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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
This book discusses relevant microgrid technologies in the context of integrating renewable energy and also addresses challenging issues. The authors summarize long term academic and research outcomes and contributions. In addition, this book is influenced by the authors' practical experiences on microgrids (MGs), electric network monitoring, and control and power electronic systems. A thorough discussion of the basic principles of the MG modeling and operating issues is provided. The MG structure, types, operating modes, modelling, dynamics, and control levels are covered. Recent advances in DC microgrids, virtual synchronousgenerators, MG planning and energy management are examined. The physical constraints and engineering aspects of the MGs are covered, and developed robust and intelligent control strategies are discussed using real time simulations and experimental studies.
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Veröffentlichungsjahr: 2017
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Table of Contents
Cover
Title Page
Copyright
Dedication
Foreword
Preface
Acknowledgments
Chapter 1: Grid-connected Renewable Energy Sources
1.1 Introduction
1.2 Renewable Power Generation
1.3 Grid-connected Wind Power
1.4 Grid-Connected PV Power
1.5 Summary
References
Chapter 2: Renewable Power for Control Support
2.1 Introduction
2.2 Wind-Energy-based Control Support
2.3 Renewable Primary Power Reserve
2.4 PV-Energy-Based Control Support
2.5 Integration of Renewable Energy Systems Through Microgrids
2.6 Summary
References
Chapter 3: Microgrids: Concept, Structure, and Operation Modes
3.1 Introduction
3.2 Microgrid Concept and Structure
3.3 Operation Modes
3.4 Control Mechanism of the Connected Distributed Generators in a Microgrid
3.5 Contribution in the Upstream Grid Ancillary Services: Frequency Control Support Example
3.6 Microgrids Laboratory Technologies
3.7 Summary
References
Chapter 4: Microgrid Dynamics and Modeling
4.1 Introduction
4.2 Distribution Network (Main Grid) and Connection Modeling
4.3 Overall Representation of the Grid-Connected Microgrid
4.4 Microgrid Components Dynamics and Modeling
4.5 Simplified Microgrid Frequency Response Model
4.6 A Detailed State-Space Dynamic Model
4.7 Microgrid Dynamic Modeling and Analysis as a Multivariable System
4.8 Summary
References
Chapter 5: Hierarchical Microgrid Control
5.1 Introduction
5.2 Microgrid Control Hierarchy
5.3 Droop Control
5.4 Hierarchical Power Management and Control
5.5 Design Example
5.6 Summary
References
Chapter 6: DC Microgrid Control
6.1 Introduction
6.2 DC Microgrid for a Residential Area
6.3 Low-voltage Bipolar-type DC Microgrid
6.4 Stability Evaluation
6.5 Experimental Study and Results
6.6 A Voltage Control Approach
6.7 Simulation Results
6.8 Experimental Results
6.9 Summary
References
Chapter 7: Virtual Synchronous Generators: Dynamic Performance and Characteristics
7.1 Introduction
7.2 Virtual Synchronous Generator (VSG) and Droop Control
7.3 Virtual Synchronous Generator-Based Oscillation Damping
7.4 A Virtual Synchronous Generator Scheme with Emulating More Synchronous Generator Characteristics
7.5 Active Power Performance Analysis in a Microgrid with Multiple Virtual Synchronous Generators
7.6 Summary
References
Chapter 8: Virtual Inertia-based Stability and Regulation Support
8.1 Introduction
8.2 An Enhanced Virtual Synchronous Generator Control Scheme
8.3 Virtual Synchronous Generator Control in Parallel Operation with Synchronous Generator
8.4 Alternating Inertia-based Virtual Synchronous Generator Control
8.5 Voltage Sag Ride-through Enhancement Using Virtual Synchronous Generator
8.6 Performance Evaluation of the Virtual Synchronous Generator with More Synchronous Generator Characteristics
8.7 Summary
References
Chapter 9: Robust Microgrid Control Synthesis
9.1 Introduction
9.2 Case Study and State-Space Model
9.3 H
∞
and Structured Singular Value (μ) Control Theorems
9.4 H
∞
-Based Control Design
9.5 µ-Based Control Design
9.6 Order Reduction and Application Results
9.7 Robust Multivariable Microgrid Control Design
9.8 Robust Tuning of VSG Parameters
9.9 Summary
References
Chapter 10: Intelligent Microgrid Operation and Control
10.1 Introduction
10.2 Intelligent Control Technologies
10.3 ANN-based Power and Load Forecasting in Microgrids
10.4 Intelligent Frequency and Voltage Control in Microgrids
10.5 Summary
References
Chapter 11: Emergency Control and Load Shedding in Microgrids
11.1 Introduction
11.2 Load Shedding as a Well-known Emergency Control Strategy
11.3 Load Shedding Algorithm: Example 1
11.4 Load Shedding Algorithm: Example 2
11.5 Undervoltage–frequency Load Shedding
11.6 Summary
References
Chapter 12: Microgrid Planning and Energy Management
12.1 Introduction
12.2 Microgrid Planning: An Example
12.3 Forecasting Techniques
12.4 Energy Management
12.5 Emission Reduction and Economical Optimization
12.6 Day-ahead Optimal Operation and Power Reserve Dispatching
12.7 Robust Energy Consumption Scheduling in Interconnected Microgrids
12.8 Summary
References
Appendix A: Appendix
Index
End User License Agreement
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Guide
Cover
Table of Contents
Foreword
Preface
Begin Reading
List of Illustrations
Chapter 1: Grid-connected Renewable Energy Sources
Figure 1.1 Power characteristics of renewable and conventional generators: (a) renewable power and (b) conventional power.
Figure 1.2 Wind generator with power electronics: (a) minimum electronics unit, (b) partial power converter, (c) full-scale power converter with gearbox, and (d) full-scale power converter without gearbox.
Figure 1.3 A conventional variable-speed WPG.
Figure 1.4 Real recorded wind speed-power pattern: (a) wind speed and (b) wind power.
Figure 1.5 Equivalent average model of the power electronic converters.
Figure 1.6 Block diagram of the considered wind energy generation system.
Figure 1.7 Blade characteristic:
C
T
versus
λ
for a fixed blade angle.
Figure 1.8 Block diagram of the grid connection system.
Figure 1.9 Block diagram of the DC bus.
Figure 1.10 Block diagram of the entire wind energy conversion system.
Figure 1.11 Hierarchical control structure of the wind energy conversion system.
Figure 1.12 Turbine power-speed characteristic.
Figure 1.13 Control scheme of the wind energy generation system.
Figure 1.14 Block diagram of the oriented field control of the electrical machine.
Figure 1.15 Control scheme of the grid connection system.
Figure 1.16 Block diagram of the line current control in the grid connection system.
Figure 1.17 Control scheme of the DC bus.
Figure 1.18 Control scheme of the entire wind energy conversion system.
Figure 1.19 Block diagram of the automatic control units for the wind energy conversion system.
Figure 1.20 Power flow exchanges around the DC bus.
Figure 1.21 Power flow exchange inside the wind energy conversion system.
Figure 1.22 Multilevel representation of the wind energy conversion system.
Figure 1.23 Block diagram of the hierarchical control for the wind energy conversion system.
Figure 1.24 Equivalent average modeling of the power conversion chain with a wind power emulator.
Figure 1.25 Power electronic stage of the wind power emulator.
Figure 1.26 Implementation of the wind energy conversion experimental test bench; (a) block diagram and (b) laboratory experiment.
Figure 1.27 Model representation of the wind energy conversion experimental test bench.
Figure 1.28 Test results of the wind energy conversion experimental test bench.
Figure 1.29 Structures of hybrid power systems for distributed generation.
Figure 1.30 A distribution grid example with connected SPGs.
Figure 1.31 Various structures of HPSs: (a) AC-coupled, (b) DC-coupled, and (c) mixed structure.
Figure 1.32 Grid-connected PV-based active generator with the control system.
Figure 1.33 Equivalent electrical diagram of the PV-based active generator.
Figure 1.34 Equivalent electrical diagram of PV power conversion system.
Figure 1.35 Equivalent electrical diagram of the batteries energy storage system.
Figure 1.36 Electrical diagram of the grid connection.
Figure 1.37 The block diagram of the PV active generator.
Figure 1.38 Hierarchical control structure for the active PV generator.
Figure 1.39 Power characteristics of one PV module.
Figure 1.40 Block diagram of a grid-connected PV system with control loops.
Figure 1.41 Block diagram of the automatic control units for the active PV generator.
Figure 1.42 Power modeling and control.
Figure 1.43 The grid-connected active PV generator (HPG) test system: (a) system configuration and (b) experimental units.
Figure 1.44 Operation strategies: (a) grid-following strategy and (b) source supplying strategy.
Figure 1.45 Experimental results for an active PV generator.
Figure 1.46 Experimental results in the nighttime.
Figure 1.47 General control structure of a grid-connected SPG.
Figure 1.48 Simplified DC/AC PWM converter structure.
Figure 1.49 Coordinate transformation of line current and voltage from the stationary
α–β
to the rotating
d–q
coordinates.
Chapter 2: Renewable Power for Control Support
Figure 2.1 Integration of the inertial controller in the control system.
Figure 2.2 Wind turbine inertial response at different operating points ((a)–(c) at full load and (d)–(f) at partial load).
Figure 2.3 The HV grid, main generation plants, and DG locations in Guadeloupe (France).
Figure 2.4 (a) Frequency response, (b) frequency nadir difference, (c) inertial response at partial load, and (d) rotor speed.
Figure 2.5 Influences of the limit of the ROCOP.
Figure 2.6 Influence of the limit of the ROCOP on frequency response.
Figure 2.7 Grid frequency and wind turbine dynamic behavior.
Figure 2.8 Contribution of wind turbine frequency control (full-load case).
Figure 2.9 Contribution of wind turbine frequency control (partial-load case).
Figure 2.10 Wind turbine dynamic behavior with various control schemes.
Figure 2.11 Inertial, primary, and secondary frequency control supports by WT.
Figure 2.12 Operating principle of both reserve allocation strategies.
Figure 2.13 Wind variability while providing reserve from wind farms with the PCS (for 15- and 30-min time steps).
Figure 2.14 The CDF of wind power fluctuations at 15-min intervals (zoom on the last percentile).
Figure 2.15 Duration curve of the instantaneous available reserve and the curtailed power of the aggregated wind farm (application of the PCS).
Figure 2.16 Duration of the instantaneous available reserve of the three wind farms and of the aggregated farm (application of the CCS).
Figure 2.17 Comparison of the reserve allocation strategies during the required period: (a) total curtailed WG, (b) average reserve in percentage of the installed capacity of the aggregated farm, and (c) efficiency indicator.
Figure 2.18 Proposed combined strategy.
Figure 2.19 Duration curve of the instantaneous available reserve and of the curtailed power of the aggregated wind farm with a 30-min time step (application of the combined reserve allocation strategy).
Figure 2.20 SPG response: (a) generated power
P
G
(pu) and nominal power
P
N
and (b) nonactive current (pu).
Figure 2.21 An example for injecting the harmonic component: (a) harmonic injection turned OFF and (b) harmonic injection turned ON. Curves from top to bottom: phase-to-neutral grid voltage, inverter current, local load current, and current flowing to the substation.
Figure 2.22 A microgrid example.
Figure 2.23 Example for organizing interconnected MGs in a smart grid.
Chapter 3: Microgrids: Concept, Structure, and Operation Modes
Figure 3.1 Basic MG architecture with an MGCC.
Figure 3.2 A typical MG structure.
Figure 3.3 MG in an interconnected distribution power grid.
Figure 3.4 Scheme of an isochronous speed control system of gas turbine.
Figure 3.5 Grid-following strategy of a PV generator with a variable DC bus voltage for MPPT.
Figure 3.6 Grid-following strategy of a PV generator with a generator-side converter for MPPT.
Figure 3.7 Grid-following strategy of a dispatchable generator in the PQ mode.
Figure 3.8 Power dispatching strategy of a gas microturbine for a VSI control.
Figure 3.9 Equivalent single-phase circuit of the grid connection side and vector diagram.
Figure 3.10 Increase of the shift.
Figure 3.11 The MG case study for supporting the main grid frequency regulation.
Figure 3.12 General organization of the system.
Figure 3.13 Idealized power frequency control characteristic.
Figure 3.14 Block diagram representation for the ancillary services provided by the MG.
Figure 3.15 The proposed power management scheme for the given case study.
Figure 3.16 Power management.
Figure 3.17 Energy limitation for the supercapacitor storage level: (a) restitution mode and (b) accumulation mode.
Figure 3.18 System response without MG support: (a) frequency, (b)
P
hv
, and (c) HV line current.
Figure 3.19 System response with MG support: (a) frequency, (b)
P
hv
, (c) HV line current, (d)
P
mg_dno_ref
, (e) powers from sources inside the MG, (f) injected power at the PCC, (g) supercapacitor terminal voltage, and (h) supercapacitor storage energy.
Figure 3.20 A real MG under High-Tech Green Campus project at the Kyushu Institute of Technology (Kitakyushu, Japan, September 2012).
Figure 3.21 A simulation software-based MG laboratory (an MG example in MATLAB–Simulink
™
environment).
Figure 3.22 Analog power system simulator as an HIL-based power system laboratory in Kyushu Power Electric Co. (Fukuoka, Japan, June 2010).
Figure 3.23 A hybrid laboratory (Power System Control laboratory, Kumamoto University, Kumamoto, Japan, August 2010).
Figure 3.24 HIL simulation structure: (a) subsystems of an electric drive, (b) signal level HIL simulation, (c) power level HIL simulation, and (d) mechanical level HIL simulation.
Figure 3.25 The HIL test setup.
Figure 3.26 A view of SMGRC laboratory (University of Kurdistan, Sanandaj, Iran, May 2016).
Figure 3.27 A view of the L2EP laboratory (Lille, France, January 2016).
Figure 3.28 A view of the PE&EE laboratory (Osaka University, Osaka, Japan, August 2015).
Chapter 4: Microgrid Dynamics and Modeling
Figure 4.1 Global scheme of an MG in a grid-connected mode.
Figure 4.2 Diesel group structure.
Figure 4.3 Block diagram of the diesel group standard model.
Figure 4.4 The main grid (diesel group) representation as a single block.
Figure 4.5 Model of one phase of an MV transmission line.
Figure 4.6 Simplified model of an MV transmission line (a) and its equivalent symbol (b).
Figure 4.7 MV line model: (a) block diagram and (b) single block representation.
Figure 4.8 Transformer model: (a) block diagram and (b) single block representation.
Figure 4.9 Modeling of a load as a current-type source.
Figure 4.10 Modeling of a load as a voltage-type source.
Figure 4.11 Coupling at the grid bus: (a) single-line diagram and (b) block diagram representations.
Figure 4.12 Coupling at the MV bus: (a) single-line diagram and (b) block diagram representations.
Figure 4.13 Coupling at the MG bus.
Figure 4.14 Global architecture representation.
Figure 4.15 Global architecture modeling for contribution of MG in the power grid frequency regulation.
Figure 4.16 The islanded MG described in Figure 3.11: (a) case study and (b) overall representation.
Figure 4.17 Equivalent circuit of a PV cell.
Figure 4.18
I
(
V
) characteristic of a PV panel.
Figure 4.19 Equivalent electrical circuit of a typical battery model.
Figure 4.20 Discharge curve (
Q–V
) of a battery.
Figure 4.21 Transmission line model of a supercapacitor.
Figure 4.22 Three-branch model of a supercapacitor.
Figure 4.23 Supercapacitor equivalent circuit model: (a) classical model and (b) simplified model.
Figure 4.24 Supercapacitor power conversion system.
Figure 4.25 Block diagram representation of the connected SBC to the MG.
Figure 4.26 Diagram of the DC chopper in the supercapacitor storage system: (a) electric diagram of the DC chopper and (b) DC chopper with ideal switches.
Figure 4.27 Classical PWM method.
Figure 4.28 Equivalent average electrical diagram of the DC chopper.
Figure 4.29 Diagram of the three-phase inverter in the grid power conversion system: (a) electrical diagram of the three-phase inverter and (b) three-phase inverter with ideal switches.
Figure 4.30 Classical sinusoidal PWM method.
Figure 4.31 Equivalent electrical diagram of the three-phase inverter.
Figure 4.32 Equivalent electrical average diagram of the three-phase rectifier.
Figure 4.33 Simplified schematic of an islanded MG: (a) single-line diagram and (b) dynamic frequency response model.
Figure 4.34 An MG case study: (a) single-line diagram and (b) frequency response model.
Figure 4.35 An MG with
N
DGs: (a) schematic diagram and (b) equivalent circuit.
Figure 4.36 Single-line diagram of the simulated MG.
Figure 4.37 System eigenvalues for initial conditions with (a) R load, (b) RL load, and (c) RLC load.
Figure 4.38 System eigenvalues for changes in VSC filter impedance with (a) R load, (b) RL load, and (c) RLC load.
Figure 4.39 Closed-loop block diagram.
Figure 4.40 Single-line diagram of the MG example.
Figure 4.41
abc
, , and frames in relation to each other.
Chapter 5: Hierarchical Microgrid Control
Figure 5.1 Hierarchical control levels in MGs.
Figure 5.2 A general scheme for MG controls.
Figure 5.3 Local control loops in a typical voltage-controlled VSC-based DG.
Figure 5.4 Local, secondary, central, and global controls.
Figure 5.5 Single machine infinite bus system model.
Figure 5.6 Load tracking by generators with different droops.
Figure 5.7 An inverter-based DG.
Figure 5.8 Droop characteristics for inverter-based DGs with inductive line impedance: (a)
f–p
droop, (b)
V–Q
droop.
Figure 5.9 An example for GDC-based droop control.
Figure 5.10 System response following a load disturbance: (a) load change pattern, (b) voltage and frequency for different values.
Figure 5.11 A power converter with virtual output impedance loop.
Figure 5.12 Timing classification of power control functions in the context of MG.
Figure 5.13 Droop controllers for the power dispatching strategy.
Figure 5.14 Centralized control of an MG in an interactive control framework.
Figure 5.15 Architecture of EMS layer of an MG.
Figure 5.16 HIL test setup.
Figure 5.17 Signals communication.
Figure 5.18 The MG noncritical loads (real) change pattern.
Figure 5.19 The MT (fictitious) response: (a) power, (b) shaft speed, (c) gas mass flow rate, (d) DC bus terminal voltage, and (e) a zoomed view of (d).
Figure 5.20 PV power (fictitious).
Figure 5.21 The SC unit response (real): (a) On/Off state, (b) exchanged power, and (c) terminal voltage.
Chapter 6: DC Microgrid Control
Figure 6.1 System configuration of the DC MG for residential area.
Figure 6.2 Interconnected operation.
Figure 6.3 Intentional islanding operation.
Figure 6.4 Flowcharts of disconnection (a) and reconnection (b).
Figure 6.5 A low-voltage bipolar-type DC MG.
Figure 6.6
V–I
curve of constant power load.
Figure 6.7 Simplified DC distribution circuit.
Figure 6.8 Stability condition of the simplified circuit.
Figure 6.9 Target model in this study.
Figure 6.10 Simulation circuit.
Figure 6.11 Equivalent circuit and control block of rectifier.
Figure 6.12 Circuit of the experimental system.
Figure 6.13 Laboratory-scale DC MG experimental system.
Figure 6.14 Gas engine unit and hot water tank.
Figure 6.15 DC power output.
Figure 6.16 Experimental results of a voltage sag (50%, 0.5 s).
Figure 6.17 Experimental results of disconnection from the utility grid.
Figure 6.18 Experimental results of reconnection from the utility grid.
Figure 6.19 Configuration of the case study in the laboratory.
Figure 6.20 Control for DC/DC converter for energy storage.
Figure 6.21 Droop control feature.
Figure 6.22 Circuit and control diagrams to obtain the relation between the steady-state error and the gain
K
c
.
Figure 6.23 Voltage–gain and gain–output power characteristics of the DC/DC converter (voltage variation 2%).
Figure 6.24 Voltage–gain and gain–input power characteristics of the DC/DC converter (voltage variation 2%).
Figure 6.25 Droop control to obtain the voltage reference.
Figure 6.26 Simulation circuit.
Figure 6.27 Events pattern for the performed simulations (initial condition
W
2
/
W
1
≈ 2).
Figure 6.28 Simulation results for gain-scheduling control only (initial condition
W
2
/
W
1
≈ 2).
Figure 6.29 Simulation results for gain-scheduling control and droop control with
K
v
= 10 (initial condition
W
2
/
W
1
≈ 2).
Figure 6.30 Simulation results for gain-scheduling control and droop control with
K
v
= 50 (initial condition
W
2
/
W
1
≈ 2).
Figure 6.31 Circuit of experimental system.
Figure 6.32 Experimental results of case I (gain-scheduling control only).
Figure 6.33 Experimental results of case II (gain-scheduling control only).
Chapter 7: Virtual Synchronous Generators: Dynamic Performance and Characteristics
Figure 7.1 Conceptual structure of the VSG.
Figure 7.2 A general block diagram for the VSG system.
Figure 7.3 Detailed blocks of Figure 7.2: (a) frequency detector block, (b) governor model block, and (c)
Q
droop block.
Figure 7.4 Calculating
ω
m
by Runge–Kutta method.
Figure 7.5 Droop control for inverter systems.
Figure 7.6
P
droop control.
Figure 7.7 The stand-alone mode model: (a) single-line diagram and (b) simulation circuit.
Figure 7.8 Step responses of DG frequency during a loading transition in stand-alone mode with various parameters.
Figure 7.9 SG-connected mode model: (a) single-line diagram and (b) simulation circuit.
Figure 7.10 (a) Step responses of SG frequency during a loading transition in SG-connected mode with various parameters. (b) A zoomed view of (a).
Figure 7.11
P
Droop control with a first-order lead–lag unit.
Figure 7.12 (a) Step responses of SG frequency during a loading transition in SG-connected mode with specified lag or lead–lag unit in droop control. (b) Zoom in of (a).
Figure 7.13 Eigenvalues when (a)
J
dg
of VSG varies from 1.5
−4
J
0
to 1.5
5
J
0
, (b)
D
dg
of VSG varies from 2
−4
D
0
to 2
5
D
0
, (c)
X
dg
of VSG varies from 2.5
−4
× 0.1374 to 2.5
5
× 0.1374 pu, (d)
T
d_dg
of VSG varies from 1.09
−4
× 0.1 to 1.09
5
× 0.1 s, and (e)
T
d_dg
of droop control varies from 1.25
−4
× 0.1 to 1.25
5
× 0.1 s.
Figure 7.14 Experimental circuit of (a) stand-alone mode and (b) SG-connected mode.
Figure 7.15 TU.
Figure 7.16 Experimental results of stand-alone mode to verify (a) effects of parameters and (b) effects of delays.
Figure 7.17 Experimental results of SG-connected mode to verify the effects of (a) parameters, (b) delays, and (c) inertial droop control.
Figure 7.18 (a) Control of
P
+
mQ
axis, (b)
P
+
mQ
deviation for
P
and
Q
.
Figure 7.19 System response for conventional VSG control:
P
(up signal),
Q
*
(light down signal), and
Q
(dark down signal).
Figure 7.20 System response using proposed control method:
P
(up signal),
P
*
(light down signal), and
Q
(dark down signal).
Figure 7.21 System response using the proposed damping approach: (a)
D
= 0.045 (constant), (b)
ζ
= 0.707, and (c)
ζ
= 1.5.
Figure 7.22 Parallel running system of VSG and SG.
Figure 7.23 Power response of VSG and SG: (a)
D
= 0.045 (constant) and (b)
ζ
= 1.5.
Figure 7.24 Experimental system: (a) single diagram and (b) experimental facilities.
Figure 7.25 The experimental results of the proposed approach: (a)
D
= 0.045 (constant), (b)
ζ
= 0.707, and (c)
ζ
= 1.5.
Figure 7.26 VSG control block diagram.
Figure 7.27 VSG: (a) impedances model and (b) phasor diagram.
Figure 7.28 Characteristics of grid voltage
V
gd
and
V
gq
.
Figure 7.29 The VSG control and grid voltage characteristics.
Figure 7.30 Simplified control loops: (a) from
P
to
ω
R
in an isolated grid and (b) from
P
*
to
P
in a grid-connected operation.
Figure 7.31 Structure of an MG composed of two DGs in islanded mode.
Figure 7.32 Eigenvalue loci with a variation of (a) () or (b) ().
Figure 7.33 Poles and zeros of in the left column and in the right column with a variation of (a) (), (b) (), and (c) ().
Chapter 8: Virtual Inertia-based Stability and Regulation Support
Figure 8.1 Block diagram of the proposed enhanced VSG control.
Figure 8.2 Block diagram of (a) the “stator impedance adjuster” and (b) the “
V
bus
Estimator” blocks of the enhanced VSG control.
Figure 8.3 Block diagram of the “
Q
droop” block of enhanced VSG control.
Figure 8.4 Reactive power control loop: (a) small-signal model and (b) bode plot.
Figure 8.5 Simulation circuit.
Figure 8.6 Simulation results when both DGs are controlled by (a) the basic VSG control, (b) the basic VSG control with the proposed stator impedance adjuster, and (c) the complete proposed enhanced VSG control.
Figure 8.7 Zoom-in simulation results of reactive power and voltage of DG1 at 24 s: (a) the basic VSG control and (b) the proposed enhanced VSG control.
Figure 8.8 Experiment circuit.
Figure 8.9 Experimental results when both DGs are controlled by (a) the basic VSG control, (b) the basic VSG control with the proposed stator impedance adjuster, and (c) the complete proposed enhanced VSG control.
Figure 8.10 An islanded MG composed of an SG and an inverter-based DG: (a) case study and (b) SG's control system.
Figure 8.11 Single SG operation: (a) active power and frequency as well as reactive power and voltage and (b) steady-state SG current waveforms.
Figure 8.12 Block diagram of the proposed modified VSG control system.
Figure 8.13 Detail of existing blocks in the modified VSG control system: (a) “DDSRF” block, (b) “SG Neg.-Seq. Compensation” block, and (c) “stator impedance adjuster” block.
Figure 8.14 Eigenvalues with a variation of .
Figure 8.15 Simulation results for the governor delay: (a) and (b) .
Figure 8.16 Analysis of different values of total output reactance : (a) eigenvalues and (b) simulation results.
Figure 8.17 Simulation results for tuning the proportional gain of transient virtual stator impedance.
Figure 8.18 Simulation results for tuning the ratio of transient virtual stator impedance when (a) , (b) , and (c) .
Figure 8.19 Simulation results under unbalanced loading condition: (a) Case A and (b) Case C (see Table 8.7).
Figure 8.20 Simulation results of steady-state SG and DG current waveforms under unbalanced loading condition: (a) Case A and (b) Case C (see Table 8.7).
Figure 8.21 Power angle curve of a typical SG.
Figure 8.22 Output power, virtual angular velocity, and virtual moment of inertia of VSG with
J
= 6 kg m
2
and
D
= 17 pu: (a) fixed
J
and (b) alternating
J
.
Figure 8.23 Transient energy trajectory after a step change in power reference of VSG with: (a) fixed moment of inertia, and (b) alternating inertia.
Figure 8.24 The kinetic energy (
E
k
), potential energy (
E
P
), and total transient energy (
V
) waveforms after a step increase in power reference of VSG with: (a) fixed moment of inertia and (b) alternating inertia.
Figure 8.25 DC-link power of VSG subjected to a step change in the power reference: (a) VSG with fixed inertia and (b) VSG with alternating inertia.
Figure 8.26 VSG unit in parallel with the SG in MG.
Figure 8.27 VSG and SG powers and SG rotor angle waveforms using (a) fixed moment of inertia and
D
= 17 pu and (b) alternating inertia control and
D
= 0 pu.
Figure 8.28 SG connected to the grid via VSG unit: (a) system configuration, (b) for VSG with fixed moment of inertia, and (c) for VSG with alternating inertia.
Figure 8.29 RMS current, RMS voltage, output power, virtual angular velocity, and virtual moment of inertia of VSG with alternating
J
and
D
= 17 pu after a power command of 4.5 kW.
Figure 8.30 Currents of the VSG subjected to the applied symmetrical voltage sag with (a) the duration of 2 cycles and
h
= 0.1 and (b) the duration of 1.5 cycles and
h
= 0.1.
Figure 8.31 VSG current trajectory in phase plane during (solid line) and after (dotted line) voltage sag with
h
= 0.1: (a) voltage sag with the duration of 1.5 cycles and (b) voltage sag with the duration of 1 cycle.
Figure 8.32 An updated VSG control scheme for voltage sag ride-through enhancement.
Figure 8.33 Power angle curve of an SG subjected to a fault. When a fault occurs, operating point moves on the dotted line and reaches to the point
δ
0
. After fault clearance, it returns to the original curve and oscillates around the equilibrium point
δ
1
.
Figure 8.34 Currents of the VSG with the voltage amplitude, output power, and alternating inertia controls, subjected to a symmetrical voltage sag with the duration of 1.5 cycles and
h
= 0.1 (the severest case).
Figure 8.35 Simulation system.
Figure 8.36 Symmetrical voltage sag at PCC of simulated system due to the three-phase fault.
Figure 8.37 PCC RMS voltage, VSG currents, and DC-link voltage of the system with VSG without the additional controllers, affected by voltage sag.
Figure 8.38 VSG angular velocity, power reference calculated by the governor, VSG output active power, and VSG output reactive power of the system with VSG without the additional controllers, affected by voltage sag.
Figure 8.39 PCC RMS voltage, VSG currents, and DC-link voltage of the system with VSG with the additional controllers, affected by voltage sag.
Figure 8.40 VSG angular velocity, power reference calculated by the governor, VSG output active power, and VSG output reactive power of the system with VSG with the additional controllers, affected by voltage sag.
Figure 8.41 Experimental system.
Figure 8.42 Symmetrical voltage sag at PCC due to symmetrical three-phase fault.
Figure 8.43 Currents and DC-link voltage of the VSG without additional controller subjected to the applied voltage sag, with (a) 1 kW output power, and (b) 2.6 kW output power.
Figure 8.44 Currents and DC-link voltage of the VSG with 1 kW output power and with voltage amplitude, output power, and alternating inertia controller subjected to the applied voltage sag.
Figure 8.45 System response in the presence of a voltage sag.
Figure 8.46 The circuit of the VSG connected to a grid.
Figure 8.47 System response in grid-connected operation: (a) scenario 1-1 (
J
= 4 s,
K
= 5) and (b) scenarios 1-2 and 2-2 (
J
= 4 s,
K
= 10).
Figure 8.48 System response in islanded operation: (a) scenario 3-1 (
P
*
= 0.0 pu, load is 0.0 kW) and (b) scenario 3-2 (
P
*
= 0.0 pu, load is 5.0 kW).
Figure 8.49 Experimental test system.
Figure 8.50 System response in grid-connected operation: (a) scenario 1-1 (
P
*
= 0.5 pu,
J
= 4 s,
K
= 5), (b) scenarios 1-2 and 2-2 (
J
= 4 s,
K
= 10), (c) scenario 1-3 (
J
= 4 s,
K
= 15), (d) scenario 2-1 (
J
= 2 s,
K
= 10), and (e) scenario 2-3 (
J
= 6 s,
K
= 10).
Figure 8.51 System response in islanded operation: (a) scenario 3-1 (
P
*
= 0.0 pu, load is 0.0 kW), (b) scenario 3-2 (
P
*
= 0.0 pu, load is 5.0 kW), (c) scenario 3-3 (
P
*
= 0.5 pu, load is 0.0 kW), and (d) scenario 3-4 (
P
*
= 0.5 pu, load is 5.0 kW).
Chapter 9: Robust Microgrid Control Synthesis
Figure 9.1 Simplified schematic of an islanded MG.
Figure 9.2 MG dynamical frequency response model.
Figure 9.3 Standard
M
–Δ configuration for μ-synthesis.
Figure 9.4 Closed-loop system structure with lumped multiplicative uncertainty.
Figure 9.5 Bode diagram of the perturbed system
P
(
s
).
Figure 9.6 H
∞
standard LFT configuration.
Figure 9.7 (a) S and (b) KS functions of nominal system.
Figure 9.8 (a) S and (b) KS functions in the presence of perturbations (robust performance).
Figure 9.9 Closed-loop system diagram with structured diagonal uncertainty block.
Figure 9.10 The closed-loop configuration for using
D–K
iteration method.
Figure 9.11 Nominal performance (solid) and robust performance (dashed).
Figure 9.12 RP index of perturbed systems (solid) and μ upper and lower bounds of perturbed closed-loop (dashed).
Figure 9.13 Robust stability of
K
, upper bound (solid) and lower bound (dashed).
Figure 9.14 Sensitivity functions of perturbed systems with
K
(solid), (dashed).
Figure 9.15 Comparison between original (solid) and reduced-order (dashed) μ-controller.
Figure 9.16 Comparison between original (solid) and reduced-order (dashed) H
∞
controller.
Figure 9.17 System response for step changes in wind power: (a) wind power change pattern and (b) MG output frequency.
Figure 9.19 System response for step changes in solar power: (a) solar power change pattern and (b) MG output frequency.
Figure 9.21 System response for nonstationary fluctuations: (a) multiple disturbances in load, wind speed, and solar irradiation and (b) MG output frequency.
Figure 9.22 MG output frequency in the presence of 50% uncertainty in
H
and
D
parameters and disturbance signal in Figure 9.20a.
Figure 9.20 System response for step disturbances: (a) multiple disturbances in load, wind speed, and solar irradiation and (b) MG output frequency.
Figure 9.23 Frequency response comparison for the proposed robust control design and conventional PI control design.
Figure 9.24 Frequency response for the shown wind power change pattern in Figure 9.17a.
Figure 9.25 Frequency response for the shown multiple load change in Figure 9.18a.
Figure 9.28 Frequency response in the presence of 50% uncertainty in
H
and
D
parameters and disturbance signal in Figure 9.20a.
Figure 9.29 Frequency response for multiple disturbances in load, wind speed, and solar irradiation (Figure 9.21a).
Figure 9.18 System response for step changes in load: (a) multiple load deviation and (b) MG output frequency.
Figure 9.30 System block diagram.
Figure 9.31 Parametric uncertainty block modeling.
Figure 9.32 Closed-loop system for robust control analysis and synthesis.
Figure 9.33 Closed-loop system diagram.
Figure 9.34 Frequency responses of the original controller and reduced-order controllers using residualization and truncation methods.
Figure 9.35 Closed-loop responses for the main controller and reduced-order controllers using residualization and truncation methods.
Figure 9.36 Closed-loop for the main controller and sixth reduced-order controllers using residualization and truncation methods.
Figure 9.37 Closed-loop response comparison for the sequential tuned PID, robust truncated , and residualized controllers.
Figure 9.38 Frequency response model: (a) block diagram of swing equation, (b) inverter model, and (c) EVSG dynamics.
Figure 9.39 Frequency response model of the MG test system, including the EVSG.
Figure 9.40 Standard structure of case study MG for H
∞
synthesis with uncertain block.
Figure 9.41 μ-Synthesis: (a) robust stability and (b) robust performance (upper bound of μ must be less than 1 at all frequencies).
Figure 9.42 Frequency response of the test system following of the 0.1 pu step change in Δ
P
L
.
Figure 9.43 Robust tuning of EVSG: (a) proposed algorithm and (b) Bode diagram of
K
evsg
and
K
hinf
.
Figure 9.44 System response for simultaneous variation in load and renewable power generation: (a) disturbance pattern and (b) frequency deviation.
Figure 9.45 System response for simultaneous step load change and parameters perturbation: (a) 75% decrease in
H
and
D
and (b) 90% decrease in
H
and
D
.
Chapter 10: Intelligent Microgrid Operation and Control
Figure 10.1 A general scheme for fuzzy-logic-based MG control.
Figure 10.2 A general scheme for adaptive fuzzy logic control system.
Figure 10.3 The GDC with neuro-fuzzy system.
Figure 10.4 Validation of the ANFIS network: (a) trained network output versus real output and (b) both output together.
Figure 10.5 System voltage and frequency following a load disturbance.
Figure 10.6 Fuzzy logic system for tuning of PI controller.
Figure 10.7 Fuzzy logic tuning system for supporting MG secondary control.
Figure 10.8 Smart tuning based on fuzzy logic compared with conventional methods.
Figure 10.9 Fuzzy logic system for supporting the PI controller.
Figure 10.10 Closed-loop frequency response model.
Figure 10.11 Frequency deviation; PI and fuzzy control (solid line), and only PI control (dashed line).
Figure 10.12 Common configurations for ANN-based control schemes.
Figure 10.13 A simplified GA flowchart.
Figure 10.14 A GA-based control: (a) GA as main controller and (b) GA-based controller tuning scheme.
Figure 10.15 MAS: (a) a conceptual framework and (b) a typical intelligent agent architecture.
Figure 10.16 MAS architecture for a decentralized control of an MG in an interactive structure.
Figure 10.17 Structure of the ANN-based PV power forecasting system.
Figure 10.18 PV power prediction on the test sample on a random day.
Figure 10.19 Load prediction on the test sample on a random day.
Figure 10.20 PV power production forecasting, load and error prediction with ANNs.
Figure 10.21 PV power and load prediction errors at 2 p.m.
Figure 10.22 Normal probability and PDF of the (a) PV power errors for 2 p.m. , and (b) load errors for 2 p.m. .
Figure 10.23 PV power calculation at hour
h
with a given probability.
Figure 10.24 Forecasting with uncertainty (random day): (a) PV and (b) load.
Figure 10.25 Case study.
Figure 10.26 A basic idea for supervisory active power compensation.
Figure 10.27 The proposed supervisory control framework.
Figure 10.28 Block diagram of the proposed FLS.
Figure 10.29 Fuzzyfication, membership functions of (a) Δ
P
PV
, (b) Δ
f
, (c)
L
SC
, and (d) Δ
P
.
Figure 10.30 Defuzzyfication, membership functions of (a) MGT power and (b) SC power.
Figure 10.31 System response in comparison with basic strategy: (a) PV power, (b) MGT power, and (c) SC power and level, (d) AG power, (e) SC level, and (f) frequency.
Figure 10.32 System response in comparison with basic strategy: (a) PV power, (b) MGT power, (c) SC power and level, and (d) AG power.
Figure 10.33 Configuration of the MG case study.
Figure 10.34 Control rule of fuzzy control.
Figure 10.35 Simulation results (gain-scheduling control and fuzzy control) under different initial conditions: (a)
W
2
/
W
1
≈ 2 and (b)
W
2
/
W
1
≈ 0.5.
Figure 10.36 Relations between input and voltage reference .
Figure 10.37 Integral of the square of the current (
I
EDLC
1
and
I
EDLC
2
).
Figure 10.38 Experimental results (gain-scheduling control and fuzzy control) of (a) case I and (b) case II.
Figure 10.39 Multi-VSG system.
Figure 10.40 Automatic voltage regulator diagram.
Figure 10.41 System generators response following a fault at bus 8: (a) angular frequency and (b) VAD.
Figure 10.42 Calculated
J
and
D
by the PSO algorithm in the presence of fault.
Figure 10.43 System generators response following a fault for the PSO-based scheme: (a) angular frequency and (b) VAD.
Figure 10.44 MAS-based MG frequency regulation scheme.
Figure 10.45 Feedback control system for (a) diesel unit, (b) ECS, and (c) coordination of diesel unit.
Figure 10.46 Single-line diagram of the laboratory MG system.
Figure 10.47 MG response for step load change: (a) scenario 1 and (b) scenario 2.
Chapter 11: Emergency Control and Load Shedding in Microgrids
Figure 11.1
L
-step load shedding scheme.
Figure 11.2 Flowchart of the proposed load shedding algorithm (Example 1).
Figure 11.3 MG case study for load shedding (Example 1).
Figure 11.4 MG frequency response following loss of DG 2: (a) without load shedding and (b) with load shedding.
Figure 11.5 The effect of shedding
L
4
on frequency of MG (pu).
Figure 11.6 Flowchart of the proposed load shedding (Example 2).
Figure 11.7 The concept of average rate of drop in (a) voltage and (b) frequency.
Figure 11.8 MG case study for load shedding (Example 2).
Figure 11.9 System response following islanding at 2 s: (a) frequency and (b) voltage measurements.
Figure 11.10 System response (following islanding at 2 s) using the proposed load shedding: (a) frequency and (b) voltage measurements.
Figure 11.11 The case study for load shedding evaluation.
Figure 11.12 System response following islanding at 2 s: (a) frequency and (b) voltage at bus 6.
Figure 11.13 Δ
v
–Δ
f
trajectory postcontingency behavior: (a) without load shedding (unstable) and (b) using load shedding (stable).
Figure 11.14 Impact of shedding high-reactive-power load.
Figure 11.15 Load shedding with and without STATCOM.
Figure 11.16 Load shedding following increasing active power: (a) MG frequency and (b) Δ
v
–Δ
f
plot.
Chapter 12: Microgrid Planning and Energy Management
Figure 12.1 Prosumer with load demand response and electrical production capabilities.
Figure 12.2 An MG with a PV-based AG.
Figure 12.3 A typical residential/urban MG.
Figure 12.4 A sample of the daily load profile for the studied CNC workshops set in weekday and weekend.
Figure 12.5 The monthly averaged data: (a) solar radiation and the clearness index and (b) wind speed at 10 m above the surface of the earth.
Figure 12.6 MG case study: (a) block diagram and (b) the overall configuration in HOMER environment.
Figure 12.7 Per unit values of NPC, COE, DG pollution, and renewable fraction for the selected nine plans.
Figure 12.8 24-h-ahead PV power forecasting.
Figure 12.9 24-h-ahead load forecasting .
Figure 12.10 Determination of operating cases.
Figure 12.11 Energy analysis for (a) the daytime and (b) the nighttime.
Figure 12.12 Power references from the power planning in the central energy management.
Figure 12.13 Flow diagram for the battery charging algorithm during the daytime.
Figure 12.14 Flow diagram for the battery discharging algorithm at night.
Figure 12.15 Modified control system for the application.
Figure 12.16 MG platform at L2EP.
Figure 12.17 24-h-ahead PV power forecasting for the self-consumption of one house.
Figure 12.18 24-h-ahead load forecasting for the self-consumption of one house.
Figure 12.19 Energy analysis the self-consumption of one house: (a) daytime and (b) nighttime.
Figure 12.20 Generated powers in the MG for the self-consumption of one house.
Figure 12.21 Sensed powers inside the PV-based AG for the self-consumption of one house.
Figure 12.22 Time evolution of batteries SOC for the self-consumption of one house.
Figure 12.23 The SCs dynamic response: (a) power compensation and (b) power absorption.
p
AG_mes
(Ch 1): 100 W/div;
p
bat_mes
(Ch 2): 100 W/div;
p
uc_mes
(Ch 3): 150 W/div;
p
PV_mes
(Ch 4) : 150 W/div.
Figure 12.24 PV power and load forecasting for 24 hour.
Figure 12.25 MGCC-based power references setting for the case of one producer.
Figure 12.26 24-h-ahead PV power and load forecasting.
Figure 12.27 Full-scaled PV-based production: (a) MGCC power reference and (b) total sensed PV power.
Figure 12.28 Energy analysis for full-scaled PV-based producer: (a) daytime and (b) nighttime.
Figure 12.29 Full-scaled PV-based prosumers with energy storages: (a) MGCC power references and (b) local PV-based AG control power references.
Figure 12.30 Scheme of the day-ahead optimal operational planning.
Figure 12.31 Principle of optimal path by DP.
Figure 12.32 Power reference calculation and dispatching.
Figure 12.33 Schematic diagram of the experimental setup.
Figure 12.34 Day-ahead load forecast (kW) and PV power forecast in MPPT (kW).
Figure 12.35 Global power reference of PV-based AGs.
Figure 12.36 Occurrence of MGT 2 power set points: (a) without optimization and (b) using the equivalent emissions as objective function.
Figure 12.37 DP algorithm.
Figure 12.38 Day-ahead PV power forecast, load forecast, and power reserve with 1% of LOLP.
Figure 12.39 Power reserve dispatching in scenario 3.
Figure 12.40 Reference power of AG, battery power, and energy.
Figure 12.41 LOLP for each hour with scenarios 2 and 3.
Figure 12.42 A distribution network with connected MGs.
Figure 12.43 RCA-ECS algorithm.
Figure 12.44 RPAR-ECS algorithm.
Figure 12.45 Simulation results: (a) generated NAs demand and the system-wide optimal demand, (b) adaptive system-wide demand and Lagrange multipliers, (c) generation cost per kWh in cost formulation, (d) PAR in cost formulation, (e) generation cost per kWh in the PAR formulation, and (f) PAR in PAR formulation.
List of Tables
Chapter 1: Grid-connected Renewable Energy Sources
Table 1.1 Power calculation and control algorithms for the wind energy conversion system
Table 1.2 Power calculation and power control algorithms for the active PV generator
Table 1.3 Advantages and features of control schemes for DC/AC converter in PV applications
Chapter 2: Renewable Power for Control Support
Table 2.1 Set points for the reference scenario with 11% wind power
Table 2.2 Power reserve distribution for the 29.2% wind case
Table 2.3 Estimated amount of instantaneous available wind reserve on the Guadeloupe island
Table 2.4 Efficiency indicators of the reserve allocation strategies
Table 2.5 Comparison of the reserve allocation strategies within the aggregated wind farm during the overall studied period
Table 2.6 Critical operating points of the Guadeloupe system as a function of the total installed wind capacity
Table 2.7 Summary of the main characteristics of the proposed combined strategy
Table 2.8 General comparison of the three reserve allocation strategies
Chapter 4: Microgrid Dynamics and Modeling
Table 4.1 Parameters of the MG case study
Table 4.2 MG parameters and linearization data
Table 4.3 Three MG output
X
/
R
with the corresponding RGAs
Chapter 5: Hierarchical Microgrid Control
Table 5.1 Typical line impedance values [21]
Chapter 6: DC Microgrid Control
Table 6.1 Main parameters of the simulation system
Table 6.2 Main parameters of the experimental system
Table 6.3 Condition of each experiment
Table 6.4 Parameters
Table 6.5 Main parameters
Table 6.6 Main experiment parameters
Chapter 7: Virtual Synchronous Generators: Dynamic Performance and Characteristics
Table 7.1 VSG parameters
Table 7.2 State-space model parameters
Chapter 8: Virtual Inertia-based Stability and Regulation Support
Table 8.1 Simulation parameters
Table 8.2 Simulation sequence
Table 8.3 Experiment sequence
Table 8.4 SG parameters
Table 8.5 DG control parameters
Table 8.6 Control schemes of simulations
Table 8.7 Machine modes during oscillation
Table 8.8 Specifications of the experimental system
Table 8.9 The specifications of the simulation system
Table 8.10 The specifications of the simulation system
Table 8.11 Control parameters
Table 8.12 Circuit parameters
Table 8.13 Test scenarios and parameter in grid connected operation
Table 8.14 Test cases and parameter in islanded operation
Chapter 9: Robust Microgrid Control Synthesis
Table 9.1 The parameters of frequency response model (Fig. 9.2)
Table 9.2 γ and maximum accepTable uncertainty disk radius for scenarios 1–3
Table 9.3 Time of the events
Chapter 10: Intelligent Microgrid Operation and Control
Table 10.1 Errors of the PV power forecasting with ANNs
Table 10.2 Errors of the load demand forecasting with ANNs
Table 10.3 Results of the error estimation for the PV power forecasting
Table 10.4 Results of the error estimation for the load forecasting
Table 10.5 The parameters of the multi-VSG system
Table 10.6 System condition when the fault occurs
Table 10.7 Averaged and maximum frequency deviation under different load change scenarios
Chapter 11: Emergency Control and Load Shedding in Microgrids
Table 11.1 Lookup Table for load shedding
Table 11.2 Load shedding Table based on the proposed algorithm
Chapter 12: Microgrid Planning and Energy Management
Table 12.1 CNC workshop consumption in a normal working hour
Table 12.2 Daily load forecast data of the workshops
Table 12.3 Description and economic and technical specification for the components of the proposed MG
Table 12.4 Grid buying and selling tariffs
Table 12.5 Values of all optimization variables
Table 12.6 The best optimization results of the MG planning
Table 12.7 Day-ahead operational planning results
Table 12.8 Day-ahead operational planning results
Appendix A: Appendix
Table A.1 Parameters for the case of single-inverter operation
Table A.2 Parameters of SG
Table A.3 Experimental parameters for the case of single-inverter operation
Table A.4 Experimental parameters for the case of parallel operation with an SG
Table A.5 Simulation parameters of VSG
Microgrid Dynamics and Control
Hassan Bevrani
University of Kurdistan, Kurdistan, Iran
Bruno Francois
Centrale Lille, France
Toshifumi Ise
Osaka University, Osaka, Japan
This edition first published 2017
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Library of Congress Cataloging-in-Publication Data
Names: Bevrani, Hassan, author. | Francois, Bruno, author. | Ise, Toshifumi, 1957- author.
Title: Microgrid dynamics and control / by Hassan Bevrani, Bruno Francois, Toshifumi Ise.
Description: First edition. | Hoboken, NJ : John Wiley & Sons, 2017. | Includes bibliographical references and index. |
Identifiers: LCCN 2017016748 (print) | LCCN 2017027057 (ebook) | ISBN 9781119263692 (pdf) | ISBN 9781119359357 (epub) | ISBN 9781119263678 (cloth)
Subjects: LCSH: Microgrids (Smart power grids)
Classification: LCC TK3105 (ebook) | LCC TK3105 .B48 2017 (print) | DDC 621.31-dc23
LC record available at https://lccn.loc.gov/2017016748
Cover image: Courtesy of Hassan Bevrani
Cover design by Wiley
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Foreword
The electric power industry is in the midst of a critical period in its evolution. Today's high-voltage transmission network is reliable and controllable but suffers from cascading failures
