Emerging techniques in power system analysis

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Emerging techniques in power system analysis

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Emerging techniques in power system analysis

  1. 1. Zhaoyang Dong Pei Zhang et al. Emerging Techniques in Power System Analysis
  2. 2. Zhaoyang Dong Pei Zhang et al. Emerging Techniques in Power System Analysis With 67 Figures
  3. 3. 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: 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 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, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: Frido Steinen-Broo, EStudio Calamar, Spain Printed on acid-free paper Springer is part of Springer Science + Business Media (www.springer.com)
  4. 4. 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.
  5. 5. 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
  6. 6. Contents 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 · · · · · · · · · · · · · · · 1 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 · · · · · · · · · · · 1.6 Summary· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 19 References · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 2 16 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
  7. 7. viii Contents 2.4 Probabilistic vs Deterministic Approaches · · · · · · · · · · · · · · · 2.5 Phasor Measurement Units · · · · · · · · · · · · · · · · · · · · · · · · · · 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 · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 4 34 2.6 Topological Methods · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 3 31 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
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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;
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. 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-
  17. 17. 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
  18. 18. 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
  19. 19. 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.
  20. 20. 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-
  21. 21. 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;
  22. 22. 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
  23. 23. 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
  24. 24. 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.
  25. 25. 1.5 Control Performance 15 These schemes have become more common primarily because they are less costly and quicker to permit, design, and build than other alternatives such as constructing major transmission lines and power plants. A SPS senses abnormal system conditions and (often) takes pre-determined or pre-designed actions to prevent those conditions from escalating into major system disturbances. SPS actions minimize equipment damage and prevent cascading outages, uncontrolled loss of generation, and interruptions to customer electric service. SPS remedial actions may be initiated by critical system conditions which can be system parameter changes, events, responses, or a combination 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 in power systems. It is a hardware and software system used by operators to monitor, control, and optimize a power system. The monitor and control functions are known as SCADA; the advanced analytical functions such as state estimation, contingency analysis, and optimization are often referred to as EMS. Typical benefits of SCADA/EMS systems include: improved quality of supply, improved system reliability, and better asset utilization and allocation. An increasing interest in the EMS functions is the online security analysis software tools, which typically provide transient stability analysis, voltage security analysis, and small – signal stability analysis. The latest development in computer hardware and software and in power system simulation 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 protections and controls based on a number of literatures (4 – 10). As shown in this figure, the time delays of many different control systems or strategies have some considerable overlaps. The reason is historical. In the past, the design of different control was originally based on a single goal to solve a particular problem. As modern power systems are more interconnected and have increasing stress levels, disturbances may cause multiple controls to respond, among which some may be undesired. This trend presents great challenges and risks in protection and control, as evidence by increasing occurrences of blackout events in North America. This challenge will be illustrated with two case analyses in the next section.
  26. 26. 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-area system 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 heavy load increase on a hot summer day. Meanwhile, a transmission contingency event 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 local generation, the (reduced) import from the tie-line, and the spinning reserves. This may lead to a decrease of both frequency and voltage. Certainly, under this 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 generators’ under-frequency (UF) tripping scheme and the loads’ under-voltage (UV) shedding scheme are not well coordinated. Likely, the under-frequency generation tripping scheme will disconnect some generation from the system before the load shedding scheme is completed, since the present setting in generation tripping is usually very fast. This will worsen the imbalance between load and generation in the load pocket. Hence, both voltage and frequency may decrease further. This may lead to more generation to be quickly
  27. 27. 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 power for voltage support. Therefore, this may lead to a sharp drop of voltage and eventually a fast voltage collapse at the end. Even though this is initially a real power imbalance or frequency stability problem, the final consequence is a voltage collapse. Fig.1.8 shows the gradual process based on the above analysis. Fig. 1.8. The process to instability As previously mentioned, the root cause is the imbalance of generation and load in the load pocket. The coordination of generation tripping and load shedding is not optimized or well coordinated to perform load shedding
  28. 28. 18 1 Introduction in order to avoid the generation tripping, which eventually causes a sharp voltage collapse. 2) Zone 3 Protection The second example is from the July 2, 1996, WSCC blackout. At the very beginning of the blackout, two parallel lines were tripped due to fault and mis-operation, and consequently some generation was tripped as a correct SPS response. Then, a third line was disconnected due to bad connectors in a distance relay. More than 20 seconds after these events, the last straw of the collapse occurred. This last straw was the trip of the Mill Creek-Antelope line due to the undesired Zone 3 protective relay. After this tripping, the system collapsed 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 the observed 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 was the 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
  29. 29. 1.6 Summary 19 if the setting of the Zone 3 relay can be dynamically reconfigured, considering the heavily loaded system condition, the system operators may have enough time to perform some corrective actions to save the system from a fast collapse. 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. Increased connectivity and resultant nonlinear complexity of power system is another trend. The consequences of such changes are various uncertainties and difficulties in power system analysis. Recent major power system blackouts also remind the power industry of the need for situational awareness and more effective tools in order to ensure more secure operation of the system. This chapter has reviewed these important aspects of the power system worldwide. This chapter serves as an introduction and forms the basis for further discussion on the emerging techniques in power system analysis. References ALSTOM (2002) Network Protection & Automation Guide. ALSTOM, LevalloisPerret Buygi MO, Shanechi HM, Balzer G et al (2006) Network planning in unbundled power systems. IEEE Trans Power Syst 21(3) Concordia C, Ihara S (1982) Load representation in power systems stability studies. IEEE Trans. Power App Syst 101: 969 – 977
  30. 30. 20 1 Introduction Endsley MR (1988) Situation awareness global assessment technique. Proceedings of The National Aerospace and Electronics Conference. IEEE, pp789 – 795 EPRI Project Opportunities (2007) PMU-based Out-of-step Protection Scheme General Electric Company (1987) Load modeling for power flow and transient stability computer studies, Vol 1 – 4, EPRI Report EL-5003 IEEE Task Force on Load Representation for Dynamic Performance (1993) Load representation for dynamic performance analysis. IEEE Trans Power Syst 8(2): 472 – 482 IEEE Task Force on Load Representation for Dynamic Performance (1995) Bibliography on load models for Power flow and dynamic performance simulation. IEEE Trans Power Syst 10(1): 523 – 538 IEEE Task Force on Load Representation for Dynamic Performance (1995) Standard load models for power flow and dynamic performance simulation. IEEE Trans Power Syst 10(3): 1302 – 1313 Hill DJ (1993) Nonlinear dynamic load models with recovery for voltage stability studies. 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. Schweitzer Engineering 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 York Kosterev DN, Taylor CW, Mittelstadt WA (1999) Model validation for the august 10, 1996 WSCC system outage. IEEE Trans Power Syst 14(3): 967 – 979 Lin CJ, Chen YT, Chiang HD et al (1993) Dynamic load models in power systems using the measurement approach. IEEE Trans Power Syst 8(1) Ma J, He RM, Hill DJ (2006) Load modeling by finding support vectors of load data from field Measurements, IEEE Trans Power Syst 21(2): 726 – 735 Ma J, Han D, He R et al (2008) Reducing identified parameters of measurementbased composite load model. IEEE Trans Power Syst 23(1): 76 – 83 Ma J, Dong ZY, He R et al (2007) System energy analysis incorporating comprehensive load characteristics. IET Gen Trans Dist, 1(6): 855 – 863 Mooney J, Fischer N (2006) Application guidelines for power swing detection on transmission systems. Proceedings of the 59th annual conference for protective relay engineers. 2006 IEEE, 289 – 298 National Grid Management Council. Empowering the market–national electricity reform for australia. December 1994 Nelles O (2001) Nonlinear system identification. Springer, Heidelberg NERC (North American Electric Reliability Council) (2005) Best practices task force report. Discussions, Conclusions, and Recommendations NERC Real-Time Tools Best Practices Task Force (2008) Real-time tools survey analysis and recommendations. Final Report Pereira L, Kosterev D, Mackin P et al (2002) An interim dynamic induction motor model 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-step protection with a real time digital simulator. TP6241-01, BC hydro, Cegertec, BC Transmission Corporation and Schweitzer Engineering Laboratories inc Price WW, Wirgau KA, Murdoch A et al (1988) Load modeling for load flow and transient stability computer studies. IEEE Trans Power Syst 3, pp180 – 187 Shahidehpour M, Ymin H, Li Z (2002) Market operations in electric power systems. Forecasting, Scheduling, and Risk Management, IEEE, Wiley, New York Tzaiouvaras D (2006) Relay performance during major system disturbances.
  31. 31. References 21 TP6244 – 01, SEL Thorpe GH (1998) Competitive electricity market development in australia. Proceedings of ARC Workshop on Emerging Issues and Methods in the Restructuring of the electric Power Industry, The University of Western Australia, 20 – 22 July 1998 Wang JC, Chiang HD, Chang CL et al (1994) Development of a frequency-dependent composite load model using the measurement approach. IEEE Trans Power Syst 9(3): 1546 – 1556 Undrill JM, Laskowski TF (1982) Model selection and data assembly for power system simulation. IEEE Trans Power App Syst, 101, pp. 3333 – 3341 SEL-421 Manual, Schweitzer Engineering Laboratories, SEL-421 Relay Protection Automation 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 Performing Probabilistic Risk Assessment for Operational Planning. Proceedings of the of the14th ISAP, November, 2007
  32. 32. 2 Fundamentals of Emerging Techniques Xia Yin, Zhaoyang Dong, and Pei Zhang Following the new challenges of the power industry outlined in Chapter 1, new techniques for power system analysis are needed. These emerging techniques cover 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 techniques, it is necessary to give an overview of these emerging techniques and compare these emerging ones with traditional approaches. In this chapter, the following emerging techniques will be outlined. Some of the key techniques and their applications in power engineering will be detailed in the subsequent chapters. The main objective is to provide a holistic picture of the technological trends in power system analysis over the recent years. 2.1 Power System Cascading Failure and Analysis Techniques In 2003, there were several major blackouts, which were regarded as results of cascading failures of power systems. The increasing number of system instability events is mainly because of the operation of market mechanisms which has driven more generation investments but provided insufficient transmission expansion investments. With the increased demand for electricity, many power systems have been heavily loaded. As a result, power systems are running close to their security limits and therefore vulnerable to disturbances (Dong et al., 1995). The blackout of 14 August 2003 (Michigan Public Service Commission 2003) 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-
  33. 33. 24 2 Fundamentals of Emerging Techniques cil (NERC) report. Over 50 million people experienced that blackout over a considerable number of hours. The economic loss and political impact were enormous, and concerns regarding national security rose from the power sector. The major reasons for the blackout were identified as (U.S.-Canada Power System Outage Task Force, 2004): • failure to identify emergency conditions and communicate to neighboring 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 a significant increment in transmission congestion and has created many new bottlenecks in the flows of bulk power. This situation has further stressed the power system. It is a far more complex problem than a simple voltage collapse based on the information available so far. As clearly indicated in many literatures about this event, the reasons for such large scale blackouts are extremely complex, and have yet to be fully understood. 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 system signals and therefore unable to reach a timely remedial decision to prevent such cascading system failure. The state-of-the-art power system stability analysis leads to the following conclusions: • 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 the system; • this explicit consideration of the uncertainties in disturbances and of power system parameters can impact on the decisions on placement of correction devices such as FACTS devices or on the control design of excitation controllers; • the explicit consideration of where the system breaks under multiple contingencies can be used to adjust the controllers and the links to be strengthened in power system design;
  34. 34. 2.1 Power System Cascading Failure and Analysis Techniques 25 • the mechanism of cascading failure blackouts has not been fully understood; • if timely information about system security is available even a short time beforehand, many of the severe system security problems such as blackouts 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 in order to develop reliable algorithms to monitor, predict, and prevent blackouts. To face the impending challenges from operation and planning with respect to cascading failure avoidance, power system reliability analysis needs new evaluation tools. So far, the widely recognized contingency analytical method 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 of components of the system within a short time. The merits of the N-1 criterion are the flexibility, clarity, and simplicity of implementation. However, with the increasing risk of the occurrence of catastrophic failure and system complexity, this criterion may not provide sufficient information of the vulnerability and severity level of the system. Since catastrophic disruptions are normally caused by cascading failures of electrical components, the importance of studying the inherent mechanism of cascading outages is attracting more and more attention. So far, many models have been documented on simulating cascading failures. In the article by Dobson et al., 2003, a load-dependent model is proposed from a probabilistic point of view. At start, the system components will be allocated a virtual load randomly. Then the model will be initiated by adding a disturbance load to all the components. A component will be tripped when its load exceeds the maximum limit, and other unfailed components will receive a constant load from this failure. This cascading procedure will terminate when there are no component failures within a cascading scenario. This model can fully explore all the possibilities of cascading cases of the system. This cascading model is further improved by incorporating branching process approximation in the article by Dobson et al., 2004, so that the propagation of cascading failures can be demonstrated. However, both of them did not address the joint interactions among system components during cascading scenarios. In the article by Chen et al., 2005, cascading dynamics is investigated under different system operating conditions via a hidden failure model. This model employs linear programming (LP) generation redispatch jointed with dc load flow for power distribution and emphasizes the possible failures existing in the relay system. Chen et al. (Chen et al., 2006) study the mechanism of cascading outages by estimating the probability distribution of
  35. 35. 26 2 Fundamentals of Emerging Techniques historical data of transmission outages. However, both methods above do not consider failures of other network components, such as generators and loads. In the article by Stubna and Fowler, 2003, to describe the statistics of robust complex systems under uncertain conditions, highly optimised tolerance (HOT) model is introduced in simulating blackout phenomena in power systems. A simulation result shows that this model reasonably fits the historical data set of one realistic test power system. Besides these proposed models, the investigation of critical transitions of a system according to the system loading conditions during cascading procedure is also studied (Carreras et al., 2002). The paper finds that the size of the blackouts will experience a sharp increase once the system loading condition is over a critical transition point. Efforts also have been dedicated to understand the cascading faults from global 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 statistic theories. In the article by Carreras et al., 2004, from the detailed time series analysis of the North American Electrical Reliability Council (NERC) 15 years historical blackout data, the authors find that cascading failures occurring in the system had exhibited self organised criticality (SOC) dynamics. This work shows that the cascading collapse of systems may be caused by the power system global nonlinear dynamics instead of weather or other external triggering disturbances. This evidence provides a global philosophy for understanding the catastrophic failures in power systems. It has been recognised that the structures of complex networks always affect their functions (Strogatz, 2001). Due to the complexity inherit in power grids the study of system topology is another interesting approach. In the article by Lu et al. 2004, “small world” is introduced for analysing and comparing the topology characteristics of power networks in China and the United States. 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 indicates that the increase in the homogeneity of the network will be helpful to enhance the system stability. However, since topology analyses normally require networks to be homogeneous and non-weighted, it might need approximations when dealing with power grid issues. Recent NERC studies of major blackouts (NERC US Canada Power System Outage Task Force 2004) have shown that more than 70% of those blackouts involved hidden failures, which are incorrect relay operations, namely removing a circuit element(s) as a direct consequence of another switching event (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 lines are called as expose to hidden failures) with the tripped line may incorrectly
  36. 36. 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 analysis software, called Transmission Reliability Evaluation of Large-Scale Systems (TRELSS), which has been applied in real systems for several years (Makarov and Hardiman, 2003). The model addresses the trips of loads, generators, and protection control groups (PCG). In every cascading scenario, the value of load node voltages, generator node voltages as well as circuit overloads will be investigated sequentially, and the next cascading fault will be determined from the result. The model is very complex for application (Makarov and Hardiman, 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 in this 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 understandable information and patterns from large data bases; or in short it is the process to discover hidden patterns from data bases. It is an important step in the process of knowledge discovery in databases (Olaru and Wehenkel, 1999). It has been used in a number of areas for power system analysis where large amount data are involved such as forecasting and contingency assessment. It is well known that online contingency assessment or online dynamic security assessment (DSA) is a very complex task that requires a significant amount of computational costs for many real interconnected power systems. With increasing complexity in modern power systems, the corresponding system data are exponentially increasing. Many companies store such data but are not yet able to fully utilize them. Under such emerging complexity, it is desirable to have reliable and fast algorithms to perform such duties instead of the traditional time-consuming security assessment/dynamic simulation based ones. It should be noted that artificial intelligence (AI) techniques such as neural networks (NNs) have been used for similar purposes as well. However, AI based methods suffer a number of shortcomings which have prevented their wider application in realistic situations so far. The major shortcomings of
  37. 37. 28 2 Fundamentals of Emerging Techniques NN based online dynamic security assessment are the inference opacity, the over-fitting problem, and applicability to a large scale system. Lack of statistical information from NN outputs is also a major concern which limits its application. Data mining based real time security assessment approaches are able to provide statistically reliable results and have been widely practiced in many complex systems such as telecommunications system and internet security areas. In power engineering, data mining has been successfully employed in a number of areas including fault diagnosis and condition monitoring of power system equipment, customer load profile analysis (Figueiredo et al., 2005), nontechnical loss analysis (Nizar, 2008), electricity market demand and price forecasting (Zhao et al., 2007a; Zhao et al., 2007b; Zhao et al., 2008), power system contingency assessment (Zhao, 2008c), and many other tasks for power system operations (Madan et al., 1995; Tso et al., 2004; Pecas Lopes and Vasconcelos, 2000). However, there is still a lack in systematic application of data mining techniques in some specific areas such as large scale power system contingency assessment and predictions (Taskforce 2009). For applications such as a power system online DSA, it is critical to have assessment results within a very short time in order for the system operator to take corresponding control actions to prevent series system security problems. Data mining based approaches, with their mathematically and statistically reliable characteristics open up a realistic solution for on-line DSA type tasks. They outperform the traditional AI based approach in many aspects. First, data mining is originally designed to discover useful patterns in large-scale databases, in which AI approaches usually face unaffordable time complexity. Therefore, data mining based approach are able to provide the fast response in user friendly efficient forms. Second, a variety of data cleaning techniques have been incorporated into data mining algorithms, hence enabling data mining algorithms with strong noisy input tolerance capabilities. The most important feature is that a number of data mining methods actually come from the modification of traditional statistic theory. For instance, the Bayesian classifier is from Bayesian decision theory and support vector machine (SVM) is based on statistical learning theory. As a result, these techniques are able to handle large-scale data sets. Moreover, they have strong statistical robustness and the ability to overcome over-fitting problems as compared with AI techniques. The statistical robustness means that if the system is assessed to have a security problem, it will experience such a problem with a given probability of occurrence if no actions are taken. This characteristic is very important for the system operator managing the system security in a market environment where any major actions are associated with potentially huge financial risks. The operator needs to be sure that a costly remedial action (such as load shedding) is necessary before that action takes place. Data mining normally involves four types of tasks
  38. 38. 2.3 Grid Computing 29 including the classification, clustering, regression, and association rule learning (Wikipedia) (Han, 2006). 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 classification problem belongs to supervised learning problems, which can be described using 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 given X), 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 approximation to the supervisor’s response. Predicting the occurrence of system contingency is a typical binary classification problem. The factors which are relevant to the contingencies (e.g., demand and weather) can be seen as the dimensions of the input vector X = (x1 , x2 , . . . , xn ), and xi , i ∈ [1, n] is a relevant factor. So far, there have been a number of classification algorithms in practice. According to the article by Sebastiani, 2002, the main classification algorithms can be categorized as: decision tree and rule based approaches such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1998); examplebased 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 groups but the groups are not pre-defined. Regression is used to model the data series with the least error. Association rule learning is used to discover relationships between variables in a data base (Han, 2006). More detailed discussion on data mining will be given in Chapter 3 of this book. 2.3 Grid Computing With the deregulation and constant expansion of power systems, the demand of high performance computing (HPC) for power system adequacy and security analysis has increased rapidly. HPC also plays an important role in ensuring efficient and reliable communication for power system operation and
  39. 39. 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 infrastructure, which can provide high performance computing and a communication mechanism for providing services in these areas of the power system. It has been recognized that the commonly used Energy Management Systems (EMS) are unable to provide solutions to meet such requirements of HPC and data and resource sharing (Chen et al., 2004) for its operations. In the past, some efforts had been made in order to enhance the computational power of EMS (Chen et al., 2004) in the form of parallel processing, but only the centralized resources were used, and an equal distribution of computing tasks among participating computers was assumed. In parallel processing, the tasks can be divided into a number of subtasks of equal size to all systems. For this purpose, all machines need to be dedicated and should be homogeneous, i.e. they should have common configurations and capabilities, otherwise different computers may return results at different times depending on their availability when the tasks were assigned to the computers. In parallel processing, there is a need for collaboration of data from different organizations, which is sometimes very hard due to various technical or security issues (Chen et al., 2004). Consequently, there should be a mechanism for processing the distributed and multi-owner data repositories (Cannataro and Talia, 2001). Some distributed computing solutions also have been proposed previously for getting high efficiency computation, but they demand homogeneous resources and are not scalable. In addition, the parallel processing techniques involve tightly coupling of the machines (Chen et al., 2004). Use of super computers is another solution, but it is very expensive and often not suitable, especially for a single organization which may be constrained by resources. Grid computing is an infrastructure that can provide an integrated environment for all these participants in the electricity market and power system operations by providing secured resources as well as data sharing and high performance computing for power system analysis. Grid computing can be involved in all fields in which computers are involved, and these fields can be related to communications, analysis, and organizational decision making. Grid computing is a new technology that involves the integrated and collaborative use of computers, networks, databases, and scientific instruments owned and managed by multiple organizations (Foster and Kesselman, 1997; Foster et al., 2001). It is able to provide HPC and access to remote, heterogeneous and geographically separated data over the vast area. This technology is mainly developed by E-science community (EUROGRID, NASA IPG, PPDG, GridPP), but nowadays it is widely used in many research fields like oil and gas fields, banking, and education. Grid computing has provided large contributions in these areas. In the past few years grid computing technology has gained much attention from the power engineering field and significant research is being done at
  40. 40. 2.4 Probabilistic vs Deterministic Approaches 31 numerous places in order to investigate the potential use of grid computing technology and in order to apply this technology in power engineering field (Chen et al., 2004; Taylor et al., 2006; Ali et al., 2006; Wang and Liu, 2005; Ali et al., 2005; Axceleon and PTI, 2003). Grid computing can provide efficient and effective computing services in order to meet the increasing need of high performance computation in power system reliability and security analyses which are facing today’s power industry. It can also provide remote access to distributed resources of the power system, thereby providing effective and fast mechanisms of monitoring and control of power systems. Overall, it can provide efficient services in power system monitoring and control, scheduling, power system reliability and security analysis, planning, and electricity market forecast (Chen et al., 2004; Ali et al., 2005). Grid Computing is a form of parallel and distributed computing that involves coordination and sharing of computing, application, data storage, and network resources across dynamic and geographically distributed organizations (Asadzadeh et al., 2004). This integration creates a virtual organization where in a number of mutually distrustful participants with varying degrees of prior relationship want to share resources to perform some computational tasks (Foster and Kesselman, 1997; Foster et al., 2001). Some of the commonly used grid computing tools include Globus (Foster and Kesselman, 1997) and EnFuzion (Axceleon). EnHuzion is a distributed computing tool developed by Turbolinux. It has strong robustness, high reliability, efficient network utilization, intuitive GUI interfaces, multi platform support, multi-core processors, flexible scheduling and lights-out option, and extensive administrative tools (Axceleon). Detailed discussion on grid computing will be given in Chapter 4 of this book. 2.4 Probabilistic vs Deterministic Approaches The power systems must be planned to reliably supply electricity to end users with a high level of reliability and meet the security requirements. Fundamentally, these requirements conflict with economic concerns and usually tradeoffs have to be made in system operation and planning. Moreover, because the power system has been operating for many years following a similar pattern, system operators and engineers could predict future conditions with reasonable accuracy. However, with the changes over the past few years, especially with deregulation and increased interconnections, it is more and more difficult to predict the system conditions, although forecasting is an important task for system operators. Traditionally, the system security and reliability are evaluated under the
  41. 41. 32 2 Fundamentals of Emerging Techniques deterministic framework. The deterministic approach basically studies the system stability, given a set of network configurations, system loading conditions and disturbances, etc. (Kundur, 1994). Since the operation of the power system is stochastic in nature and so are the disturbances, engineers have to run thousands of time domain simulations to determine the system stability for a set of credible disturbances before dispatching. Under this deterministic regime, system operations and planning require experience and judgment from the system operators. Similarly, in the planning stage, planning engineers need to carry out such analysis to evaluate system reliability, and adjust the expansion plans if necessary. Despite its popularity with many research organizations and utilities, the time-domain simulation method suffers from intensive and time-consuming computation and is only feasible in recent years with progresses in computer engineering. This significant disadvantage has motivated engineers and scholars to develop new methods to account for the stochastic nature of system stability. Studying only the worst case scenario is one solution to the problem, but the obtained result is, however, too conservative and therefore unpractical for economy concerns in both operation and planning. In the articles by Billinton and Kuruganty, 1980; Billinton and Kuruganty, 1979; Hsu and Chang, 1988, probabilistic indexes for transient stability have been proposed. These methods consider various uncertainties in power systems, such as the loading conditions, fault locations and types, etc. The system stability can be assessed in the probabilistic framework which provides the system operator and planner with a clearer picture of stability status. The idea of probabilistic stability assessment is extended to the small signal stability analysis in this paper via a Monte Carlo simulation approach. In the probabilistic study of power system stability, several methods such as the cumulant and moment methods can be applied. These methods use cumulant or moment models to calculate the statistics of system eigenvalues using mathematical equations such as the Gram-Charlier equations (Hsu and Chang, 1988; Wang et al., 2000; Zhang and Lee, 2004; Da Silva et al., 1990). The advantage of these methods is fast computational speed. However, approximation is usually needed in these methods (Wang et al., 2000; Zhang and Lee, 2004). The Monte Carlo technique is another option which is more appropriate for analyzing the complexities in large-scale power systems with high accuracy, though it may require more computational effort (Robert and Casella, 2004; Billinton and Li, 1994). The Monte Carlo method involves using random numbers and probabilistic models to solve problems with uncertainties. Reliability study in power systems is a case in point (Billinton and Li, 1994). Simply speaking, it is a method for iteratively evaluating a deterministic model using sets of random numbers. Take power system small signal stability assessment for example. The Monte Carlo method can be applied for probabilistic small signal stability analysis. The method starts from the probabilistic modeling of system parameters of interest, such as the dispatching
  42. 42. 2.4 Probabilistic vs Deterministic Approaches 33 of generators, electric loads at various nodal locations and network parameters, etc. Next, a set of random numbers with a uniform distribution will be generated. Subsequently, these random numbers are fed into the probabilistic models to generate actual values of the parameters. The load flow analysis and system eigenvalue calculation can then be carried out, followed by the small signal stability assessment via system modal analysis. The Monte Carlo method can also be used for many other probabilistic system analysis tasks. For transmission system planning, the deterministic criteria may ignore important system parameters which may have significant impacts on the system reliability. The deterministic planning also favors a conservative result based on the commonly used worst case conditions. According to EPRI (EPRI, 2004), deterministic transmission planning fails to provide a measure of the reliability of the transmission system design. The techniques which can effectively consider uncertainties in the planning process have been investigated by researchers and engineers for probabilistic transmission planning practices. Under the probabilistic approach, system failure risk reduction can be clearly illustrated. The impact of system failure can be assessed and considered in the planning process. The probabilistic transmission planning methods developed enable quantification of risks associated with different planning options. They also provide useful insights to the design process as well. EPRI (EPRI, 2004; Zhang, et al., 2004; Choi et al., 2005; EPRI-PRA, 2002) proposed probabilistic power system planning to consider the stochastic nature of the power system and compared the traditional deterministic approach vs. the probabilistic approach. A summary of deterministic and probabilistic system analysis approaches are given in Table 2.1. Table 2.1. A Summary of Deterministic vs Probabilistic Approaches Deterministic Deterministic Deterministic Deterministic Deterministic Deterministic Deterministic Approach load flow stability assessment small signal stability transient stability voltage stability power system planning Probabilistic Probabilistic Probabilistic Probabilistic Probabilistic Probabilistic Probabilistic Approach load flow stability assessment small signal stability transient stability voltage stability power system planning For transmission system planning, generally speaking, the deterministic method uses simple rules compared with probabilistic methods. Deterministic methods have been implemented in computer software for easy analysis over the years of system planning practices. However, probabilistic methods normally require new software and higher computational costs in order to cope with the more comprehensive analysis tasks involved. Although the probabilistic method is more complex than the deterministic method and requires more computational power, the benefits of the probabilistic method out-weight the deterministic one because (1) it enables the tradeoff between reliability and economics in transmission planning; and (2) it is able to evalu-
  43. 43. 34 2 Fundamentals of Emerging Techniques ate risks in the process so as to enable risk management in the planning process. Transmission system planning easily involves tens of millions of dollars; the two advantages of the probabilistic approach make it a very attractive option for system planners. Detailed discussions on probabilistic vs deterministic methods will be given in Chapter 5. 2.5 Phasor Measurement Units Conventionally, power system control and protection is normally designed to respond to large disturbances, mainly faults, in the system. Following the lessons learnt from the 2003 blackout, protection system fault has been identified as a major factor leading to the cascading failure of a power system. Consequently, the traditional system protection and control need to be reviewed and new techniques are needed to cope with today’s power system operational needs (EPRI, 2007). The phasor measurement unit (PMU) is digital equipment which records the magnitude and phase angles of currents and voltages in a power system. They can be used to provide real-time power system information in a synchronized way as either standalone devices or they can be integrated into other protective devices. PMUs have been installed in the power systems across large geographical areas. They provide valuable potential for improving the monitoring, control, and protection of the power system in many countries. The synchronized phasor measurement data provides highly useful system dynamics information. Such information is particularly useful when the system is in a stressed operating state or subject to potential system instability. Such information can be used to assist the situational awareness for the system control centre operators. In the article by Sun and Lee, 2008, a method is proposed to use phase-space visualization and pattern recognition to identify abnormal patterns in system dynamics in order to predict system cascading failure. By strategically selecting the locations for PMU installations in a transmission network, the real time synchronized phasor measurement data can be used to calculate indices which can be used to measure the vulnerability of the system against possible cascading failures (IEEE PES CAMS Taskforce, 2009; Taylor et al., 2005; Zima and Andersson, 2004). The increasingly popular wide area monitoring, protection, and control scheme highly relies on synchronized real time system information. PMUs together with advanced telecommunication techniques are essential for this scheme. In summary, PMUs can be used to assist in state estimation, detect system inter-area oscillations and assist in determining corresponding controls, provide system voltage stability monitoring and control, facilitate

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