRM where all quality constraints are modified by K factors. Secondly, regressions

were made considering costs and quality indicators results from the NRM where only

quality constraints related to the representative zone of each pair “firm, province” were

modified.

Tables 8, 9 and 10 below present the estimations for each pair “firm, province”

considering results regarding distribution costs and quality indicators from the NRM

when all quality constraints vary according to each K factor.

Recalling that:

(a) β

jiji AQY ,, =

Where:

Yi,j = Total distribution network costs

Qi,j = Total TIEPI/Total NIEPI

and

(b) jiji QconsY ,, lnln β+=



Table 8 – Estimation Results for Pair (1, U) Considering Q as Total TIEPI/NIEPI Q= Total TIEPI Pair (1, U) Q= Total NIEPI Pair (1, U)lyi,j lyi,j

Coef. Std.err. t stat. P> t Coef. Std.err. t stat. P> tParametersConstant 17.54822 .010204 1719.75 0.000 17.1995 .0227573 755.78 0.000lQi,, j -.1709368 .0131304 -13.02 0.000 -.4472944 .0272652 -16.41 0.000

Regression ValuesNº Obs. R-squared Adj R-squared Nº Obs. R-squared Adj R-squared33 0.8454 0.8404 33 0.8967 0.8934

Source: Own elaboration from regressions’ results

According to the model, a reduction of 1% in the indicator total TIEPI in the

area of service (1, U) implies an increase of nearly 0,17% on distribution costs of firm

1. Regarding NIEPI, the sensibility of distribution costs in relation to a quality increase

is higher. An improvement of 1% in the index NIEPI implies an increase of

approximately 0,45% on distribution costs.

Table 9 – Estimation Results for Pair (3, S) Considering Q as Total TIEPI/NIEPI

Q= Total TIEPI Pair (3, S) Q= Total NIEPI Pair (3, S)lyi,j lyi,j




Table 9 above shows regression results for pair (3, S). For this area of service, a

reduction of 1% in the indicator TIEPI implies an increase of approximately 0,21% on

distribution costs. When NIEPI is take into consideration, a quality increase of 1%

causes a cost increment of nearly 0,39%.

Finally, results for pair (4, R) are indicated in Table 10 below.



Table 10 – Estimation Results for Pair (4, R) Considering Q as Total TIEPI/NIEPI Q= Total TIEPI Pair (4, R) Q= Total NIEPI Pair (4, R)lyi,j lyi,j




According to the model, a reduction of 1% in the indicator TIEPI in this area of

service implies an increase of nearly 0,34% on distribution costs of firm 4, while a

reduction of 1% of NIEPI causes an cost increment of approximately 0,46%.

When comparing constant’s coefficients for the three regressions, the higher

result was for pair (1, U) and the lowest was the coefficient for pair (4, R). This makes

sense since these results refers to total distribution network costs. Total distribution

costs in urban areas are, usually, higher than in rural areas: generally, in urban regions,

the network is composed mainly by underground cables while in rural areas it is

composed mainly by overhead lines; besides, rural networks usually have a radial

configuration which is cheaper than the meshed structure found in urban areas.

It was also verified a tendency, when comparing regressions’ results for the

three pairs “firm province”, that the more urban is the area of service, the lower is the

sensitivity of distribution costs in relation to quality of service.

Furthermore, it was observed that NIEPI coefficients are higher than TIEPI

coefficients for all regressions. This is probably due to the fact that improving the

former index is, usually, more costly than improving the latter one. Duration of

interruptions may be reduced at a lower cost than number of interruptions. For instance,

TIEPI’s reduction may require simply the installation of protective devices or/and better

operation and maintenance planning, while a reduction in NIEPI requires more efforts

like an increase in the number of substations, kilometres of lines, etc.

Tables 11, 12 and 13 below present regressions estimations considering the

results of the NRM when K factors modify only the quality constraints related to



representative zone of each pair “firm, province”11. These regressions take into account

Q as the quality indicators in the representative zone of each pair “firm, province”.

Table 11 – Estimation Results for Pair (1, U) Considering Q as TIEPI/NIEPI in the Urban Zone

Q= Urban TIEPI Q= Urban NIEPIlyi,j lyi,j

Coef. Std.err. t stat. P> t Coef. Std.err. t stat. P> tParametersConstant 17.53973 .0105242 1666.61 0.000 17.17711 .0257662 666.65 0.000lQi,,j -.1651684 .0131403 -12.57 0.000 -.4443824 .0290686 -15.29 0.000



Table 11 shows that, according to the model’s results, in order to reduce TIEPI

in the urban zone of area of service (1, U) in 1%, distribution costs of company 1 would

increase nearly 17%. Regarding NIEPI, a reduction of 1% would imply a cost increment

of approximately 0,44%.

Table 12 – Estimation Results for Pair (3, S) Considering Q as TIEPI/NIEPI in the Semi-urban Zone

Q= SU TIEPI Pair (3, S) Q= SU NIEPI Pair (3, S)lyi,j lyi,j




Table 12 above shows the results for pair (3, S). According to the model, a

reduction of 1% in the indicator TIEPI in the semi-urban of this area of service implies

11 For the urban province, only one type of regression was done (the one that considers NRM results where K factors modify all quality indicators) since semi-urban and rural zones represent only a very small part of this area of service (3%).



an increase of approximately 0,29% on distribution costs. Regarding NIEPI, a quality

increase of 1% causes a cost increment of 0,51%.

Table 13 – Estimation Results for Pair (4, R) Considering Q as TIEPI/NIEPI in the Rural Concentrated Zone

Q= RC TIEPI Pair (4, R) Q= RC NIEPI Pair (4, R)lyi,j lyi,j




Finally, for pair (4, R) a reduction of 1% in the indicator TIEPI in the rural

concentrated zone of this area of service implies an increase of approximately 0,30% on

distribution costs, according to the model. When NIEPI is take into consideration, a

quality increase of 1% causes a cost increment of 0,29%.

IV.3 – Setting Incentives for Continuity of Supply

In this section, unitary incentives for quality of supply in Spain will be analyzed

under the perspective of the approach adopted in this thesis. An exercise will be done

with the aim of estimating the quality level that would be achieved by the application of

the current Spanish incentive scheme for quality of service (SEE Appendix C). The

exercise is done for each pair “firm, province” considering, firstly, results from the

NRM were all quality constraints are modified by K factors, and secondly, considering

results from the NRM were only quality constraints for the representative zone of each

area of service are modified by K factors.

For this purpose, the marginal cost curve of reducing TIEPI (hours) and NIEPI

(number of interruptions) was estimated according to the parameters obtained from the

regressions considering total TIEPI and NIEPI and distribution costs resultant from the

NRM where all quality constraints vary according to K.



The level of unitary incentives considered were the values established by Order

ITC/3801/2008: 100c€/KWh or 1000€/MWh for TIEPI and 150c€/client and

interruption or 1,5€/client and interruption for NIEPI.

In order to compare the marginal cost of reducing one hour of TIEPI and the

incentives received, the former was divided by the total power installed in the area of

service of each pair “firm, province”. In the case of NIEPI, in order to compare the

marginal cost of reducing number of interruptions and the incentives received, marginal

costs were divided by the total number of customers of each pair “firm, province”.

The results of this exercise are shown in Graphs 5 and 6, where:

Optimal level: Level of TIEPI/NIEPI that would be achieved under the current

Spanish incentive scheme for quality of supply improvement (point where the marginal

cost curve crosses the reward curve);

TIEPI 2007/NIEPI 2007: Total TIEPI/NIEPI observed in each province in

2007;

Reference12: Minimum required levels for TIEPI/NIEPI (RD 1634/2006)

weighted average, using power installed as the weight.

Graph 5 – Marginal Cost Curve and Incentive for Total TIEPI

12 There is no minimum reference value for total TIEPI or NIEPI. Minimum reference values for these quality indicators are established by law per zone type.




Graph 5 presents the curve of marginal cost of reducing one hour of TIEPI

divided by the total power installed in each area of service analyzed and the marginal

reward of 1000€/MWh.

It can be observed that, according to the proposed approach, the current level of

incentives for TIEPI in Spain would provide incentives for distribution companies in the

urban and semi-urban areas of service to achieve a quality level not only higher than the

reference considered in the exercise but also higher than the level currently observed in

these provinces.

On the other hand, for the case of pair (4, R) and according to the model, the

current level of reward for quality of service in Spain does not provide enough

incentives for company 4 to achieve neither the level of TIEPI currently observed in

province R nor the reference level considered for this province.

Graph 6 below shows the same exercise but considering NIEPI as the quality

indicator.


Pair (4, R)



Graph 6 – Marginal Cost Curve and Incentive for Total NIEPI

Graph 6 above shows marginal cost curves for the three provinces divided by the

total number of customers in each area of service analyzed and the reward of

1,5€/MWh.

According to the approach presented in this thesis, the current Spanish incentive

for NIEPI would lead to quality levels below both the reference level13 and the level

currently observed in each province in 2007.

It is important to mention though that these results depends on the accuracy of

the estimations, therefore they should be read carefully. For instance, marginal costs

curves were derived from regressions results, which do not represent reality perfectly.

On the other hand, comparison between results for the studied pairs “firm, province”

13 As the reference levels for TIEPI, reference levels for NIEPI are weighted averages of minimum required levels of TIEPI per zone type. The weight used was the installed power per zone type.

Pair (4, R)





can be highly accurate since they were obtained taking into account the same

assumptions. In this sense, one important point that has to be made is that optimal

quality levels resultant from the exercises both for TIEPI and NIEPI are much higher for

the urban province, even though differences on quality requirements in different zone

types are taken into account.

The results presented in Graphs 5 and 6 above contrast the marginal cost of

incrementing total TIEPI/NIEPI and the incentive received by distribution companies

for these improving these indicators. However, incentives for quality of electricity

supply in Spain are attributed to each distribution company according to the level of

quality they provide in each zone type and not to the average quality level they provide

considering all zone types.

For this reason, the previous exercise will be repeated taken into consideration

TIEPI and NIEPI indicators for the representative zone types chosen for each pair “firm,

province”. In this new exercise, the marginal cost curve was estimated for each area of

service according to the parameters obtained from the regressions which considered

NRM results in which K factors modified only the quality constraints related to the

representative zone type of each pair “firm, province”.

Graph 7 below presents the marginal cost curves, in €/MWh14, of reducing one

hour of TIEPI in the representative zone of each pair “firm, province” and the incentive

for TIEPI of 1000€/MWh, where:

Optimal level: Level of TIEPI that would be achieved in each zone type under

the current Spanish incentive scheme for quality of supply improvement (point where

the marginal cost curve crosses the reward curve);

TIEPIurb 2007, TIEPIsu 2007, TIEPIrc 2007: TIEPI observed in the urban

zone of province U in 2007; TIEPI observed in the semi-urban zone of province S in

2007; and TIEPI observed in the rural concentrated zone of province R in 2007,

respectively.

Minimum reference level: Minimum required levels for TIEPI per zone type,

according to RD 1634/2006. These values are 1,5 hour for urban zones, 2,5 hours for

semi-urban zones and 6 hours for rural concentrated zones.

14 The marginal cost curve of each pair “firm, province” was divided by the power installed in the representative zone of each one of these pairs.



Graph 7 – Marginal Cost Curve and Incentive for TIEPI in the Representative Zone Type of each Pair “firm, province”

It can be observed from Graph 7 above that, according to the proposed approach,

the quality level in terms of TIEPI that would be achieved in the urban zone of pair (1,

U) is not only higher than the minimum reference level for TIEPI in urban zones but

also higher than the current quality level in the urban zone of province U.

The results for pair (3, S) indicate that the optimal quality level in the semi-

urban zone of this province under the Spanish incentive scheme is higher than the

minimum reference level for urban zones but lower than the level currently observed in

the semi-urban zone of province 3.

Finally, in the case of pair (4, R), the quality level that would be achieved in the

rural concentrated zone of this area of service is not only below the current quality level

Pair (4, R)





of the rural concentrated zone of province R but also below the minimum required level

for rural concentrated zones.

These results confirmed the ones presented in Graphs 5 and 6 regarding the

optimal quality level resultant from the application of the current unitary incentives for

quality of supply in Spain. Optimal quality levels achieved in the urban and semi-urban

zones are much higher than the ones achieved in the rural zone even if difference of

quality requirements in those zones are taken into account.



CONCLUSION

In this thesis the relation between quality of electricity supply improvements and

the costs incurred by companies due to these improvements was analyzed in three

Spanish areas of service.

Results from a Network Reference Model from scratch were used as a proxy for

actual distribution network costs under different quality requirements. Using

econometrics, these results were applied to estimate a total distribution network cost

function considering quality as an independent variable for each area of service.

Outcomes indicate that distribution network costs are more sensitive to quality

improvements in rural areas rather than in urban areas. In such a situation, incentives for

continuity of supply should not be the same for urban and rural zones, as it is the case of

the current Spanish incentive scheme for quality of supply. It was also observed that

reducing NIEPI is more costly than reducing TIEPI, so different incentives must also

apply to these different indicators.

The approach adopted in this study was used also as a tool to analyze the unitary

incentive for continuity of supply established by the current legislation in Spain. For

this purpose, a marginal cost curve of improving continuity of supply indicators was

derived from total distribution network cost function for each area of service analyzed.

The results of this analysis showed that the reward set by the current regulation does not

provide enough incentives for the distribution company serving the rural province to

improve quality of service.

Some points regarding results should be clarified though. As previously

mentioned, results depend on models’ precision. This precision depends, firstly, on the

capacity of network reference models to represent real networks and, secondly, on the

capacity of regressions’ parameters to measure accurately the sensibility of distribution

costs in relation to quality of service.

Regarding the sensibility of distribution costs in relation to quality of service,

attention must be paid to the fact that the parameter β estimated in this thesis always

refers to one of the two indicators of continuity of supply (TIEPI or NIEPI) since

including both of them in the same regression would case a multicollinearity problem.



Nevertheless, efforts to improve TIEPI (or NIEPI) have, generally, an impact on NIEPI

(or TIEPI). Therefore, marginal costs curves estimated in this thesis for reducing one of

the two indicators have a component of the other.

Besides, further research should include other provinces so that results can be

more accurate and generalized. Due to the scope of this thesis, it was not possible to

include more provinces in the analysis.

The main objective of this work was to introduce and test a coherent and valid

approach which can be used in order to calibrate incentives for quality of supply. The

methodology applied to the Spanish case showed that results are coherent with reality

since they prove that giving the same incentives for quality improvement in urban zones

and in rural zones will lead to much lower quality levels in the latter.

In this sense, the application of the proposed approach to different combination

of provinces, zone types and quality indicators can be a powerful tool for guiding

regulators when making decisions regarding incentives for quality of electricity supply.

Finally, regarding quality regulation, an important issue is the role of the

regulator. Regulators must be independent from the Government since they were not

created with the intention of achieving short-term political goals; instead, they have a

more permanent function which is to implement competitive markets in industries

which were traditionally organized as monopolies and to substitute markets for the parts

of the chain that remained as natural monopolies.

Political parties are greatly influenced by regulated companies, mainly due to

electoral reasons. If regulators are not given enough power in the decision making

process, the regulated companies and/or Government’s interests will be pursued rather

than the interest of society as whole.

In the particular case of Spain, the national energy regulator’ decisions are

subjected to the Government’s approval; for this reason, the regulator of last resort is,

actually, the Government.



REFERENCES

AI, C., MARTINEZ, S., SAPPINGTON, D. M., (2004). Incentive Regulation and Telecommunications Service Quality. Journal of Regulatory Economics, vol. 26, issue 3, pages 263-285.

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APPENDIX A: Network Reference Model

The Network Reference Model used in this study is based on the NRM for

distribution networks proposed by Peco (2001), with the inclusion of some constraints.

This model designs the network from scratch, that is, without considering the existing

network. According to Peco (2004), the modeling of demand and the determination of

settlements comprise four phases:

1) Identification of settlements: the settlements and their outline are

automatically identified from the customers' GPS coordinates, based on their number

and dispersion;

2) Determination of the street maps of each settlement to constraint the network

routes inside a settlement;

3) Selecting aerial and underground areas in each settlement: aerial lines and/or

underground cables may be installed within a settlement depending on the user's choice,

but outside the settlements the model only considers aerial lines;

4) Processing geographic information (orography, forbidden rights-of-way...)

that can affect the location costs of MV/LV transformers, HV/MV substations and the

LV, MV and HV network routes outside settlements;

After this initial demand modeling, the PECO model comprises several planning

modules, which can be run one after the other sequentially, or one at a time only. These

planning modules are:

● Locating MV/LV transformers;

● Locating HV/MV substations;

● LV network planning;

● MV network planning.

Regarding continuity of supply, in order to optimize both urban and rural areas

even-handedly, the model is based on the following:

● The total unavailability time due to an equipment failure depends on the

location time (to find the fault); the sectionalizing time (to isolate the fault and fault to

restore the supply) and the repairing time (to repair de the damaged equipment).



● The model obtains the optimum equilibrium in: (i) maintenance crews, (ii)

fault indicators, manual switches, automatic circuit reclosers, and (iii) feeder

reinforcements and alternative supply feeders; in order to improve continuity of supply.

● The parameters associated with the continuity assessment (failure rates, the

displacement speed of maintenance crews, repairing times...) are modelled by fuzzy

numbers to take into account the randomness and the uncertainty related to the whole

process (equipment failure, location, repair and restoration of supply).

The optimization depends on the costs of energy-not-supplied given, the

accurate modelling of reliability and the use of fuzzy parameters

In what regards results, this model computes the costs of designing a whole new

distribution network – the total amount in euro of fixed asset – plus the annual costs of

operation & maintenance and losses. In such case, this cost must be annualized. This is

done by considering the annual depreciation of assets plus a return on net assets

corresponding to distribution installations for each distribution company.

Assuming that the useful life of a distribution network is equal to 40 years, the

WACC (Weighted Average Cost of Capital) is 5,6% and that 60% of the fixed asset for

each distribution company of this studied has already been depreciated, the capital costs

are calculated according to the following:

Capital costs = Fixed Asset/40 + 0,4* Fixed Asset*0,056

The total annual distribution network costs calculated for the objective proposed

in this thesis will be:

Total annual distribution network costs = capital costs + O&M expenses



APPENDIX B: Regressions’ Statistics

Tables 12, 13 and 14 below show statistics from regressions which consider the

results regarding distribution costs and quality indicators from the NRM where all

quality constraints (TIEPI and NIEPI) vary according to each K factor.

Table 14 – Regression Pair (1, U), Q = Total TIEPI/NIEPI

Source SS df MS SS df MSModel .582265539 1 .582265539 .617629201 1 .617629201Residual .1065047 31 .003435635 .071141038 31 .002294872Total .688770239 32 .02152407 .688770239 32 .02152407

Q = Total TIEPI Q = Total NIEPI

Source: Own elaboration from regressions’ results Table 15 – Regression Pair (3, S), Q = Total TIEPI/NIEPI

Source SS df MS SS df MSModel .601609377 1 .601609377 .624469713 1 624469713 Residual .036444692 31 .001401719 .013584356 31 .000522475Total .638054069 32 .023631632 .638054069 32 .023631632


Source: Own elaboration from regressions’ results Table 16 – Regression Pair (4, R), Q = Total TIEPI/NIEPI

Source SS df MS SS df MSModel 1.29173506 1 1.29173506 1.25110405 1 1.25110405Residual .129333325 31 .004172043 .169964326 31 .00548272Total 1.42106838 32 .044408387 1.42106838 32 .044408387



Tables 15, 16 and 17 below show statistics from regressions which consider the

results regarding distribution costs and quality indicators from the NRM where K

factors modify only the quality constraints (TIEPI and NIEPI) related to the

representative zone of each pair “firm, province”.

Table 17 – Regression Pair (1, U), Q = Urban TIEPI/NIEPI

Source SS df MS SS df MSModel .575793808 1 .575793808 .608106884 1 .608106884 Residual .112976431 31 .003644401 .080663355 31 .002602044Total .688770239 32 .02152407 .688770239 32 .02152407

Q = Urban TIEPI Q = Urban NIEPI




Table 18 – Regression Pair (3, S), Q = Semi-urban TIEPI/NIEPI

Source SS df MS SS df MSModel .62988772 1 62988772 .619020085 1 .619020085Residual .204932497 31 .007319018 .215800132 31 .007707148Total .834820217 32 .028786904 .834820217 32 .028786904

Q = SU TIEPI Q = SU NIEPI

Source: Own elaboration from regressions’ results Table 19 – Regression Pair (4, R), Q = Rural Concentrated TIEPI/NIEPI

Source SS df MS SS df MSModel .456634522 1 .456634522 .518635517 1 .518635517Residual .158912428 31 .005126207 .096911433 31 .003126175Total .61554695 32 .019235842 .61554695 32 .019235842

Q = Rural concentrated TIEPI Q = Rural concentrated NIEPI




APPENDIX C: Setting Incentives for Continuity of Supply

In order to analyze the current rewards for continuity of supply in Spain, firstly,

the marginal cost curves for the pairs (1, U), (3, S) and (4, R) were calculated according

to the parameters estimated for the cost function relating quality to distribution costs for

each pair “firm, province”.

Firstly, regressions were done taking into account the NRM results for

distribution costs and total TIEPI when K factors modify all quality constraints. The

parameters of these regressions are shown in Table 20 below, recalling that:

Cost function: β

jiji AQY ,, =

Where:

Yi, j = Total distribution network costs;

Qi,j = total TIEPI/ total NIEPI.

Table 20 – Parameters of the Cost function

ln A A β ln A A β(1, U) 17,54822 41.792.188 -0,1709368 17,1995 29.488.178 -0,4472944(3, S) 17,38789 35.601.203 -0,2061203 17,2541 31.142.998 -0,3878734(4, R) 15,60795 6.004.081 -0,3395835 15,54763 5.652.622 -0,457939



The marginal cost curve is the derivative of the total cost function in relation to

Q, which is equal to: 1.. −= ββ QA

dQdy

.

The marginal cost curve for each pair “firm, province” is the total distribution

cost increment due to a reduction of hours of interruption (TIEPI)/number of

interruptions (NIEPI).

In the case of TIEPI, the marginal cost curve was divided by the total power

installed (in MW) in each area of service. In this way, it can be contrasted with the

incentive for TIEPI (1000 €/MWh). In the case of NIEPI, the marginal cost curve was

divided by the total number of customers of each area of service. In this way, it is

possible to contrast the marginal cost curve with the incentive for NIEPI (1,5€/customer

and interruption).



The data source of installed power and number of clients of each pair “firm,

province” is the CNE.

This exercise was repeated but considering now the parameters of regressions

which took into account results regarding distribution costs and quality indicators from

the NRM when K factors modify only quality constraints related to the representative

zone of each province. The objective here is to analyze the effect of the current Spanish

incentive scheme for quality of supply over the zonal continuity of supply indicators

rather than its impact on the average TIEPI and NIEPI.

Table 21 – Parameters of the Cost function Q ln A A β Q ln A A β

(1, U) TIEPIurb 17,53973 41.438.875 -0,1651684 NIEPIurb 17,17711 28.835.274 -0,4443824(3, S) TIEPIsu 17,27489 31.797.238 -0,2940436 NIEPIsu 17,23959 30.694.376 -0,5113793(4, R) TIEPIrc 15,60807 6.004.802 -0,2973645 NIEPIrc 15,54876 5.659.013 -0,2824007


In this exercise, for the case of TIEPI, the marginal cost curves of (1, U), (3, S)

and (4, R) were divided by the power installed in the urban zone, the semi-urban zone

and the rural concentrated zone, respectively. In the case of NIEPI, the marginal cost

curves of (1, U), (3, S) and (4, R) were divided by the number of clients in the urban

zone, the semi-urban zone and the rural concentrated zone, respectively.

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