Microgrids and Methods of Analysis

✍ Scribed by Gevork B. Garehpetian, Hamid Reza Baghaee, Masoud M. Shabestary

• Publisher: Academic Press
• Year: 2021
• Tongue: English
• Leaves: 349
• Edition: 1
• Category: Library

✦ Synopsis

The increasing penetration of distributed energy resource (DER), distributed generation (DG) and energy storage system (ESS) units in distribution grids leads to the emergence of the concepts of active distribution networks (ADNs), microgrids, and virtual power plants. Nowadays, the use of electronically-coupled distributed energy resources is of great interest that can provide the power of demand side alone or in a small electricity grid. A microgrid is a small-scale power grid in low voltage network that must be able to locally solve energy issues and enhance the flexibility and can operate either in grid-connected or islanded/autonomous mode of operation. To study them, researchers need an appropriate set of methods, software tools, analogous to those exist for large interconnected power systems.

The book Microgrids and Methods of Analysis addresses systematic analysis, control/protection systems design, and optimal operation of a distribution system under high penetration of DERs analogous to those that exist for large interconnected power systems.

✦ Table of Contents

Cover
Front-Matter_2020_Microgrids-and-Methods-of-Analysis
Microgrids and Methods of Analysis
Copyright_2021_Microgrids-and-Methods-of-Analysis
Copyright
Contents
Chapter-1---Introduction_2021_Microgrids-and-Methods-of-Analysis
1 . Introduction
1. Microgrids
2. Challenges
3. Purpose and target audiences
4. Benefits to the audiences
5. Outline of the book
References
Chapter-2---Microgrid-control-strategi_2021_Microgrids-and-Methods-of-Analys
2 . Microgrid control strategies
1. Introduction
2. Basic infrastructures for control and management of microgrids
3. Microgrid control schemes
3.1 Centralized control
3.2 Decentralized control
3.3 Distributed control
3.4 Hierarchical control
4. Current challenges and future trends in control and management of microgrids
5. Case study 1: design of an improved hierarchical control structure
5.1 Design procedure of control structure
5.1.1 Primary control level
5.1.1.1 Droop control
5.1.1.2 Inner control loops
5.1.2 Secondary control level
5.1.3 Tertiary control level
5.1.4 Control of reactive power reference using fuzzy logic control
5.2 Simulation results
5.2.1 Islanded operation with step load change
5.2.2 Motor starting
5.2.3 Grid-connected operation mode
5.2.4 Three-phase to ground fault in islanded mode and fault ride through capability, real-time verification
5.3 Discussions
6. Case study 2: improvement of hierarchical control performance for unbalanced and nonlinear loads
6.1 Basic structure and control levels
6.1.1 Self-tuning filter
6.1.2 Virtual impedance
6.1.3 Harmonic compensation
6.1.4 Algorithm of harmonic power flow
6.2 Simulation results
6.2.1 Nonlinear load effect
6.2.2 Unbalanced load effect
6.2.3 Distributed energy resource outage and plug-and-play operation
7. Summary
References
Chapter-3---Power-flow-analysis-of-micro_2021_Microgrids-and-Methods-of-Anal
3 . Power-flow analysis of microgrids
1. Fundamental-frequency deterministic power flow
1.1 Introduction
1.2 Microgrid dynamic and steady-state modeling
1.2.1 Voltage source converter-based distributed energy resources
1.2.2 Load modeling
1.2.3 Droop-controlled distributed energy resources
1.2.4 Nonlinear model of type-3 doubly fed induction generator-based wind generation
1.2.5 Solar photovoltaic nonlinear model
1.3 Power flow problem
2. Radial basis function neural networks
3. Proposed algorithm
3.1 Power flow algorithm
3.2 Simulation results
3.2.1 Case study 1: meshed microgrid
3.2.2 Case study 2: radial microgrid
3.2.3 Case study 3: microgrid with droop-controlled distributed energy resources
3.2.4 Case study 4: comparison between the proposed and modified ladder iterative power flow analysis algorithms
3.2.5 Case study 5: microgrid with AC and DC sections
3.3 Discussion and conclusion
4. Probabilistic power flow problem
4.1 Uncertain elements models in microgrid
4.1.1 Load probabilistic model
4.1.2 Distributed energy resource probabilistic model
4.1.2.1 Wind generation probabilistic model
4.1.2.2 Probabilistic model of photovoltaic
4.1.3 Probabilistic model of plug-in hybrid electric vehicles
4.2 Probabilistic power flow problem
4.2.1 Deterministic power flow problem
4.2.2 Probabilistic power flow
4.2.3 Correlation between two uncertain variables
4.3 Suggested radial basis function neural network-based probabilistic power flow
4.3.1 Radial basis function neural network
4.3.2 Enhanced unscented transformation
4.3.3 Theory of possibility
4.3.4 Alpha-cut method
4.3.5 Defuzzification
4.3.6 Proposed algorithm
4.4 Simulation results
4.5 Six-Bus system with wind generation system and photovoltaic panel
4.5.1 14-Bus grid containing wind generation systems and plug-in hybrid electric vehicle charging station
4.5.2 118-Bus grid including wind generation systems
4.5.3 Comparison of computation time in different methods
4.6 Main result
5. Extension of harmonic power flow
5.1 Proposed radial basis function neural network-based harmonic power flow
5.1.1 Harmonic power flow problem
References
Further reading
Chapter-4---Fault-analysis_2021_Microgrids-and-Methods-of-Analysis
4 . Fault analysis
1. Introduction
2. Droop method-based control, hierarchical organization
2.1 Droop control method
2.2 Virtual impedance
2.3 Internal control loops
2.4 Secondary control
2.5 Tertiary control
3. Direct voltage control
3.1 Power management of microgrid and its synchronization
3.2 Local controllers of inverter-based distributed energy resources
4. Fault model: general form
4.1 Droop-based control of inverter-interfaced distributed energy resources
4.2 Inverter current limiting
4.3 Directly voltage-controlled inverted-interfaced distributed energy resources
4.4 Equivalent phase network
4.5 Short-circuit contribution of wind generation and photovoltaic systems
5. Simulation results
5.1 Performance evaluation of the proposed strategy
5.2 Fault studies on islanded inverter-interfaced distributed energy resources in various reference frames
5.3 Droop- and directly voltage-controlled-based methods performance comparison versus dynamic loads
6. Summary
References
Further reading
Chapter-5---Operation-under-unbalanced-con_2021_Microgrids-and-Methods-of-An
5 . Operation under unbalanced conditions
1. Introduction
2. Recent grid code requirements
2.1 Low-voltage ride-through requirements
2.2 High-voltage ride-through requirements
2.3 Reactive current injection requirements
2.4 Frequency control and active power restoration
2.5 Asymmetric ride-through scheme
2.5.1 Two-sample asymmetric ride-through schemes
2.5.2 ART-1 and ART-2 schemes with single-phase and double-phase faults
3. Operation of interconnecting converters under unbalanced conditions
3.1 Minimum oscillation on active and reactive powers
3.2 Minimum fault current
3.3 Maximum allowable active and reactive power injection
3.4 Voltage support and maximum allowable active power injection
4. Voltage support methods under unbalanced conditions
4.1 Positive-sequence reactive current injection
4.2 Mixed-sequence reactive current injection
4.3 Dynamic regulation of phase-voltage magnitudes
5. Summary
References
Chapter-6---Microgrid-protection_2021_Microgrids-and-Methods-of-Analysis
6 . Microgrid protection
1. Introduction
2. Control system
2.1 Hierarchical droop-based control structure
2.2 Direct voltage control scheme
3. Fault model of inverter-interfaced distributed energy resource units
3.1 HDRC-based inverter-interfaced distributed energy resource units
3.2 Inverter current limiting
3.3 Direct voltage control-based inverter-interfaced distributed energy resource units
4. Proposed fault detection method
4.1 Transient monitoring function
4.2 Proposed fault detection scheme
4.3 Artificial neural network-based single-phase auto reclosing scheme
5. Overcurrent/overload protection scheme
5.1 Overload protection
5.2 Overcurrent protection
6. Simulation results
6.1 Case 1: motor starting
6.2 Case 2: overload protection
6.3 Case 3: fault response of isolated inverter-interfaced distributed energy resource for different reference frames
6.4 Case 4: real-time verification using real-time digital simulator-Three-phase fault, overcurrent protection, and improvement ...
6.5 Discussions
7. Pareto-optimal solution for coordination of overcurrent relays in interconnected networks and Multi-distributed energy reso ...
7.1 Problem formulation
7.2 Proposed method
7.2.1 Challenges for time setting multiplier: continuous or discrete
7.2.2 Optimization algorithm
7.2.3 Objective functions
7.2.4 Software implementation and coefficients of characteristic curves
7.3 Simulation results and discussion
7.3.1 Case 1: 8-bus system-comparison with Refs. [22] and [31].
7.3.2 Case 2: IEEE 30-bus system-comparison with Refs. [21] and [31].
7.3.3 Case 3: Wood and Woollenberg 6-bus system- comparison with Ref. [43].
7.3.4 Case 4: OCRC problem in microgrids-comparison with Refs. [61,62].
7.3.5 Application of directional OC relays and OCRC problem in microgrids - comparison with [61].
8. Conclusion
References
Further reading
Chapter-7---Optimal-sizing-of-microgri_2021_Microgrids-and-Methods-of-Analys
7 . Optimal sizing of microgrids
1. Introduction
2. Optimal hybrid wind generation/photovoltaic/battery energy storage system
2.1 Modeling and simulation of wind generation/photovoltaic system
2.2 System objective function
3. Hybrid photovoltaic/wind generation/fuel cell network optimal design
3.1 Modeling
3.1.1 Photovoltaic and wind generation units
3.1.2 Electrolyzer, hydrogen storage tank, and fuel cell
3.1.3 Converter
3.1.3.1 Operation philosophy
3.1.3.1.1 Power generated by renewable units
3.1.3.1.2 Operation strategy
3.2 Reliability/cost assessment
3.2.1 Reliability indices
3.2.1.1 Loss of load expected
3.2.1.2 Loss of energy expected/Expected energy not supplied
3.2.1.3 Loss of power supply probability
3.2.1.4 Equivalent loss factor
3.2.2 System reliability
3.2.2.1 Cost of loss of load
3.2.2.2 Approximate method
3.2.3 Problem statement
4. Particle swarm optimization and multiple-objective particle swarm optimization algorithms
4.1 Particle swarm optimization algorithm
4.2 Multiobjective optimization
4.3 Multiobjective particle swarm optimization
4.4 Best trade-off solution
5. Simulation results
5.1 Case study 1: hybrid system including photovoltaic/wind generation/battery energy storage system
5.1.1 Optimal installation angle of photovoltaic panels
5.1.2 Hybrid system optimal design
5.1.3 Comparison with genetic algorithm
5.2 Case study 2: hybrid system including photovoltaic/wind generation/fuel cell
5.2.1 Base case
5.2.2 100% available components
5.2.3 Impact of DC/AC convertor on reliability
5.2.4 Accurate and approximate methods comparison
5.2.5 Proposed multiple-objective particle swarm optimization and single-objective optimization algorithms comparison
6. Conclusion
Appendix
References
Chapter-8---Power-management-in-hybrid-mic_2021_Microgrids-and-Methods-of-An
8 . Power management in hybrid microgrids
1. Introduction
2. Basic control and management structure of microgrid
3. Hierarchical power management
3.1 Primary control
3.2 Secondary control
3.3 Power management in tertiary control
4. Generalization of power management concepts to DC and hybrid AC/DC microgrids
4.1 AC microgrids
4.2 DC microgrids
4.3 AC/DC microgrids
5. Summary
References
Further reading
Index_2021_Microgrids-and-Methods-of-Analysis
Index
A
B
C
D
E
F
G
H
I
J
L
M
N
O
P
R
S
T
U
V
W
Backcover