Microgrids : theory and practice / edited by Peng Zhang.

Contributor(s): Zhang, Peng, Dr [editor.]
Language: English Series: IEEE Press series on power and energy systems: 128.Publisher: Hoboken, New Jersey : John Wiley & Sons, Inc., [2024]Description: xli, 896 pages : illustrations (chiefly color)Content type: text Media type: unmediated Carrier type: volumeISBN: 9781119890850; 9781119890881; 1119890888; 9781119890874; 111989087X; 9781119890867; 1119890861Subject(s): Microgrids (Smart power grids)DDC classification: 621.31 LOC classification: TK3105 | .M557 2024
Contents:
About the Editor xxix -- List of Contributors xxxi -- Preface xxxix -- Acknowledgments xli -- 1 Introduction 1 Peng Zhang -- 1.1 Background 1 -- 1.2 Reader's Manual 2 -- 2 AI-Grid: AI-Enabled, Smart Programmable Microgrids 7 Peng Zhang, Yifan Zhou, Scott A. Smolka, Scott D. Stoller, Xin Wang, Rong Zhao, Tianyun Ling, Yucheng Xing, Shouvik Roy, and Amol Damare -- 2.1 Introduction 7 -- 2.2 AI-Grid Platform 8 -- 2.3 AI-Enabled, Provably Resilient NM Operations 9 -- 2.4 Resilient Modeling and Prediction of NM States Under Uncertainty 12 -- 2.5 Runtime Safety and Security Assurance for AI-Grid 20 -- 2.6 Software Platform for AI-Grid 41 -- 2.7 AI-Grid for Grid Modernization 55 -- 2.8 Exercises 55 -- References 55 -- 3 Distributed Power Flow and Continuation Power Flow for Steady-State Analysis of Microgrids 59 Fei Feng, Peng Zhang, and Yifan Zhou -- 3.1 Background 59 -- 3.2 Individual Microgrid Power Flow 60 -- 3.3 Networked Microgrids Power Flow 64 -- 3.4 Numerical Tests of Microgrid Power Flow 71 -- 3.5 Exercises 78 -- References 78 -- 4 State and Parameter Estimation for Microgrids 81 Yuzhang Lin, Yu Liu, Xiaonan Lu, and Heqing Huang -- 4.1 Introduction 81 -- 4.2 State and Parameter Estimation for Inverter-Based Resources 82 -- 4.3 State and Parameter Estimation for Network Components 94 -- 4.4 Conclusion 102 -- 4.5 Exercise 103 -- 4.6 Acknowledgment 103 -- References 103 -- 5 Eigenanalysis of Delayed Networked Microgrids 107 Lizhi Wang, Yifan Zhou, and Peng Zhang -- 5.1 Introduction 107 -- 5.2 Formulation of Delayed NMs 107 -- 5.3 Delayed NMs Eigenanalysis 110 -- 5.4 Case Study 111 -- 5.5 Conclusion 115 -- 5.6 Exercises 115 -- References 116 -- 6 AI-Enabled Dynamic Model Discovery of Networked Microgrids 119 Yifan Zhou and Peng Zhang -- 6.1 Preliminaries on ODE-Based Dynamical Modeling of NMs 119 -- 6.2 Physics-Data-Integrated ODE Model of NMs 124 -- 6.3 ODE-Net-Enabled Dynamic Model Discovery for Microgrids 126 -- 6.4 Physics-Informed Learning for ODE-Net-Enabled Dynamic Models 130 -- 6.5 Experiments 132 -- 6.6 Summary 139 -- 6.7 Exercises 139 -- References 139 -- 7 Transient Stability Analysis for Microgrids with Grid-Forming Converters 141 Xuheng Lin and Ziang Zhang -- 7.1 Background 141 -- 7.2 System Modeling 142 -- 7.3 Metric for Transient Stability 146 -- 7.4 Microgrid Transient Stability Analysis 147 -- 7.5 Conclusion and Future Directions 151 -- 7.6 Exercises 152 -- References 152 -- 8 Learning-Based Transient Stability Assessment of Networked Microgrids 155 Tong Huang -- 8.1 Motivation 155 -- 8.2 Networked Microgrid Dynamics 156 -- 8.3 Learning a Lyapunov Function 158 -- 8.4 Case Study 162 -- 8.5 Summary 164 -- 8.6 Exercises 164 -- References 164 -- 9 Microgrid Protection 167 �Rmulo G. Bainy and Brian K. Johnson -- 9.1 Introduction 167 -- 9.2 Protection Fundamentals 167 -- 9.3 Typical Microgrid Protection Schemes 180 -- 9.4 Challenges Posed by Microgrids 182 -- 9.5 Examples of Solutions in Practice 187 -- 9.6 Summary 192 -- 9.7 Exercises 192 -- References 194 -- 10 Microgrids Resilience: Definition, Measures, and Algorithms 197 Zhaohong Bie and Yiheng Bian -- 10.1 Background of Resilience and the Role of Microgrids 197 -- 10.2 Enhance Power System Resilience with Microgrids 199 -- 10.3 Future Challenges 216 -- 10.4 Exercises 216 -- References 217 -- 11 In Situ Resilience Quantification for Microgrids 219 Priyanka Mishra, Peng Zhang, Scott A. Smolka, Scott D. Stoller, Yifan Zhou, Yacov A. Shamash, Douglas L. Van Bossuyt, and William W. Anderson Jr. -- 11.1 Introduction 219 -- 11.2 STL-Enabled In Situ Resilience Evaluation 220 -- 11.3 Case Study 222 -- 11.4 Conclusion 227 -- 11.5 Exercises 227 -- 11.6 Acknowledgment 227 -- References 227 -- 12 Distributed Voltage Regulation of Multiple Coupled Distributed Generation Units in DC Microgrids: An Output Regulation Approach 229 Tingyang Meng, Zongli Lin, Yan Wan, and Yacov A. Shamash -- 12.1 Introduction 229 -- 12.2 Problem Statement 230 -- 12.3 Review of Output Regulation Theory 232 -- 12.4 Distributed Voltage Regulation in the Presence of Time-Varying Loads 239 -- 12.5 Simulation Results 241 -- 12.6 Conclusions 261 -- 12.7 Exercises 261 -- 12.8 Acknowledgment 262 -- References 262 -- 13 Droop-Free Distributed Control for AC Microgrids 265 Sheik M. Mohiuddin and Junjian Qi -- 13.1 Cyber-Physical Microgrid Modeling 265 -- 13.2 Hierarchical Control of Islanded Microgrid 267 -- 13.3 Droop-Free Distributed Control with Proportional Power Sharing 271 -- 13.4 Droop-Free Distributed Control with Voltage Profile Guarantees 273 -- 13.5 Steady-State Analysis for the Control in Section 13.4 277 -- 13.6 Microgrid Test System and Control Performance 279 -- 13.7 Steady-State Performance Under Different Loading Conditions and Controller Settings 282 -- 13.8 Exercises 284 -- References 284 -- 14 Optimal Distributed Control of AC Microgrids 287 Sheik M. Mohiuddin and Junjian Qi -- 14.1 Optimization Problem for Secondary Control 287 -- 14.2 Primal-Dual Gradient Based Distributed Solving Algorithm 291 -- 14.3 Microgrid Test Systems 297 -- 14.4 Control Performance on 4-DG System 298 -- 14.5 Control Performance on IEEE 34-Bus System 300 -- 14.6 Exercises 304 -- References 304 -- 15 Cyber-Resilient Distributed Microgrid Control 307 Pouya Babahajiani and Peng Zhang -- 15.1 Push-Sum Enabled Resilient Microgrid Control 307 -- 15.2 Employing Interacting Qubits for Distributed Microgrid Control 313 -- References 330 -- 16 Programmable Crypto-Control for Networked Microgrids 335 Lizhi Wang, Peng Zhang, and Zefan Tang -- 16.1 Introduction 335 -- 16.2 PCNMs and Privacy Requirements 336 -- 16.3 Dynamic Encrypted Weighted Addition 340 -- 16.4 DEWA Privacy Analysis 343 -- 16.5 Case Studies 345 -- 16.6 Conclusion 354 -- 16.7 Exercises 355 -- References 355 -- 17 AI-Enabled, Cooperative Control, and Optimization in Microgrids 359 Ning Zhang, Lingxiao Yang, and Qiuye Sun -- 17.1 Introduction 359 -- 17.2 Energy Hub Model in Microgirds 360 -- 17.3 Distributed Adaptive Cooperative Control in Microgrids 361 -- 17.4 Optimal Energy Operation in Microgrids Based on Hybrid Reinforcement Learning 369 -- 17.5 Conclusion 384 -- 17.6 Exercises 384 -- References 385 -- 18 DNN-Based EV Scheduling Learning for Transactive Control Framework 387 Aysegul Kahraman and Guangya Yang -- 18.1 Introduction 387 -- 18.2 Transactive Control Formulation 388 -- 18.3 Proposed Deep Neural Networks in Transactive Control 391 -- 18.4 Case Study 392 -- 18.5 Simulation Results and Discussion 394 -- 18.6 Conclusion 396 -- 18.7 Exercises 398 -- References 398 -- 19 Resilient Sensing and Communication Architecture for Microgrid Management 401 Yuzhang Lin, Vinod M. Vokkarane, Md. Zahidul Islam, and Shamsun Nahar Edib -- 19.1 Introduction 401 -- 19.2 Resilient Sensing and Communication Network Planning Against Multidomain Failures 404 -- 19.3 Observability-Aware Network Routing for Fast and Resilient Microgrid Monitoring 412 -- 19.4 Conclusion 420 -- 19.5 Exercises 420 -- References 422 -- 20 Resilient Networked Microgrids Against Unbounded Attacks 425 Shan Zuo, Tuncay Altun, Frank L. Lewis, and Ali Davoudi -- 20.1 Introduction 425 -- 20.2 Adaptive Resilient Control of AC Microgrids Under Unbounded Actuator Attacks 427 -- 20.3 Distributed Resilient Secondary Control of DC Microgrids Against Unbounded Attacks 437 -- 20.4 Conclusion 449 -- 20.5 Acknowledgment 451 -- 20.6 Exercises 451 -- References 453 -- 21 Quantum Security for Microgrids 457 Zefan Tang and Peng Zhang -- 21.1 Background 457 -- 21.2 Quantum Communication for Microgrids 459 -- 21.3 The QKD Simulator 463 -- 21.4 Quantum-Secure Microgrid 467 -- 21.5 Quantum-Secure NMs 471 -- 21.6 Experimental Results 474 -- 21.7 Future Perspectives 481 -- 21.8 Summary 483 -- 21.9 Exercises 483 -- References 484 -- 22 Community Microgrid Dynamic and Power Quality Design Issues 487 Phil Barker, Tom Ortmeyer, and Clayton Burns -- 22.1 Introduction 487 -- 22.2 Potsdam Resilient Microgrid Overview 488 -- 22.3 Power Quality Parameters and Guidelines 490 -- 22.4 Microgrid Analytical Methods 498 -- 22.5 Analysis of Grid Parallel Microgrid Operation 499 -- 22.6 Fault Current Contributions and Grounding 515 -- 22.7 Microgrid Operation in Islanded Mode 529 -- 22.8 Conclusions and Recommendations 551 -- 22.9 Exercises 552 -- 22.10 Acknowledgment 553 -- References 553 -- 23 A Time of Energy Transition at Princeton University 555 Edward T. Borer, Jr.
-- 23.1 Introduction 555 -- 23.2 Cogeneration 556 -- 23.3 The Magic of The Refrigeration Cycle 560 -- 23.4 Capturing Heat, Not Wasting It 562 -- 23.5 Multiple Forms of Energy Storage 565 -- 23.6 Daily Thermal Storage - Chilled or Hot Water 569 -- 23.7 Seasonal Thermal Storage - Geoexchange 571 -- 23.8 Moving to Renewable Electricity as the Main Energy Input 574 -- 23.9 Water Use Reduction 575 -- 23.10 Closing Comments 577 -- 24 Considerations for Digital Real-Time Simulation, Control-HIL, and Power-HIL in Microgrids/DER Studies 579 Juan F. Patarroyo, Joel Pfannschmidt, K. S. ..
Summary: "A microgrid is a decentralized group of electricity sources and loads that normally operates, connected to and synchronous with the traditional wide area synchronous grid (macrogrid), but is able to disconnect from the interconnected grid and to function autonomously in "island mode" as technical or economic conditions dictate. Another use case is the off-grid application, it is called an autonomous, stand-alone or isolated microgrid. These microgrids are best served by local energy sources where power transmission and distribution from a major centralized energy source is too far and costly to execute. They offer an option for rural electrification in remote areas and on smaller geographical islands. As a controllable entity, a microgrid can effectively integrate various sources of distributed generation (DG), especially renewable energy sources (RES)."-- Provided by publisher.
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Includes bibliographical references and index.

About the Editor xxix -- List of Contributors xxxi -- Preface xxxix -- Acknowledgments xli -- 1 Introduction 1 Peng Zhang -- 1.1 Background 1 -- 1.2 Reader's Manual 2 -- 2 AI-Grid: AI-Enabled, Smart Programmable Microgrids 7 Peng Zhang, Yifan Zhou, Scott A. Smolka, Scott D. Stoller, Xin Wang, Rong Zhao, Tianyun Ling, Yucheng Xing, Shouvik Roy, and Amol Damare -- 2.1 Introduction 7 -- 2.2 AI-Grid Platform 8 -- 2.3 AI-Enabled, Provably Resilient NM Operations 9 -- 2.4 Resilient Modeling and Prediction of NM States Under Uncertainty 12 -- 2.5 Runtime Safety and Security Assurance for AI-Grid 20 -- 2.6 Software Platform for AI-Grid 41 -- 2.7 AI-Grid for Grid Modernization 55 -- 2.8 Exercises 55 -- References 55 -- 3 Distributed Power Flow and Continuation Power Flow for Steady-State Analysis of Microgrids 59 Fei Feng, Peng Zhang, and Yifan Zhou -- 3.1 Background 59 -- 3.2 Individual Microgrid Power Flow 60 -- 3.3 Networked Microgrids Power Flow 64 -- 3.4 Numerical Tests of Microgrid Power Flow 71 -- 3.5 Exercises 78 -- References 78 -- 4 State and Parameter Estimation for Microgrids 81 Yuzhang Lin, Yu Liu, Xiaonan Lu, and Heqing Huang -- 4.1 Introduction 81 -- 4.2 State and Parameter Estimation for Inverter-Based Resources 82 -- 4.3 State and Parameter Estimation for Network Components 94 -- 4.4 Conclusion 102 -- 4.5 Exercise 103 -- 4.6 Acknowledgment 103 -- References 103 -- 5 Eigenanalysis of Delayed Networked Microgrids 107 Lizhi Wang, Yifan Zhou, and Peng Zhang -- 5.1 Introduction 107 -- 5.2 Formulation of Delayed NMs 107 -- 5.3 Delayed NMs Eigenanalysis 110 -- 5.4 Case Study 111 -- 5.5 Conclusion 115 -- 5.6 Exercises 115 -- References 116 -- 6 AI-Enabled Dynamic Model Discovery of Networked Microgrids 119 Yifan Zhou and Peng Zhang -- 6.1 Preliminaries on ODE-Based Dynamical Modeling of NMs 119 -- 6.2 Physics-Data-Integrated ODE Model of NMs 124 -- 6.3 ODE-Net-Enabled Dynamic Model Discovery for Microgrids 126 -- 6.4 Physics-Informed Learning for ODE-Net-Enabled Dynamic Models 130 -- 6.5 Experiments 132 -- 6.6 Summary 139 -- 6.7 Exercises 139 -- References 139 -- 7 Transient Stability Analysis for Microgrids with Grid-Forming Converters 141 Xuheng Lin and Ziang Zhang -- 7.1 Background 141 -- 7.2 System Modeling 142 -- 7.3 Metric for Transient Stability 146 -- 7.4 Microgrid Transient Stability Analysis 147 -- 7.5 Conclusion and Future Directions 151 -- 7.6 Exercises 152 -- References 152 -- 8 Learning-Based Transient Stability Assessment of Networked Microgrids 155 Tong Huang -- 8.1 Motivation 155 -- 8.2 Networked Microgrid Dynamics 156 -- 8.3 Learning a Lyapunov Function 158 -- 8.4 Case Study 162 -- 8.5 Summary 164 -- 8.6 Exercises 164 -- References 164 -- 9 Microgrid Protection 167 �Rmulo G. Bainy and Brian K. Johnson -- 9.1 Introduction 167 -- 9.2 Protection Fundamentals 167 -- 9.3 Typical Microgrid Protection Schemes 180 -- 9.4 Challenges Posed by Microgrids 182 -- 9.5 Examples of Solutions in Practice 187 -- 9.6 Summary 192 -- 9.7 Exercises 192 -- References 194 -- 10 Microgrids Resilience: Definition, Measures, and Algorithms 197 Zhaohong Bie and Yiheng Bian -- 10.1 Background of Resilience and the Role of Microgrids 197 -- 10.2 Enhance Power System Resilience with Microgrids 199 -- 10.3 Future Challenges 216 -- 10.4 Exercises 216 -- References 217 -- 11 In Situ Resilience Quantification for Microgrids 219 Priyanka Mishra, Peng Zhang, Scott A. Smolka, Scott D. Stoller, Yifan Zhou, Yacov A. Shamash, Douglas L. Van Bossuyt, and William W. Anderson Jr. -- 11.1 Introduction 219 -- 11.2 STL-Enabled In Situ Resilience Evaluation 220 -- 11.3 Case Study 222 -- 11.4 Conclusion 227 -- 11.5 Exercises 227 -- 11.6 Acknowledgment 227 -- References 227 -- 12 Distributed Voltage Regulation of Multiple Coupled Distributed Generation Units in DC Microgrids: An Output Regulation Approach 229 Tingyang Meng, Zongli Lin, Yan Wan, and Yacov A. Shamash -- 12.1 Introduction 229 -- 12.2 Problem Statement 230 -- 12.3 Review of Output Regulation Theory 232 -- 12.4 Distributed Voltage Regulation in the Presence of Time-Varying Loads 239 -- 12.5 Simulation Results 241 -- 12.6 Conclusions 261 -- 12.7 Exercises 261 -- 12.8 Acknowledgment 262 -- References 262 -- 13 Droop-Free Distributed Control for AC Microgrids 265 Sheik M. Mohiuddin and Junjian Qi -- 13.1 Cyber-Physical Microgrid Modeling 265 -- 13.2 Hierarchical Control of Islanded Microgrid 267 -- 13.3 Droop-Free Distributed Control with Proportional Power Sharing 271 -- 13.4 Droop-Free Distributed Control with Voltage Profile Guarantees 273 -- 13.5 Steady-State Analysis for the Control in Section 13.4 277 -- 13.6 Microgrid Test System and Control Performance 279 -- 13.7 Steady-State Performance Under Different Loading Conditions and Controller Settings 282 -- 13.8 Exercises 284 -- References 284 -- 14 Optimal Distributed Control of AC Microgrids 287 Sheik M. Mohiuddin and Junjian Qi -- 14.1 Optimization Problem for Secondary Control 287 -- 14.2 Primal-Dual Gradient Based Distributed Solving Algorithm 291 -- 14.3 Microgrid Test Systems 297 -- 14.4 Control Performance on 4-DG System 298 -- 14.5 Control Performance on IEEE 34-Bus System 300 -- 14.6 Exercises 304 -- References 304 -- 15 Cyber-Resilient Distributed Microgrid Control 307 Pouya Babahajiani and Peng Zhang -- 15.1 Push-Sum Enabled Resilient Microgrid Control 307 -- 15.2 Employing Interacting Qubits for Distributed Microgrid Control 313 -- References 330 -- 16 Programmable Crypto-Control for Networked Microgrids 335 Lizhi Wang, Peng Zhang, and Zefan Tang -- 16.1 Introduction 335 -- 16.2 PCNMs and Privacy Requirements 336 -- 16.3 Dynamic Encrypted Weighted Addition 340 -- 16.4 DEWA Privacy Analysis 343 -- 16.5 Case Studies 345 -- 16.6 Conclusion 354 -- 16.7 Exercises 355 -- References 355 -- 17 AI-Enabled, Cooperative Control, and Optimization in Microgrids 359 Ning Zhang, Lingxiao Yang, and Qiuye Sun -- 17.1 Introduction 359 -- 17.2 Energy Hub Model in Microgirds 360 -- 17.3 Distributed Adaptive Cooperative Control in Microgrids 361 -- 17.4 Optimal Energy Operation in Microgrids Based on Hybrid Reinforcement Learning 369 -- 17.5 Conclusion 384 -- 17.6 Exercises 384 -- References 385 -- 18 DNN-Based EV Scheduling Learning for Transactive Control Framework 387 Aysegul Kahraman and Guangya Yang -- 18.1 Introduction 387 -- 18.2 Transactive Control Formulation 388 -- 18.3 Proposed Deep Neural Networks in Transactive Control 391 -- 18.4 Case Study 392 -- 18.5 Simulation Results and Discussion 394 -- 18.6 Conclusion 396 -- 18.7 Exercises 398 -- References 398 -- 19 Resilient Sensing and Communication Architecture for Microgrid Management 401 Yuzhang Lin, Vinod M. Vokkarane, Md. Zahidul Islam, and Shamsun Nahar Edib -- 19.1 Introduction 401 -- 19.2 Resilient Sensing and Communication Network Planning Against Multidomain Failures 404 -- 19.3 Observability-Aware Network Routing for Fast and Resilient Microgrid Monitoring 412 -- 19.4 Conclusion 420 -- 19.5 Exercises 420 -- References 422 -- 20 Resilient Networked Microgrids Against Unbounded Attacks 425 Shan Zuo, Tuncay Altun, Frank L. Lewis, and Ali Davoudi -- 20.1 Introduction 425 -- 20.2 Adaptive Resilient Control of AC Microgrids Under Unbounded Actuator Attacks 427 -- 20.3 Distributed Resilient Secondary Control of DC Microgrids Against Unbounded Attacks 437 -- 20.4 Conclusion 449 -- 20.5 Acknowledgment 451 -- 20.6 Exercises 451 -- References 453 -- 21 Quantum Security for Microgrids 457 Zefan Tang and Peng Zhang -- 21.1 Background 457 -- 21.2 Quantum Communication for Microgrids 459 -- 21.3 The QKD Simulator 463 -- 21.4 Quantum-Secure Microgrid 467 -- 21.5 Quantum-Secure NMs 471 -- 21.6 Experimental Results 474 -- 21.7 Future Perspectives 481 -- 21.8 Summary 483 -- 21.9 Exercises 483 -- References 484 -- 22 Community Microgrid Dynamic and Power Quality Design Issues 487 Phil Barker, Tom Ortmeyer, and Clayton Burns -- 22.1 Introduction 487 -- 22.2 Potsdam Resilient Microgrid Overview 488 -- 22.3 Power Quality Parameters and Guidelines 490 -- 22.4 Microgrid Analytical Methods 498 -- 22.5 Analysis of Grid Parallel Microgrid Operation 499 -- 22.6 Fault Current Contributions and Grounding 515 -- 22.7 Microgrid Operation in Islanded Mode 529 -- 22.8 Conclusions and Recommendations 551 -- 22.9 Exercises 552 -- 22.10 Acknowledgment 553 -- References 553 -- 23 A Time of Energy Transition at Princeton University 555 Edward T. Borer, Jr.

-- 23.1 Introduction 555 -- 23.2 Cogeneration 556 -- 23.3 The Magic of The Refrigeration Cycle 560 -- 23.4 Capturing Heat, Not Wasting It 562 -- 23.5 Multiple Forms of Energy Storage 565 -- 23.6 Daily Thermal Storage - Chilled or Hot Water 569 -- 23.7 Seasonal Thermal Storage - Geoexchange 571 -- 23.8 Moving to Renewable Electricity as the Main Energy Input 574 -- 23.9 Water Use Reduction 575 -- 23.10 Closing Comments 577 -- 24 Considerations for Digital Real-Time Simulation, Control-HIL, and Power-HIL in Microgrids/DER Studies 579 Juan F. Patarroyo, Joel Pfannschmidt, K. S. ..

Available to OhioLINK libraries.

"A microgrid is a decentralized group of electricity sources and loads that normally operates, connected to and synchronous with the traditional wide area synchronous grid (macrogrid), but is able to disconnect from the interconnected grid and to function autonomously in "island mode" as technical or economic conditions dictate. Another use case is the off-grid application, it is called an autonomous, stand-alone or isolated microgrid. These microgrids are best served by local energy sources where power transmission and distribution from a major centralized energy source is too far and costly to execute. They offer an option for rural electrification in remote areas and on smaller geographical islands. As a controllable entity, a microgrid can effectively integrate various sources of distributed generation (DG), especially renewable energy sources (RES)."-- Provided by publisher.

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