{"product_id":"microgrids-isbn-9781119890850","title":"Microgrids","description":"\u003cb\u003eMicrogrids\u003c\/b\u003e \u003cp\u003e \u003cb\u003eUnderstand microgrids and networked microgrid systems\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eMicrogrids are interconnected groups of energy sources that operate together, capable of connecting with a larger grid or operating independently as needed and network conditions require. They can be valuable sources of energy for geographically circumscribed areas with highly targeted energy needs, and for remote or rural areas where continuous connection with a larger grid is difficult. Microgrids’ controllability makes them especially effective at incorporating renewable energy sources. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMicrogrids: Theory and Practice \u003c\/i\u003eintroduces readers to the analysis, design, and operation of microgrids and larger networked systems that integrate them. It brings to bear both cutting-edge research into microgrid technology and years of industry experience in designing and operating microgrids. Its discussions of core subjects such as microgrid modeling, control, and optimization make it an essential short treatment, valuable for both academic and industrial study. Readers will acquire the skills needed to address existing problems and meet new ones as this crucial area of power engineering develops. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMicrogrids: Theory and Practice \u003c\/i\u003ealso features:  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eIncorporation of new cyber-physical system technologies for enabling microgrids as resiliency resources \u003c\/li\u003e\n\u003cli\u003eTheoretical treatment of a wide range of subjects including smart programmable microgrids, distributed and asynchronous optimization for microgrid dispatch, and AI-assisted microgrid protection \u003c\/li\u003e\n\u003cli\u003ePractical discussion of real-time microgrids simulations, hybrid microgrid design, transition to renewable microgrid networks, and more\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMicrogrids: Theory and Practice \u003c\/i\u003eis ideal as a textbook for graduate and advanced undergraduate courses in power engineering programs, and a valuable reference for power industry professionals looking to address the challenges posed by microgrids in their work. \u003c\/p\u003e\u003cp\u003eAbout the Editor xxix\u003c\/p\u003e \u003cp\u003eList of Contributors xxxi\u003c\/p\u003e \u003cp\u003ePreface xxxix\u003c\/p\u003e \u003cp\u003eAcknowledgments xli\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePeng Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Background 1\u003c\/p\u003e \u003cp\u003e1.2 Reader’s Manual 2\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 AI-Grid: AI-Enabled, Smart Programmable Microgrids 7\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePeng Zhang, Yifan Zhou, Scott A. Smolka, Scott D. Stoller, Xin Wang, Rong Zhao, Tianyun Ling, Yucheng Xing, Shouvik Roy, and Amol Damare\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 7\u003c\/p\u003e \u003cp\u003e2.2 AI-Grid Platform 8\u003c\/p\u003e \u003cp\u003e2.3 AI-Enabled, Provably Resilient NM Operations 9\u003c\/p\u003e \u003cp\u003e2.4 Resilient Modeling and Prediction of NM States Under Uncertainty 12\u003c\/p\u003e \u003cp\u003e2.5 Runtime Safety and Security Assurance for AI-Grid 20\u003c\/p\u003e \u003cp\u003e2.6 Software Platform for AI-Grid 41\u003c\/p\u003e \u003cp\u003e2.7 AI-Grid for Grid Modernization 55\u003c\/p\u003e \u003cp\u003e2.8 Exercises 55\u003c\/p\u003e \u003cp\u003eReferences 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Distributed Power Flow and Continuation Power Flow for Steady-State Analysis of Microgrids 59\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFei Feng, Peng Zhang, and Yifan Zhou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Background 59\u003c\/p\u003e \u003cp\u003e3.2 Individual Microgrid Power Flow 60\u003c\/p\u003e \u003cp\u003e3.3 Networked Microgrids Power Flow 64\u003c\/p\u003e \u003cp\u003e3.4 Numerical Tests of Microgrid Power Flow 71\u003c\/p\u003e \u003cp\u003e3.5 Exercises 78\u003c\/p\u003e \u003cp\u003eReferences 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 State and Parameter Estimation for Microgrids 81\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYuzhang Lin, Yu Liu, Xiaonan Lu, and Heqing Huang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 81\u003c\/p\u003e \u003cp\u003e4.2 State and Parameter Estimation for Inverter-Based Resources 82\u003c\/p\u003e \u003cp\u003e4.3 State and Parameter Estimation for Network Components 94\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 102\u003c\/p\u003e \u003cp\u003e4.5 Exercise 103\u003c\/p\u003e \u003cp\u003e4.6 Acknowledgment 103\u003c\/p\u003e \u003cp\u003eReferences 103\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Eigenanalysis of Delayed Networked Microgrids 107\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLizhi Wang, Yifan Zhou, and Peng Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 107\u003c\/p\u003e \u003cp\u003e5.2 Formulation of Delayed NMs 107\u003c\/p\u003e \u003cp\u003e5.3 Delayed NMs Eigenanalysis 110\u003c\/p\u003e \u003cp\u003e5.4 Case Study 111\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 115\u003c\/p\u003e \u003cp\u003e5.6 Exercises 115\u003c\/p\u003e \u003cp\u003eReferences 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 AI-Enabled Dynamic Model Discovery of Networked Microgrids 119\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYifan Zhou and Peng Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Preliminaries on ODE-Based Dynamical Modeling of NMs 119\u003c\/p\u003e \u003cp\u003e6.2 Physics-Data-Integrated ODE Model of NMs 124\u003c\/p\u003e \u003cp\u003e6.3 ODE-Net-Enabled Dynamic Model Discovery for Microgrids 126\u003c\/p\u003e \u003cp\u003e6.4 Physics-Informed Learning for ODE-Net-Enabled Dynamic Models 130\u003c\/p\u003e \u003cp\u003e6.5 Experiments 132\u003c\/p\u003e \u003cp\u003e6.6 Summary 139\u003c\/p\u003e \u003cp\u003e6.7 Exercises 139\u003c\/p\u003e \u003cp\u003eReferences 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Transient Stability Analysis for Microgrids with Grid-Forming Converters 141\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eXuheng Lin and Ziang Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Background 141\u003c\/p\u003e \u003cp\u003e7.2 System Modeling 142\u003c\/p\u003e \u003cp\u003e7.3 Metric for Transient Stability 146\u003c\/p\u003e \u003cp\u003e7.4 Microgrid Transient Stability Analysis 147\u003c\/p\u003e \u003cp\u003e7.5 Conclusion and Future Directions 151\u003c\/p\u003e \u003cp\u003e7.6 Exercises 152\u003c\/p\u003e \u003cp\u003eReferences 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Learning-Based Transient Stability Assessment of Networked Microgrids 155\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTong Huang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Motivation 155\u003c\/p\u003e \u003cp\u003e8.2 Networked Microgrid Dynamics 156\u003c\/p\u003e \u003cp\u003e8.3 Learning a Lyapunov Function 158\u003c\/p\u003e \u003cp\u003e8.4 Case Study 162\u003c\/p\u003e \u003cp\u003e8.5 Summary 164\u003c\/p\u003e \u003cp\u003e8.6 Exercises 164\u003c\/p\u003e \u003cp\u003eReferences 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Microgrid Protection 167\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRômulo G. Bainy and Brian K. Johnson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 167\u003c\/p\u003e \u003cp\u003e9.2 Protection Fundamentals 167\u003c\/p\u003e \u003cp\u003e9.3 Typical Microgrid Protection Schemes 180\u003c\/p\u003e \u003cp\u003e9.4 Challenges Posed by Microgrids 182\u003c\/p\u003e \u003cp\u003e9.5 Examples of Solutions in Practice 187\u003c\/p\u003e \u003cp\u003e9.6 Summary 192\u003c\/p\u003e \u003cp\u003e9.7 Exercises 192\u003c\/p\u003e \u003cp\u003eReferences 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Microgrids Resilience: Definition, Measures, and Algorithms 197\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eZhaohong Bie and Yiheng Bian\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Background of Resilience and the Role of Microgrids 197\u003c\/p\u003e \u003cp\u003e10.2 Enhance Power System Resilience with Microgrids 199\u003c\/p\u003e \u003cp\u003e10.3 Future Challenges 216\u003c\/p\u003e \u003cp\u003e10.4 Exercises 216\u003c\/p\u003e \u003cp\u003eReferences 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 In Situ Resilience Quantification for Microgrids 219\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePriyanka Mishra, Peng Zhang, Scott A. Smolka, Scott D. Stoller, Yifan Zhou, Yacov A. Shamash, Douglas L. Van Bossuyt, and William W. Anderson Jr.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 219\u003c\/p\u003e \u003cp\u003e11.2 STL-Enabled In Situ Resilience Evaluation 220\u003c\/p\u003e \u003cp\u003e11.3 Case Study 222\u003c\/p\u003e \u003cp\u003e11.4 Conclusion 227\u003c\/p\u003e \u003cp\u003e11.5 Exercises 227\u003c\/p\u003e \u003cp\u003e11.6 Acknowledgment 227\u003c\/p\u003e \u003cp\u003eReferences 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Distributed Voltage Regulation of Multiple Coupled Distributed Generation Units in DC Microgrids: An Output Regulation Approach 229\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTingyang Meng, Zongli Lin, Yan Wan, and Yacov A. Shamash\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 229\u003c\/p\u003e \u003cp\u003e12.2 Problem Statement 230\u003c\/p\u003e \u003cp\u003e12.3 Review of Output Regulation Theory 232\u003c\/p\u003e \u003cp\u003e12.4 Distributed Voltage Regulation in the Presence of Time-Varying Loads 239\u003c\/p\u003e \u003cp\u003e12.5 Simulation Results 241\u003c\/p\u003e \u003cp\u003e12.6 Conclusions 261\u003c\/p\u003e \u003cp\u003e12.7 Exercises 261\u003c\/p\u003e \u003cp\u003e12.8 Acknowledgment 262\u003c\/p\u003e \u003cp\u003eReferences 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Droop-Free Distributed Control for AC Microgrids 265\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSheik M. Mohiuddin and Junjian Qi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Cyber-Physical Microgrid Modeling 265\u003c\/p\u003e \u003cp\u003e13.2 Hierarchical Control of Islanded Microgrid 267\u003c\/p\u003e \u003cp\u003e13.3 Droop-Free Distributed Control with Proportional Power Sharing 271\u003c\/p\u003e \u003cp\u003e13.4 Droop-Free Distributed Control with Voltage Profile Guarantees 273\u003c\/p\u003e \u003cp\u003e13.5 Steady-State Analysis for the Control in Section 13.4 277\u003c\/p\u003e \u003cp\u003e13.6 Microgrid Test System and Control Performance 279\u003c\/p\u003e \u003cp\u003e13.7 Steady-State Performance Under Different Loading Conditions and Controller Settings 282\u003c\/p\u003e \u003cp\u003e13.8 Exercises 284\u003c\/p\u003e \u003cp\u003eReferences 284\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Optimal Distributed Control of AC Microgrids 287\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSheik M. Mohiuddin and Junjian Qi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Optimization Problem for Secondary Control 287\u003c\/p\u003e \u003cp\u003e14.2 Primal–Dual Gradient Based Distributed Solving Algorithm 291\u003c\/p\u003e \u003cp\u003e14.3 Microgrid Test Systems 297\u003c\/p\u003e \u003cp\u003e14.4 Control Performance on 4-DG System 298\u003c\/p\u003e \u003cp\u003e14.5 Control Performance on IEEE 34-Bus System 300\u003c\/p\u003e \u003cp\u003e14.6 Exercises 304\u003c\/p\u003e \u003cp\u003eReferences 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Cyber-Resilient Distributed Microgrid Control 307\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePouya Babahajiani and Peng Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Push-Sum Enabled Resilient Microgrid Control 307\u003c\/p\u003e \u003cp\u003e15.2 Employing Interacting Qubits for Distributed Microgrid Control 313\u003c\/p\u003e \u003cp\u003eReferences 330\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Programmable Crypto-Control for Networked Microgrids 335\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLizhi Wang, Peng Zhang, and Zefan Tang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 335\u003c\/p\u003e \u003cp\u003e16.2 PCNMs and Privacy Requirements 336\u003c\/p\u003e \u003cp\u003e16.3 Dynamic Encrypted Weighted Addition 340\u003c\/p\u003e \u003cp\u003e16.4 DEWA Privacy Analysis 343\u003c\/p\u003e \u003cp\u003e16.5 Case Studies 345\u003c\/p\u003e \u003cp\u003e16.6 Conclusion 354\u003c\/p\u003e \u003cp\u003e16.7 Exercises 355\u003c\/p\u003e \u003cp\u003eReferences 355\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 AI-Enabled, Cooperative Control, and Optimization in Microgrids 359\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNing Zhang, Lingxiao Yang, and Qiuye Sun\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 359\u003c\/p\u003e \u003cp\u003e17.2 Energy Hub Model in Microgirds 360\u003c\/p\u003e \u003cp\u003e17.3 Distributed Adaptive Cooperative Control in Microgrids 361\u003c\/p\u003e \u003cp\u003e17.4 Optimal Energy Operation in Microgrids Based on Hybrid Reinforcement Learning 369\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 384\u003c\/p\u003e \u003cp\u003e17.6 Exercises 384\u003c\/p\u003e \u003cp\u003eReferences 385\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 DNN-Based EV Scheduling Learning for Transactive Control Framework 387\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAysegul Kahraman and Guangya Yang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 387\u003c\/p\u003e \u003cp\u003e18.2 Transactive Control Formulation 388\u003c\/p\u003e \u003cp\u003e18.3 Proposed Deep Neural Networks in Transactive Control 391\u003c\/p\u003e \u003cp\u003e18.4 Case Study 392\u003c\/p\u003e \u003cp\u003e18.5 Simulation Results and Discussion 394\u003c\/p\u003e \u003cp\u003e18.6 Conclusion 396\u003c\/p\u003e \u003cp\u003e18.7 Exercises 398\u003c\/p\u003e \u003cp\u003eReferences 398\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Resilient Sensing and Communication Architecture for Microgrid Management 401\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYuzhang Lin, Vinod M. Vokkarane, Md. Zahidul Islam, and Shamsun Nahar Edib\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 401\u003c\/p\u003e \u003cp\u003e19.2 Resilient Sensing and Communication Network Planning Against Multidomain Failures 404\u003c\/p\u003e \u003cp\u003e19.3 Observability-Aware Network Routing for Fast and Resilient Microgrid Monitoring 412\u003c\/p\u003e \u003cp\u003e19.4 Conclusion 420\u003c\/p\u003e \u003cp\u003e19.5 Exercises 420\u003c\/p\u003e \u003cp\u003eReferences 422\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Resilient Networked Microgrids Against Unbounded Attacks 425\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShan Zuo, Tuncay Altun, Frank L. Lewis, and Ali Davoudi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 425\u003c\/p\u003e \u003cp\u003e20.2 Adaptive Resilient Control of AC Microgrids Under Unbounded Actuator Attacks 427\u003c\/p\u003e \u003cp\u003e20.3 Distributed Resilient Secondary Control of DC Microgrids Against Unbounded Attacks 437\u003c\/p\u003e \u003cp\u003e20.4 Conclusion 449\u003c\/p\u003e \u003cp\u003e20.5 Acknowledgment 451\u003c\/p\u003e \u003cp\u003e20.6 Exercises 451\u003c\/p\u003e \u003cp\u003eReferences 453\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Quantum Security for Microgrids 457\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eZefan Tang and Peng Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Background 457\u003c\/p\u003e \u003cp\u003e21.2 Quantum Communication for Microgrids 459\u003c\/p\u003e \u003cp\u003e21.3 The QKD Simulator 463\u003c\/p\u003e \u003cp\u003e21.4 Quantum-Secure Microgrid 467\u003c\/p\u003e \u003cp\u003e21.5 Quantum-Secure NMs 471\u003c\/p\u003e \u003cp\u003e21.6 Experimental Results 474\u003c\/p\u003e \u003cp\u003e21.7 Future Perspectives 481\u003c\/p\u003e \u003cp\u003e21.8 Summary 483\u003c\/p\u003e \u003cp\u003e21.9 Exercises 483\u003c\/p\u003e \u003cp\u003eReferences 484\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Community Microgrid Dynamic and Power Quality Design Issues 487\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePhil Barker, Tom Ortmeyer, and Clayton Burns\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 487\u003c\/p\u003e \u003cp\u003e22.2 Potsdam Resilient Microgrid Overview 488\u003c\/p\u003e \u003cp\u003e22.3 Power Quality Parameters and Guidelines 490\u003c\/p\u003e \u003cp\u003e22.4 Microgrid Analytical Methods 498\u003c\/p\u003e \u003cp\u003e22.5 Analysis of Grid Parallel Microgrid Operation 499\u003c\/p\u003e \u003cp\u003e22.6 Fault Current Contributions and Grounding 515\u003c\/p\u003e \u003cp\u003e22.7 Microgrid Operation in Islanded Mode 529\u003c\/p\u003e \u003cp\u003e22.8 Conclusions and Recommendations 551\u003c\/p\u003e \u003cp\u003e22.9 Exercises 552\u003c\/p\u003e \u003cp\u003e22.10 Acknowledgment 553\u003c\/p\u003e \u003cp\u003eReferences 553\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 A Time of Energy Transition at Princeton University 555\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eEdward T. Borer, Jr.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 555\u003c\/p\u003e \u003cp\u003e23.2 Cogeneration 556\u003c\/p\u003e \u003cp\u003e23.3 The Magic of The Refrigeration Cycle 560\u003c\/p\u003e \u003cp\u003e23.4 Capturing Heat, Not Wasting It 562\u003c\/p\u003e \u003cp\u003e23.5 Multiple Forms of Energy Storage 565\u003c\/p\u003e \u003cp\u003e23.6 Daily Thermal Storage – Chilled or Hot Water 569\u003c\/p\u003e \u003cp\u003e23.7 Seasonal Thermal Storage – Geoexchange 571\u003c\/p\u003e \u003cp\u003e23.8 Moving to Renewable Electricity as the Main Energy Input 574\u003c\/p\u003e \u003cp\u003e23.9 Water Use Reduction 575\u003c\/p\u003e \u003cp\u003e23.10 Closing Comments 577\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Considerations for Digital Real-Time Simulation, Control-HIL, and Power-HIL in Microgrids\/DER Studies 579\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJuan F. Patarroyo, Joel Pfannschmidt, K. S. Amitkumar, Jean-Nicolas Paquin, and Wei li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 579\u003c\/p\u003e \u003cp\u003e24.2 Considerations and Applications for Real-Time Simulation 580\u003c\/p\u003e \u003cp\u003e24.3 Considerations and Applications of Control Hardware-in-the-Loop 593\u003c\/p\u003e \u003cp\u003e24.4 Considerations and Applications of Power Hardware-in-the-Loop 602\u003c\/p\u003e \u003cp\u003e24.5 Concluding Remarks 612\u003c\/p\u003e \u003cp\u003e24.6 Exercises 612\u003c\/p\u003e \u003cp\u003eReferences 613\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Real-Time Simulations of Microgrids: Industrial Case Studies 615\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHui Ding, Xianghua Shi, Yi Qi, Christian Jegues, and Yi Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Universal Converter Model Representation 615\u003c\/p\u003e \u003cp\u003e25.2 Practical Microgrid Case 1: Aircraft Microgrid System 617\u003c\/p\u003e \u003cp\u003e25.3 Practical Microgrid Case 2: Banshee Power System 620\u003c\/p\u003e \u003cp\u003e25.4 Summary 630\u003c\/p\u003e \u003cp\u003e25.5 Exercises 630\u003c\/p\u003e \u003cp\u003eReferences 630\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Coordinated Control of DC Microgrids 633\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eWeidong Xiao and Jacky Xiangyu Han\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 dc Droop 634\u003c\/p\u003e \u003cp\u003e26.2 Hierarchical Control Scheme 639\u003c\/p\u003e \u003cp\u003e26.3 Average Voltage Sharing 639\u003c\/p\u003e \u003cp\u003e26.4 Bus Line Communication 645\u003c\/p\u003e \u003cp\u003e26.5 Summary 651\u003c\/p\u003e \u003cp\u003e26.6 Exercises 654\u003c\/p\u003e \u003cp\u003eReferences 654\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Foundations of Microgrid Resilience 655\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eWilliam W. Anderson, Jr. and Douglas L. Van Bossuyt\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 655\u003c\/p\u003e \u003cp\u003e27.2 Background\/Problem Statement 656\u003c\/p\u003e \u003cp\u003e27.3 Defining Resilience 657\u003c\/p\u003e \u003cp\u003e27.4 Resilience Analysis Examples 662\u003c\/p\u003e \u003cp\u003e27.5 Discussion and Future Work 671\u003c\/p\u003e \u003cp\u003e27.6 Conclusion 672\u003c\/p\u003e \u003cp\u003e27.7 Acknowledgments 672\u003c\/p\u003e \u003cp\u003e27.8 Exercises 673\u003c\/p\u003e \u003cp\u003eReferences 677\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Reliability Evaluation and Voltage Control Strategy of AC–DC Microgrid 681\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eQianyu Zhao, Shouxiang Wang, Qi Liu, Zhixin Li, Xuan Wang, and Xuan Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 681\u003c\/p\u003e \u003cp\u003e28.2 Typical Topology Evaluation of AC–DC Microgrid 682\u003c\/p\u003e \u003cp\u003e28.3 Coordinated Optimization for the AC–DC Microgrid 690\u003c\/p\u003e \u003cp\u003e28.4 Case Study 696\u003c\/p\u003e \u003cp\u003e28.5 Actual Project Construction 707\u003c\/p\u003e \u003cp\u003e28.6 Conclusion 708\u003c\/p\u003e \u003cp\u003e28.7 Exercises 710\u003c\/p\u003e \u003cp\u003eReferences 710\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Self-Organizing System of Sensors for Monitoring and Diagnostics of a Modern Microgrid 713\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMichael Gouzman, Serge Luryi, Claran Martis, Yacov A. Shamash, and Alex Shevchenko\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 713\u003c\/p\u003e \u003cp\u003e29.2 Structures for Building Modern Microgrids 713\u003c\/p\u003e \u003cp\u003e29.3 Requirements for the Monitoring and Diagnostics System of Modern Microgrids 715\u003c\/p\u003e \u003cp\u003e29.4 Communication Systems in Microgrids 716\u003c\/p\u003e \u003cp\u003e29.5 Sensors 717\u003c\/p\u003e \u003cp\u003e29.6 Network Topology Identification Algorithm 721\u003c\/p\u003e \u003cp\u003e29.7 Implementation 725\u003c\/p\u003e \u003cp\u003e29.8 Exercise 725\u003c\/p\u003e \u003cp\u003eReferences 727\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Event Detection, Classification, and Location Identification with Synchro-Waveforms 729\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMilad Izadi and Hamed Mohsenian-Rad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e30.1 Introduction 729\u003c\/p\u003e \u003cp\u003e30.2 Event Detection 732\u003c\/p\u003e \u003cp\u003e30.3 Event Classification 737\u003c\/p\u003e \u003cp\u003e30.4 Event Location Identification 743\u003c\/p\u003e \u003cp\u003e30.5 Applications 756\u003c\/p\u003e \u003cp\u003e30.6 Exercises 757\u003c\/p\u003e \u003cp\u003eReferences 758\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 Traveling Wave Analysis in Microgrids 761\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSoumitri Jena and Peng Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e31.1 Introduction 761\u003c\/p\u003e \u003cp\u003e31.2 Background Theories 761\u003c\/p\u003e \u003cp\u003e31.3 Challenges for TW Applications in Microgrid 763\u003c\/p\u003e \u003cp\u003e31.4 Proposed Traveling Wave Protection Scheme 765\u003c\/p\u003e \u003cp\u003e31.5 Performance Analysis 774\u003c\/p\u003e \u003cp\u003e31.6 Conclusion 781\u003c\/p\u003e \u003cp\u003e31.7 Exercises 781\u003c\/p\u003e \u003cp\u003eReferences 783\u003c\/p\u003e \u003cp\u003e\u003cb\u003e32 Neuro-Dynamic State Estimation of Microgrids 785\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFei Feng, Yifan Zhou, and Peng Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e32.1 Background 785\u003c\/p\u003e \u003cp\u003e32.2 Preliminaries of Physics-Based DSE 786\u003c\/p\u003e \u003cp\u003e32.3 Neuro-DSE Algorithm 786\u003c\/p\u003e \u003cp\u003e32.4 Self-Refined Neuro-DSE 790\u003c\/p\u003e \u003cp\u003e32.5 Numerical Tests of Neuro-DSE 792\u003c\/p\u003e \u003cp\u003e32.6 Exercises 798\u003c\/p\u003e \u003cp\u003eReferences 799\u003c\/p\u003e \u003cp\u003e\u003cb\u003e33 Hydrogen-Supported Microgrid toward Low-Carbon Energy Transition 801\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJianxiao Wang, Guannan He, and Jie Song\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e33.1 Introduction 801\u003c\/p\u003e \u003cp\u003e33.2 Hydrogen Production in Microgrid Operation 802\u003c\/p\u003e \u003cp\u003e33.3 Hydrogen Utilization in Microgrid Operation 805\u003c\/p\u003e \u003cp\u003e33.4 Case Studies 810\u003c\/p\u003e \u003cp\u003e33.5 Exercises 812\u003c\/p\u003e \u003cp\u003e33.6 Acknowledgement 813\u003c\/p\u003e \u003cp\u003eReferences 813\u003c\/p\u003e \u003cp\u003e\u003cb\u003e34 Sharing Economy in Microgrid 815\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJianxiao Wang, Feng Gao, Tiance Zhang, and Qing Xia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e34.1 Introduction 815\u003c\/p\u003e \u003cp\u003e34.2 Aggregation of Distributed Energy Resources in Energy Markets 816\u003c\/p\u003e \u003cp\u003e34.3 Aggregation of Distributed Energy Resources in Energy and Capacity Markets 819\u003c\/p\u003e \u003cp\u003e34.4 Case Studies 824\u003c\/p\u003e \u003cp\u003e34.5 Exercises 829\u003c\/p\u003e \u003cp\u003e34.6 Acknowledgement 830\u003c\/p\u003e \u003cp\u003eReferences 830\u003c\/p\u003e \u003cp\u003e\u003cb\u003e35 Microgrid: A Pathway to Mitigate Greenhouse Impact of Rural Electrification 831\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJianxiao Wang, Haiwang Zhong, and Jing Dai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e35.1 Introduction 831\u003c\/p\u003e \u003cp\u003e35.2 System Model 832\u003c\/p\u003e \u003cp\u003e35.3 Case Studies 838\u003c\/p\u003e \u003cp\u003e35.4 Discussion 845\u003c\/p\u003e \u003cp\u003e35.5 Exercises 846\u003c\/p\u003e \u003cp\u003e35.6 Acknowledgement 847\u003c\/p\u003e \u003cp\u003eReferences 847\u003c\/p\u003e \u003cp\u003e\u003cb\u003e36 Operations of Microgrids with Meshed Topology Under Uncertainty 849\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMikhail A. Bragin, Bing Yan, Akash Kumar, Nanpeng Yu, and Peng Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e36.1 Self-sufficiency and Sustainability of Microgrids Under Uncertainty 849\u003c\/p\u003e \u003cp\u003e36.2 Microgrid Model: Proactive Operation Optimization Under Uncertainties 853\u003c\/p\u003e \u003cp\u003e36.3 Solution Methodology 854\u003c\/p\u003e \u003cp\u003e36.4 Conclusions 858\u003c\/p\u003e \u003cp\u003e36.5 Exercises 859\u003c\/p\u003e \u003cp\u003eReferences 860\u003c\/p\u003e \u003cp\u003e\u003cb\u003e37 Operation Optimization of Microgrids with Renewables 863\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBing Yan, Akash Kumar, and Peng Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e37.1 Introduction 863\u003c\/p\u003e \u003cp\u003e37.2 Existing Work 864\u003c\/p\u003e \u003cp\u003e37.3 Mathematical Modeling 865\u003c\/p\u003e \u003cp\u003e37.4 Solution Methodology 870\u003c\/p\u003e \u003cp\u003e37.5 Exercises 871\u003c\/p\u003e \u003cp\u003eReferences 872\u003c\/p\u003e \u003cp\u003eIndex 875\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePeng Zhang, Ph.D, \u003c\/b\u003eis Professor of Electrical and Computer Engineering and an Affiliate Professor of Computer Science and Applied Mathematics and Statistics at Stony Brook University, New York. He is a Senior Member of the IEEE and has published widely on microgrids and networked microgrid systems.   \u003c\/p\u003e\u003cp\u003e \u003cb\u003eUnderstand microgrids and networked microgrid systems\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eMicrogrids are interconnected groups of energy sources that operate together, capable of connecting with a larger grid or operating independently as needed and network conditions require. They can be valuable sources of energy for geographically circumscribed areas with highly targeted energy needs, and for remote or rural areas where continuous connection with a larger grid is difficult. Microgrids’ controllability makes them especially effective at incorporating renewable energy sources. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMicrogrids: Theory and Practice \u003c\/i\u003eintroduces readers to the analysis, design, and operation of microgrids and larger networked systems that integrate them. It brings to bear both cutting-edge research into microgrid technology and years of industry experience in designing and operating microgrids. Its discussions of core subjects such as microgrid modeling, control, and optimization make it an essential short treatment, valuable for both academic and industrial study. Readers will acquire the skills needed to address existing problems and meet new ones as this crucial area of power engineering develops. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMicrogrids: Theory and Practice \u003c\/i\u003ealso features:  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eIncorporation of new cyber-physical system technologies for enabling microgrids as resiliency resources \u003c\/li\u003e\n\u003cli\u003eTheoretical treatment of a wide range of subjects including smart programmable microgrids, distributed and asynchronous optimization for microgrid dispatch, and AI-assisted microgrid protection \u003c\/li\u003e\n\u003cli\u003ePractical discussion of real-time microgrids simulations, hybrid microgrid design, transition to renewable microgrid networks, and more\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMicrogrids: Theory and Practice \u003c\/i\u003eis ideal as a textbook for graduate and advanced undergraduate courses in power engineering programs, and a valuable reference for power industry professionals looking to address the challenges posed by microgrids in their work.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989620900069,"sku":"NP9781119890850","price":140.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119890850.jpg?v=1761784842","url":"https:\/\/k12savings.com\/products\/microgrids-isbn-9781119890850","provider":"K12savings","version":"1.0","type":"link"}