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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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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 DEVELOPMENT OF TARGETS ....................................................................................................................... 41
4.2.1 International Comparisons.............................................................................................................. 41
4.2.2 Comparisons Saudi versus International ......................................................................................... 42
4.2.3 Conclusions ..................................................................................................................................... 44
4.3 INCENTIVE MECHANISMS........................................................................................................................... 45
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 INCENTIVE MECHANISMS........................................................................................................................... 55
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
6.2 DEVELOPMENT OF TARGETS ....................................................................................................................... 64
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 (%)
Technology-Peer group Q1 Median Q3
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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Diesel generator (2-35 MW) 0.5 0.1 0.01
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 (%)
Technology-Peer group Q1 Median Q3
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
Technology-Peer group Q1 Median Q3
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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
Diesel generator (2-35 MW) 12.0 5.1 1.4
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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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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
median value of peer group
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
Based on discussions with the different stakeholders of the electricity sector in Saudi Arabia
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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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
Based on discussions with the different stakeholders of the electricity sector in Saudi Arabia
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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