Details

Autor(en) / Beteiligte

• Zhang, Peng,

Titel

Microgrids : Theory and Practice

Auflage

First edition

Ort / Verlag

Hoboken, New Jersey : Wiley,

Erscheinungsjahr

2024

Beschreibungen/Notizen

• Includes bibliographical references and index.
• Cover -- Title Page -- Copyright -- Contents -- About the Editor -- List of Contributors -- Preface -- Acknowledgments -- Chapter 1 Introduction -- 1.1 Background -- 1.2 Reader's Manual -- 1.2.1 Volume I: Theory -- 1.2.1.1 Platform -- 1.2.1.2 Steady‐State Analysis -- 1.2.1.3 Dynamics and Stability -- 1.2.1.4 Resilience -- 1.2.1.5 Control and Optimization -- 1.2.1.6 Cyber Infrastructure and Cybersecurity -- 1.2.2 Volume II: Practice -- 1.2.2.1 Community Microgrids -- 1.2.2.2 Control, Protection, and Analytics -- 1.2.2.3 Microgrid as a Service -- Chapter 2 AI‐Grid: AI‐Enabled, Smart Programmable Microgrids -- 2.1 Introduction -- 2.2 AI‐Grid Platform -- 2.3 AI‐Enabled, Provably Resilient NM Operations -- 2.3.1 Neuro‐Reachability: AI‐Enabled Dynamic Verification of NMs Dynamics -- 2.3.2 Neuro‐DSE: AI‐Enabled Dynamic State Estimation -- 2.3.3 Neural‐Adaptability: AI‐Based Resilient Microgrid Control -- 2.4 Resilient Modeling and Prediction of NM States Under Uncertainty -- 2.4.1 Hybrid Neural ODE‐SDE Graph Modeling of NM Dynamics -- 2.4.2 NeuralODE with Soft‐Masking to Adapt to Spatially Partial Observations -- 2.4.3 NeuralSDE with Wasserstein Adversarial Training for Efficient Learning of Process Uncertainty -- 2.4.4 Experiments -- 2.4.4.1 Overall Performance -- 2.4.4.2 Performance in the Presence of Noise -- 2.4.4.3 Prototyping -- 2.5 Runtime Safety and Security Assurance for AI‐Grid -- 2.5.1 Introduction -- 2.5.1.1 Architectural Overview of Bb‐Simplex -- 2.5.2 Preliminaries -- 2.5.2.1 BaC Synthesis Using SOS Optimization -- 2.5.2.2 BaC Synthesis Using Deep Learning -- 2.5.3 Deriving the Switching Condition -- 2.5.3.1 Forward Switching Condition -- 2.5.3.2 Reverse Switching Condition -- 2.5.3.3 Decision Logic -- 2.5.4 Application to Microgrids -- 2.5.4.1 Baseline Controller -- 2.5.4.2 Neural Controller -- 2.5.4.3 Adaptation Module.
• 2.5.5 Implementation and Experiments -- 2.5.5.1 Integration of Bb‐Simplex in RTDS -- 2.5.5.2 User Interface -- 2.5.5.3 Consistency of RTDS and MATLAB Models -- 2.5.5.4 Evaluation of Forward Switching Condition -- 2.5.5.5 Evaluation of Neural Controller -- 2.5.5.6 Evaluation of Adaptation Module -- 2.5.6 Extension to Approximate Dynamics -- 2.5.6.1 Impact of Approximate Dynamics on BaC -- 2.5.6.2 Impact of Approximate Dynamics on FSC -- 2.5.7 Extension to Hybrid Systems -- 2.5.7.1 Switching Logic for Hybrid Systems -- 2.5.7.2 FSC for Hybrid Systems -- 2.5.8 Related Work -- 2.6 Software Platform for AI‐Grid -- 2.6.1 Infrastructure Overview -- 2.6.1.1 Real Time Digital Simulator (RTDS) -- 2.6.1.2 Windows -- 2.6.1.3 Network -- 2.6.1.4 SQL Server -- 2.6.1.5 Python -- 2.6.1.6 DNP3 -- 2.6.1.7 Asp.net Web Server -- 2.6.2 Software Architecture Overview -- 2.6.3 Software Architecture Component -- 2.6.3.1 RTDS -- 2.6.3.2 DNP3 -- 2.6.3.3 Special Encoding/Decoding Process -- 2.6.3.4 SQL Server -- 2.6.3.5 AI‐Grid Control Function -- 2.6.4 Customization for Each Team -- 2.6.4.1 SDC -- 2.6.4.2 Power Flow -- 2.6.4.3 Digital Twin User Interface -- 2.7 AI‐Grid for Grid Modernization -- 2.8 Exercises -- References -- Chapter 3 Distributed Power Flow and Continuation Power Flow for Steady‐State Analysis of Microgrids -- 3.1 Background -- 3.2 Individual Microgrid Power Flow -- 3.2.1 Enhanced Newton‐Type Power Flow -- 3.2.1.1 EMPF Formulation -- 3.2.1.2 Modified Jacobian Matrix -- 3.2.2 Revisited Implicit Zbus Power Flow -- 3.2.2.1 Basic GRev Formulation -- 3.2.2.2 GRev with Hierarchical Control -- 3.2.3 Generalized Back/Forward Sweep Power Flow -- 3.2.3.1 Direct Back/Forward Sweep Method -- 3.2.3.2 Generalized Microgrid Power Flow Algorithm -- 3.3 Networked Microgrids Power Flow -- 3.3.1 Networked Microgrids Architecture -- 3.3.2 Distributed NMPF Formulation.
• 3.3.2.1 Power Sharing (PS) Mode -- 3.3.2.2 Voltage Regulation (VR) Mode -- 3.3.3 Distributed NMPF Algorithm -- 3.3.4 APF‐Based Continuation Power Flow -- 3.4 Numerical Tests of Microgrid Power Flow -- 3.4.1 Validity of Individual Microgrid Power Flow -- 3.4.1.1 Power Flow Results for Different Microgrid Configurations -- 3.4.1.2 Power Flow Results Under Various Control Modes -- 3.4.2 Validity of Networked Microgrids Power Flow -- 3.4.2.1 APF Results Under Droop Control -- 3.4.2.2 APF Results Under PS Control -- 3.4.2.3 APF Results Under VR Control -- 3.4.2.4 Convergence Performance of APF -- 3.5 Exercises -- References -- Chapter 4 State and Parameter Estimation for Microgrids -- 4.1 Introduction -- 4.2 State and Parameter Estimation for Inverter‐Based Resources -- 4.2.1 Background and Motivation -- 4.2.2 Overview of CPDSE Framework -- 4.2.2.1 Cyber‐Physical State‐Space Representation of IBRs -- 4.2.2.2 Comparison with Conventional Single‐State‐Space Representation -- 4.2.2.3 CPDSE and CPDPE for IBRs -- 4.2.3 Examples of Cyber‐Physical State‐Space Models -- 4.2.3.1 Physical State‐Space Model -- 4.2.3.2 Cyber State‐Space Model -- 4.2.4 CKF for Dynamic State Estimation and Bad Data Processing -- 4.2.5 Simulation Results -- 4.3 State and Parameter Estimation for Network Components -- 4.3.1 Background and Motivation -- 4.3.2 Dynamic State Estimation‐Based Protection for Microgrid Circuits -- 4.3.3 Dynamic State Estimation‐Based Fault Location for Microgrid Circuits -- 4.4 Conclusion -- 4.5 Exercise -- 4.6 Acknowledgment -- References -- Chapter 5 Eigenanalysis of Delayed Networked Microgrids -- 5.1 Introduction -- 5.2 Formulation of Delayed NMs -- 5.3 Delayed NMs Eigenanalysis -- 5.3.1 Solution Operator Basics -- 5.3.2 ODE‐SOD Eigensolver -- 5.4 Case Study -- 5.4.1 Methodology Validity -- 5.4.2 Cyber Network's Impact on NMs Stability.
• 5.4.2.1 Impact of Communication Delay -- 5.4.2.2 Impact of Measurement Delay -- 5.4.3 Electrical Network's Impact on NMs Stability -- 5.5 Conclusion -- 5.6 Exercises -- References -- Chapter 6 AI‐Enabled Dynamic Model Discovery of Networked Microgrids -- 6.1 Preliminaries on ODE‐Based Dynamical Modeling of NMs -- 6.1.1 Formulation of DERs with Hierarchical Control -- 6.1.2 Formulation of Network Dynamics -- 6.1.3 ODE‐Enabled NMs Dynamic Model -- 6.2 Physics‐Data‐Integrated ODE Model of NMs -- 6.2.1 Physics‐Based InSys Formulation -- 6.2.2 Data‐Driven ExSys Formulation -- 6.3 ODE‐Net‐Enabled Dynamic Model Discovery for Microgrids -- 6.3.1 ODE‐Net‐Based State‐Space Model Formulation -- 6.3.2 Continuous‐Time Learning Model for ODE‐Net -- 6.3.2.1 Discrete‐Time Learning -- 6.3.2.2 Continuous‐Time Learning -- 6.3.3 Continuous Backpropagation -- 6.3.4 Further Discussion -- 6.4 Physics‐Informed Learning for ODE‐Net‐Enabled Dynamic Models -- 6.4.1 Physics‐Informed Formulation for ODE‐Net Training -- 6.4.2 Physics‐Informed Continuous Backpropagation -- 6.5 Experiments -- 6.5.1 Case Design -- 6.5.1.1 Test System 1 -- 6.5.1.2 Test System 2 -- 6.5.2 Method Validity -- 6.5.3 Method Scalability -- 6.5.4 Method Superiority over Discrete‐Time Learning -- 6.6 Summary -- 6.7 Exercises -- References -- Chapter 7 Transient Stability Analysis for Microgrids with Grid‐Forming Converters -- 7.1 Background -- 7.2 System Modeling -- 7.2.1 Grid‐Following Inverter -- 7.2.2 Grid‐Forming Inverter -- 7.2.3 SG Model -- 7.2.4 Network Model -- 7.2.5 Fault Model -- 7.3 Metric for Transient Stability -- 7.4 Microgrid Transient Stability Analysis -- 7.4.1 Transient Stability of an Islanded Microgrid with Single SG -- 7.4.2 Impact of GFL Inverter on Transient Stability of an Islanded Microgrid -- 7.4.3 Impact of GFM and Parameter Tuning on Transient Stability of an Islanded Microgrid.
• 7.4.4 The Transient Stability of an Islanded Microgrid with Only GFM -- 7.5 Conclusion and Future Directions -- 7.6 Exercises -- References -- Chapter 8 Learning‐Based Transient Stability Assessment of Networked Microgrids -- 8.1 Motivation -- 8.2 Networked Microgrid Dynamics -- 8.3 Learning a Lyapunov Function -- 8.3.1 Stability of Equilibrium Points -- 8.3.2 Neural Network Architecture -- 8.3.3 Neural Lyapunov Methods -- 8.4 Case Study -- 8.5 Summary -- 8.6 Exercises -- References -- Chapter 9 Microgrid Protection -- 9.1 Introduction -- 9.1.1 Motivation -- 9.2 Protection Fundamentals -- 9.2.1 Big Picture -- 9.2.2 Protection Systems and Actions -- 9.2.2.1 Overcurrent Element -- 9.2.2.2 Distance Element -- 9.2.2.3 Current Differential -- 9.2.2.4 Directional Comparison -- 9.2.3 Phasor‐Based Protection -- 9.2.4 Full‐Cycle Fourier Transformation -- 9.2.5 Superimposed Quantities -- 9.2.6 Traveling Wave‐Based Protection -- 9.2.7 Centralized Protection -- 9.3 Typical Microgrid Protection Schemes -- 9.3.1 Subtransmission -- 9.3.2 Distribution -- 9.3.2.1 Radial -- 9.3.2.2 Looped/Meshed -- 9.3.2.3 Typical Response Times -- 9.3.3 IEEE 1547 Guidelines for DERs -- 9.4 Challenges Posed by Microgrids -- 9.4.1 Challenges Posed by Changes in Operational Mode -- 9.4.1.1 Short‐Circuit Capacity -- 9.4.1.2 Current‐Flow Direction -- 9.4.2 Challenges Posed by DER Operation -- 9.4.2.1 Voltage Regulation and Stability -- 9.4.2.2 Frequency Decay and Angular Stability -- 9.4.3 IBR Challenges -- 9.4.3.1 Impacts of IBR Fault Current -- 9.4.3.2 Impacts Posed by IBR to Protection Schemes -- 9.5 Examples of Solutions in Practice -- 9.5.1 Case 1: North Bay Hydro Microgrid -- 9.5.1.1 Challenges -- 9.5.1.2 Protection Overview -- 9.5.1.3 Solution -- 9.5.2 Case 2: IIT Microgrid -- 9.5.2.1 Challenges -- 9.5.2.2 Protection Overview -- 9.5.2.3 Solution.
• 9.5.3 Case 3: Duke Energy Microgrid.
• "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)."--
• Description based on publisher supplied metadata and other sources.
• Description based on print version record.