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

Zhaoyang Dong
Pei Zhang
et al.
Emerging Techniques in Power System
Analysis


Zhaoyang Dong
Pei Zhang
et al.

Emerging Techniques in
Power System Analysis

With 67 Figures


Authors
Zhaoyang Dong

Pei Zhang

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:

E-mail:

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
This work is subject to copyright. All rights are reserved, whether the whole or part of the
material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
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The use of general descriptive names, registered names, trademarks, etc. in this publication
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Cover design: Frido Steinen-Broo, EStudio Calamar, Spain
Printed on acid-free paper
Springer is part of Springer Science + Business Media (www.springer.com)

Preface

Electrical power systems are one of the most complex large scale systems.
Over the past decades, with deregulation and increasing demand in many
countries, power systems have been operated in a stressed condition and subject to higher risks of instability and more uncertainties. System operators
are responsible for secure system operations in order to supply electricity
to consumers efficiently and reliably. Consequently, power system analysis
tasks have become increasingly challenging and require more advanced techniques. This book provides an overview of some the key emerging techniques
for power system analysis. It also sheds lights on the next generation technology 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 the
challenging issues including, load modeling, distributed generations, situational awareness, and control and protection.
Chapter 2 provides an overview of the key emerging technologies following
the evolvement of the power industry. Since it is impossible to cover all of
emerging technologies in this book, only selected key emerging technologies
are described in details in the subsequent chapters. Other techniques are
recommended 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 complex
problems, e.g. cascading failure and electricity market signal analysis. Data
mining theories and application examples are presented in this chapter.
Chapter 4 covers another important technique: grid computing. Grid computing techniques provide an effective approach to improve computational
efficiency. The methodology has been used in practice for real time power
system stability assessment. Grid computing platforms and application examples are described in this chapter.
Chapter 5 emphasizes the importance of probabilistic power system analysis, including load flow, stability, reliability, and planning tasks. Probabilistic approaches can effectively quantify the increasing uncertainties in power
systems and assist operators and planning in making objective decisions...
Various probabilistic analysis techniques are introduced in this chapter.

vi

Preface

Chapter 6 describes the application of an increasingly important device,
phasor measurement units (PMUs) in power system analysis. PMUs are able
to provide real time synchronized system measurement information which
can be used for various operational and planning analyses such as load modeling and dynamic security assessment. The PMU technology is the last key
emerging technique covered in this book.
Chapter 7 provides information leading to further reading on emerging
techniques for power system analysis.
With the new initiatives and continuously evolving power industry, technology advances will continue and more emerging techniques will appear., The
emerging technologies such as smart grid, renewable energy, plug-in electric
vehicles, emission trading, distributed generation, UVAC/DC transmission,
FACTS, and demand side response will create significant impact on power
system. Hopefully, this book will increase the awareness of this trend and
provide a useful reference for the selected key emerging techniques covered.

Zhaoyang Dong, Pei Zhang
Hong Kong and Palo Alto
August 2009


Contents

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

2

Among Protection and Control Systems · · · · · · · · · · ·

16

1.6 Summary· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · ·

19

References · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · ·

19

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

3

4

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

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

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 countries across the world, the industry has been experiencing many changes leading to increasing complexity, interconnectivity, and uncertainties. Demand
for electricity has also increased significantly in many countries, which
resulted in increasingly stressed power systems. The insufficient investment
in the infrastructure for reliable electricity supply had been regarded as a
key factor leading to several major blackouts in North America and Europe
in 2003. More recently, the initiative toward development of the smart grid
again introduced many additional new challenges and uncertainties to the
power industry. In this chapter, a general overview will be given starting
from deregulation, covering electricity markets, present uncertainties, load
modeling, situational awareness, and control issues.

1.1 Principles of Deregulation
The electricity industry has been undergoing a significant transformation
over the past decade. Deregulation of the industry is one of the most important milestones. The industry had been moving from a regulated monopoly
structure to a deregulated market structure in many countries including the
US, UK, Scandinavian countries, Australia, New Zealand, and some South
American countries. Deregulation of the power industry is also in the process

recently in some Asian countries as well. The main motivations of deregulation 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 establishing
electricity markets. They are (1) to ensure a secure operation and (2) to
facilitate an economical operation (Shahidehpour et al., 2002).

1.2 Overview of Deregulation Worldwide
In South America, Chile started the development of a competitive system
for its generation services based on marginal prices as early as the early
1980s. Argentina deregulated its power industry in 1992 to form generation,
transmission, and distribution companies into a competitive electricity market where generators compete. Other South America countries followed the
trend 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 in
England and Wales.
Prior to March 1990, the vast majority of electricity supplied in England and Wales was generated by the Central Electricity Generating Board
(CEGB), which also owned and operated the transmission system and the
interconnectors with Scotland and France. The great majority of the output

of the CEGB was purchased by the 12 area electricity boards; each of which
distributed and sold it to customers.
On March 31, 1990, the electricity industry was restructured and then
privatized under the terms of the Electricity Act 1989. The National Grid
Company plc assumed ownership and control of the transmission system and
joint ownership of the interconnectors with Scotland and France, together
with the two pumped storage stations in North Wales. But, these stations
were subsequently sold off.
In the early 1990s, the Scandinavian countries (Norway, Sweden, Finland and Denmark) created a Nordic wholesale electricity market – Nord
Pool (www.nordpool.com). The corresponding Nordic Power Exchange is
the world’s first international commodity exchange for electrical power. It
serves customers in the four Scandinavian countries. Being the Nordic Power
Exchange, Nord Pool plays a key role as a part of the infrastructure of the
Nordic electricity power market and thereby provides an efficient, publicly
known price of electricity of both the spot and the derivatives market.
In Australia, the National Electricity Market (NEM) was first commenced
in December 1998, in order to increase the transmission efficiency and
reduce electricity prices. NEM serves as a wholesale market for the supply of
electricity 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’s
longest interconnected power systems connecting 8 million end use consumers
with 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 the
major 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 from
the late 1970s, to allow non-utility generators to enter the wholesale power
market. In 1992, the Energy Policy Act (EPACT) formed the foundation for
wholesale electricity deregulation.
Similar deregulation processes have occurred in New Zealand and part of
Canada 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, a
cooperative of consumers, or privately. As the single electricity service
provider, 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 regulator sets the tariff (electricity price) to earn a fair rate of return on investments
and to recover operational expenses. Under the regulated environment, companies maximize profits while being subject to many regulatory constraints.
From microeconomics, the sole service provider of a monopoly market has the
absolute market power. In addition, because the costs are allowed by the regulator to be passed to the customers, the utility has fewer incentives to reduce
costs or to make investments considering the associated risks. Consequently,
the customers have no choices for their electricity supply service providers
and have no choices on the tariffs (except in case of service contracts).
As compared with a monopoly market, an ideal competitive market normally has many sellers/service providers and buyers/customers. As a result
of competition, the market price is equal to the cost of producing the last
unit sold, which is the economically efficient solution. The role of deregulation is to structure a competitive market with enough generators to eliminate

market power.
With the deregulation, traditional vertically integrated power utilities are
split into generation, transmission, and distribution service providers to form


4

1 Introduction

a competitive electricity market. Accordingly, the market operation decision
model 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 mechanism
responds to a decentralized process. Each participant aims at profit maximization. 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 electricity
business in a market environment.
Another key change resulted from the electricity market is the introduction of more uncertainties and stake holders into the power industry. This

helps to increase the complexity of power system analysis and leads to the
need for new techniques.

1.2.2 Typical Electricity Markets
There are three major electricity market models in practice worldwide. These
models include the PoolCo model, the bilateral contracts model, and the
hybrid model.
1) PoolCo Model
A PoolCo is defined as a centralized marketplace that clears the market
for 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 the
amounts of power they are willing to trade in the market. Sellers in an electricity market would compete for the right to supply energy to the grid and not
for 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 to
buy. 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 that
the buyers will pay at a pool price determined by the market clearing process. There is an independent system operator (ISO) in a PoolCo market to
implement 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 bidders

provided 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 normally benefit by getting dispatched by the ISO. An ideal PoolCo market is a
competitive market where the GENCOs bid with their marginal costs. When
market power exists, the dominating GENCOs may not necessarily bid with
their marginal costs.
2) Bilateral Contracts Model
Bilateral contracts are negotiable agreements on delivery and receipt of
electricity between two traders. These contracts set the terms and conditions
of agreements independent of the ISO. However, in this model the ISO will
verify that a sufficient transmission capacity exists to complete the transactions and maintain the transmission security. The bilateral contract model
is very flexible, as trading parties specify their desired contract terms. However, its disadvantages arise from the high costs of negotiating and writing
contracts and the risk of creditworthiness of counterparties.
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 any
customer will be allowed to negotiate a power supply agreement directly with
suppliers 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 with
suppliers will offer a true customer choice and an impetus for the creation of a
wide variety of services and pricing options to best meet individual customer
needs (Shahidehpour et al., 2002).

1.3 Uncertainties in a Power System
Uncertainties have existed in power systems from the beginning of the power
industry. Uncertainties from demand and generator availability have been
studied 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 of
uncertainty has been increasing dramatically. For example, in a deregulated
environment, although generation planning is considered in the overall planning process, it is difficult for the transmission planner to access accurate
information concerning generation expansion. Transmission planning is no
longer coordinated with generation planning by a single planner. Future generation 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 important
role. In addition to the uncertainties coming from forecast demand, load
models also contribute to system uncertainty, especially for power system
simulation and stability assessment tasks. Inappropriate load models may
lead 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 operation
and control of a power grid. The accuracy of the power system simulation
relies heavily on the model reliability. Among all the components in a power
system, the load model is one of the least well known elements; however,
its significant influences on the system stability and control have long been
recognized (Concordia and Ihara, 1982; Undrill and Laskowski, 1982; Kundur 1993; IEEE 1993a; IEEE 1993b). Moreover, the load model has direct

influences on power system security. On August 10, 1996, WSCC (Western Systems Coordinating Council) in the USA collapsed following power
oscillations. The blackout caused huge economic losses and endangered state
security. However, the system model guiding the WSCC operation had failed
to predict the blackout. Therefore, the model validation process, following this outage, indicated that the load model in WSCC database was not
adequate to reproduce the event. This strongly suggests that a more reliable
load model is desperately needed. The load model also has great effects on
economic operation of a power system. The available transfer capability of
the transmission corridor is highly affected by the accuracy of the load models used. Due to the limited understanding of load models, a power system is
usually operated very conservatively, leading to the poor utilization of both


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 of
various components, each with their own characteristics. Furthermore, load is
always changing, both in its amount and composition. Thus, how to describe
the aggregated dynamic characteristic of the load has been unsolved so far.
Due to the blackouts which occurred all around the world in the last few
years, load modeling has received more attention and has become a new
research focus.
The state of the art for research on load modeling is mainly dedicated to
the structure of the load model and algorithms to find its parameters.
The structure of the load model has great impacts on the results of power
system analysis. It has been observed that different load models will lead to
various, even completely contrary conclusions on system stability (Kosterev
et al., 1999; Pereira et al., 2002). The traditional production-grade power

system analysis tools often use the constant impedance, constant current, and
constant power load model, namely the ZIP load model. However, simulation
results by modeling load with ZIP often deviate from the field test results,
which indicate the inefficiency of the ZIP load model. To capture the strong
nonlinear characteristic of load under the recovery of the voltage, a load model
with a nonlinear structure was proposed by (Hill, 1993). Load structure in
terms 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 the
load characteristics better than a first order transfer function. The recent
trend 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 et
al., 1994) developed a load model as a combination of a RC circuit in parallel
with an induction motor equivalent circuit. Ma et al. (Ma et al., 2006; He et
al., 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 model
that is 80% static and 20% induction motor model is proposed by (Perira et
al., 2002) for WSCC system simulation. Except for the load model structure,
the identification algorithm to find the load model parameters is also widely
researched. Both linear and nonlinear optimization algorithms are applied
to solve the load modeling problem. However, the identification algorithm is
based on the model structure and it cannot give reliable results without a
sound model structure.
Although various model structures have been proposed for modeling load
for research purposes, the power industry still uses very simple static load
models. The reason is that some basic problems on composite load modeling
are still open, which mainly include three key points: First, which model
structure among proposed various ones is most appropriate to represent the
dynamic characteristic of the load and is it the model with the simplest
structure? 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 optimization
may easily stick into some local minima? Third, how is the generalization
capability of the proposed load model? Load is always changing; however,
a model can only be built on available measurements. So, the generalization
capability of the load model reflects its validity. Theoretically, the first point
involves the minimized realization problem, the second point addresses the
identification problem, and the third point closely relates to the statistic
distribution of the load.
A sound load model structure is the basis for all other load modeling
practice. Without a good model structure, all the efforts to find reliable load
models are in vain. Based on the Occam’s razor principle, which states that
from all models describing a process accurately, the simplest one is the best
(Nelles, 2001). Correspondingly, simplification of the model structure is an
important step in obtaining reliable load models (Ma et al., 2008). Currently,
ZIP in combination with a motor is used to represent the dynamic characteristic of the load model. However, there are various components of a
load. Take motors as an example, there are big motors and small motors,
industry motors and domestic motors, three-phase motors and single-phase
motors. Correspondingly, different load compositions are used to model different loads or loads at different operating conditions. Once the load model
structure is selected, proper load model parameter values are needed. Given
the variations of the actual loads in a power system, a proper range of
parameter values can be used to provide a useful guide in selecting suitable
load models for further simulation purposes.
Parameter estimation is required in order to calculate the parameter values for a given load model with system response measurement data. This
often involves optimization algorithms and linear/nonlinear least squares

estimation (LSE) techniques, or a combination of both approaches.
A model with the appropriate structure and parameters usually has good
performance when fitting the available data. However, it does not necessarily
mean it is a good model. A good load model must have good generalization
capability. Since a load is always changing, the model built on the available data must also have the strong capability to describe the unseen data.
Methodologies used for generalization capability analysis include statistical
analysis and various machine learning methods. Even if a model with good
generalization capability has been obtained, cross validation is still needed
because it is still possible that the derived load model may fail to present
the system dynamics in some system operating conditions involving system
transients. It is worth noting that both research and engineering practice in
load modeling are still facing many challenges. There are many complex load
modeling problems causing difficulties to the power industry; consequently,
static load models are still used by some companies in their operations and
planning practices.


10

1 Introduction

1.3.2 Distributed Generation
In addition to those uncertainty factors discussed previously, another
important issue is the potential large-scale penetration of distributed generation (DG) into the power system. Traditionally, the global power industry
has been dominated by large, centralized generation units which are able
to exploit significant economies of scale. In recent decades, the centralized
generation model has been the focus of concern on its costs, security vulnerability, and environmental impacts, while DG is expected to play an
increasingly important role in the future provision of a sustainable electricity
supply. Large-scale implementation of DG will cause significant changes in
the power industry and deeply influence the transmission planning process.

For example, DG can reduce local power demand; thus, it can potentially
defer investments in the transmission and distribution sectors. On the other
hand, when the penetration of DG in the market reaches a certain level, its
suppliers will have to get involved in the spot market and trade the electricity through the transmission and distribution networks, which may need
to be further expanded. Reliability of some types of DGs is also of a concern for the transmission and distribution network service providers (TNSPs
and DNSPs). Therefore, it is important to investigate the impacts of DG on
power system analysis, especially in the planning process. The uncertainties
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 from
the 2003 blackouts in North America and the following blackouts in UL and
Italy clearly showed the need for techniques to analyze and prevent such
devastating events. According to the Electricity Consumers Resource Council (2004), the blackout in August 2004 in America and Canada had left 50
million people without power supply and with an economic cost estimated
at up to $10 billion. The many studies of this major blackout concluded
that 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 the
lack of situational awareness was composed of a number of factors such as
deficiencies in operator training, lack of coordination and ineffectiveness in
communications, and inadequate tools for system reliability assessment. This
lack of situational awareness also applies to other major system blackouts
as well. As a result, operators and coordinators were unable to visualize the
security and reliability status of the overall power system following some
disturbance 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 Scandinavian
blackout incident of 2003. As the complexity and connectivity of power systems continue to grow, for the system operators and coordinators, situational
awareness becomes more and more important. New methodologies needed for
better awareness of system operating conditions can be achieved. The capability of control centres will be enhanced with better situational awareness.
This can be partially promoted by development of operator and control centre tools which allows for more efficient proactive control actions as compared
with the conventional preventative tools. Real time tools, which are able to
perform 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, prompt
control actions can be taken to prevent possible system wide outages.
In its report for blackouts, NERC Real-Time Tools Best Practices Task
Force (RTTBPTF) defined situational awareness as “knowing what is going
on around you and understanding what needs to be done and when to maintain, or return to, a reliable operating state.” NERC’s Real-Time Tools Survey report presented situational awareness practices and procedures, which
should be used to define requirements or guidelines in practice. According to
the article by Endsley, 1998, there are three levels for the term situational
awareness or situation awareness: (1) perception of elements, (2) comprehending the meaning of these elements, and (3) projecting future system
states based on the understanding from levels 1 and 2. For level 1 of situational awareness, operators can use tools which provide real time visual
and audio alarm signals which serve as indicators of the operating states
of the power system. According to NERC (NERC 2005, NERC 2008) there
are three ways of implementing such alarm tools which are being within the
SCADA/EMS system, external functions, or a combination of the two.
NERC Best Practices Task Force Report (2008) summarized the following
situational awareness practice areas in its report: reserve monitoring for both
reactive reserve capability and operating reserve capability; alarm response
procedures; conservative operations to move the system from unknown and
potentially risky conditions into a secure state; operating guides defining procedures 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 protection and control (EPRI, 2004; EPRI, 2007; SEL-421 Manual; ALSTOM,
2002; Mooney and Fischer, 2006; Hou et al., 1997; IEEE PSRC WG, 2005;


12

1 Introduction

Tzaiouvaras, 2006; Plumptre et al., 2006). Both protection and control can
be viewed as corrective and/or preventive activities to enhance system
security. Meanwhile, protection can be viewed as activities to disconnect and
de-energize some components, while control can be viewed as activities without physical disconnection of a significant portion of system components. In
this report, we do not intend to make a clear distinction between protection
and control. We collectively use the term “protection and control” to indicate the activities to enhance system security. In addition, although there
are a number of ways to classify the protection and control systems based on
different viewpoints, this report classifies protection and control as local and
centralized 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 of
transmission lines. Distance relays measure voltage and current and also compare the apparent impedance with relay setting. When the tripping criteria
are reached, distance relays will trip the breakers and clear the fault. Typical
forms of distance relays include impedance relay, mho relay, modified mho
relay, and combinations thereof. Usually, distance relays may have Zone 1,
Zone 2, and Zone 3 relays to cover longer distances of transmission lines with
the 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 Characteristics

Prime Mover Control and Automatic Generation Control (AGC) is
applied to maintain the power system frequency within a required range
by the control of the active power output of a generator. Prime movers of


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 load
characteristics. The AGC is used to restore the frequency and the tie-line
flow to their original and scheduled values. The input signal of AGC is called
Area Control Error (ACE), which is the sum of the tie-line flow deviation
and the frequency deviation multiplied by a frequency-bias factor.
Power System Stabilizer (PSS) technology’s purpose is to improve small
signal stability or improve damping. PSSs are installed in the excitation system to provide auxiliary signals to the excitation system voltage regulating
loop. The input signals of PSSs are usually signals that reflect the oscillation
characteristics, such as the shaft speed, terminal frequency, and power.
Generator Excitation System is utilized to improve power system stability
and power transfer capability, which are the most important issues in bulk
power systems under heavy load flow. The primary task of the excitation
system in synchronous generators is to maintain the terminal voltage of the
generator at a constant level and guarantee reliable machine operations for
all operating points. The governing functions achieved are (1) voltage control,
(2) reactive power control, and (3) power factor control. The power factor

control uses the excitation current limitation, stator current limitation, and
rotor displacement angle limitation linked to the governor.
On-Load Tap Changer (OLTC) is applied to keep the voltage on the low
voltage (LV) side of a power transformer within a preset dead band, such that
the power supplied to voltage sensitive loads is restored to the pre-disturbance
level. Usually, OLTC takes tens of seconds to minutes to respond to the low
voltage event. OLTC may have a negative impact to voltage stability, because
the higher voltage at the load side may demand higher reactive current to
worsen the reactive problem during a voltage instability event.
Shunt Compensation in bulk power systems includes traditional technology like capacitor banks and new technologies like the static var compensator
(SVC) and the static compensator (STATCOM). An SVC consists of shunt
capacitors and reactors connected via thyristors that operate as power electronics switches. They can consume or produce reactive power at speeds in
the order of milliseconds. One main disadvantage of the SVC is that their
reactive power output varies according to the square of the voltage they are
connected to, which is similar to capacitors. STATCOMs are power electronics based SVCs. They use gate turn off thyristors or insulated gate bipolar
transistors (IGBTs) to convert a DC voltage input to an AC signal that
is chopped into pulses that are then recombined to correct the phase angle
between voltage and current. STATCOMs have a response time in the order
of microseconds.
Load shedding is performed only under an extreme emergency in modern
electric power system operation, such as faults, loss of generation, switching
errors, lightning strikes, and so on. For example, when system frequency drops
due to insufficient generation under a large system disturbance, load shedding
should 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 should
also be performed to bring voltage back to normal. The formal load shedding
scheme can be realized via under-frequency load shedding (UFLS) while the
latter 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 separate the system when loss of synchronism occurs. Ideally, the system should
be separated at such points as to maintain a balance between load and generation in each separated area. Moreover, separation should be performed
quickly and automatically in order to minimize the disturbance to the system and to maintain maximum service continuity via the OOS blocking relay
and tripping relay. During a transient swing, the OOS condition can be
detected by using two relays having vertical (or circular) characteristics on an
R-X plane as shown in Fig.1.5. If the time required to cross the two characteristics (OOS1 and OOS2) of the apparent impedance locus exceeds a specified
value, the OOS function is initiated. Otherwise, the disturbance will be identified as a line fault. The OOS tripping relays should not operate for stable
swings. They must detect all unstable swings and must be set so that normal
load conditions are not picked up. The OOS blocking relays must detect the
condition before the line protection operates. To ensure that line relaying is
not blocked for fault conditions, the setting of the relays must be such that
normal load conditions are not in the blocking area.

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 more
widely used in recent years to provide protection for power systems against
problems that do not directly involve specific equipment fault protection. A
SPS is applied to solve single and credible multiple contingency problems.