Target Incentive

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Development of KPIs for the ElectricitySector in the Kingdom of Saudi Arabia

Targets & Incentives Report

Submitted to: Electricity & Co-generation Regulatory Authority of the

Kingdom of Saudi Arabia

Submitted by: KEMA International B.V., The Netherlands

Arnhem 22 May 2009


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Targets & Incentives Report Development of KPIs for the SaudiElectricity Sector

22 May 2009 2

TABLE OF CONTENTS

1 INTRODUCTION ....................................................................................................................................... 4

1.1 BACKGROUND ........................................................................................................................................... 4

1.2 REPORT OUTLINE ....................................................................................................................................... 4

2 DEVELOPMENT KPI FRAMEWORK ............................................................................................................ 6

2.1 IDENTIFICATION OF KPITARGETS .................................................................................................................. 7

2.2 REGULATORY INCENTIVE MECHANISMS ............................................................. ............................................. 9

2.3 SELECTION OF INCENTIVE MECHANISMS ....................................................................................................... 12

3 GENERATION ......................................................................................................................................... 15

3.1 KPIOVERVIEW ........................................................................................................................................ 15

3.2 DEVELOPMENT OF TARGETS ....................................................................................................................... 16

3.2.1 International Comparisons.............................................................................................................. 16

3.2.2 Comparisons of power generation in Saudi Arabia versus International ........................................ 21

3.2.3 Power Generation of SEC ................................................................................................................ 22

3.2.4 Power Generation of Marafiq ......................................................................................................... 293.2.5 Power Generation of Saudi Aramco and SWCC .............................................................................. 32

3.2.6 Summary of KPI Targets .................................................................................................................. 34

3.3 INCENTIVE MECHANISMS........................................................................................................................... 35

3.4 RECOMMENDATIONS GENERATION.............................................................................................................. 37

4 TRANSMISSION ..................................................................................................................................... 39

4.1 KPIOVERVIEW ........................................................................................................................................ 39


4.2.1 International Comparisons.............................................................................................................. 41

4.2.2 Comparisons Saudi versus International ......................................................................................... 42

4.2.3 Conclusions ..................................................................................................................................... 44


4.3.1 Selection of Incentive Mechanism ................................................................................................... 45

4.3.2 Incentive Mechanism Conclusions .................................................................................................. 49

4.4 RECOMMENDATIONS TRANSMISSION ........................................................................................................... 50

5 DISTRIBUTION ....................................................................................................................................... 51

5.1 KPIOVERVIEW ........................................................................................................................................ 51

5.2 DEVELOPMENT OF TARGETS ....................................................................................................................... 525.2.1 International Comparisons.............................................................................................................. 52


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5.2.2 Comparisons Saudi versus International ......................................................................................... 53

5.2.3 Conclusions ..................................................................................................................................... 54


5.3.1 Selection of Incentive Mechanism ................................................................................................... 55

5.3.2 Incentive Mechanism Conclusions .................................................................................................. 59

5.4 RECOMMENDATIONS DISTRIBUTION ............................................................................................................ 61

6 CUSTOMER SERVICE .............................................................................................................................. 63

6.1 KPIOVERVIEW ........................................................................................................................................ 63


6.3 INCENTIVE MECHANISMS........................................................................................................................... 666.4 RECOMMENDATIONS CUSTOMER SERVICE..................................................................................................... 67

ANNEXES

ANNEX 1: DATA FOR TRANSMISSION KPIs

ANNEX 2: DATA FOR DISTRIBUTION KPIs

ANNEX 3: DATA FOR CUSTOMER SERVICE KPIs

ANNEX 4: PROCESS FOR SETTING LOCAL TARGETS

ANNEX 5: DETERMINATION OF THE COST OF INTERRUPTIONS


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1 INTRODUCTION

1.1 BACKGROUND

The Electricity & Co-generation Regulatory Authority of the Kingdom of Saudi Arabia

(hereafter ECRA) has initiated a project to develop Key Performance Indicators (KPIs) for

the Saudi power sector. KEMA has been asked to assist ECRA in the implementation of the

KPI project. The main objectives of this project are to develop KPIs for generation,

transmission, distribution, and customer service.

As part of this project, a KPI Report has been produced setting out the recommendations for

the KPIs to be implemented. This Targets & Incentives Report builds further on the KPI

Report and assesses the desired level for these KPIs in terms of performance targets. In

doing so, use is made of internationally available datasets and comparable performance

statistics.

A distinction has been made between long-term (to be achieved in 6 years) and short-term

targets (to be achieved in 3 years). The long-term target reflects the desired level of the KPI.

However, one also needs to take into account the time frame required to improveperformance and therefore, for the short-run at least, it will not be practical to apply long-

term targets. To bridge this gap, this report also makes recommendations on short-term

targets, which act as an intermediate between the existing performance level and the future

desired performance level.

Once targets have been determined, the next step is to identify how deviations in

performance relative to the target should be treated. A number of options exist for

implementing such incentives. In this report we develop recommendations on the type of

incentive approach which would best fit a given type of KPI. Also, where incentives are foundto be applicable, we provide recommendations on the size and limitations of such incentives.

1.2 REPORT OUTLINE

This Report is structured as follows:

Chapter 2 presents a general overview of the conceptual background of deriving the targets

for the different KPIs. Also, this chapter sets out the options for designing regulatory

incentive mechanisms and the factors that would drive the choice for a particular approach.


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Chapters 3, 4, 5, and 6 respectively deal with the development of targets for the KPIs

related to generation, transmission, distribution, and customer service. Each chapter starts

with an overview of the KPIs that were selected and present the results of the international

comparisons that have been performed in order to arrive at suitable targets. Further, each

chapter assesses the preferred regulatory incentive mechanism to be used for inducing

target performance.


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2 DEVELOPMENT KPI FRAMEWORK

In developing a KPI framework there are three key issues that need to be considered. First,

a selection of the performance areas to be included in the framework need to be identified

and the performance indicators to be used for quantifying the utilitys performance should be

selected. This issue has been the central theme for the KPI Report that has previously been

produced and in which the final set of KPIs were recommended. An overview of the selected

KPIs is provided in the following table.

Table 2.1. Overview of KPIs to be included in the KPI Framework.

Generation Transmission

G1 Availability Factor (AF) T1 ENS

G2 Forced Outage Factor (FOF) T2 SAIDI-T

G3 Scheduled Outage Factor (SOF) T3 SAIFI-T

G4 Equivalent Forced Outage Rate (EFOR) T4 MAIFI-T

G5 Starting Reliability (SR) T5 Out100 km

G6 Gross Capacity Factor (GCF) T6 Voltage Dips

G7 Net Capacity Factor (NCF) T7 Network Losses

Distribution Customer Service

D1 SAIDI

C1Average Time to Supply ExistingConnections (ATSE)D2 SAIFI

D3 MAIFI

C2Average Time to Supply NewConnections (ATSN)D4 Network Losses

C3Average Time to Reconnect AfterPayment (ATRAP)

C4 Notification of Interruption of Supply (NIS)

C5 Frequency of Complaints (FC)

C5 Frequency of Billing Complaints (FBC)

C7Average Time to Resolve BillingComplaints (ATRBC)

C8Average Waiting Time Call Center(AWTCC)

The second issue is the identification of a proper target level performance for the selected

KPIs. Thirdly and finally, there is the issue of choosing an appropriate regulatory incentive

mechanism to induce the utility to reach the target performance level.


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This chapter deals with the two latter issues and approaches these from a conceptual point

of view. The subsequent chapters 3 till 6 will deal with each of the four respective

performance areas (generation, transmission, distribution, and customer service) and detail

the issues in the context of the specific set ofKPIs.

2.1 IDENTIFICATION OF KPI TARGETS

Once the KPIs have been identified the next step is to formulate the desirable level of

performance. This is expressed in terms of the targeted performance level for the KPI.Typically, for setting the target, regulators consider the historical performance of the utility

and an assessment of this performance in, for example, a regional or international context.

The general idea is that historical performance should be something that the utility is

expected to achieve, however historic performance is not always necessarily the optimal

performance level. Comparisons with (international) counterparts can provide useful

information for the regulator in order to identify the potential for improvement and to

formulate long-term targets which the utility should achieve. Starting from the historical

situation, the utility is expected to gradually increase its performance towards this long-term

target.

Acknowledging the fact that performance improvement is difficult to achieve overnight, a

distinction can be made between long-term and short-term targets. We recommend to apply

6 years for the long-term target and 3 years for the short-term target.

The short-term target acts as an intermediate performance target for the utility which should

be achieved in 3 years. After this, the utility can improve further aiming towards the level of

the long-term target in the next 3 years. The short-term target can thus be interpreted as the

period of time considered reasonable for the utility to improve up to the final target. The

expected performance level can be gradually increased each year over the duration of thetime-period in which the long-term target should be achieved.

For identifying the KPI target we have selected the following approach based on the central

limit theorem. This states that the distribution of a sum of many independent, identically

distributed random variables tends towards the normal distribution theory of normal

distributions. This is illustrated in Figure 2.1. The mean value is equal to the median value in

a normal distribution and about 68% of the values are within 1 standard deviation of the

mean (mathematically, , where is the arithmetic mean and is the standard

deviation), about 95% of the values are within two standard deviations ( 2), and about


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99.7% lie within 3 standard deviations ( 3). This is known as the 68-95-99.7 rule or the

empirical rule.

The standard deviation () is defined as:

=1/N (x I - )

Figure 2.1 Overview of normal distributions

It is common to define the following quartiles:

first quartile (designated Q1) = lower quartile = cuts off lowest 25% of data = 25thpercentile ( 0.6745)

second quartile (designated Q2) = median= cuts data set in half = 50th percentile

third quartile (designated Q3) = upper quartile = cuts off highest 25% of data, orlowest 75% = 75th percentile ( + 0.6745 )


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For deriving the benchmark target, international data samples have been used. Information

about performance of the sample is used as a reference to set the target. Even though one

could opt for the best performing utility in the sample, this is generally problematic as

sometimes this can be driven by data issues or the best utility simply being an outlier. On the

other hand, the mean generally provides a more realistic indication of the target but at the

same time, performance better than the mean should also be considered.

A pragmatic approach is to focus on the so-called peer group which is defined as the

companies that are located two quartiles around the mean outside the standard deviation ()

as basis for setting the target. The principle is given in the following figure. This approach

provides more robust information about the range where the target should be located. We

should note however that the process of setting the target is not a mechanic one and will

involve utilization of the consultants experience and knowledge.

Statistical Analysis

Quartile

(25%)

Performance Indicator

Standard

Methodology

Data Base

PeerG

roup Quartile

(25%)

median

Figure 3.2 Flow scheme of statistical analysis of Key Performance Indicators

2.2 REGULATORY INCENTIVE MECHANISMS

Once the KPI targets have been identified, the next step is to consider the methods that can

be used to encourage the utility to achieve these targets. There are three main methods

which such incentives can be provided namely; (1) performance publication, (2) minimum

standards, and (3) penalty/reward schemes. These three methods are now described in

more detail.

Performance Publication


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Performance publication is when the regulator requires the company to disclose information

about (trends in) its performance to the regulator and/or the general public. Overviews of the

companys performance are reported to the regulator and published, for example, in the

companys annual reports, in dedicated regulatory publications, or on the companys or

regulators website.

Performance publication is relatively simple to implement and does not require the regulator

to develop a view on what should be an appropriate performance target. Such an approach

can be useful in the case where the formulation of a meaningful target is difficult. Even

through there are no financial incentives, the fact that the company is exposed by making

public its performance already creates incentives to maintain a high level of performance.

Overall Standards

An overall standard relates to the performance of the utility averaged over all customers

being served. Here, a minimum target level to be achieved is specified for a certain

performance indicator. However, the utility is generally not exposed to any financial penalties

in case of not meeting the targets. The idea of the overall standard is that the specification of

a target level provides the utility with a tangible objective to achieve that is in line with

regulatory expectations.

Guaranteed Standards

A guaranteed standard relates to the level of performance as experienced by the individual

customer. Here, a minimum target level is specified by the regulator and consequently each

customer is expected to be served according to this target. In the case that a particular

customer is served at a sub-standard level, that customer becomes entitled to a financial

compensation.

Penalty/Reward Schemes

Under a penalty/reward scheme, a more continuous relation is imposed between price and

performance. Each performance level results in a financial incentive, which varies with the

gap between actual performance level and some predefined target level. In case the

company performs below the target, the incentive is a financial penalty, while if the company

exceeds the target the incentive comes in the form of a financial reward. This financial

incentive is proportional to the gap between the actual and targeted performance.


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Different types of penalty/reward schemes exist. Price and performance can be mapped

continuously or in a discrete fashion, the level of the penalty or reward can be capped, dead

bands may be applied, etc. Some examples are provided in Figure 2.2.

Quality Level (q)

Low High

Reward

Penalty

1. Minimum Standard

2. Continuous

4. Dead Band

3. Capped

Financial Incentive ()

Figure 2.2: Examples of penalty/reward schemes. The horizontal axis represents the actual

performance, the vertical axis the financial incentive.

Under the first scheme (minimum standard), after reaching a certain performance

level, a fixed penalty is imposed. This is essentially an ordinary minimum standard.

In the second example the continuous scheme there is a continuous relation

between price and performance. For each level of performance, there is a

corresponding penalty or reward which is proportional to the gap between actual and

target performance.

The third scheme is similar to the second but now with a cap on the level of penalty

and reward. Essentially then, the scheme is only linear within a predefined band;

outside this band, the scheme is similar to a minimum standard and has similar

problems. If performance decreases beyond some minimum level, the penalty paid

by the company does not increase further. Similarly, performance levels exceeding

the maximum level would not generate any additional rewards to the firm.

The fourth scheme has a dead band; performance variations within this band do not

lead to financial consequences. The reason for this is to prevent shocks in the level


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of the financial incentive due to stochastic variations in performance. Stochastic

effects can lead to performance fluctuations and consequently also a fluctuation in

the level of penalties and rewards.

2.3 SELECTION OF INCENTIVE MECHANISMS

When comparing the different incentive mechanisms, we can note that the strength of the

incentive is progressively increased when moving from performance publication to overall

standards, then minimum standards, and finally penalty/reward schemes. At the same time

however, the degree of complexity involved and the implementation costs and risks also

increase. In the implementation of performance incentive mechanism a trade-off should thus

be made between the effectiveness of the mechanism and its costs and risks.

Table 2.2: Comparisons of the design and risks and costs of the different incentive

mechanisms

PerformancePublication

OverallStandard

GuaranteedStandard

Penalty/RewardScheme

Scheme characteristics

PerformanceScope

Average Average IndividualCustomer

Average

Target involved No Yes Yes Yes

Penalty Involved No No Yes Yes

Reward Involved No No No Yes

Incentives and Risks

Incentives Weak Moderate Strong Very Strong

Financial Risks None None Yes Yes

Implement. Costs Low Low High High

Performance publication is the least complex option and in the context of this project, can be

considered the default mechanism that will be applied to all KPIs. It is envisaged that once

the KPI system has been implemented, ECRA will collect performance data of the

companies on a regular basis and make this information available to the general public.

An important limitation of performance publication is that there are no regulatory targetsdefined. Thus, even though information about performance is available, it is unclear as to


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which target performance this should be compared with. To overcome this limitation, the

overall standard can be applied which does clearly promulgate the desired target level. In

case of not meeting that target, no penalties are however involved in general and thus the

utility is not f inancially exposed.

Guaranteed standards are one step further and do expose the utility financially as they

involve penalties to be paid to customers in the form of compensation payments. An

important characteristic of guaranteed standards thus is that they provide direct benefits to

customers who experience low performance levels. This however also involves higher

administrative costs.

Finally, the penalty/reward scheme imposes a more direct link between performance and

financial outcome as well as the possibility of symmetric incentives. That is both inferior as

superior performance lead to financial incentives in the form of penalties or rewards

respectively.

An important question is the type of incentive mechanism ECRA should adopt for each of the

selected KPIs. As mentioned, performance publication can be considered the default

approach and where targets can be clearly specified, this can be extended into overall

standards. The question however is whether it is desirable to move further into the directionof guaranteed standards and then further to penalty/reward schemes. Doing so would

introduce an element of risk to the utility and also lead to a higher regulatory burden. If, for

some reason, the company is not able to meet the target levels, this can trigger high

penalties and could cause financial trouble. Such a situation may occur if the standard level

is set at too high a level or if performance levels have a stochastic nature with large

fluctuations around the average performance over time.

Clearly, the introduction of financial risks would need to be justified by the expected benefits

from implementing more intrusive schemes as opposed to only performance publication or

overall standards. If the latter two mechanisms would be expected to provide sufficient

assurance on their own in that performance will be high, it is probably not worth opting for

more complex schemes, as these are more difficult to implement and administer and also

they introduce an element of financial risk for the utility. In particular if the existing price-

control regime is not very focused on cost reductions, regulators generally have no real

concern that performance will be low.

For example, under rate-of-return systems where the utility is assured of a sufficiently high

remuneration, there is no natural incentive to cut costs on performance. In contrast, under

so-called cap regulation there are very strong incentives to cut costs and some of this can

come at the expense of performance degradation. In such circumstances, the use of stricter


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effective means of regulatory incentive mechanisms can then be justified. Simply stated,

regulators are more willing to take the risks of introducing more complex and intrusive

mechanisms if there are sufficient concerns that the utility will not improve performance on

its own or that performance may even be reduced.

In this report, in the following respective chapters, recommendations have been developed

on the most appropriate incentive mechanism to be applied for each of the selected KPIs. In

doing so, we have taken into consideration the existing and future state of affairs regarding

the regulation of the Saudi power sector. This information has been combined with our

experience from other countries and is used to formulate what we believe is an appropriate

and balanced choice of incentive mechanisms for the different KPIs under consideration.


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3 GENERATION

3.1 KPI OVERVIEW

Based on discussions with the different stakeholders of the electricity sector in Saudi Arabia

a list of KPIs for the generation sub-sector was selected. We believe these are relevant for

ECRA to include in its KPI measurement framework. In developing our recommendations,

we have taken into account the fact that the generation market in Saudi Arabia is currently

not liberalized. It therefore becomes more important and relevant to include certain

indicators that would not be considered so important in liberalized markets.

The following table provides an overview of our recommended KPIs. All KPIs are to be

measured on an annual basis and reported, by each relevant utility, on an aggregated basis

per technology (Simple Cycle (SC), Steam Cycle, Combined Cycle (CCGT), Diesel

Generator (DG) and Cogeneration) and for each administrative region. Note however that at

this point in time, the data received from the utilities is not available at administrative region

level but only by operational area.

Table 3.1 Recommended KPIs for Generation.

Generation KPI Unit Target Level Freq.

G1 Availability Factor (AF) % Yes

Per Admin

Region and

per

Technology

Annual

G2 Forced Outage Factor (FOF) % Yes

G3 Scheduled Outage Factor (SOF) % No

G4 Equivalent Forced Outage Rate (EFOR) % Yes

G5 Starting Reliability (SR) % Yes

G6 Gross Capacity Factor (GCF) % No

G7 Net Capacity Factor (NCF) % No

In the table it is also indicated for which KPI s it will be practical to apply a target and for

which it is not. It should also be noted that the targets are not projected to be applied for

cogeneration plants where steam and water production rather than electricity production is

leading. For instance the design capacity, location and operation of Saudi Aramco and

SWCC plants are determined based on the steam requirements for hydrocarbon facilities


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and desalination facilities. Thus power generation and dispatching criteria for these

cogeneration units will not be subject to any form of dispatching and scheduling instructions

by the system operator.

In addition to the above KPI list, there is some supporting information that would need to be

collected for the purpose of cross-checking. These are shown in the following table.

Table 3.2 Supporting information to be collected for Generation.

Data Unit Target Level Freq.

Service Factor (SF) % No

Gross Maximum Capacity (GMC) MW NoPer Admin

Region and

per

Technology

Annual

Net Maximum Capacity (NMC) MW No

Gross Actual Generation (GAG) MWh No

Net Actual Generation (NAG) MWh No

Gross Annual Heat Rate (GAHR) BTU/kWh No

3.2 DEVELOPMENT OF TARGETS

3.2.1 International Comparisons

The KPIs for which a target is projected will be based on the comparison with international

peer groups. In order to assure comparability, the peer group was selected for different sub-

groups, consisting of units of similar technologies, similar capacity ranges and similar fueltypes as in Saudi Arabia. The following KPIs was investigated and compared with

international values of peer groups:

Availability Factor (AF);

Forced Outage Factor (FOF);

Starting Reliability (SR);

Equivalent Forced Outage Rate (EFOR).


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Targets of these KPIs will be derived for the long term and short term based on

comparisons with the values of the peer groups and utilization of the consultants experience

and knowledge.

The data base of NERC-GADS (Generation Availability Data System) was used to carry out

the statistical analysis of key performance indicators of the different peer groups. This

NERCGADS reporting system, initiated by the electric utility industry in 1982, maintains

complete operating histories on more than 5000 generating units representing 72% of the

installed generating capacity in the United States supplemented by other units in Canada

and in other parts of the world.

The NERC-GADS system is using clearly defined indicators according to the IEEE-762

definitions and is fully in line with the definitions used in the KPI-Report. Before selecting the

different peer groups it is important to compare the dispatch of different units of the peer

group of the NERC-GADS and the units in Saudi Arabia. Peaking units will have other

operational characteristics and requirements than base load units. A comparison is made in

the following table.

Table 3.3 Characteristics of peaking, cycling and base load units

Peaking Cycling Base load

Starting reliability High High Low

Running reliability Low Medium Very High

Thermal efficiency Low Medium Very high

O&M costs High Medium Low

Base load units require low O&M costs, high efficiency (low fuel costs) and a high operating

reliability. This implies a high availability factor and low forced outages.

Peaking units will have low operational hours, which implies that the efficiency of these units

is less important. Peaking units require a high starting reliability because these units must

ramp-up very quickly. Peaking units will have a low capacity factor. Simple cycles are

normally used as peaking units.


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It should be noted that the NERC-GADS sample for simple cycle units represents low

service factors and low net capacity factors and this implies that the NERC-GADS data base

for simple cycle units is a group of peaking units. For this reason the service factor and the

net capacity factor was investigated from the NERC-GADS data base and this was

compared with the units of SEC.

The capacity factor is related to the dispatch of the unit; in very simple terms it shows the

extent to which the generator is used. A high capacity factor implies that the unit is used as

base load unit while a low capacity factor indicates that a unit is used as peak load unit.

Table 3.4 Net Capacity Factor (NCF) and weighted Service Factor (SF) of different technologiesin Saudi Arabia compared to peer group

Technology NCF of SEC

units in 2007

NCF (median of

peer group

Weighted SF

of SEC units

in 2007

SF (median

peer group)

Simple cycle 38 2 58 2

Steam cycle 72 19 78 50

Combined cycle 57 24 71 35

Diesel Generator 17 6 35 9

From this table it can be concluded that the simple cycle units of SEC in Saudi Arabia are

used as base load units and probably also as peaking units.

According to General Electric

1

it turns out that using FOR (Forced Outage Rate) and EFOR(Equivalent Forced Outage Rate) creates an unrealistic, optically poor, misleading

measurement for simple cycle units used as peak load units. The main reason is that there is

no credit for available standby reserve time; and worse, all forced outage hours (nights,

weekends, holidays, unapplied time etc.) are counted. The gas turbine industry is focused on

the FOF (Forced Outage Factor) instead of EFOR. The simple cycle units of SEC have a

service factor and a capacity factor that is comparable with base load units and this means

that EFOR and FOR data of the simple cycle units in the NERC-GADS data base (peaking

1GE Power Systems, Predicted Reliability, Availability, Maintainability for the General Electric 7H gas turbine,


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units) cannot be compared with the data of simple cycle units (base load units) in Saudi

Arabia. Unfortunately, NERC separates simple cycle plants from combined cycle plants and

does not separate out the gas turbine portion of the combined cycle plants.

As a first conservative approach we propose to use the equivalent forced outage rate of the

peer group of combined cycles for simple cycle units that are used as base load and cycling

units. For instance 2/3 of the capacity of combined cycles is contributed by gas turbines.

Combined cycles in the range of (50-125 MW) are corresponding to gas turbines in the

range of 30-90 MW. It is well known that gas turbines contribute significantly to forced

outages in combined cycles due to the complicated technology that is sensitive to forced

outages.

The results of the statistical analysis of the different peer groups are given in next tables

Table 3.5 Overview of quartile spread of weighted availability factor of different peer groups

Weighted Availability factor (%)

Technology-Peer group Q1 Median Q3

Simple cycle (10-125 MW) 83.6 91.2 95.4

Steam plant (100-700 MW 79.4 86.4 90.9

Combined cycle (100-500 MW) 86.2 90.5 95.0

Diesel generator (2-35 MW) 93.4 98.9 99.8

Table 3.6 Overview of quartile spread of weighted forced outage factor of different peer groups

Weighted Forced Outage Factor (%)


Simple cycle (10-125 MW) 5.4 2.0 0.8

Steam plant (100-700 MW 4.4 2.2 1.2

Combined cycle (100-500

MW)

4.3 1.6 0.5


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The starting reliability is much more important for peaking units than for base load units

because peak load have to ramp up very quickly in case of forced outages of the units. If a

unit has many starting problems this may also lead to frequency and voltage changes in the

system. Simple cycle units are often used as peaking units and therefore the statistical

analysis of starting reliability will be only carried out for simple cycle units.

Table 3.7 Overview of quartile spread of starting reliability of different peer groups

Starting Reliability (%)


Simple cycle (10-125 MW) 93.5 98.4 100

Units can be derated due to forced or planned outage. It is very common for a unit to be

partially derated due to technical problems. The equivalent outage factor refers to the

conversion of partial outages including capacity constraints to equivalent full outages. For

this reason the equivalent forced outage rate is defined. The disadvantage is that the

equivalent forced outage rate is more difficult to calculate and this may lead to

misunderstandings if the definitions are not correctly applied or if some data are not

available.

The next table presents the median value of the equivalent forced outage rate of differentpeer groups.

Table 3.8 Overview of quartile spread of weighted equivalent forced outage rate of different

peer groups

Weighted Equivalent Forced Outage Rate



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Simple cycle (10-125 MW)

Peer group of NERC-GADS

Combined cycle (50-125 MW)

9.5 4.0 1.8

Steam plant (100-700 MW 14.0 6.9 3.4

Combined cycle (100-500 MW) 15.4 6.5 2.0


The presented equivalent forced outage rate of the peer group for simple cycles (4.0) is in

line with the weighted EFOR as reported by ISO New England for gas turbines23.

From the statistical analysis it can be concluded that the availability factors and forced

outage factors of simple cycles and combined cycles do not differ substantially. The main

reason is that the gas turbine in combined cycles is the most advanced and complicated

equipment and therefore sensitive to forced outages.

3.2.2 Comparisons of power generation in Saudi Arabia versus International

The key performance indicators were investigated based the information received from SEC,

Marafiq, Saudi Aramco and SWCC. Key performance indicators per unit and per group of

units were received from the different companies were compared with the statistical values

of the KPIs of the different peer groups Simple Cycle (SC), Steam Cycle (ST), Combined

Cycle (CCGT), Diesel Generator (DG) and Cogeneration (COGEN).

Data of SEC were obtained per operating area:

Central Operational Area (COA)

Eastern Operational Area (EOA)

Southern Operational Area (SOA)

Western Operational Area (WOA)

Note that in future, data will need to be reported per administrative region.

22 Interim Review of Resource Adequacy, ISO New England, 2005


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3.2.3 Power Generation of SEC

3.2.3.1 Availability factor

The weighted availability factors of the generation units in different areas of SEC were

compared with the median values of the peer group. The weighted availability factors of the

simple cycle units in WOA and COA were lower than the median value of the peer group.

The weighted availability factor of simple cycle units in EOA and in SOA were almost equal

to median value of the peer group.

Weighted Availability Factor- SC

50.0

60.0

70.0

80.0

90.0

100.0

2005 2006 2007 2008

Year

WAF(%) WOA

COA

EOA

SOA

median value of peer group

Figure 3.1 Weighted availability factor of simple cycle units of SEC compared to a peer group

The weighted availability factor of steam cycles in WOA was lower than the median value of

the peer group and the weighted availability factor in EOA was equal to the median value

(See Figure 3.2).


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22 May 2009 23

Weighted Availability Factor- Steam Cycle

50.0

60.0

70.0

80.0

90.0

100.0

2005 2006 2007 2008

Year

WAF(%)

WOA

EOA


Figure 3.2 Weighted availability factor of steam cycle units of SEC compared to a peer group

The weighted availability factors of the combined cycles operating in COA and WOA were

low compared to the median value of the peer group (see Figure 3.3).

Weighted Availability Factor- CCGT

50.0

60.0

70.0

80.0

90.0

100.0

2005 2006 2007 2008

Year

WAF(%)

WOA

COA


Figure 3.3 Weighted availability factor of combined cycle units of SEC compared to a peer

group


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Weighted Availability Factor- DG

50.0

60.0

70.0

80.0

90.0

100.0

2005 2006 2007 2008

Year

WAF(%)

EOA

SOA


Figure 3.4 Weighted availability factor of diesel generators of SEC compared to a peer group

From Figure 3.4 it can be concluded that the weighted availability factor of diesel generators

in SOA was lower than the median value of the peer group and in EOA equal to the median

value of the peer group.

3.2.3.2 Forced outage factor

The weighted forced outage factors of the generation units of SEC were compared with the

median values of the peer group. (See Figures 3.5-3.8)

Weighted Forced Outage Factor- SC

0.0

2.0

4.0

6.0

8.0

10.0

2005 2006 2007 2008

Year

WFOF(%) WOA

COA

EOA

SOA


Figure 3.5 Weighted forced outage factor of simple cycle units of SEC compared to a peer

group


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Weighted Forced Outage Factor- Steam Cycle

0.0

1.0

2.0

3.0

4.0

5.0

2005 2006 2007 2008Year

WFOF(%)

WOA

EOA


Figure 3.6 Weighted forced outage factor of steam cycle units of SEC compared to a peer

group

Weighted Forced Outage Factor- CCGT

0.0

1.0

2.0

3.0

4.0

5.0

6.0

2005 2006 2007 2008

Year

WFOF(%)

WOA

COA


Figure 3.7 Weighted forced outage factor of combined cycle units of SEC compared to a peer

group


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Weighted Forced Outage Factor- DG

0.0

1.0

2.0

3.0

4.0

5.0

2005 2006 2007 2008

Year

WFOF(%)

EOA

SOA


Figure 3.8 Weighted forced outage factor of diesel generators of SEC compared to a peer

group

From these figures it can be concluded that:

The weighted forced outage factors for simple cycles and combined cycles in COA

were higher than the median value of the peer group;

The weighted forced outage factor for simple cycles and combined cycles in WOA

were lower than the median values of the peer group;

Steam cycles in all relevant operating areas have a lower weighted forced outage

factor than the median value of the peer group;

Diesel generators in EOA and SOA have a higher weighted forced outage factor than

median value of the peer group.

3.2.3.3 Starting reliability

The starting reliability of peaking units was compared to the values of the peer group. From

Figure 3.9 it can be concluded that the starting reliability of the simple cycle units in SOA is

substantially lower than in other regions. The starting reliability of all other units is close to

the median value of the peer group.


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Starting Reliability- SC

50.0

60.0

70.0

80.0

90.0

100.0

2005 2006 2007 2008

Year

SR(%)

WOA

COA

EOA

SOA


Figure 3.9 Starting reliability of simple cycle units of SEC compared to a peer group

3.2.3.4 Equivalent forced outage rate

Equivalent forced outage rates were recently implemented by SEC and for that reason SEC

could only provide data of each quarter of the year 2008. It should be noted that the south

operating area (SOA) was not able to submit data on equivalent forced outage rate onsimple cycles and diesel generators at this moment. The equivalent forced outage rate of the

peer group was compared for the different technologies in the year 2008.

Weighted Equivalent Forced Outage Rate- SC

0.0

5.0

10.0

15.0

20.0

25.0

1Q-2008 2Q-2008 3Q-2008 4Q-2008

Quarter of Year 2008

WEFO

R(%)

WOA

COA

EOA


Figure 3.10 Weighted equivalent forced outage rate of simple cycle units of SEC

compared to a peer group


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Weighted Equivalent Forced Outage Rate- Steam Cycle

0.0

2.0

4.0

6.0

8.0

10.0

1Q-2008 2Q-2008 3Q-2008 4Q-2008Quarter of year 2008

WEFOR(%)

WOA

EOA

mean value of peer group

Figure 3.11 Weighted equivalent forced outage rate of steam cycle units of SEC compared to a

peer group

Weighted Equivalent Forced Outage Rate-CCGT

0.0

2.0

4.0

6.0

8.0

10.0

1Q-2008 2Q-2008 3Q-2008 4Q-2008

Quarter of year 2008

WEFOR(%)

WOA

COA


Figure 3.12 Weighted equivalent forced outage rate of combined cycle units of SEC compared

to a peer group


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Weighted Equivalent Forced Outage Rate- DG

0.0

10.0

20.0

30.0

40.0

50.0

1Q-2008 2Q-2008 3Q-2008 4Q-2008

Quarter of year 2008

WEFOR(%)

EOA

mean value of peer group

Figure 3.13 Weighted equivalent forced outage rate of diesel generator units of SEC compared

to a peer group

From these figures it can be concluded that the weighted equivalent forced outage rates of

simple cycles of SEC in COA and EOA and diesel generators of SEC in EOA were higher

than the median vales of the peer group. The weighted equivalent forced outage rates of

simple cycles of SEC in WOA were considerable lower than the median values of the peer

group. For combined cycles and steam cycles, the weighted equivalent forced outage rates

were much lower than the median values of the peer group.

3.2.4 Power Generation of Marafiq

Marafiqs core business is the operation, maintenance, management, expansion and

construction of power and water systems to provide essential utility services to industrial,

commercial and residential customers in the industrial cities of Jubail and Yanbu. The power

generation units are consisting of steam cycles fuelled with HFO and of gas turbines

provided with heat recovery steam generators to produce steam for desalination plants.

Each heat recovery steam generator can receive exhaust gases from only one gas turbine

while the other gas turbine is either operating in simple cycle mode or on standby. It was

agreed that the gas turbines have to be considered as simple cycles and not as co-

generation units.

3.2.4.1 Availability factor

Gas turbines were considered as simple cycle units and the weighted availability factor ofthe gas turbines units were compared with the median value of the peer group. From figure


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3.14 it can be seen that the weighted availability factor of simple cycle units is equal tomedian value of the peer group.

Weighted Availability factor-SC

50.0

60.0

70.0

80.0

90.0

100.0

2005 2006 2007

Year

WAF(%)

Marafiq


Figure 3.14 Weighted availability factor of simple cycle units of Marafiq compared to

a peer group

The weighted availability factor of steam cycles of Marafiq is lower than the median value of

the peer group.

Weighted Availability Factor-Steam Cycle

50.0

60.0

70.0

80.0

90.0

100.0

2005 2006 2007

Year

WAF(%)

Marafiq


Figure 3.15 Weighted availability factor of steam cycle units of Marafiq compared to

a peer group


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3.2.4.2 Forced outage factor

The forced outage factor of the units of Marafiq was compared to the median value of the

peer group. (See Figure 3.16 and Figure 3.17)

Weighted Forced Outage Factor-SC

-

1.0

2.0

3.0

4.0

5.0

2005 2006 2007

Year

WFOF

(%)

Marafiq


Figure 3.16 Weighted forced outage factor of simple cycles of Marafiq compared to

a peer group.

Weighted Forced Outage Factor-Steam Cycle

-

1.0

2.0

3.0

4.0

5.0

2005 2006 2007

Year

WFOF(%)

Marafiq


Figure 3.17 Weighted forced outage factor of steam cycle units of Marafiq compared

a peer group.

From these figures it can be concluded that the forced outage factors of simple cycles and of

steam cycles are lower than the median value of the peer group.


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3.2.4.3 Starting reliability

The starting reliability is important for the simple cycle units because these units are acting

as peaking units. The starting reliability of the simple cycles is lower than the median value

of the peer group. The reason for this low starting reliability is the decreasing quality of the

fuel and the fluctuating quality of the fuel.

Starting Reliability-SC

50.0

60.0

70.0

80.0

90.0

100.0

2005 2006 2007

Year

SR(%)

Marafiq


Figure 3.18 Starting reliability of simple cycle units of Marafiq compared to a peer

group.

3.2.5 Power Generation of Saudi Aramco and SWCC

The power generation units of Saudi Aramco and SWCC are cogeneration units producing

besides steam for the hydrocarbon facilities and desalination facilities also electricity. In

these types of units the production of steam and water is leading and electricity isconsidered as by-product. The design capacity, location and operation of these plants are

based on the steam requirements. The dispatching criteria for these cogeneration units are

not subject to any form of dispatching and scheduling instructions by the system operator.

Data were received from Saudi Aramco and SWCC. It should be noted that a complete set

of data was only available of the year 2007. Data of previous years were not complete.

Moreover, data of Yanbu and Riyadh refinery of Saudi Aramco were not available and were

not incorporated in the figures below.


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The availability factor and forced outage factor was investigated based on the data received

and the results are plotted in next figures.

Weighted Availability Factor

75.0

80.0

85.0

90.0

95.0

100.0

WAF

(%)

Saudi Aramco

SWCC

Figure 3.19 Weighted availability factors of cogeneration units of Saudi Aramco and SWCC

in Saudi Arabia

Weighted Forced Outage Factor

-

2.0

4.0

6.0

8.0

10.0

12.0

WFOF(%)

Saudi Aramco

SWCC

Figure 3.20 Weighted forced outage factor of cogeneration units of Saudi Aramco and

SWCC in Saudi Arabia


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3.2.6 Summary of KPI Targets

Targets for cogeneration units (Saudi Aramco, SWCC) cannot be applied because steam

production for hydrocarbon facilities and desalination facilities are business driving factors

instead of electricity. For this reason we will focus the targets on those units where electricity

is the main product.

Based on the results the following targets for the short term can be determined for the

different technologies.

Table 3.9 Short term targets for KPIs in generation.

KPI (Weighted) Simplecycle(SC)

Steamcycle(ST)

Combinedcycle

(CCGT)

Dieselgenerator

(DG)

Weighted Availability Factor (WAF) > 85% > 85% > 85% >93%

Weighted Forced Outage Factor (WFOF) < 4%


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Long term targets which are based on the median values of the different peer groups. It

should be noted that the peer groups will also improve the operational aspects such as

availability and forced outages in the coming years as a result of investments in efficient

technologies and reducing operational & maintenance costs. This implies that long term

targets have to change as a result of a continuous improvement of the availability and

decrease of the forced outage of power generation plants.

Table 3.10 Long term targets for KPI's in generation.

KPI (Weighted)Simplecycle(SC)

Steamcycle(ST)

Combinedcycle

(CCGT)

Dieselgenerator

(DG)

Weighted Availability Factor (WAF) > 90% > 85% > 90% >95%

Weighted Forced Outage Factor(WFOF)


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not apply penalties or awards in fully liberalized markets for generators. Rather, the

incentives for high performance are inherently present and/or are contained in Power

Purchasing Agreements (PPAs). In Saudi Arabia, a Single Buyer system is envisaged to be

implemented in the near future which will also be accompanied by a (updated) set of PPAs.

The basic information contained in a Power Purchase Agreement includes the following

items:

Definitions

Purchase and Sale of Contracted Capacity and Energy (such as steam, hot water

and/or chilled water in the case of cogeneration and trigeneration plants

Operation of the Power Plant

Guarantees of Performance

Penalties

Payments

Force Majeure

Default and Early Termination

Liquidated damages

Miscellaneous

The PPAs also prescribe the performance targets to be met by the generator and the

penalties applicable in case of under-performance. In this light, the application of regulatory

penalties will be less desirable. Nevertheless, the targets for the various KPIs formulated

here can act as a useful reference.

Furthermore there is often performance standards (unit availability) tied to rewards orpenalties for meeting the availability criteria. In PPAs sometimes penalties are applied for

de-rating due to the difference, if any, between the dependable capacity, as measured by a

test (being either an acceptance test or a dependable capacity test, as the case may be),

and contract capacity, such difference is defined as deficit capacity.

The penalty mechanism in PPAs is typically used as tools to safeguard the availability of

power plants. Penalties are depending on the requirements of buyer of the electricity. The

buyer may require a high availability and low forced outages. High requirements of the buyer

regarding availability and forced outages will result in the higher operational andmaintenance costs and thus in higher tariffs.


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As an example the Power and Water Purchase Agreement for a 2,600 MW and 55 MIGD in

Ras Laffan C in Qatar is given. In this PPA, there are stringent values for forced outages and

availability. For instance penalties will be applied if capacity is reduced in a given period with

be more than 4%. These penalties will depend on the period of a year. Penalties in summer

time will be higher than in winter time in the Middle East.

In conclusion, for generation, our recommendation is not to apply any penalty regimes but

rather limit regulatory incentives to the publication of performance as compared to the

targets. These targets can be formulated in terms of overall standards that indicate the

desired minimum level of performance however without imposing any penalties in case of

sub-standard performance.

3.4 RECOMMENDATIONS GENERATION

We summarize the recommendations with respect to the targets and incentive mechanisms

for generation in the following table.


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Table 3.11. Summary of recommendations regarding targets and incentive mechanisms forGeneration. SC=Simple Cycle, ST=Steam Cycle, CCGT=Combined Cycle, DG= Diesel

Generator.

KPI (Weighted) Short-Term Targets Long-Term TargetsIncentive

Mechanism

SC ST CCGT DG SC ST CCGT DG

G1: WeightedAvailabilityFactor (WAF)

> 85% > 85% > 85% >93% > 90% > 85% > 90% >95%Overall

Standard,No penalty

G2: WeightedForced OutageFactor (WFOF)

< 4%


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4 TRANSMISSION

4.1 KPI OVERVIEW


a list of KPIs for the Transmission sub-sector was selected that we believe is relevant for

ECRA to include in its KPI measurement framework. The following table provides an

overview of the recommended KPIs for transmission. All KPIs are measured on an annual

basis and reported, by each relevant utility. Since SEC Transmission supplies at voltage

levels down to 13.8 kV and the peer group consist of transmission utilities (voltages above

50 kV), the KPIs related to interruptions are limited to the voltage levels above 50 kV,

namely 69 kV, 110 kV, 115 kV, 230kV, 380 kV.

Table 4.1 Recommended KPIs for Transmission.

Transmission KPI Unit Target Freq.

T1Energy Not Supplied

(ENS)

% Yes Voltage Level:

69 kV, < 69 kV

Type: Planned,

Unplanned,

Generation,

Force MajeureAnnual

T2 SAIDI-T Min/year No

T3 SAIFI-T Int/year No

T4 MAIFI-T Int/year No

T5Out100km

Outages/ye

ar per 100

km

No

T6 Voltage Dips Nr/year NoTotal, Affecting

load

T7 Network Losses % No System


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The KPIs related to interruptions and voltage dips should also be broken down into four

types: Planned, Unplanned, Generation, Force Majeure8.

In case of tie-lines between two utilities, the Consultant suggests that the contribution to the

KPI (that is the duration, frequency and energy lost) related to the tie-line to be divided by

two and share among the companies.

In addition to the above KPI list, there is some supporting information that would need to be

collected for the purpose of cross-checking or for calculation of other indices. These are

shown in the following table.

Table 4.2 Supporting information to be collected for Transmission

Data Unit Level Frequency

Energy Injected GWh Two classes of voltage levels:

69380 kV and < 69 kV

Annual

Energy Supplied GWh Two classes of voltage levels:

69380 kV and < 69 kV

Annual

Peak Power MW Global (delivered to the

transmission network)

Annual

Date and time of the

peak power

Date, time Global Annual

Length of networks km Two classes of voltage levels:

380 kV and below 380 kV

Annual

Number of Delivery

Points

Nr Two classes of voltage levels:

69380 kV and < 69 kV

Annual

Nb of Voltage Dips

That Reduced Load

Nr Two classes of voltage levels:

69380 kV and < 69 kV

Annual

8For SEC T, if a fault occurs at distribution, it may be the case that the distribution operator closes the

breaker that tripped while the fault is still there and as a consequence, the main breaker in

transmission trips, causing a large interruption to many customers (this consist in a human error). In

that case, the behaviour has been out of control of the transmission company and should be classified

as Force Majeure.


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4.2 DEVELOPMENT OF TARGETS

4.2.1 International Comparisons

The regulatory request for monitoring the continuity performance is relatively recent for both

transmission and distribution activities. This regulatory process started out by using the data

collected by the utilities with their own criteria and rules for measurement and calculation.

Consequently there is remains little harmony in the monitoring of continuity performance

around the world.

Some efforts for progressive harmonization are however ongoing, notably in Europe with the

Council of European Energy Regulators (CEER) and European Regulators' Group for

Electricity and Gas (ERGEG). When comparing globally all countries, the diversity of the

transmission contexts and their supply continuity results makes the drafting of a conclusion a

delicate process. On the opposite, when focusing on those countries having set up a

regulatory process for improving the continuity performance, there is more ground for

launching a comparison process.

Comparing does not mean that all values are expected to be equal: this is why even a long

term target is not proposed to be at the level of the best continuity performance but rather atthe level of an average continuity performance. Also, depending on the societal cost of the

non-continuity (often expressed by the value of the Energy Not Supplied, in currency per

kWh), a target can be set higher or lower.

The following tables consist of data from the most recent years of availability9.

Table 4.3 Overview of quartile spread of Transmission KPIS of different countries (for later

comparison with Saudi Arabia values). Note that only ENS will be provided a target.

KPI Q1 Median Q3

T1: ENS 0.00015% 0.0005% 0.0012%

T2: SAIDI-T (min) 0.8 2.1 2.6

T3: SAIFI-T 0.04 0.06 0.09

T4: MAIFI-T (values for T&D) 0.07 0.85 6.0

9As a consequence, some data may refer to different years.


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T5: Outages100km n.a. n.a. n.a.

T6: Nr Voltage Dips 77 146 257

T7: Network losses (%) 2.9% 4.8% 6.85%

n.a.: not available

Targets for transmission KPIs will only apply to ENS. The international statistics on this KPI

come from a set of statistics issued by the CEER 3 rd Benchmarking report dealing with

transmission only for 9 countries. The list of other KPIs (without target) has been prepared

from a set of 7 countries (for SAIFI-T and MAIFI-T) and 11 countries (for SAIDI-T). The

annexes contain the raw data and their country of origin.10 Since the data availability is

increasing during the years and the number of countries participating in benchmarking

studies is increasing as well, it is recommended that the above statistics are to be revised at

regular time intervals (say, every two to four years).

4.2.2 Comparisons Saudi versus International

The following table compares the values from SAUDI ARABIA to those found for the

international peer group.

Table 4.4 Comparison of continuity performance for the Energy not Supplied (ENS)

KPI SEC

Trans

Marafiq

Trans

Q1 Median Q3

T1: ENS 0.00081% n.a. 0.00015% 0.0005% 0.0012%

T2: SAIDI-T (min) 3.67 n.a. 0.8 2.1 2.6

T3: SAIFI-T 0.068 n.a. 0.04 0.06 0.09

T4: MAIFI-T (T&D) n.a. n.a. 0.07 0.85 6.0

10 The countries included in the sample are: Finland, Hungary, United Kingdom, France, Spain, Italy, Portugal,

Norway, Australia, Lithuania, Denmark, Canada, Netherlands.


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T5: Outages100km n.a. n.a. n.a. n.a. n.a.

T6: Nr Voltage Dips n.a. n.a. 77 146 257

T7: Losses (%) 2.66% n.a. 2.9% 4.8% 6.85%

The ENS value of SEC for 2007 has been derived from the SIMLI ( Total energy lost divided

by system peak, reaching 2.57 minutes in 2007) and the peak of 2007 (34,953 MW) for the

whole country: an ENS value of 1,497 MWh is then found for the SEC Transmission System.Then, considering the Energy Supplied by the Transmission network (185,471 GWh in 2007)

a figure of 0.00081% can be derived for the ENS of SEC Transmission in 2007. This figure is

higher than the international median (0.0005%) but less than the international Q3 (0.0012

%).

The SAIDI-T of SEC-Transmission (3.67minutes) appears to be above the median and the

Q3 of international values (2.6 minutes).

The SAIFI-T value of SEC-Transmission (0.068) and of Marafiq (0.81) are also above the

international median found (0.06) but SEC-Transmission is below the international Q3 (0.09).

The number of voltage dips is reported by seven countries11 out of the data collected and the

diversity of the values is higher than for the others KPIs values collected (see Annex). From

Saudi Arabia no value has been submitted (yet) by the companies. Comparisons would only

make sense at a later stage of the benchmarking process.

The transmission losses (2.66%) seem to be a good performance when compared to

international data. This is probably linked to the fact that in Saudi Arabia generators are

relatively close to consumption centers.

The comparisons provided here indicate possible improvements of the performance in Saudi

Arabia since:

- exceptional events (i.e. Force Majeure and Generation failures) have been excluded

from all above statistics,

11Namely Norway, the Netherlands, Australia Queensland, Italy, Portugal, Hungary, France


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- the TSO of the compared countries are supposed to adapt their network to the

context they face in terms of non-exceptional events.

As a consequence, the above set of continuity data presents some homogeneity and

presents a reasonable basis for a regulatory authority to set targets for continuity of supply.

For long term target, the median value of the ENS parameters is proposed.

4.2.3 Conclusions

The median of the international ENS performance (0.0005%) is proposed as a long term

target, which represents an improvement expectation of 0.00031%. Long-term here can be

interpreted as a period of 10 years, which takes into account the high capital intensity of the

transmission business and the long lead times necessary to implement structural

improvements. On the short run, while progressing towards the long-run target, the

intermediate target is based on half this improvement (0.00015%) is deemed to be a

reasonable milestone for setting a short term target of 0.00065%.

To much extent, the time scope for defining the short term can be negotiated taking into

account the implementation delays of the actions that are felt necessary to reach the short

term target.

The continuity KPIs that are without targets like SAIDI-T and SAIFI-T confirm the position of

the Transmission continuity of supply relatively in Saudi Arabia to the international median,

indicating that some improvements are needed if the international median level is to be met.

Other KPIs like voltage dips are more difficult to compare since this is not measured by

most countries, and therefore no comparison will be presented here on this issue. Losses of

SEC Transmission appear to indicate a good performance for a country as large as Saudi

Arabia.

The only transmission KPI to be subject to a target scheme in Transmission is the Energy

Not Supplied (ENS). The long term target is proposed to be the median value of the peer

group, which is 0.0005% of the Energy Supplied.

Note that the target for ENS only applies to the unplanned interruptions category. Events

associated with planned, generation, and force majeure are not subject to a target.

The short term target is a value well above the long term target. This allows sufficient time

for the transmission companies of Saudi Arabia to adapt their means in order to meet the

long term target: The short term target is proposed here at 0.00065%.


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Table 4.5 Short and long term targets for KPIs in Transmission

KPI Present Short Term Long Term

T1: ENS 0.00081% 0.00065% 0.0005%

The targets are set here for the ENS computed from unplanned interruptions of the voltage

range 69 to 380 kV so that the performance can be compared to transmission networks

consisting HV and EHV levels. For the ENS computed from interruptions originating below

69 kV (hence concerning HV/MV transformers of SEC Transmission), the target should be

set at the historical average of SEC Transmission (3 last years), international data not beingavailable on this specific component and voltage levels.

4.3 INCENTIVE MECHANISMS

4.3.1 Selection of Incentive Mechanism

As discussed in Chapter 2, in the context of a cost plus regulation, which is the context of

Saudi Arabia in 2009, there is usually no immediate need for incentives scheme on quality ofservice because the companies are not limited in their investments by a cap on their

revenues and consequently on their costs. On the contrary, in context of a price-cap

regulation, some control of the quality supply is to be implemented. In this light, the use of an

overall standard seems to be the most appropriate approach at this point in time.

Nevertheless, in the future ECRA may wish to adopt stricter incentive mechanisms if a

change towards a stricter form of price control is adopted. Anticipating such a transition in

future, it may however be worthwhile to investigate here the specifications of a

penalty/reward scheme to be applied at that point in time.

In terms of setting a penalty/incentive scheme for ENS, two main schemes are usually

considered: the minimum standard and the penalty/reward system. The drawbacks and

advantages of both are summarized in the table below:

Advantages Disadvantages

Minimum Standard

Scheme

All customers benefit from a

same Guaranteed Service

Level (GSL)

The verification that the GSL

is met at each customer

location implies a the


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presence of a meter able to

count the interruptions

Penalty/Reward Scheme Is compatible with the classic

type of meters

Tends to avoid that the utility

profits from too low

investment in reliability

Customers are treated as

the average customer

enjoying an average

continuity of supply

Since the two schemes do not counter each other out, they can be implemented to

complement each other. However, as may be observed, the penalty/reward scheme is in

principle more desirable. International experience has been gained in this field in several12

countries.

The penalty/reward schemes are all based on the following principle: the allowed revenues

of the TSO are modified upwards or downwards depending on the continuity performance

achieved. The experience of countries having set a penalty/reward system suggests two

additional criteria:

in order to avoid every year a computation of financial transfers and their realization

for performances that are in fact close to the target, a dead-band should be

considered around the target: in that way, no penalty and no reward is applied if the

performance observed is within the dead-band.

In order to limit the financial exposure of both the TSO and the public financing the

scheme, a cap is applied to the penalty and to the reward for the reporting period (the

year, normally). This cap is usually between 1% and 2% of the turnover.

Based on the above concepts, the following characteristics are proposed for the scheme to

be implemented in Saudi Arabia:

1. The dead-band is proposed to be at levels 95 % and 105% of the target.

2. The cap on the reward and on the penalty to be at 1% of the company turnover

3. Smoothing the performance by using the average performance on 3 years for the

incentive scheme

12Great Britain, Hungary, Italy, Estonia


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With respect to the level of the financial penalty/reward, economic theory suggests that this

should be set equal to the societal costs of the average interruption CENS expressed by

the value of the ENS (in Riyals per kWh not supplied). At country level, this value is usually

between 1 USD/kWh and 10 USD/kWh and varies from one country to another. Within a

country and from one type of customer to another, the cost of interruptions CENS varies

even more. For this reason it should preferably be set on the basis of a customer survey. For

specific customers with a high sensitivity to the electricity supply13, this value can be very

high particularly if no back-up supply is implemented.

An initial estimate is often proposed as the GDP divided by the gross electricity demand. For

2007, the energy sold to the customers in 2007 has been 169,750 GWh and the GDP has

been 1 401.3 billion SR14 or 374.2 billion USD. Hence, for Saudi Arabia in 2007, the value

CENS has been 8.255 SR/kWh, equivalent to 2.203 USD/kWh. However, we should point

out that this estimate is only indicative because by essence it includes the economic product

of activities that are almost independent from the electricity consumption (in many countries,

the agriculture does not depend on electricity but only on some types of fuel for the

transportation). Also, it hides the diversity of the consumers and their willingness to pay for

an improved continuity. The CENS concept also ignores the difference between short and

long interruptions, while in fact the incurred costs vary a lot with the interruption duration.

Basically, the CENS concept assumes that all economic activities depend on the availability

of electricity: the idea is that every kWh represents a fraction of the electricity consumption

and makes possible the creation of economic activities on average up to the same fraction of

the GDP. As a whole and although imperfect, the above estimate of CENS is the most

practical value to start with in the frame of this project. The annex 5 proposes a basic

questionnaire for a customer survey to assess the willingness to pay for avoiding

interruptions, so that estimates of the CENS can be developed.

The financial transaction can then be represented as a function of the continuity

performance (the average ENS on 3 years, as indicated above, expressed in percent of theenergy supplied to the delivery points), where ENSt is the target level and TO is the turnover

of the utility. This function is represented by the graphic below.

As can be observed, the incentive scheme only translates into monetary terms the actual

level of performance when it is outside the dead band of +/- 5% around the target level.

Furthermore, the level of the penalty or reward is capped at 1% of the turnover.

13hospitals, airports, banks, industries that may lose the daily batch production in case of an interruption 14

http://www.economist.com/Countries/SaudiArabia/profile.cfm?folder=Profile-Economic%20Structure


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Figure 4.1: Proposed incentive scheme for ENS in Saudi Arabia

Having defined the cap and the dead band by Cap and db, and a base revenue

allowance Ro independent from the quality performance, the revenue allowance R of each

transmission company would then typically be adjusted by its quality performance by the

following expression:

If ENS < ENSS*(1- db) a reward

G= (ENSs-ENS)* CENS > 0

is given, so that the total revenue of the utility becomes

)*)();1(*max( CENSENSENSsRoCapRoR

If ENS > ENSS*(1+ db) a penalty

P= (ENS-ENSs)* CENS > 0

High Performance

Reward

Penalty

0.95*ENSt ENSt 1.05*ENSt ENS (% of ES)

0.01*TO

0

0.01*TO

Low Performance


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22 May 2009 49

is applied, so that the total revenue of the utility becomes

)*)();1(*max( CENSENSsENSRoCapRoR

4.3.2 Incentive Mechanism Conclusions

In Saudi Arabia, the regulation in place in 2009 is cost plus regulation. Under cost plus

regulation, companies may introduce in their eligible costs for tariff setting all their

investments costs plus an agreed rate of return: in such a context, there is no factor

encouraging the utility to under invest for preserving the quality of supply. Therefore, there is

no real need for an incentive scheme as far as the regulation regime remains as cost plus:

A proposed incentive scheme is only recommended when another regulation regime (for

example price-cap) is put in place.

The incentive scheme described above will however only become important if price-cap

regulation is introduced and where companies tend to be investing in too low amounts in the

quality of supply. This scheme is configured such that around the target, i.e. beyond a

certain dead-band, a reward proportional to the excess of quality is awarded to the

company, while a penalty proportional to the deficit of quality is imposed to the company for

any such deficit of quality. These rewards and penalties are also limited by a cap, and the

whole scheme can be summarized by the following three characteristics:

Smoothing by using the average performance on 3 years for the incentive scheme

(but the yearly performance is to be reported)

The dead-band is proposed to be at levels 95 % and 105% of the target.

The cap on the reward and on the penalty is set at 1% of the turnover of the

regulated company

The slope of the reward and of the penalty should be equal to the value attributed to

the ENS namely 8.255 SRD/kWh

In terms of implementation, the scheme can consider the filling of a so-called account where

companies can record the financial sums i.e. penalties/reward as a result of the incentive

scheme over a certain period. This could be done for example every three or four years. In

this way, the low quality in one year and its related penalty can be compensated by high

quality of the next year and its related reward, and the difference would be subject to the

financial transaction as the end of the period.


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As per the experience gained in countries that applied the scheme, these characteristics

proved to be successful and has to lead to the continuity of improvements while limiting the

financial exposure of the company to the scheme.

As already stated above, this scheme could be applied from the moment when the regulation

in place changes from the existing cost plus to a possible price -cap regulation regime.

4.4 RECOMMENDATIONS TRANSMISSION

We can now summary the recommendations with respect to the targets and incentivemechanisms for transmission in the following Table.

Table 4.6 Summary of recommendations regarding targets and incentive mechanisms for

Transmission.

KPIUnit

Short-Term

Target

Long-Term

Target

IncentiveMechanism

T1

Energy Not Supplied (ENS) MWh/year 0.00065% 0.0005%

Overall Standard(now)

Penalty/Reward(in future)

T2 SAIDI-T Min/Year

T3SAIFI-T Int/Year

T4 MAIFI-T Int/Year

T5Out100km

Outages/year per

100 km

T6 Voltage Dips Nr/year

T7Network Losses %


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5 DISTRIBUTION

5.1 KPI OVERVIEW


a list of KPIs for the distribution sub-sector was selected. This list has been complied based

on its relevancy for ECRA to include in its KPI measurement framework. The following table

provides an overview of the recommended KPIs for distribution. All KPIs are measured on

an annual basis and reported, by each relevant utility.

Table 5.1 Recommended KPIs for Distribution.

Distribution KPI Unit Target Lev

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