Renewable Integrated Power System Stability and Control by Hemin Golpira

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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