内容简介：　　

　 The book aims to equalize the theoretical involvement with industrial practicality and build a bridge between academia and industry by reducing the mathematical difficulties. It provides an overview of distributed control and distributed optimization theory, followed by specific details on industrial applications to smart grid systems, with a special focus on micro grid systems. Each of the chapters is written and organized with an introductory section tailored to provide the essential background of the theories required. The text includes industrial applications to realistic renewable energy systems problems and illustrates the application of proposed toolsets to control and optimization of smart grid systems.


英文目录：
List of Figures
List of Tables
Preface
Authors
List of Symbols
SECTION I: INTRODUCTION
1 Introduction
　　1.1 Background and Motivation
　　1.2 Objectives and Scope
　　1.3 Microgrid
　　1.4 Control Strategies of MGs
　　　　1.4.1 Primary control
　　　　1.4.2 Secondary control
　　　　1.4.3 Tertiary control
　　　　　　1.4.3.1 Optimal power flow
　　　　　　1.4.3.2 Economic dispatch
　　　　　　1.4.3.3 Optimal energy scheduling
　　1.5 Major Contributions of the Book
　　1.6 Organization of the Book
2 Preliminaries
　　2.2 Distributed Finite-Time Average Consensus Algorithm
　　　　2.2.1 Distributed FACA
　　2.3 Finite-Time Control
　　2.4 Multi-Agent Optimization
　　　　2.4.1 Synchronous optimization
　　　　2.4.2 Sequential optimization
　　　　2.4.3 Projection
　　　　　　2.4.3.1Projection operator (Case 1)
　　　　　　2.4.3.2Projection operator (Case 2)
SECTION II: DISTRIBUTED SECONDARY CONTROL
3 Distributed Voltage and Frequency Restoration Control
　　3.1 Introduction
　　3.2 Modeling of MG
　　　　3.2.1 DG model
　　　　3.2.2 Network model
　　3.3 Distributed Secondary Controller Design
　　　　3.3.1 Control objective
　　　　3.3.2 Distributed secondary controller design
　　　　　　3.3.2.1Finite-time voltage restoration
　　　　　　3.3.2.2Frequency restoration
　　3.4 Simulation Results
4 Distributed Voltage Unbalance Compensation
　　4.1 Introduction
　　4.2 Distributed Cooperative Secondary Control Scheme for
　　　　Voltage Unbalance Compensation
　　　　4.2.1 A centralized VUC approach
　　　　4.2.2 Distributed VUC approach
　　　　　　4.2.2.1 Preliminary setup
　　　　　　4.2.2.2 Distributed VUC
　　　　　　4.2.2.3 Distributed cooperative secondary control scheme (DCSCS) design
　　　　　　4.2.2.4 Stability analysis of distributed VUC
　　4.3 Case Studies
　　　　4.3.1 Testing of the overall distributed control system under various cases
　　　　　　4.3.1.1 Case A: Communication failure
　　　　　　4.3.1.2 Case B: Contribution level variation
　　　　　　4.3.1.3 Case C: Backup DG plug-and-play
　　　　4.3.2 System stability and performance
　　　　4.3.3 Comparisons with centralized secondary control in [65]
　　　　4.3.4 Distributed voltage unbalance compensation using negative
　　　　　　　sequence current feedback
SECTION III: DISTRIBUTED TERTIARY OPTIMIZATION
5 Distributed Single-Area Economic Dispatch
　　5.1 Introduction
　　5.2 Problem Formulation
　　5.3 Total Load Demand Discovery
　　5.4 Distributed Economic Dispatch
　　　　5.4.1Distributed projected gradient method (DPGM)
　　　　5.4.2 Implementation of distributed ED
　　　　5.4.3 Complexity analysis
　　5.5 Case Studies
　　　　5.5.1 Case study 1: Implementation on 6-bus power system
　　　　5.5.2 Case study 2: Plug-and-play capability
　　　　　　5.5.2.1 Generator plug-and-play
　　　　　　5.5.2.2 Load plug-and-play
　　　　5.5.3 Case study 3: Implementation on IEEE 30-bus test system
　　　　5.5.4 Case study 4: Comparison with heuristic search method
6 Distributed Multi-Area Economic Dispatch
　　6.1 Introduction
　　6.2 Problem Formulation
　　6.3 Distributed Optimization Algorithm
　　　　6.3.1 Distributed synchronous optimization algorithm
　　　　6.3.2 Distributed sequential optimization algorithm
　　　　6.3.3 Virtual agent
　　6.4 Convergence analysis
　　　　6.4.1 Distributed synchronous algorithm
　　　　6.4.2 Distributed sequential algorithm
　　6.5 Economic Dispatch in Multi-Area Power System
　　　　6.5.1 Problem statement
　　　　6.5.2 Case studies
　　　　　　6.5.2.1 Case study 1: Distributed synchronous algorithm
　　　　　　6.5.2.2 Case study 2: Distributed sequential algorithm
　　　　　　6.5.2.3 Case study 3: Distributed sequential algorithm
　　　　　　 　　　with random communication strategy
　　　　　　6.5.2.4 Case study 4: Fast gradient
7 Hierarchical Decentralized Architecture for ED Problem
　　7.1 Introduction
　　7.2 Problem Formulation
　　7.3 Hierarchical Decentralized Economic Dispatch
　　　　7.3.1 Distributed algorithm in Chapter 5
　　　　7.3.2 Hierarchical decentralized algorithm
　　　　7.3.3 Convergence analysis
　　　　　　7.3.3.1 Problem decomposition
　　　　　　7.3.3.2 Dummy agent
　　7.4 Case Studies
　　　　7.4.1 IEEE 30-bus system
　　　　　　7.4.1.1 Case study 1: Without generator constraints
　　　　　　7.4.1.2 Case study 2: With generator constraints
　　　　　　7.4.1.3 Case study 3: Varying load
　　　　　　7.4.1.4 Case study 4: Plug-and-play capability
　　　　　　7.4.1.5 Case study 5: Comparison with different α values
　　　　　　7.4.1.6Case study 6: Fast gradient
　　　　7.4.2 IEEE 118-bus test system
8 Distributed Optimal Energy Scheduling
　　8.1 Introduction
　　8.2 Problem Formulation
　　　　8.2.1 System model
　　　　8.2.2 Cost function
　　　　8.2.3 Pricing function
　　　　8.2.4 Optimization problem formulation
　　8.3 Distributed Optimal Energy Scheduling
　　　　8.3.1 Distributed algorithm with synchronous communication
　　　　8.3.2 Distributed algorithm with sequential communication
　　8.4 Case Studies
　　　　8.4.1 Distributed optimization
　　　　　　8.4.1.1 Distributed optimization with synchronous communication
　　　　　　8.4.1.2 Distributed optimization with sequential communication
　　　　8.4.2 Comparison between P pricing and PD pricing
9 Conclusion and Future Works
　　9.1 Conclusion
　　9.2 Recommendations for Future Research
　　　　9.2.1 Distributed adaptive control of the MG
　　　　9.2.2 Stability analysis of the distributed secondary control with
　　　　　　　　communication time delay
　　　　9.2.3 Distributed event-triggered optimization of the MG
References
Index