Intelligent Automatic Generation Control

Intelligent Automatic Generation Control

by Hassan Bevrani, Takashi Hiyama
Released December 2017
Publisher(s): CRC Press
ISBN: 9781351833295

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Book description

Automatic generation control (AGC) is one of the most important control problems in the design and operation of interconnected power systems. Its significance continues to grow as a result of several factors: the changing structure and increasing size, complexity, and functionality of power systems, the rapid emergence (and uncertainty) of renewable energy sources, developments in power generation/consumption technologies, and environmental constraints.

Delving into the fundamentals of power system AGC, Intelligent Automatic Generation Control explores ways to make the infrastructures of tomorrow smarter and more flexible. These frameworks must be able to handle complex multi-objective regulation optimization problems, and they must be highly diversified in terms of policies, control strategies, and wide distribution in demand and supply sources—all via an intelligent scheme. The core of such intelligent systems should be based on efficient, adaptable algorithms, advanced information technology, and fast communication devices to ensure that the AGC systems can maintain generation-load balance following serious disturbances.

This book addresses several new schemes using intelligent control techniques for simultaneous minimization of system frequency deviation and tie-line power changes, which is required for successful operation of interconnected power systems. It also concentrates on physical and engineering aspects and examines several developed control strategies using real-time simulations. This reference will prove useful for engineers and operators in power system planning and operation, as well as academic researchers and students in field of electrical engineering.

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright
  5. Dedication
  6. Preface
  7. Acknowledgments
  8. 1 Intelligent Power System Operation and Control: Japan Case Study
    1. 1.1 Application of Intelligent Methods to Power Systems
    2. 1.2 Application to Power System Planning
      1. 1.2.1 Expansion Planning of Distribution Systems
      2. 1.2.2 Load Forecasting
      3. 1.2.3 Unit Commitment
      4. 1.2.4 Maintenance Scheduling
    3. 1.3 Application to Power System Control and Restoration
      1. 1.3.1 Fault Diagnosis
      2. 1.3.2 Restoration
      3. 1.3.3 Stabilization Control
    4. 1.4 Future Implementations
    5. 1.5 Summary
    6. References
  9. 2 Automatic Generation Control (AGC): Fundamentals and Concepts
    1. 2.1 AGC in a Modern Power System
    2. 2.2 Power System Frequency Control
      1. 2.2.1 Primary Control
      2. 2.2.2 Supplementary Control
      3. 2.2.3 Emergency Control
    3. 2.3 Frequency Response Model and AGC Characteristics
      1. 2.3.1 Droop Characteristic
      2. 2.3.2 Generation-Load Model
      3. 2.3.3 Area Interface
      4. 2.3.4 Spinning Reserve
      5. 2.3.5 Participation Factor
      6. 2.3.6 Generation Rate Constraint
      7. 2.3.7 Speed Governor Dead-Band
      8. 2.3.8 Time Delays
    4. 2.4 A Three-Control Area Power System Example
    5. 2.5 Summary
    6. References
  10. 3 Intelligent AGC: Past Achievements and New Perspectives
    1. 3.1 Fuzzy Logic AGC
      1. 3.1.1 Fuzzy logic Controller
      2. 3.1.2 Fuzzy-based Pi (PiD) Controller
    2. 3.2 Neuro-Fuzzy and Neural-Networks-Based AGC
    3. 3.3 Genetic-Algorithm-Based AGC
    4. 3.4 Multiagent-Based AGC
    5. 3.5 Combined and Other Intelligent Techniques in AGC
    6. 3.6 AGC in a Deregulated Environment
    7. 3.7 AGC and Renewable Energy Options
      1. 3.7.1 Present Status and Future Prediction
      2. 3.7.2 New Technical Challenges
      3. 3.7.3 Recent achievements
    8. 3.8 AGC and Microgrids
    9. 3.9 Scope for Future Work
      1. 3.9.1 improvement of Modeling and analysis Tools
      2. 3.9.2 Develop effective intelligent Control Schemes for Contribution of Dgs/ReSs in the agC issue
      3. 3.9.3 Coordination between Regulation Powers of Dgs/ReSs and Conventional generators
      4. 3.9.4 improvement of Computing Techniques and Measurement Technologies
      5. 3.9.5 Use of advanced Communication and information Technology
      6. 3.9.6 Update/Define New grid Codes
      7. 3.9.7 Revising of existing Standards
      8. 3.9.8 Updating Deregulation Policies
    10. 3.10 Summary
    11. References
  11. 4 AGC in Restructured Power Systems
    1. 4.1 Control Area in New Environment
    2. 4.2 AGC Configurations and Frameworks
      1. 4.2.1 AGC Configurations
      2. 4.2.2 AGC Frameworks
    3. 4.3 AGC Markets
    4. 4.4 AGC Response and an Updated Model
      1. 4.4.1 AGC System and Market Operator
      2. 4.4.2 AGC Model and Bilateral Contracts
      3. 4.4.3 Need for Intelligent AGC Markets
    5. 4.5 Summary
    6. References
  12. 5 Neural-Network-Based AGC Design
    1. 5.1 An Overview
    2. 5.2 ANN-Based Control Systems
      1. 5.2.1 Fundamental Element of ANNS
      2. 5.2.2 Learning and Adaptation
      3. 5.2.3 ANNS in Control Systems
    3. 5.3 Flexible Neural Network
      1. 5.3.1 Flexible Neurons
      2. 5.3.2 Learning Algorithms in an FNN
    4. 5.4 Bilateral AGC Scheme and Modeling
      1. 5.4.1 Bilateral AGC Scheme
      2. 5.4.2 Dynamical Modeling
    5. 5.5 FNN-Based AGC System
    6. 5.6 Application Examples
      1. 5.6.1 Single-Control area
      2. 5.6.2 Three-Control area
    7. 5.7 Summary
    8. References
  13. 6 AGC Systems Concerning Renewable Energy Sources
    1. 6.1 An Updated AGC Frequency Response Model
    2. 6.2 Frequency Response Analysis
    3. 6.3 Simulation Study
      1. 6.3.1 Nine-bus Test System
      2. 6.3.2 Thirty-Nine-bus Test System
    4. 6.4 Emergency Frequency Control and RESs
    5. 6.5 Key Issues and New Perspectives
      1. 6.5.1 Need for Revision of Performance Standards
      2. 6.5.2 Further Research Needs
    6. 6.6 Summary
    7. References
  14. 7 AGC Design Using Multiagent Systems
    1. 7.1 Multiagent System (MAS): An Introduction
    2. 7.2 Multiagent Reinforcement-Learning-Based AGC
      1. 7.2.1 Multiagent Reinforcement learning
      2. 7.2.2 area Control agent
      3. 7.2.3 Rl algorithm
      4. 7.2.4 application to a Thirty-Nine-bus Test System
    3. 7.3 Using GA to Determine Actions and States
      1. 7.3.1 Finding individual’s Fitness and Variation Ranges
      2. 7.3.2 application to a Three-Control area Power System
    4. 7.4 An Agent for β Estimation
    5. 7.5 Summary
    6. References
  15. 8 Bayesian-Network-Based AGC Approach
    1. 8.1 Bayesian Networks: An Overview
      1. 8.1.1 BNs at a Glance
      2. 8.1.2 Graphical Models and Representation
      3. 8.1.3 A Graphical Model Example
      4. 8.1.4 Inference
      5. 8.1.5 Learning
    2. 8.2 AGC with Wind Farms
      1. 8.2.1 Frequency Control and Wind Turbines
      2. 8.2.2 Generalized ACE Signal
    3. 8.3 Proposed Intelligent Control Scheme
      1. 8.3.1 Control Framework
      2. 8.3.2 BN Structure
      3. 8.3.3 Estimation of Amount of Load Change
    4. 8.4 Implementation Methodology
      1. 8.4.1 BN Construction
      2. 8.4.2 Parameter Learning
    5. 8.5 Application Results
      1. 8.5.1 Thirty-Nine-Bus Test System
      2. 8.5.2 A Real-Time Laboratory Experiment
    6. 8.6 Summary
    7. References
  16. 9 Fuzzy Logic and AGC Systems
    1. 9.1 Study Systems
      1. 9.1.1 Two Control Areas with Subareas
      2. 9.1.2 Thirty-Nine-Bus Power System
    2. 9.2 Polar-Information-Based Fuzzy Logic AGC
      1. 9.2.1 Polar-Information-Based Fuzzy Logic Control1,2
      2. 9.2.2 Simulation Results
        1. 9.2.2.1 Trunk Line Power Control
        2. 9.2.2.2 Control of Regulation Margin
    3. 9.3 PSO-Based Fuzzy Logic AGC
      1. 9.3.1 Particle Swarm Optimization
      2. 9.3.2 AGC Design Methodology
      3. 9.3.3 PSO Algorithm for Setting of Membership Functions
      4. 9.3.4 Application Results
    4. 9.4 Summary
    5. References
  17. 10 Frequency Regulation Using Energy Capacitor System
    1. 10.1 Fundamentals of the Proposed Control Scheme
      1. 10.1.1 Restriction of Control action (Type I)
      2. 10.1.2 Restriction of Control action (Type II)
      3. 10.1.3 Prevention of excessive Control action (Type III)
    2. 10.2 Study System
    3. 10.3 Simulation Results
    4. 10.4 Evaluation of Frequency Regulation Performance
    5. 10.5 Summary
    6. References
  18. 11 Application of Genetic Algorithm in AGC Synthesis
    1. 11.1 Genetic Algorithm: An Overview
      1. 11.1.1 GA Mechanism
      2. 11.1.2 GA in Control Systems
    2. 11.2 Optimal Tuning of Conventional Controllers
    3. 11.3 Multiobjective GA
      1. 11.3.1 Multiobjective Optimization
      2. 11.3.2 application to agC Design
    4. 11.4 GA for Tracking Robust Performance Index
      1. 11.4.1 Mixed H2/H∞
      2. 11.4.2 Mixed H2/H∞ SOF Design
      3. 11.4.3 AGC Synthesis Using GA-Based Robust Performance Tracking
    5. 11.5 GA in Learning Process
      1. 11.5.1 GA for Finding Training Data in a BN-Based AGC Design
      2. 11.5.2 Application Example
    6. 11.6 Summary
    7. References
  19. 12 Frequency Regulation in Isolated Systems with Dispersed Power Sources
    1. 12.1 Configuration of Multiagent-Based AGC System
      1. 12.1.1 Conventional agC on Diesel Unit
      2. 12.1.2 Coordinated agC on the eCS and Diesel Unit
    2. 12.2 Configuration of Laboratory System
    3. 12.3 Experimental Results
    4. 12.4 Summary
    5. References
  20. Index

Product information

  • Title: Intelligent Automatic Generation Control
  • Author(s): Hassan Bevrani, Takashi Hiyama
  • Release date: December 2017
  • Publisher(s): CRC Press
  • ISBN: 9781351833295