Emerging techniques in power system analysis Kỹ thuật mới nổi trong phân tích hệ thống điện

Chapter 1 introduces the recent changes of the power industry and thechallenging issues including, load modeling, distributed generations, situa-tional awareness, and control and protect

Zhaoyang Dong

Pei Zhang

et al.

Emerging Techniques in Power System Analysis

With 67 Figures

Department of Electrical Engineering Electric Power Research Institute The Hong Kong Polytechnic University 3412 Hillview Ave, Palo Alto,

Hong Kong, China CA 94304-1395, USA

E-mail: eezydong@polyu.edu.hk E-mail: pzhang@epri.com

ISBN 978-7-04-027977-1

Higher Education Press, Beijing

ISBN 978-3-642-04281-2 e-ISBN 978-3-642-04282-9

Springer Heidelberg Dordrecht London New York

Library of Congress Control Number: 2009933777

c

 Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg 2010

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Electrical power systems are one of the most complex large scale systems.Over the past decades, with deregulation and increasing demand in manycountries, power systems have been operated in a stressed condition and sub-ject to higher risks of instability and more uncertainties System operatorsare responsible for secure system operations in order to supply electricity

to consumers efficiently and reliably Consequently, power system analysistasks have become increasingly challenging and require more advanced tech-niques This book provides an overview of some the key emerging techniquesfor power system analysis It also sheds lights on the next generation tech-nology innovations given the rapid changes occurring in the power industry,especially with the recent initiatives toward a smart grid

Chapter 1 introduces the recent changes of the power industry and thechallenging issues including, load modeling, distributed generations, situa-tional awareness, and control and protection

Chapter 2 provides an overview of the key emerging technologies followingthe evolvement of the power industry Since it is impossible to cover all ofemerging technologies in this book, only selected key emerging technologiesare described in details in the subsequent chapters Other techniques arerecommended for further reading

Chapter 3 describes s the first key emerging technique: data mining.Data mining has been proved an effective technology to analyze very complexproblems, e.g cascading failure and electricity market signal analysis Datamining theories and application examples are presented in this chapter.Chapter 4 covers another important technique: grid computing Grid com-puting techniques provide an effective approach to improve computationalefficiency The methodology has been used in practice for real time powersystem stability assessment Grid computing platforms and application ex-amples are described in this chapter

Chapter 5 emphasizes the importance of probabilistic power system ysis, including load flow, stability, reliability, and planning tasks Probabilis-tic approaches can effectively quantify the increasing uncertainties in powersystems and assist operators and planning in making objective decisions Various probabilistic analysis techniques are introduced in this chapter

Chapter 7 provides information leading to further reading on emergingtechniques for power system analysis.

With the new initiatives and continuously evolving power industry, nology advances will continue and more emerging techniques will appear., Theemerging technologies such as smart grid, renewable energy, plug-in electricvehicles, emission trading, distributed generation, UVAC/DC transmission,FACTS, and demand side response will create significant impact on powersystem Hopefully, this book will increase the awareness of this trend andprovide a useful reference for the selected key emerging techniques covered

tech-Zhaoyang Dong, Pei ZhangHong Kong and Palo Alto

August 2009

1 Introduction· · · · 1

1.1 Principles of Deregulation· · · · 1

1.2 Overview of Deregulation Worldwide· · · · 2

1.2.1 Regulated vs Deregulated· · · · 3

1.2.2 Typical Electricity Markets· · · · 5

1.3 Uncertainties in a Power System· · · · 6

1.3.1 Load Modeling Issues· · · · 7

1.3.2 Distributed Generation· · · 10

1.4 Situational Awareness · · · 10

1.5 Control Performance · · · 11

1.5.1 Local Protection and Control · · · 12

1.5.2 Centralized Protection and Control· · · 14

1.5.3 Possible Coordination Problem in the Existing Protection and Control System· · · 15

1.5.4 Two Scenarios to Illustrate the Coordination Issues Among Protection and Control Systems · · · 16

1.6 Summary· · · 19

References· · · 19

2 Fundamentals of Emerging Techniques· · · 23

2.1 Power System Cascading Failure and Analysis Techniques · · · 23

2.2 Data Mining and Its Application in Power System Analysis · · · 27

2.3 Grid Computing· · · 29

viii Contents

2.4 Probabilistic vs Deterministic Approaches· · · 31

2.5 Phasor Measurement Units· · · 34

2.6 Topological Methods · · · 35

2.7 Power System Vulnerability Assessment· · · 36

2.8 Summary· · · 39

References· · · 39

3 Data Mining Techniques and Its Application in Power Industry· · · 45

3.1 Introduction · · · 45

3.2 Fundamentals of Data Mining· · · 46

3.3 Correlation, Classification and Regression· · · 47

3.4 Available Data Mining Tools· · · 49

3.5 Data Mining based Market Data Analysis· · · 51

3.5.1 Introduction to Electricity Price Forecasting · · · 51

3.5.2 The Price Spikes in an Electricity Market · · · 52

3.5.3 Framework for Price Spike Forecasting · · · 54

3.5.4 Problem Formulation of Interval Price Forecasting· · · · 63

3.5.5 The Interval Forecasting Approach· · · 65

3.6 Data Mining based Power System Security Assessment· · · 70

3.6.1 Background· · · 72

3.6.2 Network Pattern Mining and Instability Prediction · · · 74

3.7 Case Studies · · · 79

3.7.1 Case Study on Price Spike Forecasting · · · 80

3.7.2 Case Study on Interval Price Forecasting· · · 83

3.7.3 Case Study on Security Assessment· · · 89

3.8 Summary· · · 92

References· · · 92

4 Grid Computing · · · 95

4.1 Introduction · · · 95

4.2 Fundamentals of Grid Computing· · · 96

4.2.1 Architecture· · · 97

4.2.2 Features and Functionalities· · · 98

Contents ix

4.2.3 Grid Computing vs Parallel and Distributed

Computing · · · 100

4.3 Commonly used Grid Computing Packages· · · 101

4.3.1 Available Packages· · · 101

4.3.2 Projects· · · 102

4.3.3 Applications in Power Systems · · · 104

4.4 Grid Computing based Security Assessment· · · 105

4.5 Grid Computing based Reliability Assessment· · · 107

4.6 Grid Computing based Power Market Analysis · · · 108

4.7 Case Studies · · · 109

4.7.1 Probabilistic Load Flow · · · 109

4.7.2 Power System Contingency Analysis· · · 111

4.7.3 Performance Comparison · · · 111

4.8 Summary· · · 113

References· · · 113

5 Probabilistic vs Deterministic Power System Stability and Reliability Assessment · · · 117

5.1 Introduction · · · 117

5.2 Identify the Needs for The Probabilistic Approach· · · 118

5.2.1 Power System Stability Analysis· · · 118

5.2.2 Power System Reliability Analysis· · · 119

5.2.3 Power System Planning · · · 120

5.3 Available Tools for Probabilistic Analysis · · · 121

5.3.1 Power System Stability Analysis· · · 121

5.3.2 Power System Reliability Analysis· · · 123

5.3.3 Power System Planning · · · 123

5.4 Probabilistic Stability Assessment· · · 125

5.4.1 Probabilistic Transient Stability Assessment Methodology· · · 125

5.4.2 Probabilistic Small Signal Stability Assessment Methodology· · · 127

x Contents

5.5 Probabilistic Reliability Assessment · · · 128

5.5.1 Power System Reliability Assessment · · · 128

5.5.2 Probabilistic Reliability Assessment Methodology · · · · 131

5.6 Probabilistic System Planning· · · 135

5.6.1 Candidates Pool Construction· · · 136

5.6.2 Feasible Options Selection · · · 136

5.6.3 Reliability and Cost Evaluation· · · 136

5.6.4 Final Adjustment· · · 136

5.7 Case Studies · · · 137

5.7.1 A Probabilistic Small Signal Stability Assessment Example · · · 137

5.7.2 Probabilistic Load Flow · · · 140

5.8 Summary· · · 142

References· · · 143

6 Phasor Measurement Unit and Its Application in Modern Power Systems · · · 147

6.1 Introduction · · · 147

6.2 State Estimation · · · 151

6.2.1 An Overview· · · 151

6.2.2 Weighted Least Squares Method · · · 152

6.2.3 Enhanced State Estimation· · · 154

6.3 Stability Analysis· · · 157

6.3.1 Voltage and Transient Stability· · · 158

6.3.2 Small Signal Stability — Oscillations· · · 160

6.4 Event Identification and Fault Location· · · 162

6.5 Enhance Situation Awareness· · · 164

6.6 Model Validation· · · 167

6.7 Case Study · · · 169

6.7.1 Overview· · · 170

6.7.2 Formulation of Characteristic Ellipsoids· · · 170

6.7.3 Geometry Properties of Characteristic Ellipsoids · · · 172

6.7.4 Interpretation Rules for Characteristic Ellipsoids· · · 173

Contents xi

6.7.5 Simulation Results· · · 175

6.8 Conclusion· · · 179

References· · · 179

7 Conclusions and Future Trends in Emerging Techniques· · · 185

7.1 Identified Emerging Techniques· · · 185

7.2 Trends in Emerging Techniques· · · 186

7.3 Further Reading· · · 187

7.3.1 Economic Impact of Emission Trading Schemes and Carbon Production Reduction Schemes· · · 187

7.3.2 Power Generation based on Renewable Resources such as Wind· · · 189

7.3.3 Smart Grid · · · 190

7.4 Summary· · · 191

References· · · 191

Appendix· · · 195

A.1 Weibull Distribution · · · 195

A1.1 An Illustrative Example· · · 196

A.2 Eigenvalues and Eigenvectors· · · 197

A.3 Eigenvalues and Stability· · · 198

References· · · 200

Index · · · 201

1 Introduction

Zhaoyang Dong and Pei Zhang

With the deregulation of the power industry having occurred in many tries across the world, the industry has been experiencing many changes lead-ing to increasing complexity, interconnectivity, and uncertainties Demandfor electricity has also increased significantly in many countries, whichresulted in increasingly stressed power systems The insufficient investment

coun-in the coun-infrastructure for reliable electricity supply had been regarded as akey factor leading to several major blackouts in North America and Europe

in 2003 More recently, the initiative toward development of the smart gridagain introduced many additional new challenges and uncertainties to thepower industry In this chapter, a general overview will be given startingfrom deregulation, covering electricity markets, present uncertainties, loadmodeling, situational awareness, and control issues

1.1 Principles of Deregulation

The electricity industry has been undergoing a significant transformationover the past decade Deregulation of the industry is one of the most impor-tant milestones The industry had been moving from a regulated monopolystructure to a deregulated market structure in many countries including the

US, UK, Scandinavian countries, Australia, New Zealand, and some SouthAmerican countries Deregulation of the power industry is also in the processrecently in some Asian countries as well The main motivations of deregula-tion are to:

• increase efficiency;

• reduce prices;

• improve services;

• foster customer choices;

• foster innovation through competition;

• ensure competitiveness in generation;

2 1 Introduction

• promote transmission open access.

Together with deregulation, there are two major objectives for establishingelectricity markets They are (1) to ensure a secure operation and (2) tofacilitate an economical operation (Shahidehpour et al., 2002)

1.2 Overview of Deregulation Worldwide

In South America, Chile started the development of a competitive systemfor its generation services based on marginal prices as early as the early1980s Argentina deregulated its power industry in 1992 to form generation,transmission, and distribution companies into a competitive electricity mar-ket where generators compete Other South America countries followed thetrend as well

In the UK, the National Grid Company plc was established on March 31,

1990, as the owner and operator of the high voltage transmission system inEngland and Wales

Prior to March 1990, the vast majority of electricity supplied in land and Wales was generated by the Central Electricity Generating Board(CEGB), which also owned and operated the transmission system and theinterconnectors with Scotland and France The great majority of the output

Eng-of the CEGB was purchased by the 12 area electricity boards; each Eng-of whichdistributed and sold it to customers

On March 31, 1990, the electricity industry was restructured and thenprivatized under the terms of the Electricity Act 1989 The National GridCompany plc assumed ownership and control of the transmission system andjoint ownership of the interconnectors with Scotland and France, togetherwith the two pumped storage stations in North Wales But, these stationswere subsequently sold off

In the early 1990s, the Scandinavian countries (Norway, Sweden, land and Denmark) created a Nordic wholesale electricity market – NordPool (www.nordpool.com) The corresponding Nordic Power Exchange isthe world’s first international commodity exchange for electrical power Itserves customers in the four Scandinavian countries Being the Nordic PowerExchange, Nord Pool plays a key role as a part of the infrastructure of theNordic electricity power market and thereby provides an efficient, publiclyknown price of electricity of both the spot and the derivatives market

Fin-In Australia, the National Electricity Market (NEM) was first commenced

in December 1998, in order to increase the transmission efficiency andreduce electricity prices NEM serves as a wholesale market for the supply ofelectricity to retailers and end use customers in five interconnected regions:Queensland (QLD), New South Wales (NSW), Snowy, Victoria (VIC), and

1.2 Overview of Deregulation Worldwide 3

South Australia (SA) Tasmania (TAS) joined the Australian NEM on May

29, 2005, through Basslink The Snowy region was later abolished on July 1,

2008 In 2006 – 2007, the average daily demands in the current five regions

of QLD, NSW, VIC, SA, and TAS are 5 886 MW, 8 944 MW, 5 913 MW, 1

524 MW, and 1 162 MW, respectively The NEM system is one of the world’slongest interconnected power systems connecting 8 million end use consumerswith AUD 7 billion of electricity traded annually (2004 data) and spans over

4 000 km The Unserved Energy (USE) of the NEM system is 0.002%

In the United States, deregulation occurred in several regions One of themajor electricity markets is the California electricity market, which is part

of the PJM (Pennsylvania-New Jersey-Maryland) market The deregulation

of the California electricity market followed a series of stages, starting fromthe late 1970s, to allow non-utility generators to enter the wholesale powermarket In 1992, the Energy Policy Act (EPACT) formed the foundation forwholesale electricity deregulation

Similar deregulation processes have occurred in New Zealand and part ofCanada as well (Shahidehpour et al., 2002)

1.2.1 Regulated vs Deregulated

Traditionally the power industry is a vertically integrated single utility and

a monopoly in its service area It normally is owned by the government, acooperative of consumers, or privately As the single electricity serviceprovider, the industry is also obligated to provide electricity to all customers

in the service area

With the electricity supply service provider’s monopoly status, the tor sets the tariff (electricity price) to earn a fair rate of return on investmentsand to recover operational expenses Under the regulated environment, com-panies maximize profits while being subject to many regulatory constraints.From microeconomics, the sole service provider of a monopoly market has theabsolute market power In addition, because the costs are allowed by the reg-ulator to be passed to the customers, the utility has fewer incentives to reducecosts or to make investments considering the associated risks Consequently,the customers have no choices for their electricity supply service providersand have no choices on the tariffs (except in case of service contracts)

regula-As compared with a monopoly market, an ideal competitive market mally has many sellers/service providers and buyers/customers As a result

nor-of competition, the market price is equal to the cost nor-of producing the lastunit sold, which is the economically efficient solution The role of deregula-tion is to structure a competitive market with enough generators to eliminatemarket power

With the deregulation, traditional vertically integrated power utilities aresplit into generation, transmission, and distribution service providers to form

4 1 Introduction

a competitive electricity market Accordingly, the market operation decisionmodel also changes as shown in Figs 1.1 and 1.2

Fig 1.1 Market Operation Decision Model for the Regulated Power Industry –

Central Utility Decision Model

Fig 1.2 Market Operation Decision Model for the Deregulated Power Utility –

Competitive Market Decision Model

In the deregulated market, the economic decision making mechanismresponds to a decentralized process Each participant aims at profit max-imization Unlike that of the regulated environment, the recovery of the

1.2 Overview of Deregulation Worldwide 5

investment in a new plan is not guaranteed in a deregulated environment.Consequently, risk management has become a critical part of the electricitybusiness in a market environment

Another key change resulted from the electricity market is the tion of more uncertainties and stake holders into the power industry Thishelps to increase the complexity of power system analysis and leads to theneed for new techniques

introduc-1.2.2 Typical Electricity Markets

There are three major electricity market models in practice worldwide Thesemodels include the PoolCo model, the bilateral contracts model, and thehybrid model

1) PoolCo Model

A PoolCo is defined as a centralized marketplace that clears the marketfor buyers and sellers A typical PoolCo model is shown in Fig.1.3

Fig 1.3 Spot Market Structure (National Grid Management Council, 1994)

In a PoolCo market, buyers and sellers submit bids to the pool for theamounts of power they are willing to trade in the market Sellers in an electric-ity market would compete for the right to supply energy to the grid and notfor specific customers If a seller (normally a generation company or GENCO)bids too high, it may not be able to sell In some markets, buyers also bid

6 1 Introduction

into the pool to buy electricity If a buyer bids too low, it may not be able tobuy It should be noted that in some markets such as the Australian NEM,only the sellers bid into the pool while the buyers do not, which means thatthe buyers will pay at a pool price determined by the market clearing pro-cess There is an independent system operator (ISO) in a PoolCo market toimplement economic dispatch and produce a single spot price for electricity

In an ideal competitive market, the market dynamics will drive the spot price

to a competitive level equal to the marginal cost of the most efficient biddersprovided the GENCOs bid into the market with their marginal costs in order

to get dispatched by the ISO In such a market low cost generators will mally benefit by getting dispatched by the ISO An ideal PoolCo market is acompetitive market where the GENCOs bid with their marginal costs Whenmarket power exists, the dominating GENCOs may not necessarily bid withtheir marginal costs

nor-2) Bilateral Contracts Model

Bilateral contracts are negotiable agreements on delivery and receipt ofelectricity between two traders These contracts set the terms and conditions

of agreements independent of the ISO However, in this model the ISO willverify that a sufficient transmission capacity exists to complete the transac-tions and maintain the transmission security The bilateral contract model

is very flexible, as trading parties specify their desired contract terms ever, its disadvantages arise from the high costs of negotiating and writingcontracts and the risk of creditworthiness of counterparties

How-3) Hybrid Model

The hybrid model combines various features of the previous two models

In the hybrid model, the utilization of a PoolCo is not obligatory, and anycustomer will be allowed to negotiate a power supply agreement directly withsuppliers or choose to accept power at the spot market price In the model,PoolCo will serve all participants who choose not to sign bilateral contracts.However, allowing customers to negotiate power purchase arrangements withsuppliers will offer a true customer choice and an impetus for the creation of awide variety of services and pricing options to best meet individual customerneeds (Shahidehpour et al., 2002)

1.3 Uncertainties in a Power System

Uncertainties have existed in power systems from the beginning of the powerindustry Uncertainties from demand and generator availability have beenstudied in reliability assessment for decades However, with the deregula-

1.3 Uncertainties in a Power System 7

tion and other new initiatives happening in the power industry, the level ofuncertainty has been increasing dramatically For example, in a deregulatedenvironment, although generation planning is considered in the overall plan-ning process, it is difficult for the transmission planner to access accurateinformation concerning generation expansion Transmission planning is nolonger coordinated with generation planning by a single planner Future gen-eration capacities and system load flow patterns also become more uncertain

In this new environment, other possible sources of uncertainty include (Buygi

et al., 2006; Zhao et al., 2009):

• system load;

• bidding behaviors of generators;

• availability of generators, transmission lines, and other system facilities;

• installation/closure/replacement of other transmission facilities;

• carbon prices and other environmental costs;

• market rules and government policies.

1.3.1 Load Modeling Issues

Among the sources of uncertainties, power system load plays an importantrole In addition to the uncertainties coming from forecast demand, loadmodels also contribute to system uncertainty, especially for power systemsimulation and stability assessment tasks Inappropriate load models maylead to the wrong conclusion and possibly cause serious damage to the system

It is necessary to give a brief discussion of the load modeling issues here.Power system simulation is the most important tool guiding the operationand control of a power grid The accuracy of the power system simulationrelies heavily on the model reliability Among all the components in a powersystem, the load model is one of the least well known elements; however,its significant influences on the system stability and control have long beenrecognized (Concordia and Ihara, 1982; Undrill and Laskowski, 1982; Kun-dur 1993; IEEE 1993a; IEEE 1993b) Moreover, the load model has directinfluences on power system security On August 10, 1996, WSCC (West-ern Systems Coordinating Council) in the USA collapsed following poweroscillations The blackout caused huge economic losses and endangered statesecurity However, the system model guiding the WSCC operation had failed

to predict the blackout Therefore, the model validation process, ing this outage, indicated that the load model in WSCC database was notadequate to reproduce the event This strongly suggests that a more reliableload model is desperately needed The load model also has great effects oneconomic operation of a power system The available transfer capability ofthe transmission corridor is highly affected by the accuracy of the load mod-els used Due to the limited understanding of load models, a power system isusually operated very conservatively, leading to the poor utilization of both

follow-8 1 Introduction

the transmission and the generation assets

Nevertheless, it is also widely known that modeling the load is difficult due

to the uncertainty and the complexity of the load The power load consists ofvarious components, each with their own characteristics Furthermore, load isalways changing, both in its amount and composition Thus, how to describethe aggregated dynamic characteristic of the load has been unsolved so far.Due to the blackouts which occurred all around the world in the last fewyears, load modeling has received more attention and has become a newresearch focus

The state of the art for research on load modeling is mainly dedicated tothe structure of the load model and algorithms to find its parameters.The structure of the load model has great impacts on the results of powersystem analysis It has been observed that different load models will lead tovarious, even completely contrary conclusions on system stability (Kosterev

et al., 1999; Pereira et al., 2002) The traditional production-grade powersystem analysis tools often use the constant impedance, constant current, andconstant power load model, namely the ZIP load model However, simulationresults by modeling load with ZIP often deviate from the field test results,which indicate the inefficiency of the ZIP load model To capture the strongnonlinear characteristic of load under the recovery of the voltage, a load modelwith a nonlinear structure was proposed by (Hill, 1993) Load structure interms of nonlinear dynamic equations was later proposed by (Karlsson, Hill,1994; Lin et al., 1993) identified two dynamic load model structures based

on measurements, stating that a second order transfer function captures theload characteristics better than a first order transfer function The recenttrend has been to combine the dynamic load model with the static model(Lin et al., 1993; Wang et al., 1994; He et al., 2006; Ma et al., 2006; Wang etal., 1994) developed a load model as a combination of a RC circuit in parallelwith an induction motor equivalent circuit Ma et al (Ma et al., 2006; He etal., 2006; Ma et al., 2007; Ma et al., 2008) proposed a composite load model

of the ZIP in combination with the motor An interim composite load modelthat is 80% static and 20% induction motor model is proposed by (Perira etal., 2002) for WSCC system simulation Except for the load model structure,the identification algorithm to find the load model parameters is also widelyresearched Both linear and nonlinear optimization algorithms are applied

to solve the load modeling problem However, the identification algorithm isbased on the model structure and it cannot give reliable results without asound model structure

Although various model structures have been proposed for modeling loadfor research purposes, the power industry still uses very simple static loadmodels The reason is that some basic problems on composite load modelingare still open, which mainly include three key points: First, which modelstructure among proposed various ones is most appropriate to represent thedynamic characteristic of the load and is it the model with the simpleststructure? Second, can this model structure be identified? Is the parameter

1.3 Uncertainties in a Power System 9

set given by the optimization process really the true one, since optimizationmay easily stick into some local minima? Third, how is the generalizationcapability of the proposed load model? Load is always changing; however,

a model can only be built on available measurements So, the generalizationcapability of the load model reflects its validity Theoretically, the first pointinvolves the minimized realization problem, the second point addresses theidentification problem, and the third point closely relates to the statisticdistribution of the load

A sound load model structure is the basis for all other load modelingpractice Without a good model structure, all the efforts to find reliable loadmodels are in vain Based on the Occam’s razor principle, which states thatfrom all models describing a process accurately, the simplest one is the best(Nelles, 2001) Correspondingly, simplification of the model structure is animportant step in obtaining reliable load models (Ma et al., 2008) Currently,ZIP in combination with a motor is used to represent the dynamic char-acteristic of the load model However, there are various components of aload Take motors as an example, there are big motors and small motors,industry motors and domestic motors, three-phase motors and single-phasemotors Correspondingly, different load compositions are used to model dif-ferent loads or loads at different operating conditions Once the load modelstructure is selected, proper load model parameter values are needed Giventhe variations of the actual loads in a power system, a proper range ofparameter values can be used to provide a useful guide in selecting suitableload models for further simulation purposes

Parameter estimation is required in order to calculate the parameter ues for a given load model with system response measurement data Thisoften involves optimization algorithms and linear/nonlinear least squaresestimation (LSE) techniques, or a combination of both approaches

val-A model with the appropriate structure and parameters usually has goodperformance when fitting the available data However, it does not necessarilymean it is a good model A good load model must have good generalizationcapability Since a load is always changing, the model built on the avail-able data must also have the strong capability to describe the unseen data.Methodologies used for generalization capability analysis include statisticalanalysis and various machine learning methods Even if a model with goodgeneralization capability has been obtained, cross validation is still neededbecause it is still possible that the derived load model may fail to presentthe system dynamics in some system operating conditions involving systemtransients It is worth noting that both research and engineering practice inload modeling are still facing many challenges There are many complex loadmodeling problems causing difficulties to the power industry; consequently,static load models are still used by some companies in their operations andplanning practices

10 1 Introduction

1.3.2 Distributed Generation

In addition to those uncertainty factors discussed previously, anotherimportant issue is the potential large-scale penetration of distributed gen-eration (DG) into the power system Traditionally, the global power industryhas been dominated by large, centralized generation units which are able

to exploit significant economies of scale In recent decades, the centralizedgeneration model has been the focus of concern on its costs, security vul-nerability, and environmental impacts, while DG is expected to play anincreasingly important role in the future provision of a sustainable electricitysupply Large-scale implementation of DG will cause significant changes inthe power industry and deeply influence the transmission planning process.For example, DG can reduce local power demand; thus, it can potentiallydefer investments in the transmission and distribution sectors On the otherhand, when the penetration of DG in the market reaches a certain level, itssuppliers will have to get involved in the spot market and trade the elec-tricity through the transmission and distribution networks, which may need

to be further expanded Reliability of some types of DGs is also of a cern for the transmission and distribution network service providers (TNSPsand DNSPs) Therefore, it is important to investigate the impacts of DG onpower system analysis, especially in the planning process The uncertainties

con-DG brings to the system also need to be considered in power system analysis

1.4 Situational Awareness

The huge impact in economic terms as well as interruptions of daily life fromthe 2003 blackouts in North America and the following blackouts in UL andItaly clearly showed the need for techniques to analyze and prevent suchdevastating events According to the Electricity Consumers Resource Coun-cil (2004), the blackout in August 2004 in America and Canada had left 50million people without power supply and with an economic cost estimated

at up to $10 billion The many studies of this major blackout concludedthat a lack of situational awareness is one of the key factors that resulted

in the wide spread power system outage It has been concluded that thelack of situational awareness was composed of a number of factors such asdeficiencies in operator training, lack of coordination and ineffectiveness incommunications, and inadequate tools for system reliability assessment Thislack of situational awareness also applies to other major system blackouts

as well As a result, operators and coordinators were unable to visualize thesecurity and reliability status of the overall power system following somedisturbance events Such poor understanding of the system modes of opera-

1.5 Control Performance 11

tions and health of the network equipments also resulted in the Scandinavianblackout incident of 2003 As the complexity and connectivity of power sys-tems continue to grow, for the system operators and coordinators, situationalawareness becomes more and more important New methodologies needed forbetter awareness of system operating conditions can be achieved The capa-bility of control centres will be enhanced with better situational awareness.This can be partially promoted by development of operator and control cen-tre tools which allows for more efficient proactive control actions as comparedwith the conventional preventative tools Real time tools, which are able toperform robust real time system security assessment even with the presence

of system wide structural variations, are very useful in allowing operators

to have the better mental model of the system’s health Therefore, promptcontrol actions can be taken to prevent possible system wide outages

In its report for blackouts, NERC Real-Time Tools Best Practices TaskForce (RTTBPTF) defined situational awareness as “knowing what is going

on around you and understanding what needs to be done and when to tain, or return to, a reliable operating state.” NERC’s Real-Time Tools Sur-vey report presented situational awareness practices and procedures, whichshould be used to define requirements or guidelines in practice According tothe article by Endsley, 1998, there are three levels for the term situationalawareness or situation awareness: (1) perception of elements, (2) compre-hending the meaning of these elements, and (3) projecting future systemstates based on the understanding from levels 1 and 2 For level 1 of sit-uational awareness, operators can use tools which provide real time visualand audio alarm signals which serve as indicators of the operating states

main-of the power system According to NERC (NERC 2005, NERC 2008) thereare three ways of implementing such alarm tools which are being within theSCADA/EMS system, external functions, or a combination of the two.NERC Best Practices Task Force Report (2008) summarized the followingsituational awareness practice areas in its report: reserve monitoring for bothreactive reserve capability and operating reserve capability; alarm responseprocedures; conservative operations to move the system from unknown andpotentially risky conditions into a secure state; operating guides defining pro-cedures about preventive actions; load shed capability for emergency control;system reassessment practices, and blackstart capability practices

1.5 Control Performance

This section provides a review of the present framework of power system tection and control (EPRI, 2004; EPRI, 2007; SEL-421 Manual; ALSTOM,2002; Mooney and Fischer, 2006; Hou et al., 1997; IEEE PSRC WG, 2005;

pro-12 1 Introduction

Tzaiouvaras, 2006; Plumptre et al., 2006) Both protection and control can

be viewed as corrective and/or preventive activities to enhance systemsecurity Meanwhile, protection can be viewed as activities to disconnect andde-energize some components, while control can be viewed as activities with-out physical disconnection of a significant portion of system components Inthis report, we do not intend to make a clear distinction between protectionand control We collectively use the term “protection and control” to indi-cate the activities to enhance system security In addition, although thereare a number of ways to classify the protection and control systems based ondifferent viewpoints, this report classifies protection and control as local andcentralized to emphasize the need for better coordination in the future

1.5.1 Local Protection and Control

A distance relay is the mostly commonly used relay for local protection oftransmission lines Distance relays measure voltage and current and also com-pare the apparent impedance with relay setting When the tripping criteriaare reached, distance relays will trip the breakers and clear the fault Typicalforms of distance relays include impedance relay, mho relay, modified mhorelay, and combinations thereof Usually, distance relays may have Zone 1,Zone 2, and Zone 3 relays to cover longer distances of transmission lines withthe delayed response time as shown below:

• Zone 1 relay time and the circuit breaker response time may be as fast

as 2 – 3 cycles;

• Zone 2 relay response time is typically 0.3 – 0.5 seconds;

• Zone 3 relay response time is about 2 seconds.

Fig.1.4 shows the Zone 1, Zone 2, and Zone 3 distance relay characteristics

Fig 1.4 R-X diagram of Zone 1, Zone 2, and Zone 3 Distance Relay

1.5 Control Performance 13

a synchronous generator can be either hydraulic turbines or steam turbines.The control of prime movers is based on the frequency deviation and loadcharacteristics The AGC is used to restore the frequency and the tie-lineflow to their original and scheduled values The input signal of AGC is calledArea Control Error (ACE), which is the sum of the tie-line flow deviationand the frequency deviation multiplied by a frequency-bias factor

Power System Stabilizer (PSS) technology’s purpose is to improve smallsignal stability or improve damping PSSs are installed in the excitation sys-tem to provide auxiliary signals to the excitation system voltage regulatingloop The input signals of PSSs are usually signals that reflect the oscillationcharacteristics, such as the shaft speed, terminal frequency, and power.Generator Excitation System is utilized to improve power system stabilityand power transfer capability, which are the most important issues in bulkpower systems under heavy load flow The primary task of the excitationsystem in synchronous generators is to maintain the terminal voltage of thegenerator at a constant level and guarantee reliable machine operations forall operating points The governing functions achieved are (1) voltage control,(2) reactive power control, and (3) power factor control The power factorcontrol uses the excitation current limitation, stator current limitation, androtor displacement angle limitation linked to the governor

On-Load Tap Changer (OLTC) is applied to keep the voltage on the lowvoltage (LV) side of a power transformer within a preset dead band, such thatthe power supplied to voltage sensitive loads is restored to the pre-disturbancelevel Usually, OLTC takes tens of seconds to minutes to respond to the lowvoltage event OLTC may have a negative impact to voltage stability, becausethe higher voltage at the load side may demand higher reactive current toworsen the reactive problem during a voltage instability event

Shunt Compensation in bulk power systems includes traditional ogy like capacitor banks and new technologies like the static var compensator(SVC) and the static compensator (STATCOM) An SVC consists of shuntcapacitors and reactors connected via thyristors that operate as power elec-tronics switches They can consume or produce reactive power at speeds inthe order of milliseconds One main disadvantage of the SVC is that theirreactive power output varies according to the square of the voltage they areconnected to, which is similar to capacitors STATCOMs are power electron-ics based SVCs They use gate turn off thyristors or insulated gate bipolartransistors (IGBTs) to convert a DC voltage input to an AC signal that

technol-is chopped into pulses that are then recombined to correct the phase anglebetween voltage and current STATCOMs have a response time in the order

of microseconds

Load shedding is performed only under an extreme emergency in modernelectric power system operation, such as faults, loss of generation, switchingerrors, lightning strikes, and so on For example, when system frequency dropsdue to insufficient generation under a large system disturbance, load sheddingshould be done to bring frequency back to normal Also, if bus voltage slides

14 1 Introduction

down due to an insufficient supply of reactive power, load shedding shouldalso be performed to bring voltage back to normal The formal load sheddingscheme can be realized via under-frequency load shedding (UFLS) while thelatter scheme can be realized via under-voltage load shedding (UVLS)

1.5.2 Centralized Protection and Control

Out-of-step (OOS) relaying provides blocking or tripping functions to rate the system when loss of synchronism occurs Ideally, the system should

sepa-be separated at such points as to maintain a balance sepa-between load and eration in each separated area Moreover, separation should be performedquickly and automatically in order to minimize the disturbance to the sys-tem and to maintain maximum service continuity via the OOS blocking relayand tripping relay During a transient swing, the OOS condition can bedetected by using two relays having vertical (or circular) characteristics on anR-X plane as shown in Fig.1.5 If the time required to cross the two character-istics (OOS1 and OOS2) of the apparent impedance locus exceeds a specifiedvalue, the OOS function is initiated Otherwise, the disturbance will be iden-tified as a line fault The OOS tripping relays should not operate for stableswings They must detect all unstable swings and must be set so that normalload conditions are not picked up The OOS blocking relays must detect thecondition before the line protection operates To ensure that line relaying isnot blocked for fault conditions, the setting of the relays must be such thatnormal load conditions are not in the blocking area

gen-Fig 1.5 Tripping zones and out-of-step relay

Special Protection Systems (SPS), also known as Remedial Action Schemes(RAS) or System Integrity Protection Systems (SIPS), have become morewidely used in recent years to provide protection for power systems againstproblems that do not directly involve specific equipment fault protection ASPS is applied to solve single and credible multiple contingency problems

of them SPS remedial actions include generation rejection, load shedding,controlling reactive units, or/and using braking resistors.

SCADA/EMS is the most typical application of centralized control inpower systems It is a hardware and software system used by operators tomonitor, control, and optimize a power system The monitor and controlfunctions are known as SCADA; the advanced analytical functions such asstate estimation, contingency analysis, and optimization are often referred

to as EMS Typical benefits of SCADA/EMS systems include: improvedquality of supply, improved system reliability, and better asset utilizationand allocation An increasing interest in the EMS functions is the onlinesecurity analysis software tools, which typically provide transient stabilityanalysis, voltage security analysis, and small – signal stability analysis Thelatest development in computer hardware and software and in power systemsimulation algorithms has at present more accurate results for these functions

in real-time, which could not be achieved online in the past

1.5.3 Possible Coordination Problem in the Existing Protection

and Control System

Fig.1.6 summarizes the time delay, in the logarithmic scale, of various tions and controls based on a number of literatures (4 – 10) As shown in thisfigure, the time delays of many different control systems or strategies havesome considerable overlaps The reason is historical In the past, the design

protec-of different control was originally based on a single goal to solve a ular problem As modern power systems are more interconnected and haveincreasing stress levels, disturbances may cause multiple controls to respond,among which some may be undesired This trend presents great challengesand risks in protection and control, as evidence by increasing occurrences ofblackout events in North America This challenge will be illustrated with twocase analyses in the next section

partic-16 1 Introduction

Fig 1.6 Time frame of the present protection and control system

1.5.4 Two Scenarios to Illustrate the Coordination Issues among

Protection and Control Systems

1) Load Shedding or Generator Tripping

This case analysis shows a potential coordination problem in a two-areasystem with a generation center (see the left part in Fig.1.7) and a load pocket(see the right part in Fig.1.7) Assume the load pocket experiences a heavyload increase on a hot summer day Meanwhile, a transmission contingencyevent occurs in the tie-line between the generation center and the load pocket

to cause a reduction of the power import to the load pocket Then, the load

in the load pocket may be significantly greater than the sum of total localgeneration, the (reduced) import from the tie-line, and the spinning reserves.This may lead to a decrease of both frequency and voltage Certainly, underthis scenario, excessive load is the root cause of imbalance, and load shedding

in the load pocket is an effective short-term solution

However, there may be a potential risk of blackouts if the local ators’ under-frequency (UF) tripping scheme and the loads’ under-voltage(UV) shedding scheme are not well coordinated Likely, the under-frequencygeneration tripping scheme will disconnect some generation from the sys-tem before the load shedding scheme is completed, since the present setting

gener-in generation trippgener-ing is usually very fast This will worsen the imbalancebetween load and generation in the load pocket Hence, both voltage and fre-quency may decrease further This may lead to more generation to be quickly

1.5 Control Performance 17

Fig 1.7 A two-area sample system

tripped and the local load pocket will lose a large amount of reactive powerfor voltage support Therefore, this may lead to a sharp drop of voltage andeventually a fast voltage collapse at the end Even though this is initially areal power imbalance or frequency stability problem, the final consequence

is a voltage collapse Fig.1.8 shows the gradual process based on the aboveanalysis

Fig 1.8 The process to instability

As previously mentioned, the root cause is the imbalance of generationand load in the load pocket The coordination of generation tripping andload shedding is not optimized or well coordinated to perform load shedding

a distance relay More than 20 seconds after these events, the last straw of thecollapse occurred This last straw was the trip of the Mill Creek-Antelope linedue to the undesired Zone 3 protective relay After this tripping, the systemcollapsed within 3 seconds The relay of the Mill Creek-Antelope line did as

it should do based on its Zone 3 setting, which was to trip the line when theobserved apparent impedance encroached upon the circle of the Zone 3 relay

as shown in Figs.1.9 and 1.10 In this case, the low apparent impedance wasthe consequence of the power system conditions at that moment Obviously,

Fig 1.9 The line tripping immediately leading to a fast, large-area collapse during

the WSCC July 2, 1996, Blackout

1.6 Summary 19

if the setting of the Zone 3 relay can be dynamically reconfigured, ering the heavily loaded system condition, the system operators may haveenough time to perform some corrective actions to save the system from afast collapse

consid-Fig 1.10 Observed impedance encroaching the Zone 3 circle

1.6 Summary

Power systems have been experiencing dramatic changes over the past decade.Deregulation is one of the main changes occurring across the world Increasedconnectivity and resultant nonlinear complexity of power system is anothertrend The consequences of such changes are various uncertainties and diffi-culties in power system analysis Recent major power system blackouts alsoremind the power industry of the need for situational awareness and moreeffective tools in order to ensure more secure operation of the system Thischapter has reviewed these important aspects of the power system worldwide.This chapter serves as an introduction and forms the basis for furtherdiscussion on the emerging techniques in power system analysis

20 1 Introduction

Endsley MR (1988) Situation awareness global assessment technique Proceedings

of The National Aerospace and Electronics Conference IEEE, pp789 – 795EPRI Project Opportunities (2007) PMU-based Out-of-step Protection SchemeGeneral Electric Company (1987) Load modeling for power flow and transient sta-bility computer studies, Vol 1 – 4, EPRI Report EL-5003

IEEE Task Force on Load Representation for Dynamic Performance (1993) Loadrepresentation for dynamic performance analysis IEEE Trans Power Syst 8(2):

472 – 482

IEEE Task Force on Load Representation for Dynamic Performance (1995) ography on load models for Power flow and dynamic performance simulation.IEEE Trans Power Syst 10(1): 523 – 538

Bibli-IEEE Task Force on Load Representation for Dynamic Performance (1995) dard load models for power flow and dynamic performance simulation IEEETrans Power Syst 10(3): 1302 – 1313

Stan-Hill DJ (1993) Nonlinear dynamic load models with recovery for voltage stabilitystudies IEEE Trans Power Syst 8(1): 166 – 176

He RM, Ma J, Hill DJ (2006) Composite load modeling via measurement approach.IEEE Trans Power Syst 21(2): 663 – 672

Hou D, Chen S, Turner S (1997) SEL – 321 – 5 relay out-of-step logic SchweitzerEngineering Laboratories, Inc Application Guide AG97-13

Karlsson D, Hill DJ (1994) Modeling and identification of nonlinear dynamic loads

in power systems IEEE Trans Power Syst 9(1): 157 – 166

Kundur P (1993) Power system stability and control McGraw-Hill, New YorkKosterev DN, Taylor CW, Mittelstadt WA (1999) Model validation for the august

10, 1996 WSCC system outage IEEE Trans Power Syst 14(3): 967 – 979Lin CJ, Chen YT, Chiang HD et al (1993) Dynamic load models in power systemsusing the measurement approach IEEE Trans Power Syst 8(1)

Ma J, He RM, Hill DJ (2006) Load modeling by finding support vectors of loaddata from field Measurements, IEEE Trans Power Syst 21(2): 726 – 735

Ma J, Han D, He R et al (2008) Reducing identified parameters of based composite load model IEEE Trans Power Syst 23(1): 76 – 83

measurement-Ma J, Dong ZY, He R et al (2007) System energy analysis incorporating hensive load characteristics IET Gen Trans Dist, 1(6): 855 – 863

compre-Mooney J, Fischer N (2006) Application guidelines for power swing detection ontransmission systems Proceedings of the 59th annual conference for protectiverelay engineers 2006 IEEE, 289 – 298

National Grid Management Council Empowering the market–national electricityreform for australia December 1994

Nelles O (2001) Nonlinear system identification Springer, Heidelberg

NERC (North American Electric Reliability Council) (2005) Best practices taskforce report Discussions, Conclusions, and Recommendations

NERC Real-Time Tools Best Practices Task Force (2008) Real-time tools surveyanalysis and recommendations Final Report

Pereira L, Kosterev D, Mackin P et al (2002) An interim dynamic induction motormodel for stability studies in the WSCC IEEE Trans Power Syst 17(4): 1108 –1115

Plumptre F, Brettschneider S, Hiebert A et al (2006) Validation of out-of-stepprotection with a real time digital simulator TP6241-01, BC hydro, Cegertec,

BC Transmission Corporation and Schweitzer Engineering Laboratories incPrice WW, Wirgau KA, Murdoch A et al (1988) Load modeling for load flow andtransient stability computer studies IEEE Trans Power Syst 3, pp180 – 187Shahidehpour M, Ymin H, Li Z (2002) Market operations in electric power systems.Forecasting, Scheduling, and Risk Management, IEEE, Wiley, New YorkTzaiouvaras D (2006) Relay performance during major system disturbances

References 21

TP6244 – 01, SEL

Thorpe GH (1998) Competitive electricity market development in australia ceedings of ARC Workshop on Emerging Issues and Methods in the Restructur-ing of the electric Power Industry, The University of Western Australia, 20 – 22July 1998

Pro-Wang JC, Chiang HD, Chang CL et al (1994) Development of a frequency-dependentcomposite load model using the measurement approach IEEE Trans PowerSyst 9(3): 1546 – 1556

Undrill JM, Laskowski TF (1982) Model selection and data assembly for powersystem simulation IEEE Trans Power App Syst, 101, pp 3333 – 3341

SEL-421 Manual, Schweitzer Engineering Laboratories, SEL-421 Relay ProtectionAutomation Control, 2001

Zhao J, Dong ZY, Lindsay P et al (2009) Flexible transmission expansion planning

in a market environment IEEE Trans Power Syst 24(1): 479 – 488

Zhang P, Min L, Hopkins L, Fardanesh B (2007) Utility Experience PerformingProbabilistic Risk Assessment for Operational Planning Proceedings of the ofthe14th ISAP, November, 2007

2 Fundamentals of Emerging Techniques

Xia Yin, Zhaoyang Dong, and Pei Zhang

Following the new challenges of the power industry outlined in Chapter 1, newtechniques for power system analysis are needed These emerging techniquescover various aspects of power system analysis including stability assessment,reliability, planning, cascading failure analysis, and market analysis In order

to better understand the functionalities and needs for these emerging niques, it is necessary to give an overview of these emerging techniques andcompare these emerging ones with traditional approaches

tech-In this chapter, the following emerging techniques will be outlined Some

of the key techniques and their applications in power engineering will bedetailed in the subsequent chapters The main objective is to provide a holisticpicture of the technological trends in power system analysis over the recentyears

2.1 Power System Cascading Failure and Analysis

Techniques

In 2003, there were several major blackouts, which were regarded as results ofcascading failures of power systems The increasing number of system insta-bility events is mainly because of the operation of market mechanisms whichhas driven more generation investments but provided insufficient transmis-sion expansion investments With the increased demand for electricity, manypower systems have been heavily loaded As a result, power systems are run-ning close to their security limits and therefore vulnerable to disturbances(Dong et al., 1995)

The blackout of 14 August 2003 (Michigan Public Service Commission2003) in the USA has so far been the worst case which affected Michigan,Ohio, New York City, Ontario, Quebec, northern New Jersey, Massachusetts,and Connecticut, according to a North American Electric Reliability Coun-

24 2 Fundamentals of Emerging Techniques

cil (NERC) report Over 50 million people experienced that blackout over aconsiderable number of hours The economic loss and political impact wereenormous, and concerns regarding national security rose from the power sec-tor The major reasons for the blackout were identified as (U.S.-Canada PowerSystem Outage Task Force, 2004):

• failure to identify emergency conditions and communicate to

neighbor-ing systems;

• inefficient communication and/or sharing of system wide data;

• failure to ensure operation within secure limits;

• failure to assess system stability conditions in some affected areas;

• inadequate regional-scale visibility over the bulk power system;

• failure of the reliability organizations to provide effective real-time

diagnostic support;

• a number of other reasons.

According to an EPRI report (Lee, 2003), in the 1990s, electricity demand

in the US grew by 30%, but for the same period there was only a 15%increase in new transmission capacity Such imbalance continues to grow;

it is estimated that from 2002 to 2011, demand will grow a further 20%with only a 3.5% increase in new transmission capacity This has caused asignificant increment in transmission congestion and has created many newbottlenecks in the flows of bulk power This situation has further stressedthe power system It is a far more complex problem than a simple voltagecollapse based on the information available so far

As clearly indicated in many literatures about this event, the reasons forsuch large scale blackouts are extremely complex, and have yet to be fullyunderstood Although there are established system security assessment tools

in operation with the power companies over the blackout affected region,the system operators were unable to identify the severity of emerging systemsignals and therefore unable to reach a timely remedial decision to preventsuch cascading system failure

The state-of-the-art power system stability analysis leads to the followingconclusions:

• many power systems are vulnerable to multiple contingency events;

• the current design approaches to maintain stability are based on

deterministic approaches which do not correctly include the uncertainty

in the power system parameters or the failures which can impact thesystem;

• this explicit consideration of the uncertainties in disturbances and of

power system parameters can impact on the decisions on placement ofcorrection devices such as FACTS devices or on the control design ofexcitation controllers;

• the explicit consideration of where the system breaks under multiple

contingencies can be used to adjust the controllers and the links to bestrengthened in power system design;

2.1 Power System Cascading Failure and Analysis Techniques 25

• the mechanism of cascading failure blackouts has not been fully

under-stood;

• if timely information about system security is available even a short

time beforehand, many of the severe system security problems such asblackouts could be avoided

It can be seen that the information involved to properly assess the security

of a power system is increasingly complex with open access and deregulation.New techniques are needed to handle such problems

Cascading failure is a main form of system failure leading to blackouts.However, the mechanism of cascading failure is still difficult to analyze inorder to develop reliable algorithms to monitor, predict, and prevent black-outs

To face the impending challenges from operation and planning withrespect to cascading failure avoidance, power system reliability analysis needsnew evaluation tools So far, the widely recognized contingency analyticalmethod of large interconnection power systems is the N-1 criterion (CIGRE,1992) In some cases, the N-1 even can be defined as the loss of a set ofcomponents of the system within a short time The merits of the N-1 cri-terion are the flexibility, clarity, and simplicity of implementation However,with the increasing risk of the occurrence of catastrophic failure and systemcomplexity, this criterion may not provide sufficient information of the vul-nerability and severity level of the system Since catastrophic disruptions arenormally caused by cascading failures of electrical components, the impor-tance of studying the inherent mechanism of cascading outages is attractingmore and more attention

So far, many models have been documented on simulating cascading ures In the article by Dobson et al., 2003, a load-dependent model is proposedfrom a probabilistic point of view At start, the system components will beallocated a virtual load randomly Then the model will be initiated by adding

fail-a disturbfail-ance lofail-ad to fail-all the components A component will be tripped whenits load exceeds the maximum limit, and other unfailed components willreceive a constant load from this failure This cascading procedure will ter-minate when there are no component failures within a cascading scenario.This model can fully explore all the possibilities of cascading cases of the sys-tem This cascading model is further improved by incorporating branchingprocess approximation in the article by Dobson et al., 2004, so that the prop-agation of cascading failures can be demonstrated However, both of themdid not address the joint interactions among system components during cas-cading scenarios In the article by Chen et al., 2005, cascading dynamics isinvestigated under different system operating conditions via a hidden failuremodel This model employs linear programming (LP) generation redispatchjointed with dc load flow for power distribution and emphasizes the possiblefailures existing in the relay system Chen et al (Chen et al., 2006) study themechanism of cascading outages by estimating the probability distribution of

26 2 Fundamentals of Emerging Techniques

historical data of transmission outages However, both methods above do notconsider failures of other network components, such as generators and loads

In the article by Stubna and Fowler, 2003, to describe the statistics ofrobust complex systems under uncertain conditions, highly optimised toler-ance (HOT) model is introduced in simulating blackout phenomena in powersystems A simulation result shows that this model reasonably fits the histori-cal data set of one realistic test power system Besides these proposed models,the investigation of critical transitions of a system according to the systemloading conditions during cascading procedure is also studied (Carreras etal., 2002) The paper finds that the size of the blackouts will experience asharp increase once the system loading condition is over a critical transitionpoint

Efforts also have been dedicated to understand the cascading faults fromglobal system perspectives Since the inherent differences of systems make

it difficult to propose a generalized mathematic model for all the networks,these analysis approaches are normally established by probabilistic and statis-tic theories In the article by Carreras et al., 2004, from the detailed timeseries analysis of the North American Electrical Reliability Council (NERC)

15 years historical blackout data, the authors find that cascading failuresoccurring in the system had exhibited self organised criticality (SOC) dynam-ics This work shows that the cascading collapse of systems may be caused

by the power system global nonlinear dynamics instead of weather or otherexternal triggering disturbances This evidence provides a global philosophyfor understanding the catastrophic failures in power systems

It has been recognised that the structures of complex networks alwaysaffect their functions (Strogatz, 2001) Due to the complexity inherit in powergrids the study of system topology is another interesting approach In thearticle by Lu et al 2004, “small world” is introduced for analysing and com-paring the topology characteristics of power networks in China and the UnitedStates The result shows that many power grids fall within the “small world”category Paper (Xu and Wang, 2005) employs scale-free coupled map lattices(CML) models to investigate the cascading phenomena The result indicatesthat the increase in the homogeneity of the network will be helpful to enhancethe system stability However, since topology analyses normally require net-works to be homogeneous and non-weighted, it might need approximationswhen dealing with power grid issues

Recent NERC studies of major blackouts (NERC US Canada Power tem Outage Task Force 2004) have shown that more than 70% of those black-outs involved hidden failures, which are incorrect relay operations, namelyremoving a circuit element(s) as a direct consequence of another switchingevent (Chen et al., 2005; Jun et al., 2006) When a transmission line trip,there is a small but significant probability that lines sharing a bus (those linesare called as expose to hidden failures) with the tripped line may incorrectly

Sys-2.2 Data Mining and Its Application in Power System Analysis 27

trip due to the relay malfunctioning The Electric Power Research Institute(EPRI) and Southern Company jointly developed a cascading failure analysissoftware, called Transmission Reliability Evaluation of Large-Scale Systems(TRELSS), which has been applied in real systems for several years (Makarovand Hardiman, 2003) The model addresses the trips of loads, generators, andprotection control groups (PCG) In every cascading scenario, the value ofload node voltages, generator node voltages as well as circuit overloads will

be investigated sequentially, and the next cascading fault will be determinedfrom the result The model is very complex for application (Makarov andHardiman, 2003)

IEEE PES CAMS Task Force (2008, 2009) on Understanding, Prediction,Mitigation and Restoration of Cascading Failures provides a detailed review

of the issues of cascading failure analysis The research and development inthis area continue with various techniques (Liu et al., 2007; Nedic et al., 2006;Kirschen et al., 2004; Dobson et al., 2005; Dobson et al., 2007; Chen et al.,2005; Sun and Lee, 2008; Hung and Nieplocha, 2008; Zhao et al., 2007; Mili

et al., 2004; Kinney et al., 2005)

2.2 Data Mining and Its Application in Power System Analysis

Data mining is the process to identify hidden, potentially useful and standable information and patterns from large data bases; or in short it is theprocess to discover hidden patterns from data bases It is an important step inthe process of knowledge discovery in databases (Olaru and Wehenkel, 1999)

under-It has been used in a number of areas for power system analysis where largeamount data are involved such as forecasting and contingency assessment

It is well known that online contingency assessment or online dynamicsecurity assessment (DSA) is a very complex task that requires a significantamount of computational costs for many real interconnected power systems.With increasing complexity in modern power systems, the corresponding sys-tem data are exponentially increasing Many companies store such data butare not yet able to fully utilize them Under such emerging complexity, it isdesirable to have reliable and fast algorithms to perform such duties instead

of the traditional time-consuming security assessment/dynamic simulationbased ones

It should be noted that artificial intelligence (AI) techniques such as ral networks (NNs) have been used for similar purposes as well However, AIbased methods suffer a number of shortcomings which have prevented theirwider application in realistic situations so far The major shortcomings of

neu-28 2 Fundamentals of Emerging Techniques

NN based online dynamic security assessment are the inference opacity, theover-fitting problem, and applicability to a large scale system Lack of statis-tical information from NN outputs is also a major concern which limits itsapplication

Data mining based real time security assessment approaches are able toprovide statistically reliable results and have been widely practiced in manycomplex systems such as telecommunications system and internet securityareas In power engineering, data mining has been successfully employed

in a number of areas including fault diagnosis and condition monitoring ofpower system equipment, customer load profile analysis (Figueiredo et al.,2005), nontechnical loss analysis (Nizar, 2008), electricity market demandand price forecasting (Zhao et al., 2007a; Zhao et al., 2007b; Zhao et al.,2008), power system contingency assessment (Zhao, 2008c), and many othertasks for power system operations (Madan et al., 1995; Tso et al., 2004; PecasLopes and Vasconcelos, 2000) However, there is still a lack in systematicapplication of data mining techniques in some specific areas such as largescale power system contingency assessment and predictions (Taskforce 2009).For applications such as a power system online DSA, it is critical to haveassessment results within a very short time in order for the system opera-tor to take corresponding control actions to prevent series system securityproblems Data mining based approaches, with their mathematically andstatistically reliable characteristics open up a realistic solution for on-lineDSA type tasks They outperform the traditional AI based approach in manyaspects First, data mining is originally designed to discover useful patterns inlarge-scale databases, in which AI approaches usually face unaffordable timecomplexity Therefore, data mining based approach are able to provide thefast response in user friendly efficient forms Second, a variety of data clean-ing techniques have been incorporated into data mining algorithms, henceenabling data mining algorithms with strong noisy input tolerance capabil-ities The most important feature is that a number of data mining meth-ods actually come from the modification of traditional statistic theory Forinstance, the Bayesian classifier is from Bayesian decision theory and sup-port vector machine (SVM) is based on statistical learning theory As aresult, these techniques are able to handle large-scale data sets Moreover,they have strong statistical robustness and the ability to overcome over-fittingproblems as compared with AI techniques The statistical robustness meansthat if the system is assessed to have a security problem, it will experiencesuch a problem with a given probability of occurrence if no actions are taken.This characteristic is very important for the system operator managing thesystem security in a market environment where any major actions are asso-ciated with potentially huge financial risks The operator needs to be surethat a costly remedial action (such as load shedding) is necessary before thataction takes place Data mining normally involves four types of tasks

2.3 Grid Computing 29

including the classification, clustering, regression, and association rule ing (Wikipedia) (Han, 2006)

learn-Classification is an important task in the data mining and so is presented

in more detail here According to the article by Vapnik, 1995, the classificationproblem belongs to supervised learning problems, which can be describedusing three components:

• a generator of random vectors X, drawn independently from a fixed

but unknown distribution P ( X);

• a supervisor that returns an output value y for every input vector (in

classification problems, y should be discrete and is called class label for

a givenX), according to a conditional distribution function P (y|X),

also fixed but unknown;

• a learning machine capable of implementing a set of functions f(X, α),

α ∈ Λ.

The object of a classifier is to give the f ( X, α), α ∈ Λ with best

approx-imation to the supervisor’s response Predicting the occurrence of systemcontingency is a typical binary classification problem The factors which arerelevant to the contingencies (e.g., demand and weather) can be seen as thedimensions of the input vectorX = (x1, x2, , x n ), and x i , i ∈ [1, n] is a

relevant factor

So far, there have been a number of classification algorithms in tice According to the article by Sebastiani, 2002, the main classificationalgorithms can be categorized as: decision tree and rule based approachessuch as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier(Lewis, 1998); on-line methods such as Winnow (Littlestone, 1998); example-

prac-based methods such as k-nearest neighbors (Duda and Hart, 1973); and SVM

(Cortes and Vapnik, 1995)

Similar to classification, clustering also allocates similar data into groupsbut the groups are not pre-defined Regression is used to model the data serieswith the least error Association rule learning is used to discover relationshipsbetween variables in a data base (Han, 2006)

More detailed discussion on data mining will be given in Chapter 3 of thisbook

2.3 Grid Computing

With the deregulation and constant expansion of power systems, the demand

of high performance computing (HPC) for power system adequacy andsecurity analysis has increased rapidly HPC also plays an important role inensuring efficient and reliable communication for power system operation and

30 2 Fundamentals of Emerging Techniques

control In the past few years, grid computing technology has been catching

up and is receiving much attention from power engineers and researchers (Ali

et al., 2009; Irving et al., 2004) Grid computing technology is an ture, which can provide high performance computing and a communicationmechanism for providing services in these areas of the power system

infrastruc-It has been recognized that the commonly used Energy Management tems (EMS) are unable to provide solutions to meet such requirements ofHPC and data and resource sharing (Chen et al., 2004) for its operations Inthe past, some efforts had been made in order to enhance the computationalpower of EMS (Chen et al., 2004) in the form of parallel processing, but onlythe centralized resources were used, and an equal distribution of computingtasks among participating computers was assumed In parallel processing,the tasks can be divided into a number of subtasks of equal size to all sys-tems For this purpose, all machines need to be dedicated and should behomogeneous, i.e they should have common configurations and capabilities,otherwise different computers may return results at different times depend-ing on their availability when the tasks were assigned to the computers Inparallel processing, there is a need for collaboration of data from differentorganizations, which is sometimes very hard due to various technical or secu-rity issues (Chen et al., 2004) Consequently, there should be a mechanism forprocessing the distributed and multi-owner data repositories (Cannataro andTalia, 2001) Some distributed computing solutions also have been proposedpreviously for getting high efficiency computation, but they demand homo-geneous resources and are not scalable In addition, the parallel processingtechniques involve tightly coupling of the machines (Chen et al., 2004) Use

Sys-of super computers is another solution, but it is very expensive and Sys-oftennot suitable, especially for a single organization which may be constrained

by resources

Grid computing is an infrastructure that can provide an integrated ronment for all these participants in the electricity market and power systemoperations by providing secured resources as well as data sharing and highperformance computing for power system analysis Grid computing can beinvolved in all fields in which computers are involved, and these fields can berelated to communications, analysis, and organizational decision making.Grid computing is a new technology that involves the integrated and col-laborative use of computers, networks, databases, and scientific instrumentsowned and managed by multiple organizations (Foster and Kesselman, 1997;Foster et al., 2001) It is able to provide HPC and access to remote, hetero-geneous and geographically separated data over the vast area This technol-ogy is mainly developed by E-science community (EUROGRID, NASA IPG,PPDG, GridPP), but nowadays it is widely used in many research fields likeoil and gas fields, banking, and education Grid computing has provided largecontributions in these areas

envi-In the past few years grid computing technology has gained much tion from the power engineering field and significant research is being done at