The importance of incorporating reliability of supply criteria in a regulatory system of electricity...

The Importance of Incorporating Reliability of Supply

Criteria in a Regulatory System of Electricity

Distribution: An Empirical Analysis for Austria.

Johannes Reichl, Andrea Kollmann, Robert Tichler, Friedrich Schneider

Energieinstitut GmbH, Altenberger Strasse 69, 4040 Linz, 0732 24 68 5652, [email protected], www.energieinstitu-linz.at

6. Internationalen Energiewirtschaftstagung an der TU Wien IEWT 2009

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Abstract:

Grid tariffs are the main source of income for distribution system operators (DSOs). Reductions of tariffs increase the cost pressure on DSOs; assuming they work efficiently tariff reductions potentially lead to a decrease of the electricity system's quality if no reliability of supply criteria are incorporated in the regulatory system. Our statistically analysis shows that the correlation between grid tariffs and electricity supply interruptions in a regulatory regime neglecting this incorporation is significant and that furthermore decreasing tariffs harm the reliability of supply even in the short run. Our econometric analysis of the influence of tariffs on reliability of supply shows a significant correlation between the grid tariffs and the duration of power outages in the Austrian electricity grid; an annual average interruption duration per installed capacity of a specific grid increases ceteris paribus by 1.36 minutes if the grid tariff of this specific grid is decreased in the previous year by 1€ per MWh.

Keywords: regulation, grid tariffs, security of supply


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1 Introduction

In the Austrian electricity sector, the first stage of the liberalisation process was initiated by the

EU Directive 96/92/EC on electricity liberalisation in 1996 (European Parliament and the Council,

1997). In 1999 the Electricity Business and Organisation Act (ElWOG) in combination with the

formal implementation of the EU directive in 1998 in Austria, a standardised law for the

organization and regulation of the Austrian electricity sector, was established. After various

intermediate steps, the Austrian electricity sector was fully opened to competition and

consumers were given the possibility to switch freely between electricity suppliers in October

2001. While the competitive electricity market now covers generation, trade and supply of

electricity, the transmission and distribution of electricity1 are regulated by the energy regulating

authority, E-Control. Distribution is subject to economies of scale and the existence of a natural

monopoly in this part of the energy sector does not allow its full liberalisation from a theoretical

point of view. The introduction of a regulator, who provides incentives for cost-efficient grid

operation, is considered to be the second-best solution to deal with the natural monopoly.

Before market liberalisation took place in 2001, distribution of electricity in Austria was regulated

according to a rate-of-return (RoR) system, which is typically applied in monopoly regulation.

RoR regulation offers utilities a refunding of their operating expenditures and the employed

capital additional to a rate-of-return. Shortly after market liberalisation in 2001, RoR regulation

was replaced by an incentive-orientated regulation regime. This regime primarily follows a price-

cap regulation, but prices are determined at fixed values and are not only given an upper limit

(for an excellent review of the different models of incentive regulation and their theoretical

properties see Joskow (forthcoming)). The Austrian electricity grid is partitioned into 7 levels, of 1 In the following only the distribution of electricity is discussed.


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which 3 levels are high voltage grids, 2 levels are medium voltage grids and 2 levels are low

voltage grids. 4 tariffs (summer-high, summer-low, winter-high and winter-low) are assigned to

each of the grid levels. Shortly after his appointment, the regulator started to cut grid tariffs which

was unsurprisingly opposed by the Distribution System Operators (DSOs). As grid tariffs are the

main income of DSOs under the new regime, in their opinion a cut in tariffs would lead to the

erosion of the grid's quality, to a danger for security of supply and thus to a threat for Austria's

economy (VEÖ, 2003).

The term security of supply is used in a broad range of different meanings which can basically

be divided into economic and technical aspects of the overall supply with energy. From an

economic point of view security of supply can be improved by reducing energy import

dependence and by guaranteeing long-run capacity generation, whereas a more technical

access highlights the importance of grid stability and short-run operational reliability of the grid.

The latter, reliability of supply (as a component of the quality of service), is the aspect of the

overall security of supply that we focus on in this paper. Reliability of supply describes the

undisturbed functioning of parts of the grid as well as of the whole electricity grid within a given

region. Our empirical analysis does not differ between planned and unplanned power outages

because only aggregated data is available on DSO-level.

Despite strong opposition, the regulator cut grid tariffs by nearly 250 million altogether in the

years 2001 to 2003, which resulted in a 12% decrease of overall electricity prices (Kratena,

2004).

But not only the regulatory changes of the electricity market but also the first experiences with

market liberalisation seemed to keep their promises. In 2002 the regulator already stated that

electricity pricing had become more efficient and that the productivity of market players had

grown (Haberfellner, 2002). Similar effects were seen in other European countries, which - like


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Austria - liberalised their energy markets according to Directive 96/92/EC (European Parliament

and the Council, 1997) concerning common rules for the internal market in electricity. Jamasb

and Pollitt (2005) show that between 1997 and 2003 average real electricity prices declined for

European households, small industrial customers and large industrial users by 6, 20 and 9.5

percent, respectively. By now, the promising pace of the implementation of a competitive and

efficient energy market in the European Union as a whole and in the single member states has

significantly slowed down. A recent communication from the European Commission to the

Council and the European Parliament about the prospects for the internal gas and electricity

market clearly describes the problems that have come along with liberalisation: The European

Commission observes ”[…] a continued lack of competitive pressure and high levels of

concentration in wholesale markets […] as well as a lack of market transparency […]” (European

Commission, 2006b, p.3). The decline in prices seen at the beginning of the liberalisation has

also stopped, leading to an increase of wholesale electricity prices while household prices have

remained fairly stable.

Apart from these efficiency and competitiveness concerns the communication lays major

emphasis on security of supply. Already in EU directive 2003/54/EC (European Commission,

2003) security of supply was addressed and measures for it were recommended. Opposed to

some European countries, Austria has not adopted a legal framework for incorporating concerns

of reliability of supply in its regulatory system. The importance of a clear focus on service

reliability has been demonstrated by the pan-European blackout of 4 November 2006, which has

shown the potential weaknesses of the European electricity supply. The commission concludes

that while investment in generation capacity clearly responds to price signals (European

Commission, 2006b), not enough progress is made in the development of networks (European

Commission, 2006a). The latter point is an urgent one, as the physical limits of the existing

networks are nearly exhausted which increases the probability of temporary supply interruptions


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(European Commission, 2006a). The continuous growth of electricity demand adds further

pressure to increasing network investments as does the connection of electricity from renewable

energy sources. The Commission notes that the necessary investments shall be made by the

“economic operators concerned” (European Commission, 2006a, p.13) and highlights the

importance of “a stable and attractive regulatory framework allowing the private sector to predict

the successful completion of its investment and guaranteeing stable rates of return, while

offering a high level of service to customers” (European Commission, 2006a, p.14). The weight

that the European Commission now puts on issues of security of supply clearly shows that the

liberalisation of electricity markets cannot only be evaluated with declines in prices but must also

incorporate the possible effects liberalisation has on reliability of supply.

We take up the claim of the European Commission that a stable and attractive regulatory

framework needs to be guaranteed so that investments in security of supply are undertaken at

an optimal rate. It is well known that security of supply covers a wide range of aspects of

electricity supply from import dependence to short-run operational reliability.

We focus on one aspect of security of supply, the reliability of supply, and benchmark the

stability of the regulatory framework using the grid tariffs set by the regulator. We analyse the

short-run effects of tariff changes on supply interruptions in an incentive regulation that neglects

setting incentives for maintaining or improving the level of supply reliability.

The paper is organized as follows: chapter 2 presents some theoretical considerations about

(de-)regulation. Chapter 3 contains the framework for measuring reliability of supply. Chapter 4

presents the empirical investigation whereas part 4.1 discusses the data and the empirical

modeling. Part 4.2 shows the empirical model and 4.3 the econometric results. Chapter 5 draws

a conclusion.


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2 Theoretical Considerations and Literature Review

The switch from rate-of-return regulation to incentive regulation is carried out mainly to improve

operational efficiency of distribution system operators. Cost reductions are then intended to be

passed on to the customers. In the rate-of-return regulatory framework utilities’ profits are

determined by the returns on the employed capital. This creates incentives for exaggerated

investments in grid capacity and maintenance, resulting in poor efficiency in the distribution of

electricity (this effect of monopoly regulation is referred to as Averch-Johnson effect (Averch and

Johnson, 1962)). Incentive regulation relaxes this relation of employment of capital and profits

and motivates DSOs to implement actions improving their efficiency. On the one hand, fixing the

grid tariffs, as conducted in Austria, clearly leads to increasing profits when the costs for the

distribution decrease, hence incentives for efficiency improvements are given. On the other hand

less attention might be payed to maintaining reliability of supply under this regulatory regime,

when quality indicators or benchmarks are not incorporated in the incentive regulation. Concerns

about reliability of supply in incentive regulation are discussed broadly in literature, see e.g.

Jamasb and Pollitt (2007) and Giannakis et al. (2005) in the electricity sector or Uri (2003) and

Sappington (2003) for the telecommunication sector.

In a comprehensive empirical analysis of quality of supply in electricity networks for the USA

Ter-Martirosyan (2003) finds, that incentive regulation harms service reliability if it is not directly

addressed by the regulatory regime. In her study she uses panel data of 78 investor-owned

utilities in 23 states of the USA over the period 1993 to 1999. In her main equation she

estimates the quality variables SAIDI and SAIFI using a dummy for the regulatory regime in a

state (either incentive regulation or no incentive regulation), a quality standards dummy (either


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quality standards are explicitly incorporated in the regulation regime or not) and utility specific

and time specific variables as explanatory variables. Furthermore an error term and a vector for

other explanatory variables are included. The estimation outcome supports her hypothesis that

incentive regulation negatively influences quality of supply if no quality standards are defined.

Also the duration of outages increases under incentive regulation when no quality standards are

set, but decreases when quality standards are implemented. If incentive regulation is combined

with quality standards the average duration of power outages is reduced by 11% according to

her model.

Jamasb and Pollitt (2007) discuss that quality regulation generally aims at achieving equilibrium

between benefits and costs of the system. Thus, a central point of quality regulation is to

determine an optimal level of requested service quality, as exceeding the requested level of

service quality leads to further inefficiencies. Theoretically, the optimal level of quality is found

when the marginal costs of improving quality equal the economic value of the quality

improvements. Practically, both factors are hardly observable and measurable, so that the

optimal level is hardly ever known. Identifying the economic value of the service quality

improvements is challenging on several levels. Besides data issues, a comprehensive

determination of the economic value has to translate the social value into monetary units and

add it up with the pure financial costs to obtain the total economic value of service reliability

improvements. Additional challenges arise as such a comprehensive determination of the

economic value needs to incorporate all concerned sectors of the economy. For the household

sector this is usually carried out by estimation of the willingness to pay (WTP). Estimates of WTP

among households to avoid power outages have e.g. been calculated by Carlsson and

Martinsson (forthcoming, 2007), Layton and Moeltner (2004) and Beenstock et al. (1998). In the

firm sector cost estimates of the value of reliability of supply can be calculated by more

straightforward approaches, see e.g. Moeltner and Layton (2002). Methods for the determination


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of the economic value of quality improvements of distribution networks for the agricultural sector,

the public service sector or for the armed forces are not known to the authors. For the

identification of the costs of improving service quality, information becomes a central issue. The

regulator has an information deficit about the associated costs of improvements, whereas the

operating utility has superior information. This deficit in information on part of the regulator may

lead to two possible outcomes. Firstly, if the grid tariffs are set too low, service quality

maintenance or improvements fall behind in the DSOs' objectives. Secondly, if the grid tariffs are

set too high, expenditures for service quality may exceed the optimal level.

As a matter of fact, costs of quality differ among distribution system operators because of the

existing heterogeneity of the characteristics of end-users and different quality objectives. For that

reason, the basic principles of quality regulation are the determination and objective evaluation

of reliability of supply in a certain regulatory regime (Yu et al., 2007).

As a an example of incentive regulation incorporating service quality broadly discussed in

literature, the efficiency and performance of the UK distribution network are analysed by e.g.

Waddams Price et al. (2003), Jamasb and Pollitt (2007) and Giannakis et al. (2005). In the

Nineties impressive cost reductions were realized and service reliability was simultaneously

improved. In Phase I (1991-2000) of the service quality incorporated incentive regulation in the

UK guaranteed standards of performance were introduced, entitling consumers to

compensations for violations of that standards. From 1991/92 to 1998/99, distribution charges

for domestic users have fallen by about 9% (adding up to a price reduction of the total price for a

kWh of 29%). Minutes lost per customer gradually decreased from about 100 in 1991/92 to

below 80 in 1999/2000 and the number of interruptions per 100 customers decreased from

about 90 to slightly below 802. The Nineties were followed by tightened standards and a one-off

2 For comparison, these figures are still double the Austrian outage indicators,


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cut in distribution revenue in 2000/01 of 23% followed by a 3% p.a. fall in unit revenue until 2005

(Phase II) (Jamasb and Pollitt, 2007). At the beginning of Phase II both indicators for service

reliability deteriorated noticeable: in 2003 the minutes lost per customer even exceeded the level

at the beginning of Phase I3.

Towards the end of Phase II these indicators returned to their levels of 2000. A further

development of the regulatory regime started in 2005 with the initiation of Phase III and will run

until 2010. Despite the temporary decrease in service reliability observed between 2000-2003,

the overall trend of an increasing service reliability continued. Of course, weather conditions and

other external effects can not be neglected when analysing outage durations.

In the case of Austria, a very special feeling and high sensitivity are asked for when defining grid

tariffs, because Austria, even though being a geographically small country, has a large number

of geographical, demographic and especially climatic sources of heterogeneity among supply

areas4.

With respect to these literature findings, it is remarkable, that the Austrian regulator has not

established any quality criteria in his regulatory regime up to now. But, the regulator has

announced that an incorporation of quality criteria in his regulatory regime will begin in 2010. In

2006, he has started to define quality standards and to develop a system for incentives and

penalties if the DSOs exceed these standards or fall behind them. What these quality criteria will

look like or how the incentives and penalties will be shaped has not yet been made public by the

regulator.

see table 1.

3 Naturally, not all variations in power quality indicators can be assigned to the regulatory regime.

4 For a descriptive overview of the different supply areas characteristics see table 1.


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Besides this delayed adoption of quality regulation, some concerns remain regarding the

customer-related rights of this regime. The ElWOG (see chapter 1) defines the collection of the

data relevant for the determination of the current quality indicators. All data has to be submitted

to the regulator, who is legally obligated to immediately make these data anonymous when the

company-related information is not essential for further statistical analysis. The obligation of

confidentiality is codified in § 52 of the ElWOG, which refers to § 19 para. 2 of the federal

statistics law (for further details see Hauer und Oberndorfer (2007)). According to this, only

highly aggregated statistics are made public, which do not allow the customer any insights in the

quality of the services he is provided with. In the authors’ opinion the customer should clearly

have access to this information and have full disclosure of the activities of the monopolists.

From the last paragraphs we resume that incentive regulation incorporating service quality

potentially improves service reliability, while setting incentives for cost reductions only (as it has

been conducted in Austria in the observation period from 2002-2005) may harm reliability of

supply. Therefore our core hypothesis is, if simultaneously incentives for maintaining and

improving the network are neglected, intensified cost pressure on DSOs leads to a deterioration

of service reliability, ceteris paribus. For the Austrian regulatory regime between 2002 and 2005

this means, that grid tariffs and service quality should be positively correlated.

Even when our hypothesis is found quite often in literature (see e.g. Giannakis et al. (2005), Yu

et al. (2007) and specific for the Austrian case Haber and Rodgarika-Dara (2005)), the work of

Ter-Martirosyan (2003) on service quality in the electricity sector in the USA is the only empirical

paper giving evidence towards it. In our opinion there are two reasons for the lack of empirical

studies about this topic: Firstly, the data needed for such an analysis is highly sensitive, which

makes it difficult to collect; secondly, liberalisation of electricity markets is still in its infancy in

most European countries so that the analysis of external effects i.e. the deterioration of reliability

of supply, has only recently become an issue.


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3 Framework for Measuring Reliability of Supply

3.1 Power Quality Indicators

For the analysis of service reliability we draw on the internationally used power quality indicators

SAIDI, CAIDI and SAIFI5. The indicator SAIDI (System Average Interruption Duration Index)

measures the average interruption duration per installed capacity in kilovolt-ampere per year; a

SAIDI of 1 min/a is interpreted as an average interruption duration of 1 minute per installed

kilovolt-ampere in the according year in the according supply network.

s

jjj

L

tISAIDI

)(=

⋅∑

KjI interrupted capacity in kVA for interruption j

jt … interruption duration in minutes for interruption j

sL … total installed capacity in kVA in system s

5 Besides the power quality indicators used in Austria, a number of additional indicators for the same

purpose exist. These are for example the CMI (Customer Minutes Lost) and CI (Customer Interruptions) in

the UK, Belgium, Italy, Hungary and Norway and the indicators TIEPI (Time of Interruption Equivalent to

the Installed Capacity) and NIEPI (Number of Interruptions Equivalent to the Installed Capacity) in Spain

and Portugal.


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The indicator CAIDI (Customer Average Interruption Duration Index) is the average duration of

an interruption if an outage is experienced. Two different reference points are in use for the

interruption durations on the customer level: a.) the number of customers, henceforth CAIDI

Type I and b.) the interrupted capacity, henceforth CAIDI Type II. In our analysis the interrupted

capacity is used as reference point, since the Austrian regulator requests DSOs to provide their

values for CAIDI with respect to this mode of calculation. Therefore the power quality indicator

CAIDI in this paper is calculated according to the following formula, i.e. CAIDI (Type II):

j

j

jjj

I

tICAIDI

∑∑ ⋅ )(

=

jI … interrupted capacity in kVA for interruption j

jt … interruption duration in minutes for interruption j

The indicator SAIFI (System Average Interruption Frequency Index) measures the average

frequency of outages per installed capacity per year and a SAIFI of 1 means that each installed

kilovolt-ampere, on average, is interrupted once in the according year.

s

jj

L

ISAIFI

∑=

jI … interrupted capacity in kVA for interruption j

sL … total installed capacity in kVA in system s


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The duration indicator SAIDI is increasing, whenever sjj LtI Δ⋅Δ∑ >)( 6. This is the case when

the number of loss minutes )( jj tI ⋅∑ increases faster (or decreases slower) than the total

installed capacity sL . Therefore a decrease in SAIDI ceteris paribus has three different kinds of

origin: a.) decreasing interruption durations, b.) decreasing interrupted capacity and/or c.) an

increase in the total installed capacity, where the new capacity is less vulnerable to interruptions

as the previous capacity.

The second duration indicator CAIDI increases, when jjj ItI ∑∑ Δ⋅Δ >)( (which is only possible

when at least one jt is greater than 1). A decrease in CAIDI is therefore ceteris paribus caused

by a.) a decrease in the interruption durations, b.) an increase of the interrupted capacity in short

outages ( 1<jt ) and/or c.) a decrease in the interrupted capacity in longer outages ( 1>jt ).

For the frequency indicator SAIFI an increase will be observed when sj LI ΔΔ∑ > . A decrease

in SAIFI is therefore ceteris paribus caused by a.) a decreasing interrupted capacity and/or b.)

an increasing total installed capacity.

From these definitions several relations between the indicators arise. First, as a technical

identity, the ratio of SAIDI and SAIFI equals CAIDI:

SAIFISAIDICAIDI = (1)

Another straightforward relationship between the indicators is the fact that a decreasing SAIFI

will involve a decreasing SAIDI (assuming that the interruption duration is constant). Whether 6 The function Δ refers to the absolute first difference throughout this paper.


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CAIDI is decreasing or increasing when reliability improvements are conducted, depends on

whether the improvements have more effect on the frequency or the duration of outages.

Figure 1 shows the overall development of SAIDI, CAIDI and SAIFI in Austria in the years 2002

to 2006. The data presented here are collected by the Austrian regulator and represent the

average development of these indicators for about 130 electricity utilities and are therefore not

directly comparable with the individual company data that we will present later on. As argued

before, the trend of the indicators shows a slow but steady decrease of service reliability. Data

for individual companies or federal states are not available due to Austria’s restrict data

protection laws (see also chapter 2). Therefore we can only show the overall average trend in

Austria but will focus on much more detailed data in the following analysis.

Figure 1: Overall development of SAIDI, CAIDI and SAIFI in Austria between 2002 and 2006.

Source: E-Control (2008).


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Furthermore, the analysis of the origins of the decrease of reliability of supply has not been

empricially analysed before for Austria. The lack of scientific literature on this topic is also mainly

due to overly tight data protection laws. Specific data about the activities of the DSOs are the

basis for assigning causes to outages. As there is no reliable data base and no reliable literature

on this topic, our analysis needs to stay theoretic, because otherwise we could only be

speculative.

3.2 Influences on Reliability of Supply

For the discussion of the expected market reaction as a response to changing tariffs we first

describe the composition of the Austrian end user electricity price. The end user price consists of

seven components, which are, ranked according to their magnitude (exemplarily for the supply

area Vienna in 2006):

• the pure energy price 34.31% (incl. a settlement price for green electricity of 2.5 - 3.5%)

• grid tariff 33.26%

• value added tax 16.70%

• energy surcharge 9.80%

• usage surcharge 3.17%

• renewables surcharge 2.80%.

As mentioned in the introduction, grid tariffs are the main source of revenues for distribution

system operators. From a revenue reduction as a consequence of a reduction in the grid tariffs,

several risks for reliability of supply arise. Under the assumption that grid tariffs have already


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been established with respect to actual distribution costs in the past, firms will have to cut costs

under reduced grid tariffs, which is the main objective of incentive regulation. In the context of

reliability of supply, DSOs have two cost intensive functions:

a.) to provide sufficient capacity for distribution and to ensure an undisturbed distribution in

the future by preventive maintenance and upgrading the existing network

and

b.) to restore service in the case of interruptions.

Under cost cutting we expect point a.) to be especially problematic in the long run, since a short-

dated lack in preventive maintenance or the delay of necessary network upgrades are not likely

to affect reliability of supply immediately on a large scale. While under point b.), that is to cut

expenditures for the restoration unit, we expect negative consequences for the reliability of

supply even in the short run. A badly maintained or overstrained network is more vulnerable to

interruptions and therefore we expect a rise in the frequency of interruptions, which is observed

via the indicator SAIFI. In the case of cuts in expenditures for the restoration unit, the capacity to

act is shortened and delays in resolving the interruptions are likely to occur, which is observed

through the indicators SAIDI and CAIDI. Resuming the last paragraph, we expect cuts in grid

tariffs to significantly influence the power quality indicators SAIDI and CAIDI in the short-run and

to influence the power quality indicators SAIFI, SAIDI and CAIDI in the long-run. Unfortunately,

only data for the analysis of short-run effects is available to us.


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4 Empirical Investigation

4.1 Data and Empirical Modeling

The Austrian regulator publishes yearly data on electricity supply interruptions which are highly

aggregated and do neither include grid specific data nor characteristics of the distribution system

operators. Therefore, data for our study had to be collected from the distribution system

operators directly, as open source data on such vulnerable aspects as supply interruptions on

company level are usually not published anywhere 7.

12 out of 13 contacted distribution system operators provided data on electricity interruptions.

These 12 companies are not only the largest players on the market they also serve 60.1% of the

Austrian territory and operate in all 9 federal states of Austria. The interruption indices could not

be differentiated between planned and unplanned supply interruption, which does not severely

influence the quality of the regression outcomes.

For most grids, statistical data was available for the whole estimation period from 2002 to 2005,

but, some distribution system operators provided their power quality indices only for the most

recent years 2004 and 2005. Our sample contains 9 observations for the year 2002, 11

observations for 2003, 12 observations for 2004 and 11 observations for 2005. Mainly this is due

to the fact, that consistent Austria-wide guidelines for the calculation of these indices where

firstly established in 2002. Prior to this, not all Austrian distribution system operators had

calculated their indices based on common standards. Only values calculated according to this

7 As data on the performance of a certain distribution network is confidential to the public, only results and

statistics can be presented in this paper, that do not reveal any specific information on the distribution

system operators.


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common standard where included in our sample, resulting in a total number of observations of

43.

The inclusion of the grid tariffs in the regression analysis requires aggregations because of the

high number of small deviations within a specific grid. The calculation of the average grid tariffs

of a certain grid is based on the determination of the share of the different seasonal tariffs in the

Austrian grid. In addition, grid tariffs differ between the grid areas; so that e.g. private household

customers pay other tariffs than industrial ones. As a consequence, each aggregated grid tariff

of a certain grid is not only based on an average of the legitimated tariffs of the energy regulator

for the grid region but is also weighted with the specific seasonal electricity consumption and

with the different electricity consumption of the certain grid areas.

Within our sample, eight out of twelve electricity distribution system operators are faced with a

decrease of reliability of supply regarding the power quality indicators SAIDI and SAIFI in the

observation period. In the case of CAIDI only for five DSOs a steadily negative development is

observed (for a discussion of CAIDI see part 3.1).

In general, extreme weather events can strongly influence data on reliability of supply of power

grids. As a consequence, we excluded two data points and modified one data point that has

been influenced by extreme weather events. First, the western part of Austria suffered from

damages of flood water in the year 2005. The most affected federal state was Vorarlberg.

Therefore the data point for Vorarlberg for 2005 is excluded from our sample due to being a

single outlier in reliability of supply as a result of flood water. Second, Austria was faced with

considerable flood water also in 2002. The centre of this natural disaster and therefore most of

the damage costs were situated in the northern part of the federal state Lower Austria. Actually,

we could not include the power grid of the northern part of this federal state into our analysis due

to the fact that the resident DSO declined to transfer data of the reliability of supply of this power


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grid. Third, in October 2003, the federal state Burgenland suffered from wet snow that

repeatedly interrupted the electricity power supply of the area. Persistent degrees around the

freezing point and a simultaneous heavy precipitation caused wet snow conditions, which

affected the land line. The resident DSO therefore adjusted the transmitted data series by

excluding the interruptions caused by these snow conditions.

Beyond it, Austria did not suffer from additional natural disasters from 2002 until 2005 which

could influence reliability of supply in a way that cannot be captured within the regression

analysis. The results of the regressions therefore are assumed to reflect the unbiased and

consistent influences of the grid tariffs on the power quality indicators.


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Table 1: Descriptive statistics of the data set.

Variable Abbrev. N Min Max Mean Sd Source

Dependent Variables

SAIDI 43 * * 40.73 32.30 DSOs

SAIFI 43 * * 0.69 0.44 — „ —

CAIDI 43 * * 55.62 26.24 — „ —

Independent Variables

Overall grid length in 1000km GL 43 0.37 28.75 10.35 8.59 DSOs and their annual reports

Percentage of network underground in % NU 43 40.43 99.10 67.11 20.48 — „ —

Population density in 1000 persons/km2 (within the supply area)

PD 43 54 3.697 573 920 Statistik Austria

Number of days with snow cover >1cm (measured in the according regional capital)

SD 43 2 112 51.17 25.73 Zentralanstalt für Meteorologie und Geodynamik (ZAMG)

Average temperature in degree Celsius (measured in the according regional capital)

AT 43 8.30 11.40 9.55 0.83 — „ —

Rainfall in mm (measured in the according regional capital)

RA 43 446 1.966 890 263.774 — „ —

Number of households per federal state in 1000 (in the federal state of the major activities of the TDSO)

HH 43 104.03 807.48 340.07 193.19 Statistik Austria


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Electricity consumption per federal state in TJ (in the federal state of the major activities of the TDSO)

EC 43 4.661 48.489 22.095 12.681 — „ —

Settlement area in % (within the supply area)

SA 43 11.81 77.70 39.61 20.90 — „ —

Grid tariffs in €/MWh GT 43 29.41 54.73 39.33 6.34

E-Control, Bundeministerium für Wirtschaft und Arbeit (SGT-VO 2002, SGT-VO 2003, SGT-VO 2004, SGT-VO 2005)

Lagged variable of the grid tariffs in €/MWh

lGT 43 31.52 59.23 41.03 6.96 — „ —

*Data cannot be shown because it would reveal distribution system operators


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4.2 The Econometric Model

In order to fulfill the ceteris paribus conditions while estimating the effect of grid tariffs on the

power outage indicators, an accurate selection of the additional explanatory variables is

important. Literature on the influence of grid characteristics on number and frequency of

power outages does not exist to our knowledge. A survey among the persons in charge of

the reliability of supply with DSOs (Reichl et al., 2007) clearly shows, that the most important

variable is the percentage of network underground as a fraction of the overall grid length,

because network underground, opposed to land line, is independent from bad weather

conditions such as heavy snowfall and storms, which are responsible for a significant number

of outages in Austria. As a second important variable, the overall grid length was identified as

influential too, which needs no further explanation. A total number of 11 variables (see table

1 for descriptive statistics of these 11 independent variables) were identified as candidates

potentially influencing the frequency and duration of supply interruptions in a certain supply

area. Due to the small sample and the high correlation between some of the independent

variables, not all variables enter the regression. Variables to enter the model were selected

according to the information obtained by the survey among the DSOs. Additional to the

reportedly most decisive variables overall grid length and percentage of network

underground, a variable to describe the habitat density in the supply area was included.

Therefore, additional to the variable of main interest, that is the lagged variable of the grid

tariffs, the variable population density enters the model. There is no evidence that the three

evaluated power quality indices depend on different variables. Therefore and for

transparency, we apply the same model to all power quality indices.


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The longitudinal structure of the data implies accounting for firm-specific heterogeneity8

between the subjects. In our case this heterogeneity is likely to be caused by a wide variety

of origins where most of them are unobserved and/or unknown. This is due to the fact, that

the supply areas are very diverse regarding their reliability relevant characteristics. Not only

the topographic and demographic situation but also variables such as different management

philosophies could have an impact on reliability of supply. Because of the restrictive sample

size and the impossibility to treat all origins of heterogeneity separately, we will apply a fixed

effects approach. The fixed effects approach, in contrast to the random effects approach, is

appropriate since one can not assume without doubt that the unobserved heterogeneity is

uncorrelated with the observed explanatory variables. Resuming the last two paragraphs, our

model KGT ,M for the power quality indices is

,= ,,15,4,3,2,1,, KitKiitKitKitKitKKKit uGTPDNUGLQI ++++++ − αβββββ (2)

where ,.GTM is our model specification with the lag of the grid tariffs as explanatory variable

and K is assigned to a specific reliability indicator. i refers to the supply area and t

identifies the year of observation. KQI ., refers to the power quality index K , GL is the

overall grid length, NU is the percentage of network underground, PD refers to the

population density and GT to the grid tariffs. Grid tariffs are included as lagged value since

effects of changing tariffs are expected to become observable earliest in the following year.

iα is the unobserved heterogeneity and u is an error term, assumed to be normally

8 For a discussion of firm-specific heterogeneity in the electricity sector see e.g. Farsi et al. (2006). In

their paper they compare a new parametric panel data model with two other models in measuring

productive efficiency of the electricity distribution sector. While their model deals with several origins of

heterogeneity the specification of the firm-specific component is identical to ours.


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distributed with mean 0. For the indicators SAIDI and SAIFI we expect a negative sign for all

slope coefficients except for the overall grid length. For the indicator CAIDI hardly any

propositions can be made, since whether CAIDI is decreasing or increasing under improved

reliability of supply depends on whether the improvements have more influence on the

duration or the frequency of outages, as discussed in section 3.1.

Additional to KGT ,M we formulate a second model to examine whether the grid tariffs give

crucial information about the origins of supply interruptions and therefore effect the quality of

supply significantly or not. For reasons of comparability, we choose the nested model KR ,M

which is exactly the same model as KGT ,M , but without the lag of the grid tariffs as

explanatory variable. Thus the nested models KR ,M are

,= ,,4,3,2,1,,'

Kit'

Kiit'

Kit'

Kit'

K'

KKit uPDNUGLQI +++++ αββββ (3)

where ,.RM refers to the reduced model specification without the grid tariffs as explanatory

variable. For the comparison of models KGT ,M and KR ,M the adjusted 2 as well as the

information criteria AIC and BIC are used and can be found in table 1. We expect the same

signs for the coefficients of the restricted model ,.RM as for our model ,.GTM , discussed in the

last paragraph.

4.3 The Econometric Results


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The econometric analysis shows a statistically significant short-run influence of the grid tariffs

on reliability of electricity supply9. The coefficients of the independent variables in the

equations of the power quality indicators SAIDI, SAIFI and CAIDI are shown in table 1.

Generally it can be stated that a decrease of grid tariffs ceteris paribus leads to an increase

in the duration of supply interruptions in Austria, observable through the duration indices

SAIDI and CAIDI. In contrast, there is no significant influence of the grid tariffs on the

frequency of supply interruptions, observable through the frequency indicator SAIFI. Figure 2

shows the effects of changes in grid tariffs on the duration indicators SAIDI and CAIDI based

on the status quo. A decrease of e.g. 7€/MWh in the grid tariffs ( lΔ GT) in this year is

followed by an increase in the average duration of supply interruptions of 6.79 minutes on the

customer level and an increase of 9.49 minutes on the system level and vice versa in the

next year. In our sample the average system interruption duration is 40.73 min, while the

average customer interruption duration is 55.62 min. At this stage an increase of SAIDI of

9.49 min represents a rise in the average interruption duration of ~25%. An increase of CAIDI

of 6.69 minutes means ~12% longer interruptions on the customer level, caused by a

decrease of grid tariffs of ~17%.

9 Data for the examination of mid-term or long-term effects were not available to the authors.


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Figure 2: Changes in grid tariffs in the previous year and their influence on the interruption

duration indices in the actual year.

Other important variables for the explanation of the variations of supply interruptions are the

percentage of network underground, the overall grid length as well as the population density.

For all indicators the overall grid length has a positive influence, expressing that the risk of an

outage is rising with the length of the grid. Grid length is measured in 1.000 kilometers; an

extension of the grid of 500 km causes an increase in the indicator SAIDI of one point. The

percentage of network underground is, as expected, most decisive for the robustness against

power outages. A rise of the percentage of network underground of one percentage point is

followed by a decrease in the duration indicators SAIDI and CAIDI of about one point. For the

indicator SAIFI we see that an increase of percentage of network underground of 10

percentage points would be followed by a decrease of the frequency of outages of 0.05.

Under consideration of an indicator value for all of Austria for the year 2005 of 0.81 (E-

Control, 2006), this would lead to the mitigation of about each fifteenth outage10. Also

significant for all indicators (at least at the 10% level) is the influence of the population

density. Interestingly the signs differ between the system indicators SAIDI and SAIFI and the

customer indicator CAIDI. From the definitions of the indicators in section 3.1 an

interpretation can be given. A decrease in SAIDI and SAIFI along with an increase in CAIDI

is observed if a change in an independent variable effects the components of the dependent

variables with respect to the following relation: jjjs ItIL ∑∑ Δ⋅ΔΔ >)(> . First of all, in case

of population density as the independent variable we expect 0>sLΔ , which represents an

increase in the total installed capacity in areas with higher population density. Secondly the

relation jjj ItI ∑∑ Δ⋅Δ >)( holds when a.) the duration of outages jt is increasing or b.) the

10According to the definition of the indicator SAIFI (see section 3.1) this represents only an

approximation.


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interrupted capacity of short outages jt is decreasing or c.) the frequency of such short

interruptions decreases. Thirdly the relation )(> jjs tIL ⋅ΔΔ ∑ reflects the response of the

total installed capacity and the loss minutes to an increase in population density. This relation

is valid in case of a decreasing frequency or duration of interruptions or an increasing total

installed capacity, where the additional capacity on average faces interruptions < 1 minute.

The last relation js IL ∑ΔΔ > reflects a decreasing interruption frequency or, for the

increased total installed capacity, an average frequency of interruptions < 1, which is valid for

most data points in our sample where the indicator SAIFI has a mean of 0.69 and a standard

deviation of 0.44 (see table 2). Taking into account these analysis we conclude that

population density and the frequency of (at least) short outages are negatively correlated,

which involves a higher relative (not absolute) proportion of longer outages in areas with

higher population density and therefore an increasing population density involves an

increasing CAIDI and a decreasing SAIDI and SAIFI.


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Table 2: MLE estimates for SAIDI, SAIFI and CAIDI using the specifications of our model

GTM an the reduced model RM with data from 12 of the 13 largest Austrian distribution

system operators for the period 2002-2005.

MGT MR

Parameter SAIDI SAIFI CAIDI SAIDI SAIFI SAIDI

143.231*** 1.028*** 143.087*** 79.606*** 0.901*** 100.950***intercept

(4.99) (3.39) (6.43) (4.90) (5.67) (8.05)

1.681*** 0.027*** 0.411*** 1.139*** 0.026*** 0.132*** GL

(3.73) (5.62) -1.201 (3.06) (5.86) (0.39)

-0.912*** 0.006*** -0.962*** -0.771*** -0.006***

-0.892*** NU

(-4.69) (-2.96) (-6.31) (-3.75) (-2.94) (-5.58)

-6.81* -0.191*** 18.305*** -3.869 -0.184 20.709*** PD

(-1.73) (-4.75) (6.06) (-0.97) (-4.77) (7.00)

-1.356** -0.003 -0.970** GT

(-2.42) (-0.47) (-2.19)

N 43 43 43 43 43 43

R2 0.6018 0.6779 0.6193 0.5797 0.67925 0.6048

¯R2 0.5480 0.6345 0.5678 0.5355 0.6455 0.5632

AICα 376.420 36.173 357.094 381.957 27.917 361.566

BICα 382.970 42.724 363.644 388.612 34.571 368.220

*/**/*** significant at the 10%/5%/1% confidence level

αmodel with smaller values of Akaike (1973) and Bayes (Geweke and Meese, 1981)

Information Criterion is supposed to fit the data better than the compared model; t-Statistic in

parenthesis

Table 2 shows that a decrease of the grid tariffs in year 1−t ( lGT) has a significant and

negative influence on SAIDI and CAIDI in year t , which indicates rising supply interruption


Seite 31 von 41

durations in case of decreasing grid tariffs. There is no statistically significant influence of the

lag of grid tariffs on SAIFI, but nevertheless the lag of grid tariffs has the expected negative

sign. Our interpretation of this result is that on the short term grid tariffs do not effect the

frequency of supply interruptions, but may well influence the duration of the interruptions.

This interpretation is supported by the fact that the origins of supply interruptions in Austria

are in the majority of cases natural phenomena. Protection from these kind of origins can

mainly be achieved by an increase of the percentage of network underground which can

never be a short-run measure due to time constraints in planning, financing and construction.

Measures to achieve a shortening of the durations of interruptions can in contrast be

implemented in the short-run. Economic literature on measures for the reduction of

interruption durations was not found by the authors, but interviews with DSOs and technical

experts revealed that the duration of interruptions is partially determined by the budget spend

for maintenance by the distribution system operators. For the engineering aspects on this

topic see e.g. Grigsby (2001; 2007), Fritz and Riechmann (2002).

The financial situation for the maintenance units may determine the replacement rate of

resigned or retired personal and influence the stock of spare parts, resulting in delayed

reparation and maintenance durations. The financial situation can, in contrast to structural

measures on the grid itself, shift in the short run and show effects (positive or negative)

within the observed period of one year.

Table 2 also shows the coefficients of the nested models KR ,M . All coefficients of the

remaining variables have the same sign as in KGT ,M and have the same levels of

significance except for the variable PD (population density). A comparison between the

models with the grid tariffs as explanatory variable and the nested models based on the 2

and the information criteria AIC and BIC shows superiority of our model formulation for the

duration indicators SAIDI and CAIDI and therefore goes along with our interpretation of the


Seite 32 von 41

non-significance of the lag of the grid tariffs as explanatory variable for the frequency

indicator SAIFI.


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Table 3: Properties of the coefficients of the lag of the grid tariffs under the restricted data

sets.

dependent variable

ID of excluded

Utility SAIDI SAIFI CAIDI

P-valueα signα P-

valueα signα P-valueα

signα

1 0.039 - 0.513 - 0.030 -

2 0.150 - 0.844 + 0.098 -

3 0.160 - 0.843 + 0.160 -

4 0.020 - 0.620 - 0.058 -

5 0.010 - 0.085 - 0.027 -

6 0.000 - 0.325 - 0.001 -

7 0.059 - 0.550 - 0.099 -

8 0.070 - 0.980 + 0.023 -

9 0.029 - 0.739 - 0.036 -

10 0.046 - 0.882 + 0.056 -

11 0.026 - 0.325 - 0.042 -

12 0.042 - 0.702 - 0.028 -

α P-values and signs of the coefficients of the lag of the grid tariffs

To examine the stability of our findings we repeated the estimation procedure for models

KGT ,M twelve times, but each time with the sample reduced by one utility. For all 36

estimations we monitor the behavior of the coefficient of the lag of the grid tariffs. Table 3

reports the P-value and the sign for all 36 estimated coefficients. For the duration indicators

SAIDI and CAIDI all signs of the coefficients of the grid tariffs are the same as for the full

sample estimators in table 2. For SAIDI four of the twelve coefficients are not significant on


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the 5% level, while two coefficients are not significant on the 10% level. For the indicator

CAIDI five coefficients are not significant on the 5% level, while only one coefficient is not

significant on the 10% level. When estimating the indicator SAIFI, all coefficients of the grid

tariffs show the expected non-significance at the 5% level and only after the exclusion of the

utility with ID-number 5 this coefficient becomes significant at the 10% level. From this we

conclude that our empirical findings (that there is a short term correlation between the grid

tariffs and the duration of supply interruptions and no such correlation between the grid tariffs

and the frequency of supply interruptions) are stable for all indicators.


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5 Conclusion

The liberalisation of the European energy market was interconnected with declines of

electricity grid tariffs by the regulating authorities, leading to lower energy costs for end-

consumers. This paper shows that the focus on cost reductions comes to short for finding an

optimal structure for the energy market, reliability of supply has to be considered as well, as it

considerably influences the quality of the energy market. The results of the empirical analysis

show that an increased pressure on the Austrian DSOs in the form of commanded reductions

of grid tariffs lead to a deterioration of reliability of supply if no incentives for improving or

maintaining service reliability are provided. The correlation between grid tariffs and electricity

supply interruptions is statistically significant so that we conclude that decreasing tariffs in

this regulatory regime harm the reliability of supply even in the short run. Emanating from the

actual levels of grid tariffs and power quality indicators we find that a decrease of the grid

tariffs of 10% in this year leads to an increase of the interruption duration of 15% on the

system level and an increase of the interruption duration of 7% on the customer level in the

next year.

In Austria, the regulator has only now realised the importance of the incorporation of quality

indicators and benchmarks as essential part of the regulatory framework. The first

implementation phase of a new incentive regulation regime in Austria started in January

2006 (which is after the observation period of this article) and will end in December 2009.

Within this first phase, the regulator announced that for incorporating quality regulation within

this new regulation regime he will define certain standards for reliability of supply and

develop a bonus-malus system for utilities that either comply with these standards or violate

them. The bonus-malus system will enter into force in the second regulation period beginning

in the year 2010. Until then, utilities can voluntarily publish data on their reliability of supply.

The regulator has compiled a standardised paper that can be used for that purpose. Within

the second regulation period the individual standards of utilities will be published by the


Seite 36 von 41

regulator. At the moment, details of the bonus-malus system are not available as they still

are under discussion. Also there is no agreement on the carry-over mechanism yet, but

utilities have already demanded that at least 50% of their efficiency gains achieved between

2006 and 2009 shall remain with the utilities in the second regulation phase.

From the previous sections the authors conclude that a comprehensive regulation regime

needs to provide incentives not only for improving efficiency but also for sufficient investment

in grid capacity and maintenance. Besides monetary incentives the regulatory framework

should be able to guarantee planning reliability for the utilities in the sense that transparency

and consistency of the incentive regime allow the development and implementation of

foresighted strategies to integrate operational objectives and quality of service.

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