Microgrids: Theory and Practice (IEEE Press Series on Power and Energy Systems)

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 α and β
		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
Series Page
EULA