Smart Microgrid Systems: Advanced Technologies

Description

This book highlights microgrids as integrating platforms for distributed generation units, energy storages and local loads, with an emphasis on system performance via innovative approaches. It explains the smart power system concept, transmission, distribution, and utilization, and then looks at distributed generation technologies and hybrid power systems. Smart approaches, an analysis of microgrid design architecture and its implementation, the mitigation of cyber threats, and system optimization are also included. Case studies related to microgrid modeling and simulation are placed at the end of each chapter. FEATURES Focuses on applications of expert systems for microgrid control Explores microgrid applications for power networks and applications of expert technologies Reviews design and development technologies related to renewable energy for a weak power network Discusses cyber security for microgrids Includes case studies related to actual developments and research This book is aimed at researchers and graduate students in power engineering and electronics. Cover Half Title Title Page Copyright Page Table of Contents Preface Acknowledgements Authors Introduction 1 Overview of Smart Power Systems 1.1 The Conventional Power Grid 1.1.1 Overview of a Conventional Power Grid 1.1.2 Problems Associated With Conventional Power Systems 1.1.2.1 Cascading Failure 1.1.2.2 Environmental Issues 1.2 Future Grid 1.2.1 What Is a Smart Grid? 1.2.2 Smart Grid Characteristics 1.2.3 Main Functionalities of a Smart Grid 1.2.4 Smart Grid Communication Network 1.2.5 Integration From Supply to Demand in a Smart Grid 2 Distributed Generation Technology 2.1 Distributed Generation 2.1.1 Introduction 2.1.2 Advantages of Distributed Generation 2.2 Renewable Energy Systems 2.3 Renewable Generation Technologies 2.3.1 Solar Energy 2.3.1.1 Available Topologies 2.3.1.2 Science Behind Solar Energy 2.3.1.3 PV Efficiency 2.3.1.4 Solar PV System to Grid 2.3.1.5 Mathematical Model of a Solar PV Cell 2.3.1.6 From Cells to Modules to Arrays Maximum Power Point 2.3.1.7 Effect of Irradiance 2.3.1.8 Effect of Temperature On I-V Curves 2.3.2 Wind Energy 2.3.2.1 Basics of Wind Energy 2.3.2.1.4 Wind Energy System Configurations 2.3.2.2 Grid Integration: Synchronizing With the Grid 2.3.2.3 Synchronization Process of Wind Energy Systems 2.3.3 Energy Storage Systems 2.3.3.1 Electrochemical Battery 2.3.3.2 Flywheel 3 Overview of Microgrids 3.1 What Is a Microgrid? 3.2 Microgrid Power Architecture 3.2.1 Microgrid Structure and Components 3.2.2 Types of Power Architecture 3.3 Operation of Microgrid 3.3.1 Modes of Operation 3.3.1.1 Grid-Connected Mode 3.3.1.2 Islanded Mode 3.3.2 Demand–Supply Balance 3.3.3 Types of Distributed Generators Based On Different Operating Conditions 3.3.3.1 Grid-Forming Units 3.3.3.2 Grid-Feeding Units 3.3.3.3 Grid-Following Units 3.3.4 Types of Electrical Load 3.3.4.1 Resistive Loads 3.3.4.2 Capacitive Loads 3.3.4.3 Inductive Loads 3.3.4.4 Combination Loads 3.4 Types of Microgrid Control Architecture 3.4.1 Centralized Control 3.4.2 Decentralized Control 3.4.3 Distributed Control 3.4.4 Hierarchical Control 3.4.4.1 Droop Control 3.4.4.2 Primary Control 3.4.4.3 Secondary Control 3.4.4.4 Tertiary Control 3.5 Advantages and Disadvantages of Microgrids 3.5.1 Advantages of Microgrids 3.5.2 Disadvantages of Microgrids 3.6 Networked Microgrids 3.7 Example: Microgrid Modeling and Simulation References 4 Novel Approaches to Microgrid Functions 4.1 Reconfigurable Power Electronic Interfaces 4.1.1 Introduction to Power Electronic Interfaces 4.1.2 DC to DC Converters 4.1.2.1 Buck Converter 4.1.2.2 Boost Converter 4.1.2.3 Buck–Boost Converter 4.1.3 DC to AC Inverters 4.1.3.1 Voltage Source Inverter 4.1.3.2 Current Source Inverter 4.1.3.3 Z Source Inverter 4.1.4 Reconfigurable Power and Control Architectures of Microgrids 4.1.4.1 Reconfigurable Systems 4.1.4.2 Existing Power Architecture-Based Reconfigurable Approaches for Microgrids 4.1.4.3 Existing Control Architecture-Based Reconfigurable Approaches for Microgrids 4.1.5 Modeling of Solar Microgrids With a Z Source Inverter 4.1.5.1 Example of Proposed System With a ZSI 4.1.5.2 Modes of Control of a ZSI 4.1.5.3 Advantages of a ZSI 4.2 Adaptive Protection for Microgrids 4.2.1 Overview of Power System Protection 4.2.1.1 Protection System Components 4.2.1.2 Properties of a Protection System 4.2.2 Present Microgrid Protection Schemes 4.2.2.1 Line Protection 4.2.2.2 Primary and Backup Protection 4.2.3 Adaptive Protection Schemes for Microgrids 4.2.3.1 What Is Adaptive Protection? 4.2.3.2 Adaptive Protection Algorithms 4.2.4 Case Study 4.3 Multi-Agent-Based Control 4.3.1 Introduction to Multi-Agent Systems 4.3.2 Multi-Agent-Based Control for Microgrids 4.3.2.1 Proposed System 4.3.2.2 Agents in the System and Their Functions 4.3.3 Simulating the Interaction Between Agents Using JAVA Agent Development Environment 4.3.3.1 JAVA Agent Development Environment 4.3.3.2 Agent Formation 4.3.3.3 Sniffing Agent References 5 Cyber Security for Smart Microgrids 5.1 Overview of Cyber Attacks 5.1.1 Types of Cyber Attack 5.1.1.1 Malware 5.1.1.2 Phishing 5.1.1.3 Man in the Middle Attack 5.1.1.4 Denial of Service Attack 5.1.1.5 Ransomware 5.1.2 Common Sources of Cyber Threats 5.2 Power Routing Concept 5.3 Cyber Security-Enabled Power Systems References 6 Expert Systems for Microgrids 6.1 Optimization of Energy Management Systems for Microgrids Using Reinforcement Learning 6.1.1 Supervised, Unsupervised, and Reinforcement Learning 6.1.2 Fundamentals of Reinforcement Learning 6.1.2.1 General Reinforcement Learning Model 6.1.2.2 Markov Decision Process 6.1.2.3 The Goal of the Reinforcement Learning Agent 6.1.2.4 Policies and Value Functions 6.1.2.5 Sample-Based Learning 6.1.2.6 On- and Off-Policy Learning Methods 6.1.2.7 SARSA Vs Q-Learning 6.1.2.8 Q-Learning Algorithm 6.1.2.9 Exploration and Exploitation Strategy 6.1.2.10 Hyperparameter Selection 6.1.3 Single and Multi-Agent Reinforcement Learning 6.1.4 Problem Formulation in RL 6.1.4.1 Defining the Goal 6.1.4.2 Mapping the Problem With RL Elements 6.1.5 Reinforcement Learning Approach for Microgrids 6.1.5.1 Grid Consumption Minimization 6.1.5.2 Minimization of Demand–Supply Deficit 6.1.5.3 Islanded Operation of Microgrids 6.1.5.4 Economic Dispatch 6.1.5.5 Energy Market 6.2 Case Study: Reinforcement Learning Approach for Minimizing the Grid Dependency of a Solar Microgrid 6.2.1 Proposed System 6.2.2 Single-Agent Reinforcement Learning Model 6.2.3 Multi-Agent Reinforcement Learning Model 6.2.4 Simulation Model 6.2.4.1 Artificial Neural Network 6.2.4.2 Feature Selection 6.2.5 RL Simulation Models in Python Results 6.2.6 Hardware Implementation 6.2.6.1 Microgrid Testbed 6.2.6.2 Agent Implementation 6.2.7 Agent Communication 6.2.8 Firebase Database References 7 Conclusion References Index