Power System Modeling, Analysis and Control

Power System Modeling, Analysis and Control by: A. P. Sakis Meliopoulos Professor Georgia Institute of Technology Copyright A. P. Meliopoulos, 1990-2006

NOTICE These notes may not be reproduced without the written consent of the author. The author expresses his gratitude to all his colleagues and students, who have contributed to the developement of these notes. I hope that proper credit has been given to works of others who contributed in this field through the list of references. If any omission has occurred, it is not intentional. Dr. A. P. Sakis Meliopoulos School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, Georgia 30332-0250 Page 2 Copyright A. P. Sakis Meliopoulos 1990-2006

Acknowledgedments The author was inspired to develop a Georgia Tech course on the subject from the IEEE short course on Energy Control Center Design which he attended in 1977. Over the years, work on several sponsored research projects have contributed to these developments. Three projects were especially pivotal: (1) a multiyear project sponsored by Westinghouse, (2) two NSF grants, one on hydrothermal coordination and another on composite power system simulation, and (3) a multiyear research project on power system reliability/security sponsored over the years by Georgia Power Company, PTI, and EPRI. Special recognition is due to Professor Webb who was the primary architect and project director of the multiyear research project sponsored by Westinghouse and Professors Debs and Alford who were PIs in the same project. Under this project the author has performed research on a variety of subjects including power flow analysis, contingency analysis, optimal power flow, hydrothermal coordination, and multiphase power flow analysis. Professors Chong, Debs, and Grigsby have inspired my work in this area. Dr Chong's teaching of the graduate course EE6500, Introduction to Management and Control of Energy Systems, introduced me to the many aspects of optimization and large scale systems. Dr. Debs' teaching of the course EE6502, Control and Operation of Interconnected Power Systems, introduced me to many aspects of power system control and operation. In the period 1976-81 I was called upon to teach EE6500, a course developed by Dr. Chong, which enhanced my understanding of optimization methods and large scale systems. Since 1978 I have benefited from collaboration with Professor Leo Grigsby during our teaching on power flow analysis in the course Modern Power System Analysis. In the period 1980-83 I was called upon to teach EE6502 which enhanced my understanding of power systems. This course was originally introduced by Dr. Debs. During the years 1980-83, I further developed this course by introducing new topics which constitute two thirds of this course as being taught today at Georgia Tech. In 1983 I introduced a short course on control centers. Later, I collaborated in the teaching of this short course with Dr. Debs. Professor Contaxis is especially acknowledged for the many stimulating discussions and collaboration during our Ph.D. research years and later during his visit to Georgia Tech. I am also indebted to my many students, past and present, who have significantly contributed to the research projects and course development. It is my pleasure to recognize Professors Cokkinides, Feliachi, and Bakirtzis, my former Ph.D. students, for their contributions during the development of the graduate course Real Time Control of Power Systems. Drs Chao and Cheng have contributed to the topics of Optimal Power Flow and Security Assessment through their Ph.D. work. I would like to thank my present students, Fan Zhang and Feng Xia who contributed to the present manuscript with their Ph.D. work and help in typing. Finally, I would like to thank PTI for giving me the opportunity to work on their corrective scheduling program and Advanced Control Systems for our cooperative effort in energy management system developments. Copyright A. P. Sakis Meliopoulos 1990-2006 Page 3

Guidance from the Past The mechanicians of Heron s School told us that the study of machines consists of theoretical and practical part. The theoretical part includes the natural sciences while the practical part consists of the engineering disciplines. They postulate that a necessary condition for an able designer of mechanical devices is a solid background in both natural sciences and practical skills. Pappos of Alexandria (4 th century A.D.) Page 4 Copyright A. P. Sakis Meliopoulos 1990-2006

DRAFT and INCOMPLETE Table of Contents from A. P. Sakis Meliopoulos Power System Modeling, Analysis and Control Chapter 1 6 The Modern Electric Power System 6 1.1 Introduction 6 1.2 Power System Control Functions 9 1.2.1 Data Acquisition and Processing Subsystem 12 1.2.2 Energy Management/Automatic Generation Control Subsystem 16 1.2.3 Security Monitoring and Control Subsystem 16 1.3 Major Elements of an Energy Management System 20 1.3.1 Supervisory Control and Data Acquisition 20 1.3.2 Computers 22 1.3.3 User Interface 23 1.3.4 Applications Software 24 1.4 Hierarchical Structures 24 1.5 Automation and Control of Distribution Systems 27 1.6 The Impact of Legislation 28 1.7 Summary and Discussion 29 Copyright A. P. Sakis Meliopoulos 1990-2006 Page 5

Chapter 1 The Modern Electric Power System 1.1 Introduction The electrification of many processes through technological advances resulted in the continuous development and evolution of the electric power system over the last one hundred plus years. As an example, in the U. S. A. today, more than one third of energy consumption is in the form of electric energy. The modern day electric power system is responsible for generating, transmitting and delivering more than one third of the total consumed energy. With this progress, the complexity of the system has grown. To manage this complex system, monitoring, control and operation functions are computer assisted. The systems for computer control of electric power systems have evolved as computer and monitoring technology evolved. Throughout the years, these systems have been named Control Centers, Energy Management Systems (EMS), Independent System Operations, etc. The names reflect the changing emphasis in the functions of these control centers. In this book, we will use the term Energy Management System. The EMS concept comprises hardware and software for the purpose of monitoring and controlling the power system. Typically, the function of monitoring is fully automated. Control functions, however, are either automated or manual. Energy Management Systems have evolved from the traditional dispatcher's office. The dispatcher had in his reach supervisory equipment. Based on his experience, he would monitor the supervisory equipment and will control the system appropriately. The control was manually executed upon communication between the dispatcher and local operator (for example, plant operator). As size and complexity of the system grew, this approach was not adequate. A number of incidents indicated that the security of the system, defined as the ability to operate in synchronism under possible random disturbances, cannot be guaranteed with this simple approach. Out of this need, a comprehensive and integrated approach to monitor and control a power system has emerged. Advances in computers and power system hardware provided new possibilities. As technology evolved so did the energy measurement system. A modern energy management system is characterized with: 1. The dispatch operation has been replaced with the fully digital Automatic Generation Control (AGC). The AGC integrates the dispatch function with the Page 6 Copyright A. P. Sakis Meliopoulos 1990-2006

load frequency control, power interchange control problem, and varying degree of power system optimization functions. 2. System security functions (monitoring and control) have been integrated in a hierarchical control scheme. 3. Advanced economy scheduling functions are an integral part of the system, including access to the power markets, if available. Copyright A. P. Sakis Meliopoulos 1990-2006 Page 7

Mimic Board Consoles Display / Control Interface Redundant Computers ICCC CIOC's Communication Links OASIS Remote Terminal Units Sensors & Controls Figure 1.1 Typical Configuration of an Energy Management System Hardware The hardware required for the new approach are illustrated in Figure 1.1. The sensors and controls are located in the field and collect data. For example, a sensor can be a Page 8 Copyright A. P. Sakis Meliopoulos 1990-2006

wattmeter, a voltmeter, a breaker status device, etc. These data are collected at the Remote Terminal Units (RTUs) which are normally hardwired to the sensors and controls. Then, the data are transferred through communication channels to Communication Input/Output Controllers (CIOCs) located normally in a central location. There the data are transferred to the computers. Computer programs evaluate the data and display them on CRTs, dynamic mimic boards or computer generated projections of system displays. The operator can visualize the operation of the system by looking at the displays. In a modern energy management system, a computer (automatically or on dispatcher command) can issue commands which are transferred through the CIOCs, communication links, and RTUs to the survey points for execution. This configuration and function is illustrated in Figure 1.2. Contact Inputs Analog Inputs Contact Outputs Analog Outputs RTU Data Commands Master Station Figure 1.2 Basic SCADA System Configuration The hardware configuration of Figure 1.1 provides the possibility of controlling and operating the system in a rather sophisticated manner. The next sections provide a qualitative review of the objectives and approaches utilized in the control of a modern power system. 1.2 Power System Control Functions The operation of an electric power system is characterized with a number of control functions. Some of them are automatic and others require operator initiation. Consider, for example, a single unit power plant as in Figure 1.3. One can recognize a number of control loops: (1) The Voltage Control Loop. The objective of this control loop is to regulate the voltage at the terminals of the generator. It consists of the voltage regulator and exciter system. Inputs to this control loop are the reference voltage V ref, which may be selected by the system dispatcher or automatically by computers (VAR dispatch), and the actual voltage at the terminals of the generator V g. (2) The Power System Stabilizer (PPS) Loop. The objective of this control loop is to slow down the oscillations of the generator following a disturbance. It consists of a feedback system which injects a stabilizing signal into the exciter system. Feedback quantities may be: frequency, f, real power, P g, etc. Copyright A. P. Sakis Meliopoulos 1990-2006 Page 9

(3) The Primary Automatic Generator Control Loop. The objective of this control loop is to regulate the real power output and the speed of the generator. It consists of the speed regulator (governor) of the prime mover. It uses feedback of the generator speed (or frequency) and the real power output of the generator. Prime Mover G Tramsmission System and Load Tie Line V g P g f Governor G(s) Exciter f sched f V ref + Σ + PSS D(s) - P g f P g f - - + Σ Σ + Bias B f K(s) Σ + + Σ - + P sched L(s) Figure 1.3 Schematic Representation of Control Schemes for a Generating Unit (4) The Secondary Automatic Generation Control Loop. The objective of the secondary automatic generation control loop is to regulate the net interchange, unit real power output, and speed (frequency). It consists of a feedback system which injects a signal into the speed regulator (governor). The signal, refered to as the Unit Control Error (UCE), is constructed from measurements of frequency, interchange schedule, unit real power output, etc. Reference quantities for this control loop are: (a) Scheduled interchange of real power, P sched, (b) Scheduled frequency, f sched, and (c) Scheduled unit real power output, P des. This control loop uses integral feedback of frequency and therefore regulates the system real time (integral of frequency). Since the electric power system is a dynamic system, the control loops should be designed so that the system is steered to desired operating conditions with minimal oscillations, and minimal control effort to avoid excessive wear of equipment. Thus the transfer functions E(s), D(s), R(s), L(s), and G(s), indicated in Figure 1.3, must be selected to meet performance objectives and to track desired operating conditions in a smooth manner.. Page 10 Copyright A. P. Sakis Meliopoulos 1990-2006

ENERGY/ECONOMY FUNCTIONS SUBSYSTEM DATA AQUISITION AND PROCESSING SUBSYSTEM Load Forecast Unit Commitment Economic Interchange Evaluation Parameter Estimation SCADA Measurements Economic Dispatch Automatic Generation Control State Estimation Network Topology Displays External Equivalents Optimal Power Flow Security Dispatch Environmental Dispatch SECURITY MONITORING AND CONTROL SUBSYSTEM Emergency State Emergency Controls Security Monitoring Normal State Contingency Analysis Extremis State Restorative Controls VAR Dispatch Insecure State Preventive Controls Figure 1.4 Functional Diagram of a Modern Energy Management System It is obvious that the controls associated with a generating unit are numerous and complex. Complexity is minimized with the utilization of a hierarchical structure. For example, at a higher level, the reference points for the generator control loops are decided such as unit output, P des, interchange power, P sched, etc. At this level, economy and security of the system is the overriding consideration. Then, the mentioned local control loops are charged with the task of tracking the reference points. The higher level is performed at a central location, the energy control center. Depending on the sophistication of the particular energy management system, the reference points may be automatically selected by computers or by operators. For example, the reference for the real power output of a unit may be automatically selected by computers based on a real time economic dispatch. Others, for example the frequency reference, may be selected at another central location for a large number of interconnected power systems based on the deviation of the integral of the frequency from an accurate time reference. All of these require that the system conditions are continuously monitored. The monitoring task consists of data acquisition and state estimation. This higher level control scheme for a Copyright A. P. Sakis Meliopoulos 1990-2006 Page 11

typical energy management system is illustrated in Figure 1.4. The functions of a typical control center can be grouped into three distinct groups resulting in the three subsystems: (a) the data acquisition and processing subsystem, (b) the energy management/automatic generation control subsystem, and (c) the security monitoring and control subsystem. The three subsystems are illustrated in Figure 1.4. A brief description is given next. 1.2.1 Data Acquisition and Processing Subsystem The objective of the data acquisition and processing subsystem is to obtain an accurate (as much as possible) estimate of the operating state of the system. This is achieved with a large number of Remote Terminal Units (RTU) and associated communication network. The system is known as SCADA (Supervisory Control And Data Acquisition). The remote terminal units collect analog measurements (i.e. voltage magnitude, power flows, etc.) and status variables (i.e. status of breakers, switches, etc.) and transmit this data to the computers of the energy management system via the communication network. There, the topology of the network is formed (network configurator) and the state of the system is constructed (on-line power flow or state estimation). The results are displayed in whatever user interface media exist at the energy management system, i.e. computer monitors, mimic boards, projection systems, etc.. The Data Acquisition and Processing Subsystem comprises a set of software which process the data collected by the SCADA system. This data is utilized in two ways. Status data (circuit breaker status, interrupt switch status, transformer tap setting, etc.) are utilized to form the system configuration and model. The software which uses the status data for the purpose of computing the system configuration and model is known as system network configurator. Typically, this software is executed only when a change in status data occurs. All other data (analog measurements) is utilized to compute the best (in some sense) estimate of the operating state of the system. Two approaches are used in practice: (1) use of an on-line power flow, or (2) use of a state estimator. AutoBank 500kV/230kV G1 AutoBank 500kV/230kV G2 (a) Page 12 Copyright A. P. Sakis Meliopoulos 1990-2006

SG1 SG2 (b) Figure 1.5. Network Configurator (a) Breaker Oriented Model (b) Bus Oriented Model The task performed by the system configurator is illustrated in Figure 1.5. The information received through the SCADA system determines the status of the breakers. The system configurator uses prestored information and the breaker/switches, etc., status to determine a 'bus oriented model', i.e., what circuits are connected to what bus and how much power is injected at a bus. This task is illustrated in Figure 1.5. The bus oriented model can be also utilized by other applications programs. The analog measurements are used to determine the operating condition of the system. For a better understanding of the mathematical model which describes the operating condition of the system, we use Tinney and Dommel s classification of the various variables in a power system into state variables, and control variables. We also recognize that there is a number of externally determined variables (exogenous) such as the electric loads. A definition of these variables follows: Electric Power Demand Variables. These consist of all externally determined real and reactive load demands. These variables will be denoted with the vector d. The electric power demand variables change as the time progresses. Control Variables. These consist of all quantities that can be independently manipulated by the system dispatcher or by existing control loops to satisfy system objectives. Examples of control variables are: 1. Voltage magnitude at certain buses. For example, generation buses, buses connected to regulating transformers, or buses with synchronous condensers, etc. 2. Real power generation at all generation buses. Note that the slack bus needs to be excluded. 3. Tap settings of transformers. 4. Switch status of capacitor and/or reactor banks (open/close). 5. Other controls such as FACTS devices, etc. Copyright A. P. Sakis Meliopoulos 1990-2006 Page 13

The control variables will be denoted with the vector u. State Variables. The state variables are defined as the minimum set of variables, the knowledge of which will enable the computation of all relevant quantities of interest, such as circuit flows, generating unit reactive power output, etc. The state variables will be denoted with the vector x. It is important to recognize that for a specified set of demand and control variables, the state variables are uniquely determined from the system model. With above definitions it is quite easy to explain how the data collected with the SCADA system are used to develop a mathematical description of the system operating state. This task can be achieved with procedures of varying degree of sophistication. In the early days of development, on-line power flows were used which were later replaced with state estimation models. A brief description of these models follows. G1 G2 1 3 T1 T2 Interconnection 2 L1 4 Interconnection L3 L2 5 T1 6 MW Flow Measurement MVAR Flow Measurement kv Measurement Transformer Tap Measurement Figure 1.6 Illustration of Measurements Necessary for an On-Line Power Flow On-Line Power Flow. The on-line power flow uses measured electric loads, the network configuration, measured generating unit power outputs, and measured tie line flows, and Page 14 Copyright A. P. Sakis Meliopoulos 1990-2006

the bus oriented model from the network configurator for the purpose of computing the system state variables. Specifically, the state variables are related to the control and demand variables with the power flow model of the system (power flow equations). Solution of these equations will yield the state variables x, thus providing the operating conditions of the system. The procedure of selecting the required measurements of demand and control variables and subsequent solution of the power flow is known as the on-line power flow. Figure 1.6 illustrates a set of measurements necessary for an on-line power flow of a small system. The on-line power flow has the disadvantage that if one measurement of a control variable or a demand variable is in error, the entire power flow solution will be in error. In other words, there is no capability to detect and reject measurement error. This problem is addressed by the state estimator. State Estimation. Measurements are usually corrupted with errors for a variety of reasons: sensor inaccuracies such as potential and current transformers, instrument error, communication error, etc. For these reasons it is important to take more measurements than the minimum required to determine the system state (redundant measurements). Then, the statistical theory of state estimation is utilized to compute the best estimate of the system state. In the process, some of the measurement errors will be filtered out. State estimation employs a stochastic model of the system to filter out measurement errors, communication errors, etc. The solution is in terms of an expected value of the system state with computable error margins. The importance of this stochastic approach lies in the ability to address issues of measurement errors and other factors of uncertainty like those arising from modeling inaccuracies. An important issue in state estimation is the issue of system observability. Loosely speaking, system observability is defined as the ability of the state estimator to determine the system state from the available measurements. In a real time environment, some measurements may be corrupted or lost (due to the loss of an RTU for example). In such cases, some part of the system may not be observable. This issue will be further addressed in Chapter 7. The state estimation is an alternative to on-line flow. Another alternative would be to measure the system state directly. Technology for this purpose has been already developed by the use of synchronized measurements with the Global Positioning System. The global positioning system consists of a large number of satellites which provide a timing and positioning signal. A receiver anywhere on earth can translate this signal into a clock output with accuracy better than one microsecond and position of the receiver with accuracy better than one meter. The clock signal is used to perform synchronized measurements from which both magnitude and phase can be extracted. This technology is available but presently is not widely used due to the cost associated with the replacement of older generation sensors and meters with the new technology. It is used for specific applications. Copyright A. P. Sakis Meliopoulos 1990-2006 Page 15

1.2.2 Energy Management/Automatic Generation Control Subsystem This subsystem manages the energy generation, controls the frequency and the power transactions (net interchange) of the system, and optimizes the operation of the system. There is a hierarchical structure within this system. An example of functions within each level is provided below: Level 1: Level 2: Level 3: Load Forecasting Unit Commitment Economy Purchases Economic Dispatch Economic Interchange Evaluation Optimal Power Flow Transfer Capability Automatic Generation Control - Frequency Control - Power Transaction (Interchange) Control - Inadvertent Power Flow Control The economic importance of the energy management system is enormous. Because of the fact that the power system tries to supply a changing electric load characterized with uncertainty, many long and medium term scheduling functions are driven by parameters which possess considerable uncertainty. Thus probabilistic approaches are typically used for level 1 functions. Level 2 and 3 functions are deterministic using the results of level 1 functions as directives. 1.2.3 Security Monitoring and Control Subsystem Security of an electric power system is loosely defined as the ability of the system to withstand major disturbances without losing synchronism. The security of the system is a very complex concept. Experience accumulated over the years indicates that the security of the system can be only insured by continuous monitoring and control of the system. Security control comprises the integration of a number of automated and manual control operations, such as: * Automatic generation control, * Economic dispatch, * Generation rescheduling, * Voltage control, * Coordination with neighboring utilities, and * Load control. The integration of all these functions is incorporated in the security monitoring and control subsystem. A hierarchical control scheme is typically employed. The functions of Page 16 Copyright A. P. Sakis Meliopoulos 1990-2006

this subsystem can be grouped into two classes: (1) Security monitoring, and (2) Security controls. A qualitative analysis of these functions is given here. Later in Chapter 11, system security is addressed in greater detail. Security Monitoring: For the purpose of explaining the security monitoring function, it is expedient to classify the infinite number of possible operating conditions of a power system in terms of security. For this purpose observe that the power system should satisfy the following requirements: (1) Operating constraints such as: limits on system frequency, limits on bus voltage magnitude, limits on circuit loading, etc. Any attribute of system component, such as loading of a circuit, reactive power generated by a unit, etc. can be expressed as a function of the system state and controls, i.e. h (x,u). Then, the operating constraints are expressed as a set of inequality constraints h (x,u) b or h(x,u) 0 where h(x,u)= h (x,u) - b (2) Load constraints which simply express the fact that any customer switching into the system must be served. They are represented with a set of equality constraints, i.e. the power flow equations: g(x,u) = 0 In terms of above expressions, the operating states of a power system are classified into (DyLiacco [5], [52], Fink[70]): (l) secure, (2) normal but insecure or vulnerable, (3) emergency, (4) extremis, and (5) restorative as follows: (1) Secure: All load and operating constraints are satisfied for the systems and for any foreseeable and probable contingency. (2) Normal But Insecure: All load and operating constraints are satisfied for the present system, but not for one or more foreseeable (and probable) contingencies. (3) Emergency: All load constraints are satisfied, but one or more operating constraints are violated. (4) Extremis: One or more load constraints are violated, and one or more operating constraints are violated. (5) Restorative: All operating constraints are satisfied, but one or more loads are disconnected. Most of the time the operating state of the system is normal (secure or vulnerable). In this case, security monitoring involves the analysis of whether the operating state of the Copyright A. P. Sakis Meliopoulos 1990-2006 Page 17

system is secure or vulnerable. For this purpose, a security assessment must be performed at appropriately selected time intervals. Security assessment involves contingency ranking and contingency analysis. Contingency Ranking is the procedure by which the critical contingencies of the system at a given time are determined. The importance of this issue can be appreciated by recalling that the number of possible contingencies is very large. Fortunately, however, the number of contingencies which may cause system problems (critical contingencies) is small. Contingency Analysis refers to the problem of determining the circuit flows and bus voltages for a system contingency. Contingency analysis is typically applied to a set of contingencies determine by the contingency ranking algorithm. Security Controls: The overall objective of system operation is to steer the system in such a way as to operate in a secure state at every instant of time. This objective is achieved most of the time. Occasionally, however, the system deviates from secure operation. In this case, controls are exercised to return the system operation to a secure state. Depending on the type of insecurity, different controls must be exercised. These controls are characterized as preventive, corrective, emergency, and restorative. The following general definitions apply: Preventive Controls are actions which bring a normal but vulnerable operating state to a secure state. Corrective Controls are actions which bring an emergency operating state to a normal state (secure or vulnerable). Emergency Controls are actions which bring an emergency operating state to a restorative or extremis state. Restorative Controls are actions which bring a restorative operating state to a normal state (secure or insecure). All these controls shall be referred to as security controls. A summary of operating states and security controls is illustrated in Figure 1.7. Page 18 Copyright A. P. Sakis Meliopoulos 1990-2006

NORMAL and SECURE System Optimization D,O Restorative Controls RESTORATIVE System Security D,O Restorative Controls Preventive Controls NORMAL but VULNERABLE/INSECURE Optimization/Security D,O Emergency Controls Emergency Controls Corrective Controls EXTREMIS System Security D,O EMERGENCY System Security D,O Transition Due to Disturbances Transition Due to Control Action Figure 1.7 Power System Operating States (after DyLiacco, Fink) Security controls can be applied in many different ways depending on the operating philosophy of the particular company. Operating philosophies for an electric power system have undergone an evolutionary change. In this process, many control functions have been automated while others still rely on operator action. Obviously, an automated function, once set, is independent of human action (except for override action). Such functions are: (1) Automatic Generation Control, (2) Economic Dispatch, (3) Load Shedding, etc. Other control actions rely on operator action. Typically, security controls fall in this category. Obviously, the practices of the operators of one power system may differ from those of another system. To understand the differences, it is important to study the operation of a power system. Figure 1.7 illustrates the possible operating states and controls of the real time operation of a system. The objective of the operator is to maintain normal operating state which is characterized with satisfaction of operating and Copyright A. P. Sakis Meliopoulos 1990-2006 Page 19

load constraints. A normal operating condition is further characterized as secure or vulnerable depending whether a single abnormal event (such as the outage of a unit or tripping of line) will not or will cause the violation of some operating or load constraints. In this sense then, an operator may try to operate the system in a secure normal state always or may allow the system to operate in a vulnerable normal state. This results to two distinct operating philosophies which shall be called predictive and non-predictive. While the advantages of the predictive operating philosophy are obvious, it is economically expensive and practically complex to be applied. Emergency conditions are typically characterized with abnormal voltages or overloaded (congested) power lines or both. Recent trends (deregulation, independent operation) have resulted in operating patterns that often generate congested lines and therefore emergency conditions. In this case one of the security controls is to alleviate the congested conditions. A common terminology used for this case is congestion management. 1.3 Major Elements of an Energy Management System From the design point of view, an Energy Management System comprises four major components: 1. Supervisory Control and Data Acquisition (SCADA) System 2. Computers 3. User Interface 4. Applications Software. An integral part of an EMS is the power system dispatchers that have the responsibility of op[erating the system. Many control functions require human input or authorization. This means that the system dispatcher must take many decisions in the course of operating the system. The issue of what control functions require human input/authorization and what are fully automated (closed loop control) is a moving target driven by technological advances. It is also dependent upon the management decisions. In subsequent paragraphs, the components of an EMS will be briefly described. 1.3.1 Supervisory Control and Data Acquisition SCADA system stands for Supervisory Control And Data Acquisition system. As the name implies, it consists of two subsystems. The supervisory control subsystem consists of hardware which (a) display at a central location (energy management system EMS) the status of circuit breakers and voltage regulating devices (tap-changers, capacitors, generator voltage regulators, etc.); (b) allow remote tripping of breakers, changes of transformer tap, etc. In most cases, supervisory control is a manual function, i.e., the dispatcher at the control center will initiate a command to open/close a breaker, etc. The data-acquisition subsystem consists of: remote terminal equipment for interfacing with power system instrumentation and control devices; interfaces with communication Page 20 Copyright A. P. Sakis Meliopoulos 1990-2006

channels; and master station equipment for interfacing with the system control center. The local equipment communicate with the energy management system via dedicated or non-dedicated communication channels. Communication media have evolved over the years, from telephone circuits to microwave to fiber optic links. Analog data is scanned periodically, typically every one second to a few seconds. Each scan is triggered by the EMS at the prescribed interval by using a request to all remote stations to send in data. Data is received at the energy management system in a random order. Status data is also processed in the same way as analog data except that there are two ways of reporting status changes. The first way is to send in all status information from all remotes at the required intervals regardless of whether or not there has been a change. This approach requires a software routine at the EMS to check each new status with the old status to determine any changes. Considering the very large number of status points that is monitored in a power system, this approach represents a sizable computational burden. The second way is to send status data from the remote only when there has been an actual change of status. Normally the system operates in a quasi-steady state mode. If there are any status changes, only certain number of stations are involved. For this reason, the second method results in a better overall system response. It is important to note that advances in hardware, software and communications have revolutionized the way the SCADA system operates. The technology is in a fast pace evolution. It is not uncommon today to have a SCADA system which comprises subsystems of old technology and subsystems of newer technologies. Independently of system configuration, SCADA system manufacturer and computer configuration, the end result of the SCADA system function is to collect a set of system data every sampling period. The data consist of: * Breaker status * Disconnect switch status * Transformer tap setting * Real power (MW) flow measurements * Reactive power (MVAr) flow measurements * Voltage magnitude (kv) measurements * Phase of voltage measurements (synchronized measurements) A simplified view of a SCADA system is illustrated in Figure 1.8. At the energy management system, the data is processed with software which: (1) initiate the collection of data and place them in the data base, (2) error-checking, (3) conversion to engineering units, (4) limit-checking, and (5) generate a reliable system model which is interfaced with application programs. In summary, the Supervisory Control and Data Acquisition System generates a filtered set of data for each data collection cycle. The filtering of the data and the generation of a reliable system model will be examined in detail in Chapter 7. Copyright A. P. Sakis Meliopoulos 1990-2006 Page 21

G1 G2 MW Flow Measurement MVAR Flow Measurement kv Measurement Disconnect Switch Status Breaker Status RTU Communication Link with Control Center (a) Contact Inputs Analog Inputs Contact Outputs Analog Outputs RTU Data Commands Master Station (b) Figure 1.8 Simplified View of a SCADA System (a) Survey Points (b) SCADA System Configuration 1.3.2 Computers Page 22 Copyright A. P. Sakis Meliopoulos 1990-2006

The brain of the Energy Management System is the computer. The computer is vital for the operation and control of the system. Since computers fail as any other equipment, redundant computers are used. A basic redundant configuration is conceptually illustrated in Figure 1.9. Computer A is the primary computer which performs the real time monitoring and control of the system. Computer B is the secondary computer which remains in a backup-ready position. In case of failure of Computer A, a failover procedure is automatically initiated. There are many variations of this arrangement which are driven by the tremendous advancements in computer hardware. Today computer hardware permit the sharing of memory, open system operation and the parallel operation of multiple computers on shared data. These advances have resulted in high reliability levels of computer hardware. Advances in software engineering have also impacted the functions of the EMS computers. In general, EMS computers may perform the following functions: 1. Real time functions (monitoring and control) 2. Support of the user interface 3. Run operating studies 4. Maintenance, testing, and development of new functions 5. Simulation studies for operator training. Computer A Computer B Figure 1.9 Dual Computer Configuration Computer capabilities have been steadily increasing. At the same time, the amount of data to be processed and the system model is also increasing. In addition, the need for new applications that will support todays operating needs of power systems is also increasing. The speed of computer and the efficiency of the software should provide a system with practical response times. While for many applications the response times are satisfactory, there are applications for which improved efficiency and response times are desirable. The recent trends towards energy management systems in the names of Independenmt System Operator and Regional Transmission Organizations have increased the size of the system to be controlled. As these trends continue, improved methods for large scale system methods will be needed to meet desirable response times. 1.3.3 User Interface The user interface comprises hardware and software. The hardware consists of: 1. Consoles, CRTs, Flat Panel Displays 2. Recorders/Loggers Copyright A. P. Sakis Meliopoulos 1990-2006 Page 23

3. Dynamic Wall Board Display. 4. Computer generated projections of system displays. Software consists of information retrieval and display functions. Human engineering is an essential part of these software. Much effort has been expended in improving user interface. Recent technology advances have facilitated progress. Today's user interfaces are extremely sophisticated encompassing full graphics, fast response, selectable level of detail, and color coding. One of the most recent developments in this area are visualization and animation methods to display the operating conditions of the system and especially visualization techniques of the security status of the system. 1.3.4 Applications Software Applications software are all computer programs which are utilized in the control and operation of a power system. Depending on the needs of a particular system, applications software may vary. A comprehensive list of applications software is listed in Table 1.1. It should be mentioned that a particular power system may not need all applications software listed in Table 1.1. For example a utility without hydro generation does not need a hydrothermal coordination program. It is important to note that recent trends and legislation towards deregulation will certainly impose the need for additional applications software for example power transaction monitoring and evaluation, etc. Table 1.1 List of Possible Applications Software On Line Power Flow Economic Dispatch State Estimation Security Assessment Pollution Monitoring and Evaluation Interchange Scheduling and Evaluation Simultaneous Transfer Capability Congestion Management Unit Commitment Load Forecasting Optimal Power Flow VAR Dispatch External System Equivalents Hydrothermal Coordination Supply Side Load Management Power Bid Evaluation 1.4 Hierarchical Structures Page 24 Copyright A. P. Sakis Meliopoulos 1990-2006

The multiplicity and complexity of control functions in a power system necessitates a multi-level hierarchical structure of the entire control scheme. The organization of the various levels may depend on the structure and composition of the specific utilitity and whether the system operates as an vertically integrated system or as part of an open power market. Figure 1.11 illustrates in a conceptual manner a typical EMS structure for a vertically integrated electric power system. At the lower level, the Regional Dispatch Center (RDC) is the lower level decision maker. The RDC has the capability to send the following control commands, automatically or manually: (a) To power plants: Scheduled power and scheduled voltage or reactive power, and (b) To substations: Position of breakers, switches, transformer taps, etc. System Power Production and Control (SPPC) Operations Coordination Office (OCO) Regional Dispatch Center (RDC) Substation Power Plant UCE V exc Power Plant Controls P sched f sched ACE V sched Figure 1.11 Conceptual Hierarchical Structure of an Energy Management System The RDC receives orders from the Operations Coordination Office (OCO) which dictates the total generation and scheduled frequency for each region. The OCO also receives directives from the System Power Production and Control Center which may dictate unit Copyright A. P. Sakis Meliopoulos 1990-2006 Page 25

commitment, hydro utilization, etc. It should be clear that within this scheme, a higher level decision is passed along to a lower level system. The system that receives the decision will operate in such a way as to track the control direction. Figure 1.12 illustrates the operation of the system in an open power market. Note that the Energy management System takes the form of an Independent System Operator. The energy management system operates on the offered bids and bilateral contract and schedules the operation of the system accordingly. The operating rules for this organization are determined by concensus of all parties involved, utilities, independent power producers, consumers and legislators. Once the rules of the market have been established, all the players have to abide by these rules. Such a system has its hidden risks. This has been demonstrated with the failure of the California ISO (2000/2001). In this book, we focus on the technical aspects of the power system operation. Occassionally, references are made to the open market operation paradigm. The reason for this approach is the fact that the open market operation is in its infancy, there are several approaches and, at this point in time, noone knows what the preferred rules of operation will be. Control Area 1 ACE 1 ACE 2 Control Area 2 ISO ACE 3 Control Area 3 Power Meter Frequency Meter Utility Generator Independent Power Producer Power Wheeling Page 26 Copyright A. P. Sakis Meliopoulos 1990-2006

Figure 1.12 Conceptual View of an Energy Management System in an Open Power Market Environment 1.5 Automation and Control of Distribution Systems The reviewed hardware, software, and procedures for power system control and operation are almost exclusively applied to the generation and transmission system, i.e. the bulk power system. The importance of the bulk power system justifies the cost and personnel training requirements of Energy Management Systems. Traditionally, distribution systems have not been implemented with centralized control hardware. Recent developments in load management, distributed generation, automation of distribution operations, and possible retail wheeling has ignited interest in the development of distribution management systems. Conceptually, the approaches and hardware are similar. Practically, both hardware and control philosophies must be different because of the differences in the operation of transmission and distribution systems. Some key points are: First, the objectives of a distribution management system are somewhat unique: (a) Survey and control points may include customers. The rights and desires of customers must be observed. (b) The extent and detail of distribution systems necessitates an accurate geographical modeling approach provided by present Geographical Information System (GIS) technology. (c) Typical unique functions of the distribution management system are: (1) Provide automation of distribution control functions, i.e., capacitor switching, (2) Provide capability of load management and automatic meter reading procedures, (3) Accommodate control requirements of distributed (customer owned) generation sources, and (4) Fault location, isolation and restoration. (d) The cost of hardware must be low. Otherwise, the investment cannot be justified. Second, the data acquisition and processing subsystem is a necessary component for a Distribution Management System as it is for the EMS. However, because a distribution system may operate in unbalanced conditions and single phase circuits may exist, the processing of the data for the purpose of establishing a reliable model for the distribution system is much different. Specifically, it is necessary to use three phase instrumentation and the distribution system model may include three phase circuits as well as single phase circuits. Thus applications software must include multiphase power flow and/or multiphase state estimation. Present developments are concerned with the mentioned key points. There are many Distribution Automation and Control products that are continuously evolving as the technology evolves. The methods and approaches to distribution automation and control are beyond the scope of this book. Copyright A. P. Sakis Meliopoulos 1990-2006 Page 27

1.6 The Impact of Legislation The electric power industry has been subjected to regulation since its early days. The majority of electric power companies in the United States are investor owned utilities, regulated by local and federal regulatory agencies. In many countries the electric power industry is nationalized. Recent trends have resulted in the deregulation of the electric power industry in many countries. In the United States, deregulation and open markets have been implemented in certain regions. Because of the recent failure of the deregulation experiment in California, the involvement and authority of regulatory agencies has increased for the intended purpose of avoiding future failures. The process of deregulation is a dynamic one. It is difficult to predict the final outcome. It is certain that legislation will be always a major factor. In this respect, it is important to mention the major legislative acts that affected the operations of an electric power system. Table 1.2 lists the major legislative acts in the past 70 years in the United States. Table 1.2 Major Legislative Acts in the Last 70 Years United States PUHCA 1935 (Public Utility Holding Company Act) PURPA 1978 (Public Utility Regulatory Policies Act) Clean Air Act - 1990 Energy Policy Act - 1992 Orders 888 & 889-1996 CECA 1998 (Comprehensive Electricity Competition Act) Order 2000 Details on this legislations can be found in the literature. By far, the Energy Policy Act of 1992 has had the most impact paving the way for open markets. Page 28 Copyright A. P. Sakis Meliopoulos 1990-2006

1.7 Summary and Discussion An overview of a modern Energy Management System has been presented. Emphasis was placed on the basic structure of an EMS. The design of an EMS evolves as technology advances: computers become faster and more powerful, display hardware improve in resolution and capability, microcontrollers dominate the design of Remote Terminal Units, etc. In the past couple of decades, we have experienced a tremendous progress in SCADA and computer hardware. Yet, central to the success of an EMS is the software for the overall coordination and optimization of power system operation. The rapid advances in hardware technology have been mediated with slow progress in software development for coordination and optimization of large scale systems. Much work remains to be done in this area. One hopes that developments and progress will continue in both hardware and software in a coordinated way. This book is focused on the concepts and objectives which drive the developments of Energy Managements Systems. Copyright A. P. Sakis Meliopoulos 1990-2006 Page 29