Identifying Critical Components for Transmission System Reliability

2106 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 27, NO. 4, NOVEMBER 2012

Identifying Critical Components for TransmissionSystem Reliability

Johan Setréus, Member, IEEE, Patrik Hilber, Member, IEEE, Stefan Arnborg, Member, IEEE, andNathaniel Taylor, Member, IEEE

Abstract—This paper presents a method to quantify and ranktransmission system components by their importance for systemreliability under different load scenarios. Each component isranked by three separate importance indexes based on its ex-pected outage rate and impact on: 1) system security margin;2) load supply; and 3) generation units. By studying these threeinterests individually, a more complete view of the risks to systemreliability can be assessed. The method is demonstrated on adetailed power system model (7000 components) of a significantpart of the Great Britain transmission system at 400 and 275 kV.The results show how sensitive the component indexes are to theload scenario. The method provides an input for decision-makingwhen planning maintenance and new investment and can be usedas a complement to deterministic criteria.

Index Terms—Component reliability importance, Great Britainpower system, power flow analysis, system adequacy, system secu-rity, transmission system reliability.

NOMENCLATURE

Outage event.

Component identifier.

System load scenario.

Component ’s expected outage duration [h].

component ’s expected forced outage rate [f/yr].

Stuck probability for circuit breaker [-].

Set of outage events caused by .

Expected unavailability for outage event [-].

Critical transfer section (CTS) in system.

Start of loading risk interval on CTS [%].

End (roof) of loading risk interval on CTS [%].

Transfer in CTS after event at scenario[MW].

Transfer limit in CTS after event at [MW].

Manuscript received August 24, 2011; revised December 09, 2011; acceptedJanuary 31, 2012. Date of publication April 03, 2012; date of current versionOctober 17, 2012. This work was supported by the Swedish Centre of Excel-lence in Electric Power Engineering (EKC2). Paper no. TPWRS-00799-2011.J. Setréus and S. Arnborg are with Svenska Kraftnät, SE-172 24 Sundbyberg,

Sweden (e-mail: [email protected]).P. Hilber and N. Taylor are with the School of Electrical Engineering, KTH

Royal Institute of Technology, 100 44 Stockholm, Sweden.Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TPWRS.2012.2188144

Consequence of event for CTS at .

Power not supplied due to event at [MW].

Disconnected generation due to event at[MW].

Component ’s importance for transmissionsystem operator (TSO’s) security margin atscenario .

Component ’s importance for DisCOs at .

Component ’s importance for GenCOs at .

I. INTRODUCTION

R ELIABILITY assessments are of great concern for thepower transmission system operator (TSO). One chal-

lenge is to identify components (e.g., lines and transformers)that are critical for system reliability by quantifying their as-sociated risk. This link between reliability of components andsystem is an important input to the decision-making when plan-ning for operation, maintenance, and investment.Planning and operation of power transmission systems (PTS)

have traditionally expressed and designed the reliability securitylevel according to the deterministic criterion and its equiv-alents. These methods require that the system can withstand theloss of any single component without violating system function(e.g., load supply) or system limits (e.g., thermal, voltage, or in-stability).The fulfilment of deterministic criteria is normally checked

for a large number of outage events (e.g., loss of busbar, line,or production unit) in an initial contingency security analysisfor a typical base case scenario. The power system model usedfor this checking may include either or both of steady-state anddynamic analyses. One result is the total transfer capability(TTC) limit [MW] that each critical transfer section (CTS)in the system can handle prior to the worst (dimensioning)outage, without violating system limits. A CTS is normallydefined as a geographical cut across certain transmission lines,where the transfer on the section is the sum of all includedlines’ active-power flows. The results from the initial base casecontingency analysis can then be used in real-time operation orplanning to estimate the CTS TTC limit for a specific systemstate (topology, demand, generation, etc).The CTS limits are set based on system violations for a lim-

ited number of major credible events that are known to be di-mensioning for the base case scenario. Given a specific systemstate, the CTS limits provide the TSO with one indicator of the

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SETRÉUS et al.: IDENTIFYING CRITICAL COMPONENTS FOR TRANSMISSION SYSTEM RELIABILITY 2107

power transfer margin of fulfilling the deterministic security cri-teria, and thereby one measure of expressing the expected se-curity margin. The above procedure thus makes it possible toidentify component outage events that are critical for system re-liability, but it does not include any estimate of the likelihoodsof different outages. The outage with the worst consequencesets the system capability, and events with lower consequencebut higher likelihood may be missed; nonoptimal decisions maytherefore be made in the planning process.Methods and models for probabilistic reliability assessments

of power systems has been a major research topic during thelast decades [1]–[5]. Relatively little work has been publishedwithin reliability importance indices, which combines thecomponent’s potential system consequence with its proba-bilistic behavior (e.g., expected outage rate) and provide a linkbetween component and system reliability. Classical indexesdeveloped for general network models are e.g., Birnbaum’sand Fussell–Vesely’s measures [6]. In [7] and [8], a numberof these classical indices have been adjusted to suit powerdistribution systems with multiple load points. Three indicesare presented in [7] that rank the components based on theirreliability and associated interruption cost. For PTS applica-tions, [9] has transformed classical importance indexes to rankcritical components in common substation configurations.However, there are few published methods that quantify the

component importance for system reliability in large PTSs withmeshed topologies. One example is [10], where 38 lines in theIEEE-RTS test system are ranked with a sensitivity analysismethod that gives a monetary measure of the component’simpact on system adequacy. In [11], a method is presentedfor PTSs that combines classical importance indexes with thesystem security impact evaluated by dynamic simulations.The approach is demonstrated on a detailed model (includingprotection system) of the Finnish PTS by identifying criticalcomponents for system instability initiated by line faults. Expertjudgment is used to classify the post-fault system condition ofthe approximately 1600 dynamic simulations. This is a detailedbut time-consuming method.Another approach to study the security margin is to use static

(power flow) analysis to study the associated risk of havingoverloaded components or under-voltages in the system, as pre-sented in, e.g., [12]–[14]. A component’s risk is determined bya number of credible outage event’s consequence (thermal over-load) and probability. In [15], we propose a similar consequencemeasure except that we study each component’s risk of causingthermal overloads in the system. Furthermore, the overloads arestudied on CTSs instead of separate components. The advan-tages are that they provide a measure of the system’s securitymargin, and many TSOs already supervise the CTS’s limits inplanning and operation of the system. In this paper, the limitsare set with regard to thermal, voltage and system stability cri-teria based on static and dynamic simulations. If the assessedCTSs are the transfer congestions in the system, their conditionis assumed to be a good indicator of the system security level.A stressed CTS implies a small security margin in the system,which in its turn implies a higher risk of system collapse.An outage event in the power transmission system (PTS) may

cause interruption of supply at load points (LPs) where distribu-

tion system companies (DisCOs) receive power to deliver to endconsumers, and may cause disconnection of generation unitsowned by generation companies (GenCOs). Since the PTS isplanned and operated secure against the majority of componentoutage events that occur, very few outages result in direct con-sequences for the DisCOs and GenCOs. There may however bethe indirect consequence of a stressed security margin that theTSO needs to handle. In this paper, we look from the TSO’sviewpoint and consider all connected DisCOs and GenCOs ascustomers of the TSO. Both request a functioning grid connec-tion from the TSO, but the TSO also has an internal require-ment of a certain security margin against severe events. Hence,we argue that the TSO needs to consider all three parties in theplanning and operation of the PTS to get a complete view of thecritical components for system reliability.The contribution of this paper is a method of quantifying

the risk associated with each component, to the system and itscustomers. Three separate importance indices are assigned toeach component based on its risk to the TSO’s security marginand interruption of the PTS connection to DisCOs and GenCOs.The method is based on availability analysis with constant com-ponent unavailability data as input and an exhaustive analysisof outage events’ impact on the three parties at different loadscenarios. In contrast to earlier studies concerning componentreliability importance indexes for power systems, we use an acpower flow method to provide a realistic consequence model.This is important for PTSs since interruptions of supply orlarger blackouts seldom occur due to pure topology reasonsbut to e.g., redirected power-flows, under-voltages and stabilityissues. We also use a power system model where the substationconfigurations are modelled in detail, which enables a moreaccurate model, for, for example, cascading outage events andnonfunctioning protection equipment. Comprehensive bulksystem reliability studies and softwares modelling these prop-erties has certainly been published before, see, e.g., [2], [4], and[5], but within component importance indices earlier publishedmethods and applications have been constrained to relativelysmall test systems. The presented method is here applied on amodel of a real PTS, the Great Britain (GB) system, providingrealistic output results for identifying the critical components.The load demand and network topology are varied by the studyof a number of system base-case scenarios with constant load,showing its effect on set of most critical components.

II. PROPOSED COMPONENT IMPORTANCE METHOD

The proposed component importance method is based ona systematic analysis of outage events’ probability and con-sequence on: 1) TSOs security margin; 2) DisCOs; and 3)GenCOs. The initiating outage events in this paper includesingle or multiple component outages caused by equipmentfailures. By allocating the outage events’ risk to the involvedcomponents, the final result is a ranking list with the mostcritical components for each of the three interests in thetransmission system. The first subsection below describes themethod’s algorithm, followed by the definitions for the threeproposed component indexes used for ranking.


A. Method Algorithm

Preparation of System ModelStep 1) The transmission system model is defined at compo-

nent level. Each line, circuit breaker, busbar, SVC,etc. is represented in the model. Impedances for thetransmission lines, together with generator and loadbus parameters are set in order to perform ac powerflow analysis.

Step 2) A base-case load scenario is selected, defining,e.g., load demand, operating topology, line capac-ities, and scheduled generation.

Step 3) The CTSs are identified by the TSO and the limitsare set secured to thermal, voltage and system

stability criteria based on static and dynamic simu-lations.

Step 4) Reliability data is set for each component in thesystem. Failure modes included are, e.g., forcedcomponent outages (short and long) and nonfunc-tioning protection equipment.

System AnalysisStep 5) The enumeration technique is adopted. A list with

single and multiple component outage events is con-structed, where . The expected un-availability for each event is estimated from theinvolved components’ reliability data.

Step 6) The impact of each outage event on: 1) CTSs; 2) load points ; and 3) generator

units is evaluated with an ac power flowanalysis, including modeling of post-contingencycorrective actions (e.g., tripping of overloaded com-ponents).

Step 7) The three proposed component reliability indices, and are calculated for load sce-

nario , given the definitions in (1), (6), and (7)below.

Step 8) Steps 2)–7) are repeated for each load scenario tobe studied.

B. Component Reliability Importance for Security Margin,

The component importance index for the security margin isdefined as

(1)

which can be interpreted as the sum of risks for all outageevents , at load scenario , where component caused theevent. “Caused” means here the final component outage thattriggered the event; this procedure of allocating an event’s riskto its involved components is presented in e.g., [8] and is toavoid double counting. The event of an outage in componentfollowed by an outage in component , adds a

risk to but not . Hence, the outage event ofalso needs to be studied. The risk contribution from the eventis defined in (1) as the product of event’s consequence to theCTS and an estimated probability of this systemstate occurring.

Fig. 1. Example of for % and %. The 100% relativeloading is the deterministic secured CTS limit set by the TSO. Outage eventsresulting in transfers close to or over this limit affect the security margin. Thisis quantified by .

The consequence of outage event on CTS is defined as

(2)

and is proportional to the square of the relative active powerloading in CTS after event , where “after” means that thesystem has reached a new steady state following e.g., operationof protection and re-dispatching of generation. The termis the new CTS limit set by the TSO based on the new systemconditions due to an outage event or changes in e.g., demand orgeneration. For the TSO, this new deterministic secured limitcan be calculated in a real-time contingency analysis with a stateestimator, given a set of contingencies that are known to be di-mensioning for the system.Parameter sets the start of the loading interval for where

the relative active loading [% of limit] in CTS constitutes aconsequence to the security margin. All transfers below this in-terval have no impact . Parameter sets the endof this interval, where the consequence on the CTS reaches itsmaximum . Fig. 1 shows an illustration offor % and %. Outage events which ac-tually result in CTS collapse or system collapse are definedas , since the consequence is “extreme” in theseevents. This is also the reason for having a maximum conse-quence set by ; we need to be able to quantify the consequenceof these extreme events.The average unavailability for outage event is used as

an approximation for the probability of the system state whereevent has occurred, and this approach is also used in, e.g., [9]for component importance indices. For large systems, this ap-proximation may result in relative large errors when calculatingsystem reliability indexes [2]. However, since it is the compo-nents’ relative importance to system reliability that is of interesthere, this approximation has minor impact on the final methodranking results.


In this paper, the initial component outages in the event are as-sumed to be independent, and the approximative methods in [1]are used. Initial outage events of first and second order are con-sidered in this paper, but this is not a restriction in the method.The following equation shows the definition of for a first-order outage event which includes a permanent outage of com-ponent :

(3)

The following equation defines for a second order outageevent, where the first permanent outage is in component andthe second in component , denoted :

(4)

The reversed event of also needs to be enumer-ated with the same equation. The event of a component failurein followed by a non-functioning (stuck) circuit breaker isdefined for in

(5)

where is the expected stuck probability for .

C. Component Reliability Importance for DisCOs,

Component ’s risk of causing interruption of supply at loadscenario is defined as

[MWh/yr] (6)

The index has the same definition as in (1), except for theconsequence [MW], which is the average power not sup-plied (PNS) due to outage event in the transmission system,given base-case scenario . The estimate of includes acpower flow calculations with corrective action models for, e.g.,alleviation of circuit overloads, generating unit rescheduling,violated voltage levels, and load shedding. can be inter-preted as component ’s contribution to the system’s annualizedexpected energy not served (EENS) [MWh/yr] at a constant loadscenario .

D. Component Reliability Importance for GenCOs,

Component ’s risk of causing disconnected generation (DG)at load scenario is defined as

(7)

The index has the same definition as in (1), except forthe different consequence [MW], which is the generatorunits’ average scheduled power not possible to supply to thegrid due to outage event , given base-case scenario . Theplanned utilization for a generator unit may not be possible dueto the outage event’s impact on network topology and system

limits. The estimate of is calculated as the total sum ofall disconnected units’ difference between its scheduled gener-ation at pre-contingency for base-case , and its generation atpost-contingency after corrective actions has been performed inthe system. It has to be noted that the disconnected generationcaused by the event normally is re-dispatched to other gener-ation units. However, the scheduled generation is affected andthis constitutes a risk for both the GenCOs and the TSO, quan-tified by the component importance index . The index willdepend on the generator units’ utilization at pre-contingency,which can vary even for the same load demand due to e.g.,economic reasons. Hence, the generators’ utilization needs tobe carefully modelled based on earlier experiences, and sev-eral system scenarios may be studied for the same load demandlevel.

E. Discussion

The index allocates each component’s risk of causingoutage events resulting in system states with load curtailments,which is the last desirable option for the TSO. However, manyevents in the PTS have none or negligible impact on the loadpoints (DisCOs) but result in violations in system limits, andcritical components contributing to these events are identifiedby . identifies critical components of causing reducedgeneration capabilities in the system, as from the GenCOs view-point an important aspect of system reliability. The three in-dexes provide a differentiated view of a particular component’simportance to system reliability. A component may constitute ahigh risk to one interest but have negligible importance the othertwo. The index results give a multi-objective decision-makingof which component that provides the highest risk to system reli-ability. This ranking has to be considered as one input to the de-cision-making, together with several other aspects such as e.g.,component aging and economic considerations.

III. GB TRANSMISSION SYSTEM MODEL

No existing test-systems for power system analysis werefound that included data for the secured CTS transfer limits,which we needed to test the proposed method. Transmissionsystem data is often classified, making it difficult to publishmodels and results from such studies. Details for the GB trans-mission system are a notable exception: the TSO ‘NationalGrid’ annually presents the GB Seven Year Statement (SYS),with extensive and unrestricted data for the 400- and 275-kVlevels [16], including system topology, circuit parameters, loaddemand, and CTS limits. The GB PTS was therefore used inthis work, to provide realistic system details and make theresults reproducible from publicly available data.The GB PTS is secured with an criterion in plan-

ning and in operation [17]. As a somewhat simplifieddescription, the implementation of in GB includes anysingle outage event of a line, busbar or generator, or loss of adouble-circuit line. The criterion further includes anysingle line outage occuring within the 60 s preceding the outageof another line or generator [17], [18].


TABLE ISIZE OF THE IMPLEMENTED GB TRANSMISSION SYSTEM MODEL

Fig. 2. The shaded area represent the implemented part of the GB transmissionsystem at 400 kV and 275 kV. The analyzed critical transfer sections B7, B8,B11 and B16 are defined in [16].

TABLE IISTUDIED SYSTEM SCENARIOS IN GB SYSTEM MODEL

A. Implemented Model

A significant part of the PTS of England and Wales (Fig. 2)has been implemented by the authors in a model for four load-scenarios, based on data from the 2007 GB SYS. Table I lists theincluded component types and their abbreviations, and indicatesthe size of the implemented model, which includes 107 detailedsubstations. The area of the implemented model is crossed byfour of the systems’s total of 17 CTSs, as shown in Fig. 2. Thetransfer limits of the CTSs are set by the TSO, against thermal,voltage, and system stability criteria. The limits [MW](for an intact system and at peak) for each CTS are:

, and [16].The four load scenarios have been implemented based on

demand data in GB SYS [16], which includes each individualLP’s summer minimum and winter peak demand. The two ad-ditional load demands have for each LP been selected in the in-terval between these two extremes: Table II shows the resultingtotal system load demand for each scenario.

B. General Assumptions for Power System Model

The substation configurations are not specified in GB SYS;assumptions are therefore made based on the voltage level, in-going branches, and the node-names which indicate if the sta-tion’s BBs are capable of being coupled but are operated sepa-rately. The latter station type is implemented where applicable.Substations at 275 kV are implemented with a single-BB config-uration if the ingoing/outgoing branches are fewer than seven,as breaker-and-a-half for seven to ten branches, and otherwisewith a 2-BB-2-CB configuration. For 400-kV substations, thesame numbers are two, three-four, and five.The base-case network topology is assumed to be intact with

no planned component outages in the four system scenarios.Since the utilizations (%) of the generation units are un-

known, these are estimated by calculating the in/out net powerflow of each substation. This is possible given the LP demands[16] and the single line diagram with the GB system powerflows at winter peak [16, Fig. C.3.1]. For sites withmultiple generators, it is assumed that the units are utilized asefficiently as possible (e.g., one unit operating at 99% outputpower instead of three at 33%). Nonutilized generators aredisconnected from the grid at lower load scenarios.The line overload protection equipment in the system is as-

sumed to trip at 120% of the line’s thermal capacity (listed in[16]) for the studied scenario. This general assumption for alllines is based on formulas in [19], given a standard conductor’sthermal capacity, a temperature of 0 C, a 0.6 m/s wind speedand 15 min overloading capability.There are 17 interconnections to the adjacent (nonmodeled)

parts of the system. These are modeled as fully reliable genera-tors: 13 are modeled as strong PV-nodes and four as the system’sdistributed slack buses. Their maximum generating limits havebeen set to reasonable values in accordance with the capabilitiesof the adjacent grids.All SVCs are configured to a zero reactive compensation at an

intact system configuration. The reactive compensation equip-ment (CAP, REA) are set (on/off) so that the system fulfills thepre-fault planning voltage limits defined in [17] for each sce-nario.All four studied CTSs are considered to be equally important

to the system security margin. The parameters and in (2)are for all CTS set to 0.95 and 1.3, respectively, which resultin the illustrated in Fig. 1. These values are based onone author’s experience of real operation of transmission systemCTSs.

C. Verification of the GB Power System ModelThe average line flow deviation is about 3% for the power

flow solution of the implemented model compared to the givensample load-flow in ([16, Fig. C.3.1]). This is well within thelimits of an acceptable error in this context.In the peak load scenario, the implemented model is secured

to the GB criterion with the exception of five lines andeight busbars. The nonsecured events are due to a few radial loadpoint connections in the model, which could be explained by thelimited topology data in [16] for voltage levels below 275 kV.The allowed pre-fault planning voltage limits are specified as97.5%–102.5% for 400 kV, 95%–105% for 275 kV, and 105%


TABLE IIICOMPONENT RELIABILITY DATA FROM THE 220- AND 400-kV LEVELS IN THE

SWEDISH PTS DURING 1997–2002 [20]

below 275 kV [17]. The intact GB system model meets thesespecifications.

D. Component Reliability Data [Step 4)]

Table III shows the component reliability data for theSwedish transmission system at 400 and 220 kV that has beenimplemented. This data from [20] is used since no source ofgood failure statistics for the GB system could be found. Thebest source for 400 and 275 kV, which is [18], includes datafor England during 2000–2007, but only with a few componenttypes and no average outage durations. However, as a com-parison, the GB statistics for permanent (long) and transient(short) overhead line faults are 0.0013 and 0.0051 (f/yr km)respectively [18], which corresponds well to the used data inTable III.Table III shows that the SVC has relatively low reliability;

this is due to major outages in this component type in the period1997–2002. The SVC is a relatively uncommon component, sothe studied population is small, leading to significant uncertaintyin the outage data [20]. The average restoration time for trans-formers is relatively short since no permanent outages occurredduring the studied period.Data from the Swedish 220-kV voltage level is used for the

GB system’s components at 275 kV. The series reactors areconsidered to be ideal since no data were found. Normally openCBs and DISCs are assumed to be ideal since they are not en-ergized in the open state [1]. Transformer data is used for mul-tiple-winding TRs and QBs. The GB reliability study includesavailability assessment of the transmission system and the gen-eration system is modelled as fully reliable since no generationunit outage data were available to the authors. However, theunits’ active and reactive power limits are implemented in ac-cordance to the GB SYS data [16].

IV. METHOD ANALYSIS ON GB SYSTEM

The previous two sections described the proposed methodand the GB system developed for its verification. The systempreparation, in steps 1)–4) of the method, has already been

done when assembling the system model. The next steps inthe method are the system analysis, where an enumerationtechnique is adopted. The initiating events of all first- andsecond-order contingency combinations of the almost 7000components in Table I have been studied in this work. Theinitiating component outages in each event are assumed tobe independent. However, dependent outage events may bepresent due for example to tripping of overloaded componentsor to nonfunctioning protection equipment. For example, thescenario of a busbar fault followed by a nonfunctioning circuitbreaker can result in multiple line outages when the secondaryprotection system is activated. These multiple related (depen-dent) events can initiate large cascading events, which aresimulated in the power system model. In total, this results inabout outage events to be evaluated for each loadscenario in the GB system. Due to model symmetries, thenumber of ac power flow calculations are reduced to about

for each scenario.

A. Implementation

The power system model was implemented in Neplan [21].A program in C++ was written to interface with Neplan. It co-ordinated Neplan across multiple computers, splitting the list ofcalculations between them. Neplan was used to calculate an acpower flow to evaluate the impact of each outage event. TheC++ program then collected the results and performed all thecalculations of the method.

B. Distributed Computing for Large-Scale PTS Analysis

A typical ac power flow on the implemented system modeltook 3.5 s on one core of a fast modern processor (3.2-GHzAMD Phenom X6). It would therefore take over two years tosolve our ac power flows sequentially. These typesof calculations have, however, the advantage of independence;they can be run across as many processors as are available. TheC++ program controlling Neplan was designed to permit effi-cient parallelization of the workload, with a central controllerallocating a new batch of 2000 power flows to each processorthat became idle.Two recent trends make it particularly easy to get access to

many processors for this type of work: computers with eight“cores” are now easily available, and “utility computing” ser-vices permit short-term hire of computing resources. The op-timal choice of resource is highly dependent on the situation; inour case, some thirty CPUs were provided in-house, and eightmore from the Amazon EC2 “cloud” service. The full set of cal-culations was thus completed in under five weeks.

C. System Analysis Assumptions

The new deterministic secured CTS limit at post-contingencyis in this paper approximated as being unchanged for all

remote events where the CTS remain intact. For nearby eventsthat result in circuit outages (e.g., lines) within the CTS, thenew limit is estimated by scaling down the previous TTC limitproportional to the disconnected circuit’s thermal rating. Thisapproximation is used since the procedure for recalculating thenew deterministic limit includes data and in-house analysis toolsfor the GB system that we did not have access to. For a method


Fig. 3. Ranking in the GB system based on the component reliability importance for system security margin .

Fig. 4. Ranking in the GB system based on the component reliability importance for DisCOs (load supply) .

implementation by the TSO, this should not be an issue, how-ever for future analysis the new limit can preferably be set se-cured for set of contingencies that are known to be dimensioningfor the system, providing a more accurate estimate.The allowed post-fault voltage limits are by [17] specified as

95%–102.5% for 400 kV and 90%–105% for 275 kV. Outageevents that result in bus voltages outside these limits are in theanalysis considered as a failure condition for the associated loadpoints.About 1.1% of the studied outage events did not converge at

the peak load scenario , and about 0.5% at lower loadscenarios. In this paper, these events are treated as a (worst case)system collapse event, which provides an upper bound of the im-portance indexes results since nonconvergence can be a resultof numerical and model inaccuracies. Moreover, 33 (of 7000)of the first-order initiating component outage events (with fol-lowing post-contingencies) did not converge: these componentsare identified as critical and excluded from the importance in-dices result. The components are CBs, main BBs, DISCs, andone TR, located in four substations.

This study includes all first- and second-order contingencies.For transmission system reliability studies, considering the gen-eration system as fully reliable, this set of contingencies rep-resents a great amount of the system’s state-space. The MonteCarlo simulation technique could be an alternative solution tothe enumeration technique in order to capture a larger part ofthe state-space, and this is especially necessary if the generatorsystem is to be included in the GB study. Reference [5] pro-vides a comprehensive comparison of the two techniques’ cov-ered state-space for the Brazilian transmission system.

V. RESULTS

Results are shown in Figs. 3–5 for the three component im-portance indexes of the modeled system for: 1) security margin;2) DisCOs, and 3) GenCOs.It is seen that the component indices are highly dependent

on the system load scenario, which ranges from summer min-imum to winter peak . The set of most crit-ical components also differs substantially between the TSO,DisCOs, and GenCOs. A few components, however, are crit-ical for more than one party: nine components are present in


Fig. 5. Ranking in the GB system based on the component reliability importance for GenCOs (generation) .

two of the three rankings, and, at substation the mainbusbar, , is top-ranked both for DisCOsand GenCOs (first in Fig. 4, and seventh in Fig. 5). The great ma-jority of the top-ranked components are located at the 400-kVlevel. Components in 2-BB-2-CB substations are under-repre-sented, which is reasonable in view of this configuration’s highredundancy.

A. Component Reliability Importance for Security Margin

Fig. 3 shows that the eleven SVCs are critical componentsfor the system security margin at load scenarios two and four.The main purpose of an SVC is to mitigate the system impact ofoutage events by rapidly controlling the system’s voltage pro-file. Outage events such as a BB fault combined with an SVCoutage tend to have a large impact on the studied CTSs in GB,particularly when reactive power reserves are low due to heavyload. However, the SVC results should be treated only as in-dicative, because (i) there is high uncertainty in component re-liability data (see Section III.D), and (ii) there was non-conver-gence in a number of the associated events.The remaining components’ importance for the security

margin is less influenced by non-convergent events. Fig. 3shows several top-ranked main BBs and CBs located in the

and substations, and six disconnectors (DISC)close to one of the main BB in . This is reasonable since

and are two of the largest substations in thesystem, and these components are included in outage eventsthat split up important parts of the system topology, resultingin high stresses on the CTSs at peak load.The component importance decreases rapidly with lower load

demand (load scenarios 1–3). This is since the base case CTSlimits in [16] are set for the winter peak scenario, resulting inlarge margins at lower load levels. However, during low-loadscenarios the transfer and generation capabilities may be re-duced due to planned outages, and this will affect the base caseCTS limits. For future studies, the summer minimum scenariocould also include a worst case topology configuration wheremajor planned outages are present.

B. Component Reliability Importance for DisCOs

As shown in Fig. 4, a majority of the 30 most important com-ponents for DisCOs’ load supply are located in the sub-station. A closer investigation shows that the two main busbarsin are included in outage events that result in four majorline outages, transformer overloads, and possible interruption ofsupply for large DisCOs. The substation configuration inhas busbars that are capable of being coupled but are normallyoperated uncoupled. A different coupling scheme or a new sub-station layout in this station will improve the DisCOs’ reliabilityclose to this station.Fig. 4 also shows that a main busbar in and an SVC in

are top ranked for the DisCOs’ load supply. The SVC’simportance is especially high at scenario 4 (peak load), but lowor negligible for load scenarios 1–3.

C. Component Reliability Importance for GenCOs

Fig. 5 shows the 30 most important components for theGenCOs’ generator units in the system. The top-ranked com-ponents are mainly main BBs and coupling CBs located in the

and substations.The annualized component indexes for and (in

MWh/yr) are relatively high. This is explained by the contribu-tion from the non-converged events and the restoration modelfor post-contingency conditions. The outage duration for anevent is estimated based on the involved components expectedrestoration times . A more detailed model could insteadestimate the time it takes to perform corrective actions for theoperator.

VI. DISCUSSION

The advantage of component importance indexes is that theyprovide a quantitative link between the reliabilities of compo-nents and of the system. This gives an important input to thedecision-making in operational planning, investment and main-tenance planning. The GB PTS is planned and operated withdeterministic criteria including component types such as lines,busbars and generators, but component types such as CBs areexcluded. By combining the method presented here with the


existing deterministic criteria, the TSO’s security frameworkcould be extended to also include these components. This couldbe implemented by setting a maximum threshold value for eachof the three proposed indices.Figs. 3–5 show that no transmission line is top ranked. A

closer investigation shows that transmission lines have gener-ally low rankings; at load scenario 4 the highest-ranked line is inposition 127 (TSO), 327 (DisCOs) or 228 (GenCOs). The rela-tively strong GB criterion for line outages (see Section III)plays a part in this result, and it indicates an uneven risk levelbetween different component types. Better choices might be toinclude CBs in the criterion instead of requiringfor lines, or to use only for lines with importance indexesexceeding a certain threshold value.The proposed component importance indices in this paper

are based on the impact on the system reliability studied fromthe TSO’s viewpoint. However, a specific customer such as aGenCO with several wind farms could adopt the indices foridentifying its grid connections’ most critical components in thePTS. The GenCO could demand a certain degree of componentprioritization from the TSO.

VII. CONCLUSION

We have presented a component importance method for PTSsand its application to the GB system using parallel distributedcomputation. The first contribution of this paper is the methodapproach of separately identifying which components are crit-ical for TSO security margin, DisCOs andGenCOs. By studyingthese three interests individually, a more complete view of therisks to system reliability can be assessed by the TSO. Each in-terest has its own set of prioritized components. A DisCO orGenCO located close to the identified components may demanda more reliable grid connection from the TSO. The second con-tribution is the approach of using CTSs as indicators of the im-pact on system security margin. This link is possible since theCTSs transfer limits are set secured to the deterministic secu-rity criteria and constrained by thermal, voltage, and system sta-bility. The proposed component importance indexes providesone important input to the TSO’s decision-making in planningand operation and can be used as a complement to existing de-terministic criteria.

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Johan Setréus (S’06–M’11) was born in Stockholm,Sweden, in 1980. He received the M.Sc. degree inelectrical engineering and Tech. Lic. and Ph.D. de-grees in electric power systems from KTH Royal In-stitute of Technology, Stockholm, in 2006, 2009, and2001, respectively.His research interests include models and methods

for combining traditional deterministic criteria withprobabilistic measures for transmission systems. In2011, he joined Svenska Kraftnät, Stockholm, theSwedish national grid company, to work with system

planning and reliability analysis.

Patrik Hilber (S’02–M’08) was born in Stockholm,Sweden, in 1975. He received the M.Sc. degreein systems engineering and Tech. Lic. and Ph.D.degrees in electric power systems from KTH RoyalInstitute of Technology, Stockholm, in 2000, 2005,and 2008, respectively.He is currently an Assistant Professor with the

School of Electrical Engineering, KTH, and since2008 has been Research Leader for the RCAMresearch group and manager of several smart gridrelated projects. His research interests include main-

tenance optimization, reliability modeling of power system,s and probabilisticmodels for age and maintenance.


Stefan Arnborg (M’97) was born in Gävle, Sweden,in 1964. He received the Tech. Lic. and Ph.D. de-grees in electric power systems from KTH Royal In-stitute of Technology, Stockholm, Sweden, in 1994and 1997, respectively.In 1997, he joined Svenska Kraftnät, Stockholm,

the Swedish national grid company. Within SvenskaKraftnät he has worked with system planning,system reliability, disturbance analysis, protectionand control. He currently works with questionsrelated to system preparedness, security, and system

reliability demands on production units.

Nathaniel Taylor (M’02) was born in Oxford,U.K., in 1978. He received the M.Eng. degree inelectrical and electronic engineering from ImperialCollege London, London, U.K., in 2001, and thePh.D. degree in electrical systems from KTH RoyalInstitute of Technology, Stockholm, Sweden, in2010, specializing in high-voltage insulation andmeasurements.He is currently a Researcher with the School

of Electrical Engineering, KTH, working onpower-system components with a focus on their

reliability and integration in power systems.

Documents

Identifying Critical Components for Transmission System Reliability