Microgrids

Name: Microgrids | Theory and Practice
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Theory and Practice

Peng Zhang(Herausgeber*in)

Wiley-ISTE (Verlag)

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Erschienen am 11. März 2024

944 Seiten

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Inhalt

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
9.5.3.1 Microgrid Overview and Challenges
9.5.3.2 Solution
9.5.4 Protection of DC Microgrids
9.6 Summary
9.7 Exercises
References
Chapter 10 Microgrids Resilience: Definition, Measures, and Algorithms
10.1 Background of Resilience and the Role of Microgrids
10.1.1 Essence of Resilience
10.1.2 Microgrids in Resilience
10.2 Enhance Power System Resilience with Microgrids
10.2.1 Investment Planning
10.2.1.1 Sitting and Sizing of Distributed Generators
10.2.1.2 Infrastructure Hardening
10.2.2 Pre-Event Preparation
10.2.2.1 Mobile Emergency Resource Prepositioning
10.2.2.2 Proactive Management of Microgrids
10.2.3 Efficient Restoration
10.2.3.1 Coordinated Operation of Networked Microgrids
10.2.3.2 Microgrids Formation in Distribution Systems
10.3 Future Challenges
10.4 Exercises
References
Chapter 11 In Situ Resilience Quantification for Microgrids
11.1 Introduction
11.1.1 Background and Motivation
11.2 STL-Enabled In Situ Resilience Evaluation
11.2.1 Robustness Computation Using STL
11.2.2 STL Requirements for Microgrids
11.2.3 In Situ Resilience Quantification Mechanism
11.3 Case Study
11.3.1 Experimental Setup
11.3.2 Experimental Results
11.3.2.1 DER Disconnection
11.3.2.2 Load Shedding
11.3.3 Comparison with Existing Method
11.4 Conclusion
11.5 Exercises
11.6 Acknowledgment
References
Chapter 12 Distributed Voltage Regulation of Multiple Coupled Distributed Generation Units in DC Microgrids: An Output Regulation Approach
12.1 Introduction
12.2 Problem Statement
12.3 Review of Output Regulation Theory
12.4 Distributed Voltage Regulation in the Presence of Time-Varying Loads
12.5 Simulation Results
12.5.1 State Feedback Design
12.5.1.1 Scenario 1: Constant Loads
12.5.1.2 Scenario 2: Sinusoidal Loads
12.5.1.3 Scenario 3: Pulsed Loads
12.5.2 Output Feedback Design
12.5.2.1 Scenario 1: Constant Loads
12.5.2.2 Scenario 2: Sinusoidal Loads
12.5.2.3 Scenario 3: Pulsed Loads
12.6 Conclusions
12.7 Exercises
12.8 Acknowledgment
References
Chapter 13 Droop-Free Distributed Control for AC Microgrids
13.1 Cyber-Physical Microgrid Modeling
13.1.1 Power Network
13.1.2 Communication Network
13.2 Hierarchical Control of Islanded Microgrid
13.2.1 Zero-Level Control
13.2.2 Primary Control
13.2.3 Distributed Secondary Control with Droop
13.3 Droop-Free Distributed Control with Proportional Power Sharing
13.3.1 Distributed Average Voltage Estimation
13.3.2 Voltage Regulation
13.3.3 Frequency Regulation
13.4 Droop-Free Distributed Control with Voltage Profile Guarantees
13.4.1 Distributed Voltage Variance Estimation
13.4.2 Voltage Variance Regulator
13.4.3 Relaxed Reactive Power Regulator
13.5 Steady-State Analysis for the Control in Section 13.4
13.6 Microgrid Test System and Control Performance
13.7 Steady-State Performance Under Different Loading Conditions and Controller Settings
13.8 Exercises
References
Chapter 14 Optimal Distributed Control of AC Microgrids
14.1 Optimization Problem for Secondary Control
14.1.1 Formulated Optimization Problem
14.1.2 Convex Optimization Problem
14.1.3 Convexity of Problem (14.8)
14.1.4 Distinct Features of the Formulation
14.2 Primal-Dual Gradient Based Distributed Solving Algorithm
14.2.1 Augmented Lagrangian
14.2.2 Standard Primal-Dual Gradient Algorithm
14.2.3 Non-Separable Objective Function
14.2.4 Distributed Average Voltage Estimation
14.2.5 Coupled Reactive Power Inequality Constraints
14.2.6 Solving Algorithm
14.3 Microgrid Test Systems
14.4 Control Performance on 4-DG System
14.4.1 Performance Under Different a and ß
14.4.2 Performance Under Heavy Load Scenario
14.5 Control Performance on IEEE 34-Bus System
14.5.1 Performance Under Load Change
14.5.2 Performance Under A Wide Range of Load Scenarios
14.5.3 Optimality Comparison
14.5.4 Performance Under Line Parameter Uncertainty
14.6 Exercises
References
Chapter 15 Cyber-Resilient Distributed Microgrid Control
15.1 Push-Sum Enabled Resilient Microgrid Control
15.1.1 Push-Sum-Based Resilient Distributed Control
15.1.1.1 Case Studies
15.2 Employing Interacting Qubits for Distributed Microgrid Control
15.2.1 Preliminaries
15.2.1.1 Graph Theory
15.2.1.2 Quantum Systems and Notations
15.2.2 Quantum Distributed Control
15.2.2.1 Algorithm
15.2.2.2 Analysis
15.2.2.3 Numerical Example
15.2.3 Quantum Distributed Controller for AC and DC Microgrids
15.2.3.1 Quantum Distributed Frequency Control
15.2.3.2 Verification on an AC Networked-Microgrid Case Study
15.2.3.3 Quantum Distributed Voltage Control for DC Microgrids
15.2.3.4 Verification on a DC Microgrid Case Study
15.2.4 Discussion and Outlook
15.2.4.1 Realization
15.2.4.2 Outlook
References
Chapter 16 Programmable Crypto-Control for Networked Microgrids
16.1 Introduction
16.2 PCNMs and Privacy Requirements
16.2.1 Architecture of PCNMs
16.2.2 Formulations of Programmable Control
16.2.3 Privacy Requirement in PCNMs
16.3 Dynamic Encrypted Weighted Addition
16.3.1 The Paillier Cryptosystem
16.3.1.1 Overview of The Paillier Cryptosystem
16.3.1.2 Homomorphism
16.3.2 Additive Zero Secret Sharing
16.3.3 The DEWA Algorithm
16.4 DEWA Privacy Analysis
16.4.1 Privacy Analysis
16.4.1.1 Scenario 1
16.4.1.2 Scenario 2
16.4.2 Security Level Analysis
16.5 Case Studies
16.5.1 The PCNMs Testbed
16.5.2 The Impact of DEWA
16.5.3 DEWA Delays and Eigen Analysis of DEWA-Based PCNMs
16.5.4 Privacy Evaluation for DEWA
16.5.5 Benefit of SDN-Enabled Crypto-Control
16.5.6 Comparison of DEWA with Existing Scheme
16.6 Conclusion
16.7 Exercises
References
Chapter 17 AI-Enabled, Cooperative Control, and Optimization in Microgrids
17.1 Introduction
17.2 Energy Hub Model in Microgirds
17.3 Distributed Adaptive Cooperative Control in Microgrids
17.3.1 Structure and Feature Analysis
17.3.2 The Control of Outputs Based on Consensus Algorithm
17.3.3 The Control of Devices Based on Improved Equal Increment Principle
17.3.4 Example
17.3.4.1 Example 1
17.3.4.2 Example 2
17.4 Optimal Energy Operation in Microgrids Based on Hybrid Reinforcement Learning
17.4.1 Multi-objective Optimization Model Formulation
17.4.1.1 Objective
17.4.1.2 Constraints
17.4.2 Multi-Policy Convex Hull Reinforcement Learning with Human-in-the-Loop
17.4.2.1 Formulation of Multi-Policy Convex Hull Reinforcement Learning
17.4.2.2 Two-Channel Human-in-the-Loop Mechanism
17.4.3 Example
17.4.3.1 System Initialization
17.4.3.2 Example 1
17.4.3.3 Example 2
17.5 Conclusion
17.6 Exercises
References
Chapter 18 DNN-Based EV Scheduling Learning for Transactive Control Framework
18.1 Introduction
18.2 Transactive Control Formulation
18.3 Proposed Deep Neural Networks in Transactive Control
18.4 Case Study
18.5 Simulation Results and Discussion
18.6 Conclusion
18.7 Exercises
References
Chapter 19 Resilient Sensing and Communication Architecture for Microgrid Management
19.1 Introduction
19.1.1 Background and Motivation
19.1.2 Overview of Microgrid Sensing Technology
19.1.2.1 Remote Terminal Units (RTUs) and Intelligent Electronic Devices (IEDs)
19.1.2.2 Phasor Measurement Units (PMUs)
19.1.2.3 Smart Meters
19.1.2.4 Smart Inverter
19.1.3 Overview of Microgrid Communication Technology
19.1.3.1 Wired Communication
19.1.3.2 Wireless Communication
19.2 Resilient Sensing and Communication Network Planning Against Multidomain Failures
19.2.1 Observability Analysis and PMUs
19.2.2 Concept Definition
19.2.3 Fundamental Design Principles
19.2.4 Resilient PMU Placement and Essential Role Selection
19.2.4.1 Resilient PMU Placement
19.2.4.2 Essential Role Selection
19.2.5 Observability-Aware Resilient Communication Link Placement
19.2.6 Optimal PMU-Communication Link Selection
19.2.7 Case Study
19.2.7.1 Simulation Setup
19.2.7.2 Simulation Results
19.3 Observability-Aware Network Routing for Fast and Resilient Microgrid Monitoring
19.3.1 Problem Statement
19.3.2 Shared Observability PMU Group (SOPG)
19.3.3 Fundamental Routing Principles
19.3.4 Cost Metric Definition
19.3.5 Routing Algorithm
19.3.6 Case Study
19.3.6.1 Test Case for IEEE 30-Bus System
19.3.6.2 Performance Comparison with Baseline
19.4 Conclusion
19.5 Exercises
References
Chapter 20 Resilient Networked Microgrids Against Unbounded Attacks
20.1 Introduction
20.1.1 Background and Motivation of Attack-Resilient Secondary Control of AC Microgrids
20.1.2 Background and Motivation of Attack-Resilient Secondary Control of DC Microgrids
20.2 Adaptive Resilient Control of AC Microgrids Under Unbounded Actuator Attacks
20.2.1 Preliminaries on Graph Theory and Notations
20.2.2 Conventional Cooperative Secondary Control of AC Microgrids
20.2.3 Problem Formulation
20.2.4 Distributed Resilient Controller Design
20.2.5 Case Studies
20.3 Distributed Resilient Secondary Control of DC Microgrids Against Unbounded Attacks
20.3.1 Preliminaries
20.3.2 Standard Cooperative Secondary Control
20.3.3 Resilient Secondary Control
20.3.3.1 Attack-Resilient Secondary Control Problem Formulation
20.3.3.2 Distributed Attack-Resilient Secondary Controller Design
20.3.4 Hardware-in-the-Loop Validation
20.3.4.1 DC Microgrid HIL Testbed
20.3.4.2 Response to the Link Failure and Load Change
20.3.4.3 Performance Assessment Under Unknown Unbounded Attacks
20.3.4.4 Performance Assessment Under Unknown Bounded Attacks
20.4 Conclusion
20.5 Acknowledgment
20.6 Exercises
References
Chapter 21 Quantum Security for Microgrids
21.1 Background
21.1.1 Securing Microgrid Data Transmission
21.1.2 The Quantum Era is Coming
21.2 Quantum Communication for Microgrids
21.2.1 Overview of Quantum Cryptography
21.2.2 Basics of Quantum Key Distribution
21.2.2.1 Quantum States
21.2.2.2 General Setting of a QKD System
21.2.2.3 The Practical Decoy-State QKD Protocol
21.2.2.4 Benefits of Using QKD for Microgrids
21.3 The QKD Simulator
21.4 Quantum-Secure Microgrid
21.4.1 Quantum Communication Architecture for Microgrid
21.4.2 Quantum-Secure Microgrid Testbed
21.4.2.1 High-Level Design
21.4.2.2 QKD-Based Microgrid Communication Network
21.4.2.3 Microgrid Modeling and Operation
21.5 Quantum-Secure NMs
21.5.1 Quantum Communication Architecture for NMs
21.5.2 Quantum-Secure NMs Testbed
21.5.2.1 QKD-Enabled Testing Environment
21.5.2.2 Quantum-Secure NMs Communication Network
21.6 Experimental Results
21.6.1 Impact of Data Transmission Speed
21.6.2 Effectiveness of QKD-Enabled Communication
21.6.3 Performance of QKD-Enabled Microgrid When Quantum Keys Are Exhausted
21.6.4 Impact of QKD on Real-Time Microgrid Operations
21.6.5 Evaluation of Quantum Key Generation Speed Under Different Fiber Lengths and Noise Levels
21.6.6 Evaluation of Quantum Key Generation Speed Under Different Receiver's Detection Efficiencies
21.6.7 Comparison of Different QKD Protocols
21.7 Future Perspectives
21.8 Summary
21.9 Exercises
References
Chapter 22 Community Microgrid Dynamic and Power Quality Design Issues
22.1 Introduction
22.2 Potsdam Resilient Microgrid Overview
22.3 Power Quality Parameters and Guidelines
22.3.1 Voltage Magnitude
22.3.2 Voltage Flicker
22.3.3 Temporary Overvoltage Guidelines
22.3.4 Disruptive Undervoltage Events
22.3.5 Harmonics
22.3.6 Frequency Variations
22.3.7 IEEE 1547.4-2011
22.4 Microgrid Analytical Methods
22.4.1 Modeling Tools
22.4.2 Simplified Spreadsheet Model Calculations
22.5 Analysis of Grid Parallel Microgrid Operation
22.5.1 Voltage Regulation (Grid Parallel)
22.5.1.1 Voltage Drop Due to Load
22.5.1.2 Voltage Rise Due to Generation
22.5.1.3 Calculating Net Voltage Change on Practical Feeders
22.5.2 Voltage Sensitivity Test (Grid-Parallel Mode of Operation)
22.5.3 Substation LTC Response and Feeder Regulator as a Solution
22.5.4 Voltage Change Within the Microgrid
22.5.5 Low Generation Case: Low Voltage Issues
22.5.6 Voltage Flicker Conditions (Grid Parallel Condition)
22.5.7 Voltage Flicker and Voltage Changes on 115?kV Transmission
22.5.8 Unusual Load and DG Interactions That Might Cause Flicker
22.5.9 High Steady-State Voltage at Generator Terminals
22.5.10 Operating Mode for Generator Controller (In Grid-Parallel State)
22.6 Fault Current Contributions and Grounding
22.6.1 Unintended Islanding Protection (in Grid-Parallel Mode)
22.6.2 Unintentional Islands of Interest
22.6.3 Alternatives to DTT
22.6.4 Ground Fault Overvoltage (Grid Parallel Mode)
22.6.4.1 Ground Fault Overvoltage Background
22.6.5 IEEE Effective Grounding
22.6.6 Effective Grounding Status of Potsdam DG Sites
22.6.7 Effective Grounding of DG with Respect to 115?kV System
22.6.8 Mitigation of Ground Fault Overvoltage
22.6.9 Load Rejection Overvoltage
22.7 Microgrid Operation in Islanded Mode
22.7.1 Initial Switching Transitions and Start-Up Procedures for Islanding Mode
22.7.2 Returning to Grid Parallel Mode from Intentional Island
22.7.3 Synchronization during Transitions and Aggregations
22.7.4 Coordinating Islanded Microgrid Operation with UPS Ride-Through Capability and UPS Frequency Limits
22.7.5 Power Mismatch Allowance for Seamless Intentional Island Formation
22.7.6 Inverter-Based Resource (IBR) Compatibility
22.7.7 Avoiding Cable-Resonances During Startup Procedure
22.7.8 Fault Levels (Islanded)
22.7.9 Voltage Flicker Levels During Motor Starts (Islanded)
22.7.10 Voltage Sags During Transformer Inrush (Islanded)
22.7.11 Cold Load Pickup
22.7.12 Microgrid Primary Voltage Target Level
22.7.13 Voltage Changes Due to PV Power Variations
22.7.14 Frequency Regulation (Islanded)
22.7.15 Harmonics (Islanded)
22.7.16 Load Unbalance (Islanded)
22.7.17 Microgrid Effective Grounding (Islanded)
22.7.18 DG Plant Stability (Grid Parallel and Islanded)
22.7.19 Energy Storage for Stability and Seamless Transition
22.8 Conclusions and Recommendations
22.9 Exercises
22.10 Acknowledgment
References
Chapter 23 A Time of Energy Transition at Princeton University
23.1 Introduction
23.2 Cogeneration
23.3 The Magic of The Refrigeration Cycle
23.4 Capturing Heat, Not Wasting It
23.5 Multiple Forms of Energy Storage
23.6 Daily Thermal Storage - Chilled or Hot Water
23.7 Seasonal Thermal Storage - Geoexchange
23.8 Moving to Renewable Electricity as the Main Energy Input
23.9 Water Use Reduction
23.10 Closing Comments
Chapter 24 Considerations for Digital Real-Time Simulation, Control-HIL, and Power-HIL in Microgrids/DER Studies
24.1 Introduction
24.2 Considerations and Applications for Real-Time Simulation
24.2.1 Challenges to Real-Time Simulation of Microgrids with Modern Converters
24.2.2 Modeling Power Converter for Real-Time Simulation and HIL Tests
24.2.3 Solving Microgrid Models Through an FPGA
24.2.4 Smart Inverters for Microgrid Applications
24.2.5 Digital Twins
24.3 Considerations and Applications of Control Hardware-in-the-Loop
24.3.1 Microgrid Control System Testing
24.3.2 GHOST Microgrid
24.3.3 CHIL Using Microcontrollers
24.4 Considerations and Applications of Power Hardware-in-the-Loop
24.4.1 Selecting the Right Power Amplifier for a PHIL Application
24.4.2 PHIL Interfacing
24.4.3 Network and DER Emulators
24.4.3.1 Network Emulator
24.4.3.2 PVGS Emulator
24.5 Concluding Remarks
24.6 Exercises
References
Chapter 25 Real-Time Simulations of Microgrids: Industrial Case Studies
25.1 Universal Converter Model Representation
25.2 Practical Microgrid Case 1: Aircraft Microgrid System
25.2.1 Electrical System Modeling
25.2.2 System Simulation
25.3 Practical Microgrid Case 2: Banshee Power System
25.3.1 System Modeling
25.3.2 System Simulation
25.4 Summary
25.5 Exercises
References
Chapter 26 Coordinated Control of DC Microgrids
26.1 DC Droop
26.1.1 Linear Droop Control Scheme
26.1.2 Piecewise Linear Formation of DC Droop
26.1.3 Case Study
26.2 Hierarchical Control Scheme
26.3 Average Voltage Sharing
26.3.1 Anti Windup
26.3.2 Pilot Bus Regulation
26.3.3 Dynamic Analysis
26.4 Bus Line Communication
26.4.1 Dual Active Bridge
26.4.2 Active DC Bus Signaling
26.4.3 Operation Principle and Control Logic
26.4.4 Case Study and Simulation
26.4.4.1 Discharging Test
26.4.4.2 Charging Test
26.5 Summary
26.6 Exercises
References
Chapter 27 Foundations of Microgrid Resilience
27.1 Introduction
27.2 Background/Problem Statement
27.3 Defining Resilience
27.3.1 Energy Security
27.3.2 Defining Resilience
27.3.3 Microgrid Cost
27.3.4 Assessing Resilience and Cost
27.3.5 Climate Resilience
27.3.6 Assessing Climate Resilience
27.4 Resilience Analysis Examples
27.4.1 Analysis of Resilience Versus Cost
27.4.2 Analysis of Climate Resilience
27.5 Discussion and Future Work
27.6 Conclusion
27.7 Acknowledgments
27.8 Exercises
Labs
27.8.0 Lab 1: Resilience-Cost Assessment
27.8.0.0 Background
27.8.0.0 Required Documents
27.8.0 Lab 2: Optimizing Renewable Energy Microgrids
27.8.0.0 Background
27.8.0 Lab 3: Optimizing Renewable Energy for McMurdo Station, Antarctica
27.8.0.0 Background
References
Chapter 28 Reliability Evaluation and Voltage Control Strategy of AC-DC Microgrid
28.1 Introduction
28.2 Typical Topology Evaluation of AC-DC Microgrid
28.2.1 Typical Topology of AC-DC Microgrid
28.2.1.1 AC Grid-Connected Type
28.2.1.2 DC Grid-Connected Type
28.2.1.3 AC-DC Bus Co-Connected Type
28.2.1.4 AC-DC Bus Co-Connected Interconnected Type
28.2.2 Reliability Modeling of AC-DC Microgrid
28.2.2.1 Reliability Model of PV Power Generation System
28.2.2.2 Reliability Model of Wind Power Generation System
28.2.2.3 Reliability Model of Diesel Generator and Energy Storage
28.2.2.4 Reliability Model of Load
28.2.3 Reliability Analysis for the AC-DC Microgrid
28.2.3.1 Equivalent Model of PCC
28.2.3.2 Reliability Assessment for the AC-DC Microgrid Based on PCC Equivalent Generator Model
28.3 Coordinated Optimization for the AC-DC Microgrid
28.3.1 Small Signal Model of Droop Control AC-DC Microgrid
28.3.1.1 VSC Model
28.3.1.2 The Line Model
28.3.1.3 The Load Model
28.3.2 Objective Function and Constraints for Droop Coefficient Optimization
28.3.2.1 Objective Function
28.3.2.2 Constraints
28.3.3 Optimal Solution Algorithm for Droop Coefficient
28.4 Case Study
28.4.1 Reliability Analysis of AC-DC Microgrid
28.4.2 Voltage Control Strategy of AC-DC Microgrid
28.4.2.1 Analysis of Optimization Results
28.4.2.2 Analysis of Droop Coefficient Optimization Results
28.4.2.3 Model Comparison Analysis
28.5 Actual Project Construction
28.6 Conclusion
28.7 Exercises
References
Chapter 29 Self-Organizing System of Sensors for Monitoring and Diagnostics of a Modern Microgrid
29.1 Introduction
29.2 Structures for Building Modern Microgrids
29.2.1 Fundamental Structures
29.2.1.1 Branching
29.2.1.2 Loops
29.2.2 Dynamic Structure
29.3 Requirements for the Monitoring and Diagnostics System of Modern Microgrids
29.4 Communication Systems in Microgrids
29.5 Sensors
29.5.1 Energy Flow Sensor
29.5.1.1 Construction of the Sensor
29.5.2 Why Energy Flow Sensors and not Ammeters?
29.6 Network Topology Identification Algorithm
29.6.1 Algorithm
29.6.2 Simplified Algorithm
29.6.3 Illustration of the Algorithm
29.7 Implementation
29.8 Exercise
References
Chapter 30 Event Detection, Classification, and Location Identification with Synchro-Waveforms
30.1 Introduction
30.2 Event Detection
30.2.1 Synchronized Lissajous Curve
30.2.2 Event Detection Methodology
30.2.3 Event Detection Results
30.3 Event Classification
30.3.1 Challenging Factors
30.3.1.1 Impact of the Event Angle
30.3.1.2 Impact of the Event Location
30.3.1.3 Impact of other Event Parameters
30.3.2 Synchronized Lissajous Curve as Image
30.3.3 Convolutional Neural Networks
30.3.4 Event Classification Results
30.3.5 Classification Based on Images Versus Time Series
30.4 Event Location Identification
30.4.1 Modal Analysis of Captured Transient Synchronized Waveform Measurements
30.4.1.1 Single-Signal Versus Multi-Signal Modal Analysis
30.4.1.2 Selecting the Time Window and the Number of Modes
30.4.1.3 Selecting the Dominant Transient Event Mode(s)
30.4.1.4 Comparison with Time-Domain Analysis
30.4.2 Constructing the Feeder Model at the Dominant Transient Modes
30.4.2.1 Case I: Transient Event Does Not Create a New Mode
30.4.2.2 Case II: Transient Event Creates a New Mode
30.4.2.3 Load Modeling in Cases I and II
30.4.3 Event Location Methodology
30.4.3.1 Forward Sweep and Backward Sweep
30.4.3.2 Minimizing Discrepancy
30.4.3.3 Algorithm
30.4.3.4 Extension to Arbitrary Number of WMUs
30.4.4 Event Location Results
30.4.4.1 Scenario I: Sub-cycle Incipient Fault
30.4.4.2 Scenario II: Multi-cycle Incipient Fault
30.4.4.3 Scenario III: Permanent Fault
30.4.4.4 Scenario IV: Capacitor Bank Switching Event
30.4.4.5 Sensitivity Analysis
30.5 Applications
30.6 Exercises
References
Chapter 31 Traveling Wave Analysis in Microgrids
31.1 Introduction
31.2 Background Theories
31.2.1 Mathematical Analysis
31.2.2 Transient Response of Instrument Transformers
31.3 Challenges for TW Applications in Microgrid
31.3.1 Simulations Scenarios
31.4 Proposed Traveling Wave Protection Scheme
31.4.1 Periodic Reflected Traveling Waves from IBR
31.4.2 Merging Unit (MU)
31.4.2.1 Preprocessing
31.4.2.2 Traveling Wave Extraction
31.4.2.3 Modal Components
31.4.2.4 Settings to Avoid Superimposition
31.4.3 Central Unit (CU)
31.4.3.1 Estimation of the Frequency of Occurrence (F.o.O)
31.4.3.2 Fault Location
31.5 Performance Analysis
31.5.1 Microgrid Under Study
31.5.2 Extracted Traveling Waves in MUs
31.5.3 Fault Location Identification
31.5.4 Performance for Faults at Different Locations
31.5.5 Performance for Different Fault Resistance
31.5.6 Performance for Different Fault Inception Angles
31.5.7 Large-Scale Analysis
31.6 Conclusion
31.7 Exercises
References
Chapter 32 Neuro-Dynamic State Estimation of Microgrids
32.1 Background
32.2 Preliminaries of Physics-Based DSE
32.3 Neuro-DSE Algorithm
32.3.1 ODE-Net-Enabled ExSys Modeling
32.3.2 Physics-Based InSys Modeling
32.3.3 Neuro-DSE Algorithm
32.3.4 Joint Estimation of Dynamic States and Parameters via Neuro-DSE
32.4 Self-Refined Neuro-DSE
32.4.1 Self-Refined Training of ODE-Net
32.4.2 Procedure of Neuro-DSE+ Algorithm
32.5 Numerical Tests of Neuro-DSE
32.5.1 Test System and Algorithm Settings
32.5.2 Validity of Neuro-DSE
32.5.2.1 Neuro-DSE Under Different Noise Levels
32.5.2.2 Comparison with Conventional DNN-Based DSE
32.5.2.3 Neuro-DSE Under Different NMs Compositions
32.5.3 Efficacy of Neuro-DSE+
32.5.3.1 Neuro-DSE+ Under Different Noise Levels
32.5.3.2 Neuro-DSE+ Under Different Measurement Availability
32.6 Exercises
References
Chapter 33 Hydrogen-Supported Microgrid toward Low-Carbon Energy Transition
33.1 Introduction
33.2 Hydrogen Production in Microgrid Operation
33.2.1 Framework
33.2.2 Modeling for Power-to-Hydrogen
33.2.3 System Model
33.3 Hydrogen Utilization in Microgrid Operation
33.3.1 Framework
33.3.2 Modeling for FCHEV
33.3.2.1 Techno-Economic Analysis
33.3.2.2 FCHEV Mileage Model
33.3.2.3 Charging/Refueling Station Model
33.3.3 Trans-Energy System Scheduling
33.3.3.1 Optimal Scheduling Model
33.3.3.2 Benefits of FCHEV
33.4 Case Studies
33.4.1 Hydrogen Production in Microgrid Operation
33.4.2 Hydrogen Utilization in Microgrid Operation
33.5 Exercises
33.6 Acknowledgement
References
Chapter 34 Sharing Economy in Microgrid
34.1 Introduction
34.2 Aggregation of Distributed Energy Resources in Energy Markets
34.2.1 Energy Sharing Scheme
34.2.1.1 Energy Trading Without Energy Sharing
34.2.1.2 Energy Trading with Energy Sharing
34.2.1.3 Decentralized Implementation
34.2.2 System Model
34.2.2.1 The Aggregator's Net Cost
34.2.2.2 A User Model Without Energy Sharing
34.2.2.3 The Energy Sharing Model
34.2.3 Profit Sharing Mechanism
34.2.3.1 Sharing Contribution Rate
34.2.3.2 Mechanism Design
34.2.4 Solution Algorithm
34.3 Aggregation of Distributed Energy Resources in Energy and Capacity Markets
34.3.1 Energy Sharing Scheme
34.3.1.1 Market Framework
34.3.1.2 Energy Trading Scheme Comparison
34.3.2 System Model
34.3.2.1 The Model Without Energy Sharing
34.3.2.2 The Energy Sharing Model
34.3.3 Profit Sharing Mechanism
34.3.3.1 Sharing Contribution Rate
34.3.3.2 Mechanism Design
34.4 Case Studies
34.4.1 Aggregation of DERs in Energy Markets
34.4.2 Aggregation of DERs in Energy and Capacity Markets
34.5 Exercises
34.6 Acknowledgement
References
Chapter 35 Microgrid: A Pathway to Mitigate Greenhouse Impact of Rural Electrification
35.1 Introduction
35.2 System Model
35.2.1 Rural Resident Data
35.2.2 Electric Power Data
35.2.3 Rural Heating in Microgrid
35.2.4 Electricity Dispatch Model
35.2.5 National Carbon Emission Estimation
35.2.6 Cost Analysis for Microgrid Heating
35.3 Case Studies
35.3.1 Provincial Carbon Emissions Caused by Electric Heating Policy
35.3.2 National Impacts of Electric Heating Policy
35.3.3 Techno-Economic Analysis for Microgrid Heating
35.4 Discussion
35.5 Exercises
35.6 Acknowledgement
References
Chapter 36 Operations of Microgrids with Meshed Topology Under Uncertainty
36.1 Self-sufficiency and Sustainability of Microgrids Under Uncertainty
36.1.1 AC Power Flow
36.1.1.1 AC Power Flow for Radial Topologies
36.1.1.2 AC Power Flow for Meshed Topologies
36.1.2 Intermittent Renewables
36.1.3 Tap Changers
36.2 Microgrid Model: Proactive Operation Optimization Under Uncertainties
36.2.1 Objective
36.2.1.1 Generation Capacity Constraints
36.2.1.2 Ramp-Rate Constraints
36.2.1.3 Droop-Control Constraints
36.2.1.4 Tap-Changer Constraints
36.3 Solution Methodology
36.3.1 Surrogate Absolute-Value Lagrangian Relaxation
36.3.1.1 Relaxed Problem
36.3.2 Surrogate "l1-Proximal" Lagrangian Relaxation
36.3.2.1 Linearized Relaxed Problem
36.3.2.2 Feasibility
36.4 Conclusions
36.5 Exercises
References
Chapter 37 Operation Optimization of Microgrids with Renewables
37.1 Introduction
37.2 Existing Work
37.3 Mathematical Modeling
37.3.1 Modeling of Devices
37.3.1.1 Modeling of CCHP
37.3.1.2 Modeling of Natural Gas Boilers
37.3.1.3 Modeling of Electric Chillers
37.3.2 Modeling of Uncertain Renewables
37.3.3 Modeling of Battery and Other Devices Based on the PV States
37.3.3.1 Modeling of Battery Based on PV States
37.3.3.2 Modeling of CCHP, Boilers, and Chillers Based on PV States
37.3.4 Modeling of System Balance
37.3.4.1 Electrical Balance
37.3.4.2 Thermal Balance
37.3.5 Objective Function
37.4 Solution Methodology
37.5 Exercises
References
Index
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