Renewable Integrated Power System Stability and Control
Description
Discover new challenges and hot topics in the field of penetrated power grids in this brand-new interdisciplinary resource
Renewable Integrated Power System Stability and Control delivers a comprehensive exploration of penetrated grid dynamic analysis and new trends in power system modeling and dynamic equivalencing. The book summarizes long-term academic research outcomes and contributions and exploits the authors' extensive practical experiences in power system dynamics and stability to offer readers an insightful analysis of modern power grid infrastructure.
In addition to the basic principles of penetrated power system modeling, model reduction, and model derivation, the book discusses inertia challenge requirements and control levels, as well as recent advances in visualization of virtual synchronous generators and their associated effects on system performance. The physical constraints and engineering considerations of advanced control schemes are deliberated at length.
Renewable Integrated Power System Stability and Control also considers robust and adaptive control strategies using real-time simulations and experimental studies. Readers will benefit from the inclusion of:
* A thorough introduction to power systems, including time horizon studies, structure, power generation options, energy storage systems, and microgrids
* An exploration of renewable integrated power grid modeling, including basic principles, host grid modeling, and grid-connected MG equivalent models
* A study of virtual inertia, including grid stability enhancement, simulations, and experimental results
* A discussion of renewable integrated power grid stability and control, including small signal stability assessment and the frequency point of view
Perfect for engineers and operators in power grids, as well as academics studying the technology, Renewable Integrated Power System Stability and Control will also earn a place in the libraries of students in Electrical Engineering programs at the undergraduate and postgraduate levels who wish to improve their understanding of power system operation and control.
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Persons
Hêmin Golpîra, earned his PhD degree from Tarbiat Modares University, Tehran, Iran. Since 2016 he has been an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Kurdistan. He was formerly an Associate Fellow at the University of Wisconsin-Madison, USA, and a Visiting Professor at the École Centrale de Lille, France.
Arturo Román-Messina earned his PhD degree from Imperial College, London, UK. Since 1997 is a Professor at the Center for Research and Advanced Studies of the National Polytechnic Institute of Mexico. A Fellow of the IEEE, he is on the editorial and advisory boards of Electric Power Systems Research, and Electric Power Components and Systems.
Hassan Bevrani, PhD, is a Professor and Head of Smart/Micro Grids Research Center at the University of Kurdistan. He received his doctorate in Electrical Engineering from Osaka University in Japan. He is the author and co-author of more than 6 books, 15 book chapters, and 350 journal/conference papers.
Content
Preface xiii
Acknowledgments xvii
Nomenclature xviii
List of Abbreviations and Acronyms xxi
1 Introduction 1
1.1 Power System Stability and Control 1
1.2 Current State of Power System Stability and Control 4
1.2.1 Frequency Control 5
1.2.2 Voltage Control 6
1.2.3 Oscillation Damping 7
1.3 Data-Driven Wide-Area Power System Monitoring and Control 8
1.4 Dynamics Modeling and Parameters Estimation 10
1.4.1 Modeling of Frequency, Voltage, and Angle Controls 11
1.4.2 Parameters Estimation 12
1.5 Summary 14
References 14
2 MG Penetrated Power Grid Modeling 25
2.1 Introduction 25
2.2 Basic Concepts 26
2.2.1 Dynamic Equivalencing 26
2.2.2 Background on Study Zone and External System 27
2.3 Power Grid Modeling 28
2.3.1 The Notion of Center-of Gravity (COG) 28
2.3.1.1 Key Concept 28
2.3.1.2 Basic Assumptions 32
2.3.1.3 Modeling Formulation 32
2.3.1.4 Local Frequency Estimation 33
2.3.1.5 Simulation Results 35
2.3.2 An Enhanced COG-Based Model 46
2.3.2.1 Key Concept 46
2.3.2.2 Simulation Results 49
2.3.3 Generalized Equivalent Model 50
2.3.3.1 Basic Logic 50
2.3.3.2 Simulation and Results 51
2.4 MG Equivalent Model 53
2.4.1 Islanded Mode 54
2.4.1.1 Synchronous-Based DG 54
2.4.1.2 Genset Model Validation 57
2.4.1.3 Inverter-Based DG 58
2.4.1.4 Inverter-Based DG Model Validation 61
2.4.2 Grid-Connected Mode 61
2.4.2.1 Basic Logic 61
2.4.2.2 Model Validation 63
2.5 Summary 67
References 67
3 Stability Assessment of Power Grids with High Microgrid Penetration 71
3.1 Introduction 71
3.1.1 Motivation 71
3.1.2 Relations with Previous Literature 72
3.2 Frequency Stability Assessment 73
3.2.1 Background on Frequency Indices 73
3.2.1.1 Rate of Change of Frequency 73
3.2.1.2 Frequency Nadir 74
3.2.1.3 Delta Frequency Detection 74
3.2.2 Frequency Stability Assessment Under High MG Penetration Levels 74
3.2.3 Sensitivity Factors 74
3.2.3.1 Frequency Response 74
3.2.3.2 Delta Frequency Detection 77
3.2.4 Simulation and Results 78
3.3 Maximum Penetration Level: Frequency Stability 80
3.3.1 Basic Principle 80
3.3.2 Background on MG Modeling 81
3.3.3 Minimum Inertia Related to Frequency Nadir 82
3.3.4 Minimum Inertia Related to Delta Frequency Detection 84
3.3.5 Minimum Inertia Related to RoCoF 85
3.3.6 Maximum Penetration Level 86
3.3.7 Simulation and Results 86
3.3.7.1 Analysis Tools 86
3.3.7.2 Dynamical Simulation Results 86
3.4 Small-Signal Stability Assessment 90
3.4.1 Basic Definition 90
3.4.2 Key Concept 90
3.4.3 Simulation and Results 91
3.5 Maximum Penetration Level: Small-Signal Stability 93
3.5.1 Basic Idea 93
3.5.2 Simulation and Results 93
3.6 Voltage-Based Realization of the MG-Integrated Power Grid 94
3.6.1 Key Concepts 95
3.6.2 Jacobian Sensitivities 95
3.6.2.1 V-P Sensitivity 95
3.6.2.2 V-Q Sensitivity 96
3.6.3 Simulation and Results 97
3.7 Summary 99
References 100
4 Advanced Virtual Inertia Control and Optimal Placement 103
4.1 Introduction 103
4.2 Virtual Synchronous Generator 104
4.2.1 Concept and Structure 105
4.2.2 Basic Control Scheme and Applications 106
4.2.3 Application in Power System Dynamic Enhancement 108
4.2.3.1 Scenario 1: 10-MW Load Increase at Bus 9 109
4.2.3.2 Scenario 2: 20-MW Power Command Decrease of G3 109
4.2.4 Application to Power Grids with HVDC Systems 110
4.3 Dispatchable Inertia Placement 113
4.3.1 Frequency Dynamics Enhancement 113
4.3.1.1 Background: Literature Review 113
4.3.1.2 Virtual Inertia Modeling 114
4.3.1.3 Experimental Verification 116
4.3.1.4 Economic Modeling 119
4.3.1.5 Simulation and Results 124
4.3.1.6 Sensitivity Analysis 134
4.3.2 Small-Signal Stability 136
4.3.2.1 Objective Function 136
4.3.2.2 Simulation Results 137
4.4 Summary 139
References 139
5 Wide-Area Voltage Monitoring in High-Renewable Integrated Power Systems 145
5.1 Introduction 146
5.2 Voltage Control Areas: A Background 147
5.2.1 Voltage Sensitivities 148
5.2.2 Electrical Distances 149
5.2.3 Reactive Control Zones and Pilot Nodes 150
5.2.3.1 Selection of Optimal Pilot Buses 151
5.2.3.2 Selection of Control Plants 151
5.2.4 Other Approaches 152
5.3 Data-driven Approaches 153
5.3.1 Wide-Area Voltage and Reactive Power Regulation 154
5.3.2 PMU-Based Voltage Monitoring 155
5.4 Theoretical Framework 155
5.4.1 Dynamic Trajectories 156
5.4.2 Spectral Graph Theory 157
5.4.3 Kernel Methods 157
5.4.3.1 Markov Matrices 159
5.4.3.2 The Markov Clustering Algorithm 162
5.4.4 Spatiotemporal Clustering 164
5.5 Case Study 165
5.5.1 Sensitivity Studies 165
5.5.2 Data-Driven Analysis 169
5.5.3 Measurement-Based Reactive Control Areas 171
5.5.3.1 Diffusion Maps 171
5.5.4 Direct Clustering 175
5.5.5 Correlation Analysis 176
5.5.5.1 Direct Analysis of Concatenated Data 178
5.5.5.2 Two-Way Correlation Analysis 179
5.5.5.3 Partial Least Squares Correlation 179
5.6 Summary 181
References 181
6 Advanced Control Synthesis 185
6.1 Introduction 185
6.2 Frequency Dynamics Enhancement 186
6.2.1 Background: The Concept of Flexible Inertia 186
6.2.2 Frequency Dynamics Propagation 189
6.2.3 Inertia-Based Control Scheme 191
6.2.4 Flexible Inertia: Practical Considerations 192
6.2.5 Results and Discussions 194
6.3 Small Signal Stability Enhancement 200
6.3.1 Key Concept 200
6.3.2 Control Scheme Design 201
6.3.3 Simulation and Results 204
6.4 Summary 207
References 207
7 Small-Signal and Transient Stability Assessment Using Data-Driven Approaches 211
7.1 Background and Motivation 212
7.2 Modal Characterization Using Data-Driven Approaches 213
7.2.1 Modal Decomposition 213
7.2.2 Multisignal Prony Analysis 215
7.2.2.1 Standard Prony Analysis 215
7.2.2.2 Modified Least-Squares Algorithm 218
7.2.2.3 Multichannel Prony Analysis 219
7.2.2.4 Hankel-SVD Methods 221
7.2.3 Koopman and Dynamic Mode Decomposition Representations 222
7.2.3.1 The Koopman Operator 223
7.2.4 Dynamic Mode Decomposition 223
7.2.4.1 SVD-Based Methods 225
7.2.4.2 The Companion Matrix Approach 228
7.2.4.3 Energy Criteria 230
7.3 Studies of a Small-Scale Power System Model 231
7.3.1 System Data and Operating Scenarios 231
7.3.2 Exploratory Small-Signal Analysis 234
7.3.3 Large System Performance 236
7.3.3.1 Cases B-C 236
7.3.3.2 Case D 238
7.3.4 Mode Shape Identification 241
7.3.5 Temporal Clustering 242
7.4 Large-Scale System Study 244
7.4.1 Case Study Description 244
7.4.2 Renewable Generator Modeling 245
7.4.3 Effect of Inverter-Based DGs on Oscillatory Stability 245
7.4.4 Large System Performance 246
7.4.5 Model Validation 246
7.4.5.1 Reconstructed Flow Fields 250
7.4.6 Identification of Mode Shapes Using DMD 253
7.5 Analysis Results and Discussion 253
References 255
8 Solar and Wind Integration Case Studies 259
8.1 General Context and Motivation 259
8.2 Study System 261
8.3 Wind Power Integration in the South Systems 263
8.3.1 Study Region 263
8.3.2 Existing System Limitations 266
8.4 Impact of Increased Wind Penetration on the System Performance 266
8.4.1 Study Considerations and Scenario Development 266
8.4.2 Base Case Assessment 267
8.4.2.1 System Oscillatory Response 269
8.4.3 High Wind Penetration Case 271
8.5 Frequency Response 274
8.5.1 Frequency Variations 274
8.5.2 Wind and Hydropower Coordination 277
8.5.3 Response to Loss-of-Generation Events 280
8.6 Effect of Voltage Control on System Dynamic Performance 283
8.6.1 Voltage Support and Reactive Power Dispatch 283
8.6.2 Effect of Voltage Control Characteristics 283
8.7 Summary 288
References 288
Index 293
1
Introduction
The term power system stability and control is used to define the application of control theorems and relevant technologies to analyze and enhance the power system functions during normal and abnormal operations. Power system stability and control refers to keep desired performance and stabilizing power system following various disturbances, such as short circuits, loss of generation, and load.
The capacity of installed inverter-based distributed generators (DGs) and renewable energy sources (RESs) individually or through the microgrids (MGs) in power systems is rapidly growing, and a high penetration level is targeted for the next few decades. In most countries including developing countries, significant targets are considered for using the distributed microsources and MGs in their power systems for near future. The increase of DGs/RESs in power systems has a significant impact on CO2 reduction; however, recent studies have shown that relatively high DGs/RESs integration will have some negative impacts on power system dynamics, frequency and voltage regulation, as well as other control and operational issues. Decreasing system inertia and highly variable dynamic nature of DGs/RESs/MGs are known as the main reasons. These impacts may increase for the dynamically weak power systems at the penetration rates that are expected over the next several years.
In this chapter, a brief discussion on the power system stability and control in modern renewable integrated power systems and the current state of this topic are given. Data-driven wide-area power system monitoring and control is emphasized, and the significance of measurement-based dynamic modeling and parameter estimation is shown.
1.1 Power System Stability and Control
Power system stability and control was first recognized as an important problem in 1920s [1]. Over the years, numerous modeling/simulation programs, synthesis/analysis methodologies, and protection schemes have been developed. Power grid control must provide the ability of an electric power to regain a state of operating equilibrium after being subjected to a physical disturbance, with most system variables, i.e., frequency, voltage, and angle, bounded so that practically the entire system remains intact. Thus, the main control loops are known as frequency control, voltage control, and rotor angle (power oscillation damping) control [2].
In many power systems, advanced measurement devices such as phasor measurement units (PMUs) and modern communication devices are already being installed. Using these facilities, the parameters of existing power system controllers can be adjusted by an online data-driven control mechanism [3]. The PMU data after filtering are used to estimate some important parameters in the system (scheduling parameters). These parameters are then used in the control tuning algorithm that will adapt the controller parameters in frequency control, voltage control, and power oscillation control. Therefore, the controller's parameters are adapted according to the current status of the system.
One of the important steps of reliable and performant control system design is defining the performance specifications. It depends on the features of the controller design method, the constraints on the controller structure, the achievable performance that is limited by the physical constraints, the industrial standards on the limit of the variables, the limits of the actuators, etc. Finding the control specifications and making them compatible with the controller design approach require a deeper understanding of the physical system to be controlled.
The characteristics of three main control loops, i.e., frequency control, voltage control, and angle control, should be studied to enable the definition of achievable performance specifications and designing an effective control system.
- Frequency control: Since the frequency generated in an electric network is proportional to the rotation speed of the generator, the problem of frequency control may be directly translated into a speed control problem of the turbine generator unit. This is initially overcome by adding a governing mechanism that senses the machine speed and adjusts the input valve to change the mechanical power output to track the load change and to restore frequency to nominal value. Depending on the frequency deviation range, different frequency control loops, i.e., primary, secondary, and tertiary, may be required to maintain power system frequency stability [4].
The secondary frequency control which is also known as load frequency control (LFC) initializes a centralized and automatic control task using the assigned spinning reserve. The LFC is the main component of an automatic generation control (AGC) system [5]. In large power systems, this control loop is activated in the time frame of few seconds to minutes after a disturbance. In a modern AGC system, based on the received area control error (ACE) signal, an online tuning algorithm must adjust the LFC parameters to restore the frequency and tie-line powers to the specified values.
- Voltage control: The generators are usually operated at a constant voltage by using an automatic voltage regulator (AVR) which controls the excitation of the machine via the electric field exciter system. The exciter system supplies the field winding of the synchronous machine with direct current to generate required flux in the rotor. A system enters a state of voltage instability when a disturbance changes the system condition to make a progressive fall or rise of voltages of some buses. Loss of load in an area, tripping transmission lines, and other protected equipment are possible results of voltage instability. Like frequency control, the voltage control is also characterized via several control loops in different system levels. The AVR loop which regulated the voltage of generator terminals is located on lower system levels and responds typically in a time scale of a second or less.
- Angle control: Rotor angle stability is the ability of the power system to maintain synchronization after being subjected to a disturbance. Angle stability refers to damping of power oscillations inside subsystems and between subsystems on an interconnected grid during variation beyond specified threshold levels. The risk of losing angle stability can be significantly reduced by using proper control devices inserted into the power grid to find a smooth shape for the system dynamic response.
The power oscillation damping has been mainly guaranteed by power system stabilizers (PSSs). A PSS is a controller, which, beside the turbine-governing system, performs an additional supplementary control loop to the AVR system of a generating unit. Depending on the type of PSS, the input signal could be the rotor speed/frequency deviation, the generator active power deviation, or a combination feedback of rotor speed/frequency and active power changes. This signal to be passed through a combination of a lead-lag compensators. The PSS output signal is amplified to provide an effective output signal.
In order to damp the inter-area oscillations, which have smaller oscillation frequency than the local oscillatory modes, a wide-area control (WAC) system is required. The WAC system is a centralized controller that uses the PMU signals and produces auxiliary control signals for the PSSs.
- Virtual synchronous generator: Additional flexibility may be required from various control levels so that the system operator can continue to balance supply and demand on the modern power grids in the presence of DGs/RESs/MGs. The contribution of DGs/RESs in regulation task refers to the ability of these grids to regulate their power output, by an appropriate control action. This can be regarded as adding virtual inertia to the grid and considered as a solution. Virtual inertia emulation requires the inverter to be able to store or release an amount of energy depending on the grid frequency's deviation from its nominal value, analogous to the inertia of a conventional generator. This setup, which is known as virtual synchronous generator (VSG), will then operate to emulate desirable dynamics, such as inertia and damping properties, by flexible shaping of its output active and reactive powers as conceptually shown in Figure 1.1.
This VSG provides a promising solution to improve power grid stability and performance in the presence of a high penetration of DGs/RESs/MGs. The VSG is not only applicable for improving of frequency regulation and oscillations damping, particularly during the transient state following a disturbance, but also it is useful to support the voltage stability. The VSG system can use the available DGs/RESs, as primary sources to participate in power oscillation damping by adjusting their active and reactive power generations. The VSG is more discussed in Chapter 4.
1.2 Current State of Power System Stability and Control
Power system stability and control can take different forms, which are influenced by the type of instability phenomena. A survey on the basics of power system controls, literature, and achievements is given in [6, 7].
PMUs are sophisticated digital recording devices that communicate global positioning system (GPS) synchronized high sampling rate dynamic power system's data to the central control and monitoring stations. The recorded data by PMUs provide valuable information about the dynamic of the power system that can be used for...
