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E-raamat: Smart Microgrid Systems: Advanced Technologies [Taylor & Francis e-raamat]

(University of Moratuwa, Sri Lanka), (University of Moratuwa, Sri Lanka)
  • Formaat: 146 pages, 10 Tables, black and white; 79 Line drawings, black and white; 35 Halftones, black and white; 114 Illustrations, black and white
  • Ilmumisaeg: 05-Aug-2022
  • Kirjastus: CRC Press
  • ISBN-13: 9781003216292
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  • Taylor & Francis e-raamat
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Smart Microgrid Systems: Advanced TechnologiesSuurem pilt
  • Formaat: 146 pages, 10 Tables, black and white; 79 Line drawings, black and white; 35 Halftones, black and white; 114 Illustrations, black and white
  • Ilmumisaeg: 05-Aug-2022
  • Kirjastus: CRC Press
  • ISBN-13: 9781003216292
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003216292
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.
Preface xi
Acknowledgements xv
Authors xvii
Introduction xix
Chapter 1 Overview of Smart Power Systems
1(8)
1.1 The Conventional Power Grid
1(3)
1.1.1 Overview of a Conventional Power Grid
1(1)
1.1.2 Problems Associated with Conventional Power Systems
2(1)
1.1.2.1 Cascading Failure
2(1)
1.1.2.2 Environmental Issues
3(1)
1.2 Future Grid
4(5)
1.2.1 What Is a Smart Grid?
4(1)
1.2.2 Smart Grid Characteristics
5(1)
1.2.3 Main Functionalities of a Smart Grid
6(1)
1.2.4 Smart Grid Communication Network
7(1)
1.2.5 Integration from Supply to Demand in a Smart Grid
7(2)
Chapter 2 Distributed Generation Technology
9(24)
2.1 Distributed Generation
9(1)
2.1.1 Introduction
9(1)
2.1.2 Advantages of Distributed Generation
9(1)
2.2 Renewable Energy Systems
10(1)
2.3 Renewable Generation Technologies
11(22)
2.3.1 Solar Energy
11(1)
2.3.1.1 Available Topologies
12(1)
2.3.1.2 Science behind Solar Energy
12(1)
2.3.1.3 PV Efficiency
13(1)
2.3.1.4 Solar PV System to Grid
13(1)
2.3.1.5 Mathematical Model of a Solar PV Cell
14(4)
2.3.1.6 From Cells to Modules to Arrays
18(1)
2.3.1.7 Effect of Irradiance
18(3)
2.3.1.8 Effect of Temperature on I-V Curves
21(4)
2.3.2 Wind Energy
25(1)
2.3.2.1 Basics of Wind Energy
25(4)
2.3.2.2 Grid Integration: Synchronizing with the Grid
29(1)
2.3.2.3 Synchronization Process of Wind Energy Systems
29(1)
2.3.3 Energy Storage Systems
30(1)
2.3.3.1 Electrochemical Battery
31(1)
2.3.3.2 Flywheel
31(2)
Chapter 3 Overview of Microgrids
33(32)
3.1 What Is a Microgrid?
33(1)
3.2 Microgrid Power Architecture
34(2)
3.2.1 Microgrid Structure and Components
34(1)
3.2.2 Types of Power Architecture
35(1)
3.3 Operation of Microgrid
36(4)
3.3.1 Modes of Operation
36(1)
3.3.1.1 Grid-Connected Mode
36(1)
3.3.1.2 Islanded Mode
36(1)
3.3.2 Demand-Supply Balance
37(1)
3.3.3 Types of Distributed Generators Based on Different Operating Conditions
38(1)
3.3.3.1 Grid-Forming Units
38(1)
3.3.3.2 Grid-Feeding Units
38(1)
3.3.3.3 Grid-Following Units
39(1)
3.3.4 Types of Electrical Load
39(1)
3.3.4.1 Resistive Loads
39(1)
3.3.4.2 Capacitive Loads
39(1)
3.3.4.3 Inductive Loads
39(1)
3.3.4.4 Combination Loads
40(1)
3.4 Types of Microgrid Control Architecture
40(5)
3.4.1 Centralized Control
40(1)
3.4.2 Decentralized Control
41(1)
3.4.3 Distributed Control
41(1)
3.4.4 Hierarchical Control
42(1)
3.4.4.1 Droop Control
42(1)
3.4.4.2 Primary Control
43(1)
3.4.4.3 Secondary Control
44(1)
3.4.4.4 Tertiary Control
44(1)
3.5 Advantages and Disadvantages of Microgrids
45(1)
3.5.1 Advantages of Microgrids
45(1)
3.5.2 Disadvantages of Microgrids
45(1)
3.6 Networked Microgrids
45(1)
3.7 Example: Microgrid Modeling and Simulation
46(19)
Chapter 4 Novel Approaches to Microgrid Functions
65(30)
4.1 Reconfigurable Power Electronic Interfaces
65(14)
4.1.1 Introduction to Power Electronic Interfaces
65(1)
4.1.2 DC to DC Converters
65(2)
4.1.2.1 Buck Converter
67(4)
4.1.2.2 Boost Converter
71(1)
4.1.2.3 Buck--Boost Converter
72(1)
4.1.3 DC to AC Inverters
72(1)
4.1.3.1 Voltage Source Inverter
72(1)
4.1.3.2 Current Source Inverter
73(1)
4.1.3.3 Z Source Inverter
74(1)
4.1.4 Reconfigurable Power and Control Architectures of Microgrids
74(1)
4.1.4.1 Reconfigurable Systems
74(1)
4.1.4.2 Existing Power Architecture-Based Reconfigurable Approaches for Microgrids
74(1)
4.1.4.3 Existing Control Architecture-Based Reconfigurable Approaches for Microgrids
75(1)
4.1.5 Modeling of Solar Microgrids with a Z Source Inverter
75(1)
4.1.5.1 Example of Proposed System with a ZSI
76(1)
4.1.5.2 Modes of Control of a ZSI
77(1)
4.1.5.3 Advantages of a ZSI
78(1)
4.2 Adaptive Protection for Microgrids
79(7)
4.2.1 Overview of Power System Protection
79(1)
4.2.1.1 Protection System Components
79(1)
4.2.1.2 Properties of a Protection System
80(1)
4.2.2 Present Microgrid Protection Schemes
81(1)
4.2.2.1 Line Protection
81(1)
4.2.2.2 Primary and Backup Protection
81(1)
4.2.3 Adaptive Protection Schemes for Microgrids
81(1)
4.2.3.1 What Is Adaptive Protection?
82(1)
4.2.3.2 Adaptive Protection Algorithms
82(1)
4.2.4 Case Study
83(3)
4.3 Multi-Agent-Based Control
86(9)
4.3.1 Introduction to Multi-Agent Systems
86(2)
4.3.2 Multi-Agent-Based Control for Microgrids
88(1)
4.3.2.1 Proposed System
88(1)
4.3.2.2 Agents in the System and Their Functions
88(1)
4.3.3 Simulating the Interaction between Agents Using JAVA Agent Development Environment
89(1)
4.3.3.1 JAVA Agent Development Environment
89(1)
4.3.3.2 Agent Formation
90(1)
4.3.3.3 Sniffing Agent
91(4)
Chapter 5 Cyber Security for Smart Microgrids
95(6)
5.1 Overview of Cyber Attacks
95(1)
5.1.1 Types of Cyber Attack
95(1)
5.1.1.1 Malware
95(1)
5.1.1.2 Phishing
95(1)
5.1.1.3 Man in the Middle Attack
95(1)
5.1.1.4 Denial of Service Attack
95(1)
5.1.1.5 Ransomware
96(1)
5.1.2 Common Sources of Cyber Threats
96(1)
5.2 Power Routing Concept
96(1)
5.3 Cyber Security-Enabled Power Systems
97(4)
Chapter 6 Expert Systems for Microgrids
101(38)
6.1 Optimization of Energy Management Systems for Microgrids Using Reinforcement Learning
101(16)
6.1.1 Supervised, Unsupervised, and Reinforcement Learning
101(1)
6.1.2 Fundamentals of Reinforcement Learning
101(1)
6.1.2.1 General Reinforcement Learning Model
101(1)
6.1.2.2 Markov Decision Process
102(1)
6.1.2.3 The Goal of the Reinforcement Learning Agent
103(1)
6.1.2.4 Policies and Value Functions
104(1)
6.1.2.5 Sample-Based Learning
105(1)
6.1.2.6 On- and Off-Policy Learning Methods
106(1)
6.1.2.7 SARSA vs Q-Learning
107(1)
6.1.2.8 Q-Learning Algorithm
107(1)
6.1.2.9 Exploration and Exploitation Sfrategy
108(1)
6.1.2.10 Hyperparameter Selection
109(2)
6.1.3 Single and Multi-Agent Reinforcement Learning
111(1)
6.1.4 Problem Formulation in RL
112(1)
6.1.4.1 Denning the Goal
112(1)
6.1.4.2 Mapping the Problem with RL Elements
112(2)
6.1.5 Reinforcement Learning Approach for Microgrids
114(1)
6.1.5.1 Grid Consumption Minimization
114(1)
6.1.5.2 Minimization of Demand-Supply Deficit
114(2)
6.1.5.3 Islanded Operation of Microgrids
116(1)
6.1.5.4 Economic Dispatch
116(1)
6.1.5.5 Energy Market
117(1)
6.2 Case Study: Reinforcement Learning Approach for Minimizing the Grid Dependency of a Solar Microgrid
117(22)
6.2.1 Proposed System
117(2)
6.2.2 Single-Agent Reinforcement Learning Model
119(2)
6.2.3 Multi-Agent Reinforcement Learning Model
121(2)
6.2.4 Simulation Model
123(1)
6.2.4.1 Artificial Neural Network
123(2)
6.2.4.2 Feature Selection
125(1)
6.2.5 RL Simulation Models in Python
125(5)
6.2.6 Hardware Implementation
130(1)
6.2.6.1 Microgrid Testbed
130(1)
6.2.6.2 Agent Implementation
130(4)
6.2.7 Agent Communication
134(2)
6.2.8 Firebase Database
136(3)
Chapter 7 Conclusion
139(6)
Index 145
Prof. K.T.M.U. Hemapala received the B.Sc. (Eng.) degree from University of Moratuwa, Sri Lanka, in 2004 and the PhD degree from University of Genova, Italy in 2009. Currently he is serving as a Professor in the Department of Electrical Engineering, University of Moratuwa. His research interests are in industrial robotics, distributed generation, power system control and smart grid.

Dr. M. K. Perera received the B.Sc. (Eng.) degree from University of Moratuwa, Sri Lanka, in 2020. Currently she is a postgraduate student and a Research Assistant in the Department of Electrical Engineering, University of Moratuwa. Her research interests are in renewable generation, smart grid and machine learning.
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