Probablistic Planning

8/9/2019 Probablistic Planning

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PARSONS BRINCKERHOFF ASSOCIATES

PROBABILISTIC TRANSMISSION PLANNING

Comparative Options & Demonstration

CONFIDENTIAL

Prepared for

August 2004


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PB Associates Quality System

Document Reference p:\pba\150251 Probabilistic Transmission Planning

Report Revision 2.0

Prepared by Michael Emmerton, Don Somatilake

Reviewed by Bruce Stedall, Ryno Verster

Approved by

Steve Wightman

Date Created 8 June 2004

Date Issued 9 August 2004


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PB Associates PROBABILISTIC TRANSMISSION PLANNINGComparative Options & Demonstration

TABLE OF CONTENTS

SECTIONS

EXECUTIVE SUMMARY.....1

1. INTRODUCTION.........................................................................................................10

1.1 Background and Scope......................................................................................10

1.2 Objectives ..........................................................................................................10

1.3 Structure of the Report.......................................................................................10

2. HISTORICAL DEVELOPMENT ..................................................................................11

2.1 Limitations of Deterministic and Probabilistic Techniques ..................................11

2.2 Methodologies....................................................................................................12

2.2.1 Enumeration Markov (Billinton (1984))...............................................12

2.2.2 Enumeration - Frequency/Duration (Halperin/Adler (1958)/ Billinton(1984)...................................................................................................13

2.2.3 Monte Carlo..........................................................................................13

2.3 Modelling ...........................................................................................................14

2.3.1 Sources of Uncertainty .........................................................................14

2.3.2 Software Tools .....................................................................................15

2.3.3 Transmission versus Generation Detail ................................................15

2.3.4 Commercial Planning Software ............................................................17

3. INTERNATIONAL PRACTICE....................................................................................18

3.1 United States .....................................................................................................18

3.1.1 ERCOT ................................................................................................19

3.1.2 California ISO.......................................................................................19

3.2 Canada ..............................................................................................................21

3.2.1 Ontario Hydro.......................................................................................21

3.2.2 BC Hydro..............................................................................................23

3.3 Australia.............................................................................................................233.3.1 Victoria - VENcorp................................................................................24

3.3.2 New South Wales - TransGrid..............................................................24

3.4 Europe ...............................................................................................................24

3.4.1 United Kingdom (National Grid PLC) and France (EDF).......................24

3.5 Asia.................................................................................................................... 25

3.5.1 Hong Kong - Kowloon...........................................................................25

3.5.2 Singapore Energy Market Authority (EMA)........................................25

3.6 New Zealand......................................................................................................25

4. NEW ZEALAND MODEL SPECIFICATION................................................................27

4.1 Value of Customer Reliability (VCR) ..................................................................27


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4.2 Demand-Side Management ...............................................................................27

4.3 Technological Advancement & Thermal limits....................................................28

4.4 Specification 1 VENCorp Model (Markov States Model) ...............................29

4.5 Specification 2 Frequency & Duration Model ................................................34

4.6 Specification 3 Monte Carlo Model ...............................................................35

4.7 Specification 4 Load Management All Methods..........................................36

5. RESULTS OF ECONOMIC PLANNING EVALUATIONS ........................................... 37

5.1 VENCorp Model (Markov States Model) ..........................................................37

5.2 Frequency- Duration Model .............................................................................48

5.3 Monte Carlo Model ..........................................................................................57

5.4 Load management Model ................................................................................58

C1 CREAM Monte Carlo Composite Reliability Model ........................5-2

C2. Hydro-Thermal Optimal Dispatch Models .........................................5-3

APPENDICES:

Appendix A: NERC N-1 Standards

Appendix B: Samples Of Model Assumptions

Appendix C: Discussion of CREAM and SDDP software packages

Appendix D: Bibliography


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This paper addresses a requirement to demonstrate the above principles using up-to-date techniques and concepts increasingly being adopted in liberalised marketsthroughout the world. To this end, the objectives of this Report are as follows:

1. Grid Benefits Test: To present a hypothetical investment plan based on aneconomic evaluation of investment alternatives for a simple transmission

system and thereby:

quantify the likelihood and consequence of a supply loss, and

apply the grid-benefits test to the plan.

2. Probabilistic Planning: To illustrate the technical application of threeprobabilistic transmission planning methods, highlighting the strengths andweaknesses of each method and making recommendations for use with theNew Zealand transmission grid;

This Report does not attempt to define a Value of Customer Reliability (VCR) 2, but it is

noted that the grid benefits test depends on having a VCR figure for New Zealand andfurther work is required to establish a robust figure.

Test System

To provide a basis for analysis associated with achieving the above objectives acommon 220kV transmission system model was used as shown in Figure 1. Thiscorresponds to the system between Islington and Kikiwa on South Island in NewZealand.

The model included load and duration curves for the 220kV busses, the failureprobabilities of transmission grid segments and the availability and output capacity of

generation sources. For the analysis involving probabilistic techniques additionalinformation was also required including the frequency and duration of grid segmentoutages.

Over a hypothetically chosen 20 year planning horizon, if nothing was done (donothing) to the transmission system the EUE would increase from about 60MWh/yearto 1,100MWh/yr as shown in Figure 2.

2For the purpose of our analysis, we use a VCR customer benefit of A$29,400 per megawatt-hour as the value of un-served energy during high demand periods. We have taken this figure,without adjustment, directly from published guidelines of the Victorian grid planning authorityVENcorp in Australia. A comparable figure is not available for New Zealand.


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Figure 1: Northern South Island System Model

0

200

400

600

800

1000

1200

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022

YEAR

ENERGYNOTSERV

ED(MWh)

Monte Carlo

Markov States

F & D

Figure 2: Comparison of EUE for different probabilistic methods

3

Application of the Grid Benefits Test

The grid benefits test of New Zealand requires that a net positive benefit is shown inorder for an investment to proceed. The grid benefit should also be maximised by

3 The Monte Carlo results were based on a simulation of 50,000 trials. By comparison, a threemillion trial version delivers exactly the same result as the Markov and Frequency and Durationmethods.

Generation

from South

210 270 MW Regional Load

ISLINGTON BUS 220kV

KIKIWA BUS 220kV

Generation at Cobb 0 - 30MWGeneration atArgyle0 10MW

Circuit A Circuit B


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choosing the optimum mix of investment alternatives and timing the investmentsappropriately.

For the Test System, a deterministic solution would require a 3rdcircuit immediately.For our demonstration, consideration was given to a range of individual investmentoptions and the expected un-served energy was calculated and valued against an N-1

criteria. A composite 20 year investment plan was also considered, comprising a mixof options including:

1. A third 220kV circuit (capital cost $15M, lead time 4 years);

2. A 20MVar capacitor bank (capital cost $600k, lead time 1 year); and

3. A 50MW diesel station supply at 110kV (capital cost $70M)4.

A demand side management (DSM) option was also considered. For this study themodel assumed that a hot water load reduction opportunity is available between 11pmand 6am, offering a 10% - 15% peak reduction in two tranches (cost $2M for first 10%,

$2M for next 5%, lead time 2 years in both cases).

The composite plan calls for a 20MVAr capacitor bank installed in Year 0; 10% DSMundertaken in Year 2; 15% DSM undertaken in Year 4; and a third 220kV circuitinstalled in Year 8.

The variation in EUE with time, for the hypothetical investment plan is show graphicallyin Figure 3 (a Composite Investment Plan):

0

20

40

60

80

100

120

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022

YEAR

EXPECT

EDUN-SERVEDNERGY(MEGAWATT-HOUR

S) 20MW CAPACITOR

10% DSM 15% DSM

3RD CIRCUIT

Figure 3: EUE For Composite Investment Plan

Table 1 shows the Net Present Value (NPV) calculation for the Composite InvestmentPlan. For comparison purposes, Table 2 shows the NPV calculation for an investment

4

The diesel power station alternative offers benefits other than the improvement of transmission gridsecurity. Accordingly, for purposes of economic evaluation the investment should only be a part cost. Thediesel power station could be treated as an ancillary service (supporting grid reliability) annual servicepayment.


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plan in which a 3rd transmission circuit only is installed (Third Circuit Investment),assuming commissioning at latest possible timing in 2009 (to maintain a criteria of N).

Table 1: Composite Investment Plan


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difficulty involved in modelling the capacity availability of hydro generating units dueto uncertainties in inflows.

3. Monte Carlo method (TMC). This method introduces a random simulation methodto model a wide range of generation, load and transmission system states. Thismodelling approach can account for the unreliability/flexibility of a mixed hydro-

thermal system but is more difficult to apply and requires long computation times,particularly when the failure rates of elements are very low.

A comparison of the EUE computed by each of the methods using the Test System isshown in Table 3.

Table 3: Forecast EUE Using Three Methods

Note that the model includes an N-2 option, necessary to incorporate a 3rdtransmissioncircuit. These results show that the methods deliver a similar forecast for EUE.

From a technical perspective, the three methods offer comparable results, with theFrequency and Duration method of greatest general applicability forconsideration of small area investment planning:

PlanningMethod

1. Markov Method2. Frequency andDuration Method

3. Monte CarloMethod

Strength

Defines adequacy ofsupply. Security isdefined from a grid

viewpoint.

Defines adequacy.Security is defined

from a grid andcustomer viewpoint.Historical frequency

and duration data canbe exploited to

improve EUE forecastaccuracy.

Defines adequacy andsecurity. Useful for

consideration offlexible hydro

generation and remotecontrol of customer

demand.


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Weakness

Relies on subjectiveassessment of

probability of gridsegment failure.

Customer risk indices

not readily available.

Relies on availability offrequency and duration

data pertaining tooutages.

Iterative solutionprocess requires manyhours for convergencewhen system is highlyreliable e.g. 20 hoursfor a simple system,

single scenario & 20year forecast

Suitability forNew Zealand?

Suitable as quickmethod of assessment

Most suitable

Least suitable, butsome useful for cases

where hydroreliability/flexibility islikely to be an issue

The Frequency-Duration method is computationally more difficult to apply, but deliverssuperior results to the Markov States method. The divergence between the Frequency

and Duration method and Markov States method is very sensitive to the choice offrequency or duration. This is because the F&D method correlates the probability ofelements failures against the demand cycle based on historical data.

Note that the F&D forecast in Table 3 has been forced to yield the same results, bychoosing frequencies and durations that ensure this outcome. In practice this may notbe so, and hence historical information pertaining to system performance and loadcycles can be exploited to make more forecasts more accurate.

The Monte Carlo method is very time consuming to apply as it requires many hours ofcomputing time to yield accurate results particularly when transmission reliability ishigh. It produces identical results to the Markov States model if the number of trials issufficiently high.

Conclusions

Suitability of Probabilistic Planning Techniques

1. The three methods have been shown to produce similar forecasts for EUE(Markov and Monte Carlo are identical if the number of trials is high).

2. The most accurate method, suitable for New Zealand, is the F&D method. It ispotentially more accurate if historical data is available.

Benefits of Probabilistic Planning Techniques

1. The economic evaluations applied to the Test System clearly demonstrate thatthe probabilistic techniques support the deferral of a third circuit for 8 years.Under an N-1 deterministic planning approach the circuit would be requiredimmediately. While the Test System is a limited application the principle can beseen to apply in other liberalised markets and will have broad application forNew Zealand.

2. The results are sensitive to the choice of VCR, and care must be taken tochoose an appropriate VCR value.


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3. In the interests of efficiency, a robust planning process would comprise an initialhigh level screening of supply adequacy, followed by the application ofprobabilistic planning techniques at a local area level.

Recommendations

Given the complexity of probabilistic planning techniques for widespread application tothe NZ grid, combined with the need for planning methods that are easy to understandand apply by the wide range of emerging stakeholders, deterministic criteria provide agood indicator as to the requirements for maintaining an appropriate security of supplyon the grid and should continue to be used. However, we recommend that theCommission implement the use of probabilistic transmission planning methods inconjunction with deterministic criteria, in the first instance, as a means of ensuringfuture investments in the New Zealand grid provide an appropriate cost/benefit, inaccordance with a transparent transmission planning standards policy guideline similarto those in force in other regulatory jurisdictions operating liberalised marketeconomies. This would require several actions:

1. The introduction of best practice system planning guidelines that specify theprocesses to be followed by a transmission planning authority, therebyensuring that least cost planning is pursued through economic consideration ofalternatives in other liberalised market jurisdictions, the guidelines include anaudit/compliance mechanism.

2. Ensure that transmission planning is performed using suitable algorithms forsecurity and adequacy assessments of the New Zealand mixed hydro-thermalsystem. Given the unique characteristics of the system, it may be moresuitable to develop specific probabilistic transmission planning models at anarea planning level (or for transmission corridors), to permit optimisation tomeet objectives. Ensure that the software employed does not simplify the taskto the extent that the value of the probabilistic planning approach is lost.

3. Consideration of a study that explores the benefits available through strategicuse of energy management tools that deliver supply side energy reduction.Chief among these tools are load management schemes such as ripple controlsystems that control energy-intense hot water heating and refrigerationsystems. It is understood that the South Island of New Zealand could gain ashort-term benefit from such an approach. A feature of price-cap regulation isthat there is no incentive for such schemes, but New Zealands regulatoryframework does not mitigate against such an approach.


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1. INTRODUCTION

This study was commissioned by the Electricity Commission.

1.1 BACKGROUND AND SCOPE

The study was prompted by a need to examine alternative options available insupport of optimised investment planning, based on a prudent and well-considered approach to the quantification of risk from a transmission system andcustomer perspective.

1.2 OBJECTIVES

The objectives of the study are to prepare a paper to the satisfaction of theElectricity Commission which will address concepts and issues with respect to

comparative options available for probabilistic planning for the interconnectedgeneration/transmission system in New Zealand, including:

1. A best practice explanation of the probabilistic planning techniques in usein other countries with national transmission grids, covering bothenumerative (Markov, F&D) and Monte Carlo methods; and

2. Demonstration of the techniques including technical and economicevaluations of feasible alternatives that would result in deferral ofaugmentation investment and least cost outcomes.

1.3 STRUCTURE OF THE REPORT

The report begins with a general description of probabilistic transmission planningmethods and how such methods differ from traditional deterministic methods.The advantages and disadvantages of the approaches are discussed in generalterms.

The discussion then provides a more detailed description of each of theprobabilistic transmission planning methods demonstrated in this paper beforemoving on to discuss the wide variety of computer-based tools available thatsupport probabilistic transmission and generation planning.

A comparison is made of international practices in use by internationaltransmission planning authorities. This information is meant to provide abackdrop against within which to consider the approaches that are of relevanceto the New Zealand environment.

At this point, models are described for each of the demonstration techniques.Then the results of the modelling are presented in summary form. Variousappendixes containing relevant support material are included at the end of thisreport.

The Executive Summary summarises the work and highlights a number of

recommendations to take the work forward.


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2. HISTORICAL DEVELOPMENT

This section of the report briefly discusses the historical development ofprobabilistic planning techniques. The limitations of both probabilistic and

deterministic planning approaches are explored. The basic analytical methodsare described and linked to the development of probabilistic planning tools, andthe limitations of current planning tools are described. This discussion isbackground to the following section where the contemporary practices of leadingutilities are examined.

2.1 LIMITATIONS OF DETERMINISTIC AND PROBABILISTIC TECHNIQUES

A transmission grid investment decision-making process, based on a probabilisticanalysis of energy at risk, includes consideration of the probability-weightedimpacts on supply reliability of unlikely, high cost events.

Typical such events are single and multiple outages of transmission elements,generation or rotating reactive compensation plant, and unexpectedly high levelsof demand.

Deterministic approaches usually consider the worst-case scenario. Theselection of the worst-case most often involves a degree of subjective judgementand is therefore difficult to justify as part of an economic decision-makingprocess. Deterministic methods also impose a hard limit on system operations.As a result, systems are often designed, planned or operated to withstand severeproblems that have a low probability of occurrence. The economic result may belower than necessary utilisation and higher investment than is warranted;

however their application is simple and therefore easily understood andinterpreted.

Probabilistic techniques consider factors that may affect the performance of thesystem and provide a quantified risk assessment using performance indices suchas probability and frequency of occurrence of an unacceptable event, durationand the severity of unacceptable events etc. These performance indices aresensitive to factors that affect the reliability of the system. Quantified descriptionsof the system performance, together with other relevant factors such asenvironment impact, social and economic benefits etc., can then be entered intothe decision-making process. The end result of the analysis is a sound actuarialestimate of the expected value of energy at risk. When investment decisions are

based on energy at risk considerations, it is on the understanding that there maybe circumstances when the planned capability of the network will be insufficientto meet actual demand.

Deterministic methods alone cannot adequately address the various transmissionchallenges such as the optimising the available transfer capability (ATC), longtransmission and related voltage/reactive and security (stability) problems,transmission project ranking, transmission congestion alleviation, uncertainty ofweather, uncertainty of customer load demand, or uncertainty of equipmentfailure and operation.

The following table compares the limitations of deterministic and probabilistic

approaches in planning.


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Deterministic Probabilistic

ContingencySelection

Typically a few probable andextreme contingencies

More exhaustive ofcontingencies

ContingencyProbabilities Implicit, based on judgement Explicit, but generallybased

Load Levels Typically seasonal peaks andselected off-peak loads

Multiple levels

Analysis Steady state/dynamics Steady state as at present

Reliability None Various indices calculated

Criteria forDecisions

Well established Need a cautious approachto select criteria due to

limitations in datacontingency probabilitiesand the models

2.2 METHODOLOGIES

Transmission grid planning methodologies fall into deterministic and probabilisticapproaches and are often of a hybrid nature.

The N-1 criterion is a common deterministic approach. The N-1 criterion statesthat a transmission grid network should be designed to maintain supply tocustomers in the event of the loss of any single load-carrying segment in the grid.Under the N-1 criterion the number of load segments and their design capacitiesare determined by the peak demand expected to be served at the bulk-supplygrid exit points.

There are three main categories of probabilistic methods :

1. Enumeration methods use Markov models and Markov chains toevaluate reliability of generation and transmission elements andsystems respectively.

2. A frequency and duration method develops reliability indices forload points and for overall system adequacy for generation only orfor a composite system evaluation.

3. Monte Carlo methods are used to run probabilistic simulations forgeneration only (production costing) and for composite systemevaluation.

2.2.1 Enumeration Markov (Billinton (1984))

Enumeration methods (sometimes referred to as Billinton method or analytical

method) calculate the probabilities of discrete states. These methods explicitlyenumerate selected configurations of randomly outaged lines and generators.The states are known are Markov states, and the states are chained together to


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There are three methods that are popularly employed in Monte Carlo analysis toanalyse composite generation-transmission networks. The methods are knownas the state-sampling method, the state-transition sampling method and thesequential method. Each method relies on enumeration to varying degrees. Thesequential method combines the first two methods.

It is beyond the scope of this discussion to concern ourselves with the details ofhow these methods work. It is sufficient to be aware that a compositegeneration-transmission analysis problem requires the generation outageconfigurations to be examined more completely than enumeration methods arecapable of providing in their own right. In general a Monte Carlo methodincreases in computer run time and decreases in accuracy in proportion togeneration reliability, which occurs when an entire network is examined. In avery large network with thousands of transmission nodes and hundreds ofgenerators, Monte Carlo methods often fail to converge and more sophisticatedmathematical techniques are employed.

As the New Zealand transmission system is relatively small, it is likely that MonteCarlo methods would converge within reasonable time limits.

2.3 MODELLING

2.3.1 Sources of Uncertainty

The sources of uncertainty that must be accounted for in a probabilistic-basedsoftware model are somewhat daunting:

Sources of Uncertainty Contributing or Causal Factors

Generation Availability

Unplanned outages, equipment failures,protective relaying, economic factorsincluding fuel prices and market prices,reserve availability, reactive powerrequirements, climactic variables such asprecipitation and hydro-power availability,environmental regulations includingemissions restrictions, generating stationopenings and closings

Transmission Capacity Line ratings, weather-related factorsincluding ambient temperature, wind and icestorms, geophysical events includinglightning and earthquakes, geomagneticstorms, unplanned outages and equipmentfailures, trans-regional power exchanges


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Load Weather-related factors includingtemperature and precipitation, economicfactors including economic growth, new typesof electronically-controlled loads, andvariations in load power factors.

Distribution System Equipment failures, unplanned outages,economic factors including distributionclasses, load shedding policies, weatherrelated factors such as ambient temperature.

2.3.2 Software Tools

Various network analysis software packages are available. These were generallydeveloped to meet a particular requirement or to overcome a limitation of an

earlier version of software. When the algorithms are iterative in nature and thetransmission network and generators achieve very high levels of reliability, it canrequire extremely long processing times for the iteration to converge, orconvergence fails.

In general, different software techniques are used for generation and fortransmission modelling. The software used for reliability evaluation of generation,often developed as production models using Monte Carlo techniques.Transmission software developed mainly using Markov chain models, andreliability index methods.

Production costing methods (Monte Carlo) tend to fail when generators and

transmission networks are highly reliable and have redundant elements.

Transmission reliability methods fail to model generators effectively when thenumber of states to be modelled becomes very large. Usually generators arecombined or treated as a single entity occupying multiple states and suchsimplifications result in low accuracy simulations.

2.3.3 Transmission versus Generation Detail

In figure 7 below, various software packages are compared. The figure showsthe trade-off between software program complexity in generation and

transmission representation of detail, both electrically and probabilistically.Programs with very detailed probabilistic transmission analysis are usuallyincomplete in the treatment of random generator outages (such as TRELSS).The opposite is also true. Programs that have a complete treatment of therandom outage of generators have a highly reduced transmission networkcapability (such as MAREL).


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Figure 7: Transmission Versus Generation Detail

Existing power system models that use probabilistic methods to perform systemstudies include EPRIs Transmission Reliability Evaluation of Large Scale System(TRELSS) and Composite Reliability Assessment by Monte-Carlo (CREAM);GEs Multi-Area Reliability Simulation (MARS) and Market Assessment andPortfolio Strategies (MAPS); Power Technologies Local Area ReliabilityAssessment (LARA) and Multi-Area Reliability AnaLysis (MAREL). Other

probabilistic-based tools from vendors outside the U.S. include Powertech LabsCOMposite RELiability (COMREL) and STAtion RELiability (STAREL) developedby the University of Saskatchewan, Canada, and BC Hydros MontecarloEvaluation of COmposite system REliability (MECORE), also developed at theUniversity of Saskatchewan. Stochastic Dynamic Programming (SDDP)developed and managed by Power Systems Research Inc of Brazil (hydro-thermal power station planning optimisation).

There are various generation reliability programs and composite reliabilityprograms being used outside of the North America. Researchers in UK, France,Italy, and Brazil etc., are actively developing and implementing probabilistictechniques in power system planning and operating. At the present time some of

the major contributors are R. Billinton (Canada), V. Vittal & J. McCalley (U.S.),A.M. Leite da Silva & J.C. Mello (Brazil) and Electricite de France (EdF, France)with National Grid (UK).

trivial

simple

none a few selected Monte Carlo all configurations

none

equivalent

simplified

detailed

TRELSS

PSSE/TPLAN

DIgSILENT RECS

SYRELSDDPCREAM

COMRELcomplex

NARPENPROMECORE

MARELGRIPPROMOD

GENH

PLF

GENERATION DETAIL

TRANSMISSIONDETAIL

TRANSMISSION VERSUS GENERATION DETAIL

trivial

simple

none a few selected Monte Carlo all configurations

none

equivalent

simplified

detailed

TRELSS

PSSE/TPLAN

DIgSILENT RECS

SYRELSDDPCREAM

COMRELcomplex

NARPENPROMECORE

MARELGRIPPROMOD

GENH

PLF

GENERATION DETAIL

TRANSMISSIONDETAIL

TRANSMISSION VERSUS GENERATION DETAIL


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As part of our investigation for this report, we looked at two specific softwarepackages in more details, to assess there suitability the CREAM (Monte CarloComposite Reliability Model) program and the SDDP program. A discussion ofthese products is provided in Appendix C.

2.3.4 Commercial Planning Software

The preferred software packages for New Zealand must have the capability tomodel mixed hydro-thermal systems. Preferably the package should use amethod similar to the Frequency-Duration method to develop both customer andsystem risk indices. Commercial loadflow packages such as DigSILENT andTPLAN (PTI Shaw) come with reliability module add-ons that generate a fullrange of indices, and facilitate investment optimisation across an entiretransmission system. Care must be taken to understand what assumptions havebeen made to simplify the computational algorithms, because probabilistictransmission planning techniques are sensitive to loss of information in themodelling stage and accuracy may be affected. For example, TPLAN uses a

simplified 10 point load duration curve instead of a more accurate 100 point curveused for this demonstration.


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3. INTERNATIONAL PRACTICE

So far we have described the difference between deterministic and probabilisticplanning techniques, and outlined the range of techniques and software tools

available. In this section we explore the practices employed by those bodiesresponsible for setting the planning standards for National transmission grids.Each description includes some historical context in terms of the challenges thatbrought about a preference for a particular set of planning standards. Thisdiscussion is necessary in order to appreciate that it is common industry practiceto employ a mix of deterministic and probabilistic techniques. Finally somecommentary is provided regarding the suitability of the planning standards in usein New Zealand.

3.1 UNITED STATES

The North American power grid comprises more than 320,000 km of transmissionlines (230kV and above) and a generation capacity sufficient to deliver acombined peak electrical demand of 950,000MW.

The power grid in North America is formed by three separate power grids; theEastern Interconnection, the Western Interconnection and the ErcotInterconnection. The power grid in each Interconnection zone is comprised of anumber of connected power grids owned by individual asset owners (utilitycompanies).

This fragmented arrangement is inherently unreliable and a managementstructure was established circa 1965 to ensure that the interconnected power

grids would operate reliably as a system.

The National Electric Reliability Council (NERC) was formed almost 40 yearsago, following a major transmission grid loss that affected 30 million people. TheNERC was charged with the responsibility to promulgate planning standards thataim to ensure the system adequacy and security of transmission systems.

NERC Operating Policy 2A Transmission Operations (September 2001) statesthe following:

All control areas shall operate so that instability, uncontrolledseparation, or cascading outages will not occur as a result of the

single most severe contingency

This statement is a definition of the N-1 criteria. It is a deterministic planningstandard that is put into practice by considering a range of normal andcontingency outages (refer to Appendix A).

Under the NERC there are ten regional reliability coordinating councils in NorthAmerica. Each council has responsibility for a number of control areas that areprimary operational entities subject to the NERC planning standards for reliability.The control areas are either referred to as Independent System Operators (ISOs)or Regional Transmission Operators (RTOs).

ISOs and RTOs do not necessarily own assets and that is why the NERCplanning standards tend to be deterministic. They are technical standards that


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ensure coordinated reliability outcomes, not economic standards for least costplanning.

However, the control area operations adapt the NERC standards for use in theirareas. As a result local reliability standards are based on probabilistic as well asdeterministic planning principles because a purely deterministic approach results

in the need for additional transmission capacity. Environmental concerns(easements, EMF) have forced transmission planners to seek alternatives totraditional grid augmentation, and probabilistic planning supports an economicalternative i.e. deferral of augmentation.

The power blackout in the United States and Canada on August 14, 2003affected 50 million people and once again brought system reliability to theattention of federal politicians and the public at large. Around 10% of the totalload of the Eastern Interconnection was lost. It is no surprise that the U.S.-Canada Power System Outage Task Force did not recommend a relaxation of N-1 standards (network to be reinstated within 30 minutes of an operationalcontingency resulting in N


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Figure 8: Relative risk index - the risk of overloads in one zone

Figure 9: Absolute risk index - the risk of overload by circuit

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0180.0

2001 2002 2003 2004

Risk,sec/busorsec/circ

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.050.0

Sharpness,%

Risk ofOverloads

Sharpness ofOverloads

0

100

200

300

400

500

600

700

Circuit1

Circuit2

Circuit3

Circuit4

Circuit5

Circuit6

Circuit7

Circuit8

Circuit9

Circuit10

Circuit11

Circuit12

Circuit13

Circuit14

Circuit15

Circuit16

Circuit17

Circuit18

Circuit19

Circuit20

Risk,min/Yr


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Extending the concept of risk indices to project evaluation, alternatives can beranked taking into account both adequacy and security concerns. This approachis supplementary in providing additional insight compared to a statement ofeconomic benefits that accrue based only on expected un-served energy.

Pro

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Improvemen

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Improvemen

tinASIFI,

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Improvemen

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,

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Annua

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Improvemen

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Annua

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Overloads 44.4 3.7 40.7 91.7 0.051

Voltage 12.7 7.8 4.9 38.6

0.15 0.27 18

0.39

1.89

Overloads 11.1 3.7 7.4 66.7 0.022

Voltage 20 7.8 12.2 61

0.61 0.51 76

0.01

0.12

Overloads 3.5 3.7 -0.2 -5.7 -0.703

Voltage 7.8 7.8 0.0 0.0

0.62 -0.11 77

0.00

0.14

The prime focus is on reliability evaluation.

3.2 CANADA

3.2.1 Ontario Hydro

Bulk transmission security criteria are addressed using deterministic methods.The definition in force follows:

Deterministic Security Criteria, measured in terms of systemperformance, (e.g. loss of load, system instability), in response to

more probable/first contingencies, less probable/secondcontingencies and extreme contingencies.

In addition, probabilistic assessment of bulk transmission grid security isconducted, and development of the grid is shaped by the Customer DeliveryInterruption Indices for load points.

CDII Customer Delivery Interruption Index

The definition of the Customer Delivery Interruption Index that has been used tocompute the theoretical CDII of the load points in the Ontario Hydro transmissionsystem is as follows:

The average annual demand is defined as the total energy supplied (or expectedto be supplied) within the specified area during a period of one year (MWh)


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divided by 8760 hours, plus the additional amount of Unsupplied Energy. Thislatter term is calculated as follows -

Unsupplied Energy = ((6 minutes x Total No. of Momentary & Sustained

Interruptions)/60 minutes)/Average MW Supplied MWh

The inclusion of a 6-minute supply interruption is intended to represent theaverage time taken for the full, pre-contingency load to be restored. Thisapproach is a form of frequency-duration assessment.

VERA (Value-Based Evaluation and System Reliability Assessment)

Ontario Hydro uses a computer program called VERA, based on the Billintonfrequency-duration method.

The VERA Package is an integrated set of computer programs developed tocalculate customer interruption costs and delivery point reliability indices. VERA

can handle a large area supply network or a single customer delivery system. Itis a tool used by a system planner for determining system expansion need basedon customer impact and for comparing alternative demand/supply plans on acommon basis.

Program Output

VERA computes customer interruption costs and system disruption indices for aspecified area. The package has a very comprehensive and efficient contingencyidentification module which can also be used as a stand alone program. Thismodule identifies all the unique post-fault connectivity states of the networkfollowing all possible design criteria faults. A unique feature of this package is its

ability to model switchyard diagrams in the network context.

Component Modelling

(a) Transmission System

Network flows are computed using a linear model. The user identifies lines andinterfaces to be monitored.

(b) Loads

Load growth is simulated by making power transfers from specified generating

units to the load busses.

Load busses are assumed to maintain the same share of the area load as in thebase case power flow.

(c) Switchyard

VERA can model switchyard elements and their impact on the network in thepost-contingency state.

VERA can systematically and automatically identify all relevant contingencies fora given network topology, and forecast the change in CDII.


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When the system is too complex, Ontario Hydro reverts to in-house softwarecalled PROCOSE (PRObabilistic COmposite System Evaluation).

The PROCOSE Program is designed to examine the impact of other generatingresources that are available on a system, in mitigating any load cuts requiredwhen transmission elements are out-of-service, but it does not consider the

impact of transmission grid (busbar) configuration, nor does it examine everypossible contingency condition.


3.2.2 BC Hydro

BC Hydro uses MECORE software developed in conjunction with the Universityof Saskatchewan. The MECORE program is a Monte Carlo based compositegeneration and transmission system reliability evaluation tool designed to performreliability and reliability worth assessment of bulk electricity systems. It can

provide reliability indices at individual load points and for the overall compositegeneration and transmission system. The MECORE software is based on acombination of Monte Carlo (state sampling) and enumeration techniques. Thestate sampling technique is used to simulate system component states and tocalculate annualized indices at the system peak load level. A hybrid methodutilizing an enumeration approach for aggregated load studies is used tocalculate annual indices using an annual load curve.

For HVDC modelling, BC Hydro uses three computing tools SPARE, NETRELand MCGSR.

SPARE calculates, among other indices, unavailability due to aging failures for

each component, which is used as part of input data. Input data is the mean lifeand the deviation of each component. The aging failures can be modeled usinga posteriori Weibull or normal distribution.

NETREL is a generic tool to calculate availability/unavailability of a networkconsisting of components in parallel and/or series. The program also providesthe average capacity for a given HVDC configuration. The results from NETRELtake into account both aging related and repairable failure modes producing acomprehensive reliability picture of HVDC Poles that are in the end-of-life stage.

MCGSR (Monte Carlo Generation System Reliability) is a generating systemreliability evaluation tool.


3.3 AUSTRALIA

The general trend in Australia, due to both economic and environmentalpressures, is to operate transmission systems closer to their limits. Liberalisationof markets also increases uncertainty regarding the location and level ofgeneration, thus making the need for and cost of transmission systemreinforcement more uncertain. In the context of a market structure, systemsecurity limits are seen as constraints preventing the system from being operatedpurely according to economic rules. Despite the changes brought about by


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market liberalisation reforms, deterministic criteria remain the centrepiece ofplanning methods.

3.3.1 Victoria - VENcorp

All regulated transmission investment decisions must satisfy the regulatory testas promulgated by the ACCC. The key economic test involves deciding whetheran investment maximises the net present value of the market benefit, which is thetotal net benefit to all those that produce, distribute and consume electricity in theNational Electricity Market.

This has led to a hybrid planning approach that relies on deterministic rules andtakes into account the probability of outage occurrences.

To satisfy these requirements, VENCorp accepts the possibility of load sheddingafter an event but includes the EUE in the cost-benefit analysis, which is used todetermine the optimum solution and implementation timing for an augmentation.

This involves investment decisions based on a probabilistic analysis of energy atrisk, which includes consideration of the probability-weighted impacts on supplyreliability of unlikely, high cost events such as single and multiple outages oftransmission elements, generation or rotating reactive compensation plant, andunexpectedly high levels of demand. This approach provides a sound actuarialestimate of the expected value of energy at risk. However, implicit in its use isacceptance of the risk that there may be circumstances when the plannedcapability of the network will be insufficient to meet actual demand.

The prime focus is on reliability, balanced by the need to achieve economicoutcomes.

3.3.2 New South Wales - TransGrid

TransGrid is also subject to the ACCC regulatory test. A deterministic Code ofpractice specifies requirements relating to planning standards. A set ofdeterministic criteria are applied as a point of first review, from which point adetailed assessment of each individual alternative is made. Both Monte Carloand enumeration methods are applied to assess system adequacy.

The prime focus is on reliability, balanced by the need to achieve economicoutcomes.

3.4 EUROPE

The general trend in Europe is similar to that in Australia. This situation has ledto renewed efforts to develop modelling tools that can be used to improve thecomprehensiveness and transparency of investment decisions.

3.4.1 United Kingdom (National Grid PLC) and France (EDF)

In response to the above issues, EDF and National Grid have developed ananalytical tool called Assess.


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Generation and load uncertainty are probabilistically modelled by Assess,however the tool does not probabilistically model the risk of transmissionequipment failure i.e. outages are still simulated deterministically on an N-1 andN-2 basis as appropriate.

The prime focus is on modelling uncertainty with regard to generation and load.

3.5 ASIA

3.5.1 Hong Kong - Kowloon

CLP Power uses deterministic criteria for bulk transmission security planning, andprobabilistic approaches for assessing customer delivery interruption indices.The CDII (Customer Delivery Interruption Index) is used as a target for mediumand long term planning scenarios.

3.5.2 Singapore Energy Market Authority (EMA)

The Energy Market Authority uses a similar approach to that used by CLP Power.

3.6 NEW ZEALAND

Transpower uses DIgSILENT for network modelling. It is understood thatrecently Transpower began to use the reliability modules of this application todevelop probabilistic risk indices. The power flow module uses deterministicalgorithms, while the reliability modules use several techniques including Monte

Carlo simulation. The package handles hydro-thermal system modelling.

The following indices are calculated by the "Network Reliability" analysisassessment:

For loads:

Average Interruption Duration (AID, hr)

Load Point Interruption Time (LPIT, customers*hr/yr)

Load Point Interruption Frequency (LPIF, customers/yr)

Load Point Energy Not Supplied (LPENS, MWh/yr)

Load Point Expected Interruption Costs (LPEIC, M$/yr)

Average Customer Interruption Frequency (ACIF, 1/yr)

Average Customer Interruption Time (ACIT, hr/yr)

The ACIF and ACIT are per customer indices, while the LPIT, LPIF, LPENS andLPEIC are summations for the number of customers at the aggregated loadmodel.

For busses:

Average Interruption Duration (AID, hr)


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Yearly Interruption Frequency (LPIF, 1/yr)

Yearly Interruption Time (LPIT, hr/yr)


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4. NEW ZEALAND MODEL SPECIFICATION

This section describes the specification for the models used for thedemonstration of comparative options for probabilistic grid planning and

economic evaluation. Included here is a description of the process stepsemployed to carry out the analysis, along with the rationale for choosing themodel specification i.e. what is the analysis aiming to demonstrate. The resultsof the analysis are presented; Appendix B contains detailed outputs from thesimulation studies.

4.1 VALUE OF CUSTOMER RELIABILITY (VCR)

For the purpose of this study the following value of energy at risk is used to

calculate the total value of EUE.

Sector VCR ($ per MWh)

Residential $11,867

Commercial $56,625

Agricultural $54,782

Industrial $18,531

VCR (average) $29,600

Note that these figures are taken from VENcorps Planning Guidelines. They aretaken and used in this study as New Zealand figures without exchange rateconversion.

4.2 DEMAND SIDE MANAGEMENT

DSM means the planning, implementation and evaluation by electric utilities oftheir activities designed to influence customer use of electricity that produce thedesired changes in the timing and/or level of electricity demand. DSM includesonly activities that involved deliberate intervention by electric utilities to alterdemand and/or energy consumption.

DSM Programs are programs designed to influence utility customer uses ofenergy to produce the desired changes in demand. Such programs include loadmanagement, efficiency resource programs and conservation.

The goals of DSM are to

increase efficiency in the generation, transmission and distribution ofelectricity and defer construction of power generation plants which wouldotherwise contribute further to environmental degradation that will hamper

the attainment of sustainable development.


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to offer the utility a set of alternatives for optimizing resources and providingflexible options for future business that will reduce the utilities operatingcosts and increase its cost-competitiveness; and

to induce customers/consumers of electricity to adopt measures to effectchanges in energy consumption and utility load shape in the most efficient

and cost-effective manner.

In the New Zealand context, the most common example of a DSM program iscontrol of electric hot water heating, enabled by sending control signals over thepower lines. In the United States, it is common to use local radio stations to cycleair-conditioning loads during the peak demand periods.

4.3 TECHNOLOGICAL ADVANCEMENT & THERMAL LIMITS

This sub-section describes the possibility that exists to increase the utilisation of

the transmission network in the short term if advanced control and monitoringcapability is available to system operators.

The transmission network must be planned to operate within the thermalcapability of transmission lines and transformers. The current carrying capacityof transmission plant is defined in terms of a maximum temperature that theequipment is able to sustain without plant damage, or in the case of transmissionlines, the necessary critical clearances to the ground being maintained.

It is possible to temporarily operate a transmission line or transformer beyond thecurrent level that would, if applied continuously, result in temperatures higherthan equipment rating. Providing the equipment is operating below the

temperature rating prior to an outage occurring, a sudden increase in current flowcan be sustained for a short period of time before the temperature of theequipment rises to the rated temperature. Time constants for transmission linesare generally less than 15 minutes due to the low mass of metal involved. On theother hand transformers have a large iron mass and a large oil volume and takequite a long time to heat up. Time constants of the order of 30 to 45 minutes aretypical.

A high number of utilities, including Transpower and VENCorp, takes the short-term overload ratings into account when calculating off-load times. The off-loadtime is a function of pre-contingency load.

For transmission planning allowance is made for the short-term operation oftransmission plant beyond continuous current ratings, allowing flows greater thanthe normal firm capability limit. The maximum acceptable normal flow (with allplant in service) is limited to a level such that plant items will not exceed theirrated temperature within 10 minutes (VENCorp level) after an item of plant fails.A 10 minute rating is normally used to provide time for manual operating action tobe taken to reduce the flow so that the temperature of the element is not drivenbeyond its rating. The actions could involve transformer tap changing,reconfiguration of the network through switching or, in some cases, generatorrescheduling and/or selective load curtailment. Where special automatic controlsare implemented, the time allowed for post event load reduction may be shorterthan 10 minutes. An automatic control scheme can permit the secure utilization

of short term ratings of transmission plant.


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4.4 SPECIFICATION 1 VENCORP MODEL (MARKOV STATES MODEL)

This sub-section describes the specification for a model that uses a mixture ofdeterministic and probabilistic (enumerative) methods for reliability analysis of thetransmission network. The process is aligned with the VENcorp Planning

Guidelines and is reproduced here for easy reference:

Read hourly demand, generation and interconnection transfer data,transmission network data and ambient temperature data

Calculate transmission plant power flows, ratings and over loads

Re-dispatch Generation/Re-distribution of ancillary services (if feasible)

Re-calculate transmission plant power flows, ratings and over loads

Calculate additional cost for generation re-dispatch, re-distribution ofancillary services for this hour

Calculate value of un-served energy per yearfor this hour

Calculate total value of additional cost for generation re-dispatch, re-distribution of ancillary services and value of un-served energy per year

Overload > 0

Overload > 0

Is This

Last Hour

Reading?

Multiply by the probability of outage and calculate the total expected costper year

No

No

No

Yes

Yes

Yes

Read hourly demand, generation and interconnection transfer data,transmission network data and ambient temperature data

Calculate transmission plant power flows, ratings and over loads

Re-dispatch Generation/Re-distribution of ancillary services (if feasible)

Re-calculate transmission plant power flows, ratings and over loads

Calculate additional cost for generation re-dispatch, re-distribution ofancillary services for this hour

Calculate value of un-served energy per yearfor this hour

Calculate total value of additional cost for generation re-dispatch, re-distribution of ancillary services and value of un-served energy per year

Overload > 0

Overload > 0

Is This

Last Hour

Reading?

Multiply by the probability of outage and calculate the total expected costper year

No

No

No

Yes

Yes

Yes


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Two cases are presented here. The first case shows a simple calculation of theEUE and VCR for the composite system being modelled. The second casebuilds on the case 1 approach to introduce a full economic evaluation ofinvestment alternatives.

The following schematic diagram illustrates the section of the New Zealand grid

that has been chosen for demonstration of the VENcorp model approach toprobabilistic planning.

For this model we have chosen a double circuit 220kV network that is routedbetween Islington and Kikiwa.

Figure 10: South Island System

The entire Nelson-Marlborough region and part of the West Coast region ismainly supplied by these two 220kV lines.

The following table summarises the transmission capacity limits of these 220kVlines, under a 10 percent voltage regulation constraint for the above circuits when

regional demand power factor is 0.95.

Available Transfer Capacity (ATC)

From Islington to Kikiwa Power Factor - 0.95

Double Circuit in Service 195MW

Single Circuit Outage 100MW

Generationfrom South

210 270 MW Regional Load

ISLINGTON BUS 220kV

KIKIWA BUS 220kV

Generation at Cobb 0 - 30MWGeneration atArgyle0 10MW

Circuit A Circuit B


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The region has two generating plants. The larger one is Cobb hydro station thathas installed capacity of 32 MW with annual average yield of 195 GWh. Thesmaller one is Argyle power station with installed capacity of 10 MW.

In addition to the 220kV network supplying these load points there is also anindependent 66kV connection that can supply part of the regional demand. This

supply amounts to 20MW capacity. It is included in the above scenarios.

The total regional peak demand in the year 2002 was approximately 180 MW andis forecast to increase to 210 MW by the year 2006 according to availabledemand projections.

According to Grid Operator publications, the combined power factor of theregional demand is varies from 0.98 to 0.95 for up to 80% of the time.

In summary the following assumptions are made for the purposes of modelling:

CASE 1

1. Peak 66kV supply 20 MW to the region during high demand periods

2. Individual circuit failure probability is 0.0082 (about 3 days a year)

3. Combined regional demand power factor being 0.98 for 50% of thetime and 0.95 for the remaining period

4. Generation within the region is available for 40% of the time with fullcapacity, 20% of the time only Cobb generating at full capacity of

30MW, further 20% of the time Argyle generating at 10MW and noregional generation available for the remaining period

Four generation scenarios are considered as follows:

1. Full generation of 40MW within the region with probability of beingavailable of 0.4

2. Cobb station only, generating 30MW (1storder contingency) with

probability of being available of 0.2

3. Argyle station only, generating 10MW (1storder contingency) withprobability of being available of 0.2

4. No regional generation (2ndorder contingency) with probability ofbeing available of 0.2


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CASE 2

Some minor changes have been made to Case 1 to better reflect actual faultstatistics for the transmission lines (refer to Frequency-Duration analysis inSection 4.4). This case incorporates the evaluation of various investment

options.

COMMON ASSUMPTIONS MADE

The following assumptions were made for the purposes of modelling:

Regional Demand

1. The demand power factor is 0.95 lagging. This is a crucialassumption because it can introduce errors into the final cost of

energy not served. According to Transpower, the regionalpower factor varies from 1.00 to 0.92 during the year but most ofthe time (about 75%) it will be between 0.98 and 0.95. At 0.95power factor transmission line voltage stability rating issomewhat 3% less than at 0.98

2. The yearly demand duration curve as given in the spreadsheet

3. The yearly demand growth rate for the region is 2.5%

4. Seasonal demand variations are built into the demand durationcurve

5. 20MW of regional demand is met by 66kV connection at peakdemand times

Regional Generation

1. The installed capacity of Cobb generating plant is 30MW and70% of the time full capacity available and remaining 30% of the

time available capacity is 0MW

2. The installed capacity of Argyle generating plant is 10MW andavailability/unavailability is as same as above

3. Therefore both plants are available for 49% of the time with fullcapacities

4. Both plants are not available for 9% of the time

5. Each plant is available without the other plant for 21% of thetime


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Transmission Lines

1. Two, single circuit lines with Zebra conductor operating atmaximum voltage of 231kV at sending end (Islington) andminimum voltage of 209kV at receiving end (Kikiwa)

2. Both circuits are identical in availability and having 0.1%probability of failure

3. Therefore, both circuits are available for 99.8% of the time

4. Both circuits are not available for 0.0001% of the time

5. Probability of one circuit being out is 0.001998

Investment Scenarios

1. Do nothing

2. 20MW PFC Capacitors with 100% availability at peak load times capital cost $600,000k

3. 50MW Diesel Plant with 100% availability at peak load times capital cost $70M

4. 220kV 3rd line with the same availability as existing circuits capital cost $15M

For simplicity sake, annual O&M and running costs are presentvalued

For simplicity sake, the investment option is considered to beavailable in the year that the investment is approved

A 10% discount rate is used for evaluation purposes over a 20 yrhorizon


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4.5 SPECIFICATION 2 FREQUENCY- DURATION MODEL

This sub-section describes the specification for a model that uses enumerative

(analytical) methods for reliability analysis of the transmission network, deliveringreliability indices and EUE figures.

The model to be used builds on the basic enumeration method used for theVENcorp Case 2 model as described in section 4.3.

In this case risk indices are defined, bearing in mind that the VENcorp approachsays nothing about the frequency and duration of violations experienced bycustomers. The VENcorp model incorporates a transmission or system operatorviewpoint of risk in the probabilistic modelling. The frequency-duration methodcan be used to explicitly define a customer viewpoint of the risk of loss of supply.

In simple terms, the customer risk is expressed as the duration multiplied by thenumber of violations (voltage or overload) divided by the ratio of buses to circuitbranches. In a large system, the relative risk is of interest as an absolute risk asit is usually accepted that it is difficult to establish a benchmark for admissiblerisk.

The advantage of the frequency duration method is that the frequencies anddurations of violations for system components are readily available from historicalperformance data. This data can be used to compute the relative probability ofavailability and unavailability of components. Once the relative andcombinational probabilities are computed for each and every component in thesystem then these probabilities can be used to compute system reliability indices

using either Markov chain method or Monte Carlo simulation method.

The system given in above figure is similar to the system used for VENCorpmodel. First, we will consider two single circuit parallel lines from Islington toKikiwa for the reliability assessment of the system using frequency of failures andfailure durations.

The following assumptions were made for the F&D analysis.

1. The 66kV connection could supply around 20 MW to the region athigh demand periods.

2. Individual and independent circuit failure probability is 0.001 (8.76hours per year).

3. Combined regional demand power factor being 0.95 for the highdemand period.

4. Availability rate of the Cobb and Argyle generating plants are to be70% and independent to each other.

Four generation scenarios that have been considered are given below.


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8760 Number of hours per year.

NUM Number of trials (an option to the user).

A violation is counted when demand goes above the supply andenergy not served is accumulated for the entire simulation.

The above model has three random variables with 10 million combinations andrequires a large number of trials, including several kinds of possible combinationsto give an accurate estimate of energy not served for each year. This model canproduce results that are on a par with other methods providing that the number oftrials is sufficiently large.

4.7 SPECIFICATION 4 LOAD MANAGEMENT ALL METHODS

This sub-section describes the specification for a model that incorporates loadmanagement or load reduction tools, as a demand-side alternative.

Once again the model uses the VENCorp Case 2 model, and in this case themodelling uses all three methods to compute Expected Un-Served Energy.

An investment plan is developed based on a more realistic assessment of thetime required to implement both supply and demand side options.

The following assumptions were made with respect to the lead times assumed foreach option under consideration:

1. 20MVAr capacitors 1 year to implement, as we believe this isalready on the grid development plan.

2. Demand side management program 2 years to initiate andanother 2 years to be effective.

3. Third circuit total of 4 years to construct.

An economic evaluation is prepared for this model.


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5. RESULTS OF ECONOMIC PLANNING EVALUATIONS

This section contains the results of the analysis for each of the models describedin the previous section.

5.1 VENCORP MODEL (MARKOV STATES MODEL)

This sub-section describes the results of the VENcorp modelling scenario. Thefirst case shows a simplified calculation of the EUE and VCR. The second caseextends the technique to include consideration of investment alternatives.

Case 1 Simple Example

The power flow study shows that the outage of one of these circuits could causesupply limitation to the region during high demand periods. Accordingly, the

amount of power that can be transmitted to Kikiwa is limited by the voltagestability limit of the circuit.

The following diagrams show the estimated load duration curves for the regionthat is supplied from Kikiwa 220kV substation. The demand duration curves andsingle circuit outage limits are used to estimate energy that cannot be suppliedunder the selected generation scenarios.

0

50

100

150

200

250

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

% of Time

Demand/Supply(MW)

LDC-2002

LDC-2006

Limit-1

Limit-2

Limit-3Limit-4

Figure 11: Load Duration Curves and Supply Limits with Single Circuit Outage

The following table summarises the estimates of Expected Un-served Energy

(EUE) under each of the four generation scenarios:


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2002 2006 2002 2006 2002 2006 2002 2006

6.382 23.333 16.762 57.968 70.539 135.952 115.939 190.307

Limit-4

Energy Not Served (GWh)

Limit-1 Limit-2 Limit-3

Probabilistic Analysis:

Circuit outage probability = 0.0082 (taken as same for both circuits)

Probability of both circuits being out = 0.0000672

For the generation scenario probabilities for regional demand when PF is 0.95,the EUE is as follows:

For year 2002, EUE = 354MWh

For year 2006, EUE = 706.6MWh

We combine the EUE figures for PF at 0.98 or 0.95 with 0.5 probability each togive a final estimate for EUE:

For year 2002, EUE under single circuit outage = 221.9MWh

For year 2006, EUE under single circuit outage = 502.1MWh

Cost of energy not served under n-1 reliability criteria using $29,600/MWh Valueof Customer Reliability (VCR) for each year is given below:

For year 2002 = $6,568,240

For year 2006 = $14,862,160

Case 2 Full Evaluation

For case 2, the load duration curves are projected out for the 20 year period from2002 to 2022.


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0

50

100

150

200

250

300

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

% of the Time

Demand(MW)

2002

2003

2004

2005

2006

2007

20082009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Figure 12: Regional Demand Duration Curves 2002 to 2022

Probabilistic Analysis

The following tables contain the estimates of EUE against the N-1 transmissionreliability standard. The forecast are prepared for each of the generationscenarios, and for the four investment alternatives. For the purposes of thedemonstration, a simplification is made by assuming that each of the investmentalternatives is available in year 2002.

1. Do Nothing


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2. Investment Alternatives

a. 20MVar PFC Capacitor


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b. 50MW Diesel Power Station

c. 220kV 3rd

Circuit


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Table 5: VCR Cost of Expected Un-Served Energy (000s)

Table 5 reveals that the Expected Un-Served Energy forecast reduces accordingto the effectiveness of the investment alternative in providing increased capacityand therefore redundancy or reliability. For any year, the VCR cost is a measureof the justifiable maximum investment above which the grid benefit test outcomewould no longer be positive.

Kikiwa 220kV Grid Exit Point Reliability Risk

The above tables do not reveal the risk that demand is not met.

When the above demands are represented as a normal distribution curve,

demands that are within low risk area are imposed on the system with aprobability of 16% or less.

In case of a load shedding event, demand that is not met is limited from 0MW to30MW. This only occurs when one circuit is down, and the probability of thatevent is 0.001998. Therefore the combinational probability of the load sheddingis 0.0003197. The energy in the load shedding area is less than 3% of the totalenergy demand of the region.

Therefore, probabilistically speaking, energy that is not served is less than0.095% (3% x 0.032%) or nearly a one hundred thousandth of the total energydemand of the region. When capacity shortage is small (say 10%) it can be metwithout supply interruptions, by temporarily over-loading transmission load-


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carrying equipment. Downstream transformers will respond automatically byboosting voltage.

Using a statistical approach (normal distribution), a demand level can beestablished at the 84% confidence level as illustrated in the following chartproduced for year 2002:

0

20

40

60

80

100

120

140

160

180

200

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

% OF TIME

DEMAN

D(MW)

2002 DDC

1st SDV

Mean

HIGH RISK AREA

MEDIUM RISK

LOW RISK AREA

Figure 13 Risk Levels for Demand That Cannot Be Met

For example:

For the year 2002

Yearly average load factor according to the load duration curve =70%

Mean demand for the year = 126.11 MW

Standard deviation of the demand distribution = 23.69 MW

84% confidence level of meeting all demands below 149.8 MW

For the year 2022

Yearly average load factor according to the load duration curve =70%

Mean demand for the year = 206.64 MW

Standard deviation of the demand distribution = 38.8 MW


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84% confidence level of meeting all demands below 245.44 MW

All demands that are below the mean demand level are considered as veryimportant and carry high risk if unable to serve.

All demands that are above 84% confidence level are considered of minorimportance and carry low risk if unable to serve.

Demands that are between these limits carry medium risk if unable to serve.

Using this method of exposition, a risk-informed table can be produced for years2002 to 2022:

Table 6: VCR Cost of Expected Un-served Energy ADJUSTED by Risk

The available VCR benefit is reduced substantially when a risk-informedapproach is adopted. Tables 5 and 6 provide the comparative VCR figures.

Figure 14 shows how the benefits of each investment alternative is used up overtime as load growth increases the value of EUE and the overall performance ofthe system degrades to its starting condition. The x-axis scale is adjustedaccording to the do-nothing base-line VCR. ($0 is actually a base-line of $1.8M).


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-$25,000.00

-$20,000.00

-$15,000.00

-$10,000.00

-$5,000.00

$0.00

$5,000.00

0 5 10 15 20 25

220kV 3rd line

PFC Capacitors

50MW Diesel

EUE returns to 2002 level

Figure 14: Total VCR Saving Relative to Existing VCR per Annum

Alternative Investment Options:

The NPV spreadsheet calculations that follow are based on the simplification thatthe alternative delivers benefits in the first year.

Do Nothing

The do-nothing economic evaluation is actually a present value calculation ofthe value of customer reliability accrued over a 20 year period.

The NPV of $69M is the benefit that could be justified with appropriatetransmission grid augmentation.


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50MW Diesel Power Station6

Table 9: 50MW Diesel Power Station

3rd220kV Circuit

6 The Diesel Power Station alternative is treated as a one-off investment and the full value of the investment is

counted towards the reduction of Expected Un-Served Energy; in practice cost recovery for such an alternativecould be facilitated by treating the facility as an ancillary service.


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Table 10: 3rd

220kV Circuit

The economic evaluations demonstrate the difference in grid benefits. Using thissimplified economic evaluation, the order of investment preference is the 20MVArcapacitor, 3rdcircuit and 50MW diesel option:

AlternativePresent Value Ratio (PVR); Net

Present Value (NPV)

20MVar Capacitor 56.8; $33.5M

50MW Diesel 0.84; ($10.8M)

3rdCircuit 4.6; $53.6M

A composite investment scenario is explored in a later section of this report.

5.2 FREQUENCY- DURATION MODEL

Firstly we consider two single circuit parallel lines from Islington to Kikiwa for the

reliability assessment of the system using frequency of failures and failuredurations.

We assume that both circuits A and B are identical in all features for the simplicityof illustration. We will also assume that both circuits were commissioned 20 yearsago and have failed 9 times independently during that period.

Therefore, the frequency of failures for one circuit is (F, per year) = 9/20 = 0.45 /year.

A components service and outage cycles during its complete service time isgraphically represented as follows:


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Where, miis the in service duration in years and riis the outage duration in yearsof the component in ith cycle.

If the total number of service/failure cycles for entire service duration is n, thenthe total number of years the component was in service is (M, years) = miyears, (i =1 to n)

Similarly, the total number of years that the component was out of service is (R,years) = riyears (i =1 to n)

The mean time in service (m) = M/n (years) and Mean outage time (r) = R/n(years).

Then, the probability that the component is in service is = M/(M + R) or m/(m + r),and similarly the probability of component being out of service is = R/(M + R) orr/(m + r).

Now, if we assume total in service time of the circuit A as = 19.98 years, and thetotal outage time of the same circuit as = 0.02 years, then the probability of circuitA in service is P(s) = 19.98/(19,98+0.02) = 0.999.

And, the probability of circuit A out of service is P(o) = 0.02/(19.98+0.02) = 0.001.

Also, the constant service rate of the component () = F/P(s) = 0.45/0.999 =0.45045.

And the constant repair rate of the component () = F/P(o) = 0.45/0.001 = 450.

A spreadsheet model was developed for the sample system given aboveaccording to the chapter 6, Reliability Evaluation of Power Systems by RoyBillinton and Ronald N. Allan using following equations to estimate AnnualizedLoad Point Indices (ALPI).

Expected number of load curtailments/Violations = Fj

Absolute risk of violations = Durations (hours) * number of violations/8760

Expected energy not supplied = Lj* Dj* Fj

Expected duration of Load curtailment = Dj*Fj

Maximum duration of load curtailment = max { D1 , D2 , D3 .}

Where,

Fj Frequency of occurrence of outage j

Lj Load curtailment at the bus arising due to the outage j

m1 r m2 r m3 r

Second service/failure cycle


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Dj Duration of the load curtailment arising due to the outage j

The model assumptions for the F&D method are shown in the following table:

Figure 15 shows the difference between forecasts of EUE for a Do Nothingscenario for both Markov probability method and frequency/duration method:

0

200

400

600

800

1000

1200

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022

YEAR

EXPECTEDUN-SERVEDENERGY(MWh

)

Markov

F&D

Figure 15: Comparison of EUE Forecasts for Do Nothing

The chart shows that the estimates of energy are in close accordance usingeither method.


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The degree of accordance is less for the 3rdcircuit model as shown in Figure 16.

0

20

40

60

80

100

120

140

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022

YEAR

EX

PECTEDUN-SERVEDENERGY(MWh)

Markov

F&D

Figure 16: Comparison of EUE Forecasts for 3rd

Circuit Model

The F&D model is sensitive to the choice of frequency of outages. The followingtable shows significant difference in the forecasts of EUE as the frequency isvaried in the F&D model, holding probability of failure constant:


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Kikiwa 220kV Grid Exit Point Reliability Risk

The F&D method lends itself to the computation of reliability risk indices.

Do Nothing

The variation of load point reliability indices - Loss of Load Expected (LOLE) andLoss of Energy Expected (LOEE) - are shown in the following tables:

The following table shows that the reliability risk indices are also sensitive tofrequency assumptions:

The expected duration and absolute risk of violations is not affected byfrequency, so long as the probability is held consta

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