Contents
1 Intelligent Power System Operation and Control: Japan Case Study
1.1 Application of Intelligent Methods to Power Systems
1.2 Application to Power System Planning
1.2.1 Expansion Planning of Distribution Systems
1.3 Application to Power System Control and Restoration
2 Automatic Generation Control (AGC): Fundamentals and Concepts
2.1 AGC in a Modern Power System
2.2 Power System Frequency Control
2.3 Frequency Response Model and AGC Characteristics
2.3.6 Generation Rate Constraint
2.3.7 Speed Governor Dead-Band
2.4 A Three-Control Area Power System Example
3 Intelligent AGC: Past Achievements and New Perspectives
3.1.2 Fuzzy-based Pi (PiD) Controller
3.2 Neuro-Fuzzy and Neural-Networks-Based AGC
3.3 Genetic-Algorithm-Based AGC
3.5 Combined and Other Intelligent Techniques in AGC
3.6 AGC in a Deregulated Environment
3.7 AGC and Renewable Energy Options
3.7.1 Present Status and Future Prediction
3.7.2 New Technical Challenges
3.9.1 improvement of Modeling and analysis Tools
3.9.2 Develop effective intelligent Control Schemes for Contribution of Dgs/ReSs in the agC issue
3.9.3 Coordination between Regulation Powers of Dgs/ReSs and Conventional generators
3.9.4 improvement of Computing Techniques and Measurement Technologies
3.9.5 Use of advanced Communication and information Technology
3.9.6 Update/Define New grid Codes
3.9.7 Revising of existing Standards
3.9.8 Updating Deregulation Policies
4 AGC in Restructured Power Systems
4.1 Control Area in New Environment
4.2 AGC Configurations and Frameworks
4.4 AGC Response and an Updated Model
4.4.1 AGC System and Market Operator
4.4.2 AGC Model and Bilateral Contracts
4.4.3 Need for Intelligent AGC Markets
5 Neural-Network-Based AGC Design
5.2.1 Fundamental Element of ANNS
5.3.2 Learning Algorithms in an FNN
5.4 Bilateral AGC Scheme and Modeling
6 AGC Systems Concerning Renewable Energy Sources
6.1 An Updated AGC Frequency Response Model
6.2 Frequency Response Analysis
6.3.2 Thirty-Nine-bus Test System
6.4 Emergency Frequency Control and RESs
6.5 Key Issues and New Perspectives
6.5.1 Need for Revision of Performance Standards
7 AGC Design Using Multiagent Systems
7.1 Multiagent System (MAS): An Introduction
7.2 Multiagent Reinforcement-Learning-Based AGC
7.2.1 Multiagent Reinforcement learning
7.2.4 application to a Thirty-Nine-bus Test System
7.3 Using GA to Determine Actions and States
7.3.1 Finding individual’s Fitness and Variation Ranges
7.3.2 application to a Three-Control area Power System
8 Bayesian-Network-Based AGC Approach
8.1 Bayesian Networks: An Overview
8.1.2 Graphical Models and Representation
8.1.3 A Graphical Model Example
8.2.1 Frequency Control and Wind Turbines
8.3 Proposed Intelligent Control Scheme
8.3.3 Estimation of Amount of Load Change
8.4 Implementation Methodology
8.5.1 Thirty-Nine-Bus Test System
8.5.2 A Real-Time Laboratory Experiment
9.1.1 Two Control Areas with Subareas
9.1.2 Thirty-Nine-Bus Power System
9.2 Polar-Information-Based Fuzzy Logic AGC
9.2.1 Polar-Information-Based Fuzzy Logic Control1, 2
9.2.2.1 Trunk Line Power Control
9.2.2.2 Control of Regulation Margin
9.3.1 Particle Swarm Optimization
9.3.3 PSO Algorithm for Setting of Membership Functions
10 Frequency Regulation Using Energy Capacitor System
10.1 Fundamentals of the Proposed Control Scheme
10.1.1 Restriction of Control action (Type I)
10.1.2 Restriction of Control action (Type II)
10.1.3 Prevention of excessive Control action (Type III)
10.4 Evaluation of Frequency Regulation Performance
11 Application of Genetic Algorithm in AGC Synthesis
11.1 Genetic Algorithm: An Overview
11.2 Optimal Tuning of Conventional Controllers
11.3.1 Multiobjective Optimization
11.3.2 application to agC Design
11.4 GA for Tracking Robust Performance Index
11.4.3 AGC Synthesis Using GA-Based Robust Performance Tracking
11.5.1 GA for Finding Training Data in a BN-Based AGC Design
12 Frequency Regulation in Isolated Systems with Dispersed Power Sources
12.1 Configuration of Multiagent-Based AGC System
12.1.1 Conventional agC on Diesel Unit
12.1.2 Coordinated agC on the eCS and Diesel Unit