Microgrid Dynamics and Control

Hassan Bevrani, PhD, is a Professor at University of Kurdistan, Kurdistan, Iran. Bruno Francois, PhD, is a Professor at Centrale Lille, Lille, France. Toshifumi Ise, PhD, is a Professor at Osaka University, Osaka, Japan.

• Foreword xixPreface xxiAcknowledgments xxvii1 Grid-connected Renewable Energy Sources 11.1 Introduction 11.2 Renewable Power Generation 31.2.1 Renewable Energy Development 51.3 Grid-connectedWind Power 61.3.1 Wind Power GeneratorWithout Power Electronic Converters 71.3.2 Wind Power Generator Using Partial-Scale Power Electronic Converters 71.3.3 Wind Power Generator Using Full-Scale Power Electronic Converters 71.4 Grid-Connected PV Power 351.4.1 Solar Power Generators with Embedded Energy Storage Systems 361.4.2 Solar Energy Conversion System: Modeling, Control, and Analysis 381.4.3 Experimental Results 551.4.4 Control of Grid-Connected Solar Power Inverters: A Review 591.5 Summary 66References 662 Renewable Power for Control Support 692.1 Introduction 692.2 Wind-Energy-based Control Support 732.2.1 Wind Turbines Inertial Response 732.2.2 Study on a Real Isolated Power System 772.2.3 Primary Frequency and Inertial Controls 812.2.4 Using Secondary Control 892.3 Renewable Primary Power Reserve 892.3.1 InstantaneousWind Power Reserve 892.3.2 An Evaluation on the Real Case Study 922.3.3 Comparison of the Reserve Allocation Strategies 962.4 PV-Energy-Based Control Support 1022.5 Integration of Renewable Energy SystemsThrough Microgrids 1052.5.1 A Solution for Renewable Power Penetration 1052.5.2 Microgrids in Future Smart Grids 1082.6 Summary 112References 1133 Microgrids: Concept, Structure, and Operation Modes 1193.1 Introduction 1193.2 Microgrid Concept and Structure 1253.3 Operation Modes 1293.4 Control Mechanism of the Connected Distributed Generators in aMicrogrid 1303.4.1 Speed Control of Classical Distributed Generators 1303.4.2 Control of Inverter-based Distributed Generators 1313.5 Contribution in the Upstream Grid Ancillary Services: FrequencyControl Support Example 1373.5.1 Participation in the Frequency Regulation 1383.5.2 Power Dispatching 1423.5.3 Simulation Results 1473.6 Microgrids Laboratory Technologies 1473.6.1 Hardware-in-the-loop-based Microgrid Laboratory 1523.6.2 Participant Laboratories to Provide the Present Book 1573.7 Summary 160References 1604 Microgrid Dynamics andModeling 1654.1 Introduction 1654.2 Distribution Network (Main Grid) and Connection Modeling 1684.2.1 Distribution Network Modeling 1684.2.2 Modeling of Connection Between the Main Grid and the Microgrid 1744.3 Overall Representation of the Grid-Connected Microgrid 1784.3.1 Microgrid Bus 1784.3.2 Global Architecture Representation 1784.3.3 Microgrid Representation in the Islanded Operation Mode 1794.4 Microgrid Components Dynamics and Modeling 1824.4.1 PV Model 1824.4.2 Energy Storage Systems Modeling 1864.4.3 Power Electronic Converters 1934.5 Simplified Microgrid Frequency Response Model 1984.5.1 Example 1 1994.5.2 Example 2 2014.6 A Detailed State-Space DynamicModel 2034.6.1 MathematicalModeling 2034.6.2 Simulation Example 2074.6.3 Closed-Loop State-Space Model 2104.7 Microgrid Dynamic Modeling and Analysis as a Multivariable System 2114.7.1 State-space Modeling 2124.7.2 Dynamic Analysis 2154.8 Summary 217References 2175 Hierarchical Microgrid Control 2215.1 Introduction 2215.2 Microgrid Control Hierarchy 2255.2.1 Local Control 2275.2.2 Secondary Control 2285.2.3 Central/Emergency Control 2295.2.4 Global Control 2315.3 Droop Control 2335.3.1 Droop Characteristic in Conventional Power Systems 2335.3.2 Droop Control in Inverter-based Distributed Generators 2355.3.3 Virtual Impedance Control 2415.4 Hierarchical Power Management and Control 2435.4.1 Operation Layers and Control Functions 2445.4.2 Timescale Analyzing and Implementation Constraints 2455.5 Design Example 2525.5.1 Power Dispatching 2535.5.2 Hardware-In-the-Loop Test Results 2545.5.3 Test Procedure 2575.6 Summary 262References 2636 DC Microgrid Control 2676.1 Introduction 2676.2 DC Microgrid for a Residential Area 2706.2.1 System Configuration and Operation 2706.2.2 Voltage Clamp Control 2736.2.3 Disconnection/Reconnection from/to the Utility Grid 2736.3 Low-voltage Bipolar-type DC Microgrid 2756.4 Stability Evaluation 2776.5 Experimental Study and Results 2806.5.1 Experimental System 2806.5.2 Voltage Sag of the Utility Grid 2846.5.3 Disconnection/Reconnection from/to the Utility Grid 2846.6 A Voltage Control Approach 2866.6.1 Case Study and Voltage Control System 2866.6.2 Energy Storage System Control 2906.7 Simulation Results 2946.7.1 Simulation Results for the Gain-scheduling Control 2966.7.2 Simulation Results for Droop Control 2966.8 Experimental Results 3006.8.1 Case I 3016.8.2 Case II 3016.9 Summary 304References 3047 Virtual Synchronous Generators: Dynamic Performance and Characteristics 3077.1 Introduction 3087.2 Virtual Synchronous Generator (VSG) and Droop Control 3147.2.1 Droop Control 3147.2.2 Transient Frequency Response 3157.2.3 Active Power Response 3237.2.4 Experimental Results 3277.3 Virtual Synchronous Generator-Based Oscillation Damping 3317.3.1 Mathematical Formulation 3317.3.2 Oscillation DampingMethodology 3347.3.3 Simulation Results 3377.3.4 Experimental Results 3417.4 A Virtual Synchronous Generator Scheme with Emulating More Synchronous Generator Characteristics 3447.4.1 Emulating Synchronous Generator Characteristics 3457.4.2 Stability Analysis and Parameters Design 3517.5 Active Power Performance Analysis in a Microgrid with Multiple Virtual Synchronous Generators 3537.5.1 Closed-Loop State-Space Model 3537.5.2 Oscillation Damping 3557.5.3 Transient Active Power Sharing 3567.6 Summary 358References 3588 Virtual Inertia-based Stability and Regulation Support 3618.1 Introduction 3618.2 An Enhanced Virtual Synchronous Generator Control Scheme 3638.2.1 Proposed Virtual Synchronous Generator Control Scheme 3648.2.2 Simulation Results 3678.2.3 Experimental Results 3738.3 Virtual Synchronous Generator Control in Parallel Operation with Synchronous Generator 3768.3.1 System Description 3778.3.2 The Proposed Modified Virtual Synchronous Generator Control Scheme 3788.3.3 Parameter Tuning Methods 3828.3.4 Simulation Results 3888.4 Alternating Inertia-based Virtual Synchronous Generator Control 3938.4.1 Control Strategy 3938.4.2 Stability Analysis 3978.4.3 Effect of Alternating Inertia on Dissipated Energy 4018.4.4 Grid Stability Improvement 4018.4.5 Experimental Results 4058.5 Voltage Sag Ride-through Enhancement Using Virtual Synchronous Generator 4068.5.1 Virtual Synchronous Generator Subjected to Voltage Sags 4068.5.2 State Variable Analysis in Phase Plane 4078.5.3 Voltage Sag Ride-through Enhancement 4098.5.4 Simulation Results 4118.5.5 Experimental Results 4158.6 Performance Evaluation of the Virtual Synchronous Generator with More Synchronous Generator Characteristics 4218.6.1 System Configuration and Parameters 4228.6.2 Simulation Results 4238.6.3 Experimental System 4258.7 Summary 430References 4329 Robust Microgrid Control Synthesis 4359.1 Introduction 4359.2 Case Study and State-Space Model 4389.3 H∞ and Structured Singular Value (μ) Control Theorems 4429.3.1 H∞ ControlTheory 4429.3.2 Structured Singular Value (μ) Control Theory 4429.4 H∞-Based Control Design 4449.4.1 UncertaintyModeling 4449.4.2 H∞ Optimal Controller 4469.4.3 Closed-Loop Nominal Stability and Performance 4469.4.4 Closed-Loop Robust Stability and Performance 4469.5 μ-Based Control Design 4479.5.1 UncertaintyModeling in μ-Synthesis 4489.5.2 D–K Iteration 4499.5.3 Closed-Loop Nominal and Robust Performance 4519.5.4 Robust Stability 4519.6 Order Reduction and Application Results 4539.6.1 Controller Order Reduction 4539.6.2 Application Results 4559.6.3 Comparison withWell-Tuned Proportional-Integral (PI) Controllers 4589.7 Robust Multivariable Microgrid Control Design 4659.7.1 Uncertainty Determination 4659.7.2 Robust Stability and Performance 4689.8 Robust Tuning of VSG Parameters 4739.8.1 The Extended VSG Dynamics 4749.8.2 Case Study and H∞ Control Synthesis 4759.8.3 Robust Tuning of Extended VSG Parameters 4789.8.4 Simulation Results 4819.9 Summary 483References 48310 IntelligentMicrogrid Operation and Control 48710.1 Introduction 48810.2 Intelligent Control Technologies 49110.2.1 Fuzzy Logic Control 49110.2.2 Artificial Neural Networks 50110.2.3 Genetic Algorithm and Particle Swarm Optimization 50410.2.4 Multiagent System 50810.3 ANN-based Power and Load Forecasting in Microgrids 51210.3.1 PV Power Prediction 51410.3.2 Load Forecasting 51510.3.3 Forecasting Error 51710.4 Intelligent Frequency and Voltage Control in Microgrids 52010.4.1 Fuzzy-logic-based Supervisory Frequency Control 52110.4.2 Fuzzy-based Distribution Voltage Control in DC Microgrids 52810.4.2.1 Proposed Control Strategy 52810.4.2.2 Simulation Results 53310.4.2.3 Experimental Results 53710.4.3 Particle Swarm Optimization (PSO)-based Stability Enhancement in a Microgrid with Virtual Synchronous Generators 53810.4.4 Multiagent-based Secondary Frequency Control 54710.5 Summary 554References 55411 Emergency Control and Load Shedding in Microgrids 56111.1 Introduction 56111.2 Load Shedding as aWell-known Emergency Control Strategy 56411.3 Load Shedding Algorithm: Example 1 56711.3.1 Proposed Algorithm 56711.3.2 Case Study 56911.3.3 Simulation Results 57111.4 Load Shedding Algorithm: Example 2 57211.4.1 Proposed Algorithm 57211.4.2 Case Study 57411.4.3 Simulation Results 57611.5 Undervoltage–frequency Load Shedding 57811.5.1 Δv–Δf Plane 57911.5.2 Voltage and Frequency Performances 58111.6 Summary 583References 58412 Microgrid Planning and EnergyManagement 58912.1 Introduction 58912.2 Microgrid Planning: An Example 59412.2.1 Description of Input Parameters 59512.2.2 System Description and Specification 59712.2.3 Numerical Results and Discussion 59812.3 Forecasting Techniques 60112.3.1 PV Power Prediction 60112.3.2 Load Forecasting 60212.3.3 Energy Estimation 60412.3.3.1 Estimation of the Available PV Power 60412.4 Energy Management 60512.4.1 Daily Power Management and Setting of Power References 60512.4.2 Medium-term Energy Management 60912.4.3 Short-term Power Management 61212.4.4 Experimental Tests 61312.5 Emission Reduction and Economical Optimization 62412.5.1 Micro-Gas Turbine (MGT) Fuel Consumption and Emissions 62512.5.2 Day-ahead Optimal Operational Planning 62612.5.3 Experimental Results 63212.6 Day-ahead Optimal Operation and Power Reserve Dispatching 63512.6.1 Scenario 1: Power Reserve Provided by MGTs 63712.6.1.1 Daytime 63712.6.1.2 Nighttime (Discharge the Battery) 63812.6.2 Scenario 2: Power Reserve Provided by Micro Gas Turbines and PV-based Active Generator 63812.6.3 Optimal Reserve Power Dispatching Application for Unit Commitment Problem 64212.7 Robust Energy Consumption Scheduling in Interconnected Microgrids 64512.7.1 Cost Minimization Formulation 64812.7.2 Peak-to-Average Ratio Minimization Formulation 65012.7.3 Simulation Results 65212.8 Summary 658References 659A Appendix 663Index 665