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Innovation in Power, Control, and Optimization

Book Description

Innovation in Power, Control, and Optimization: Emerging Energy Technologies unites research on the development of techniques and methodologies to improve the performance of power systems, energy planning and environments, controllers and robotics, operation research, and modern artificial computational intelligent techniques. Containing research on power engineering, control systems, and methods of optimization, this book is written for professionals who want to improve their understanding of strategic developments in the area of power, control, and optimization.

Table of Contents

  1. Title Page
  2. Copyright Page
  3. Editorial Advisory Board
  4. Foreword
  5. Foreword
  6. Preface
  7. Acknowledgment
  8. Chapter 1: Coordinated Intelligent Operation and Emergency Control of Electric Power Systems
    1. Abstract
    2. Introduction
    3. Conclusion
    4. Appendix:List Of Authors
  9. Chapter 2: Hopfield Lagrange Network for Economic Load Dispatch
    1. Abstract
    2. Introduction
    3. Background
    4. Hopfield Lagrange Network And Applications
  10. Chapter 3: Renewable Energy and Sustainable Development
    1. Abstract
    2. Introduction
    3. Future Research Directions
    4. Conclusion
  11. Chapter 4: Demand-Side Response Smart Grid Technique for Optimized Energy Use
    1. Abstract
    2. Introduction
    3. Electricity Industry Development In Australia (Case Study)
  12. Chapter 5: Soft Computing and Computational Intelligent Techniques in the Evaluation of Emerging Energy Technologies
    1. Abstract
    2. Introduction
    3. Emerging Energy Technologies
    4. Soft Computing And Computational Intelligent Techniques
    5. Literature Survey For Emerging Energy Technologies
    6. Future Research Directions
    7. Conclusion
  13. Chapter 6: Dynamic Analysis and Stability Improvement Concerning the Integration of Wind Farms Kurdistan Electric Network Case Study
    1. Abstract
    2. Introduction
    3. Background
    4. Future Research Directions
    5. Conclusion
  14. Chapter 7: Many-to-Many Assignment Problems
    1. Abstract
    2. Introduction
    3. Basic Constructions To Derive The Modified Lagrangian Bounds
    4. Modified Lagrangian Bound For Many-To-Many Assignment Problem
    5. Solving The Dual Problems
    6. Computational Tests
    7. Future Research Directions
    8. Final Remarks And Conclusion
    9. Appendix
  15. Chapter 8: Power Systems Investments
    1. Abstract
    2. Introduction
    3. Project Appraisal And Volatility Fundamentals
    4. Combined Cycle Natural Gas-Fired Plant
    5. Future Research Directions
    6. Conclusion
    7. Appendix
  16. Chapter 9: Optimal Configuration and Reconfiguration of Electric Distribution Networks
    1. Abstract
    2. Introduction
    3. Main Focus Of The Chapter
    4. Introduction To Distribution Networks &Amp; Their Configuration
    5. Introduction To Distribution Network’S Reconfiguration
    6. Basic Deterministic Load Flow
    7. Probability Density Function
    8. Probabilistic Load Flow
    9. Proposed Probabilistic Procedure
    10. Genetic Algorithm
    11. Proposed Fitness Function For Ga
    12. Computation Results And Discussion
    13. Conclusion
    14. Future Research Directions
  17. Chapter 10: A Descriptive Approach for Power System Stability and Security Assessment
    1. Abstract
    2. Introduction
    3. Background
    4. Problem Illustration And Modeling
    5. Proposed Methodology
    6. Simulation Results
    7. Future Research Directions
    8. Conclusion
  18. Chapter 11: Analyses and Monitoring of Power Grid
    1. Abstract
    2. Introduction
    3. Background
    4. Solutions And Recommendations
    5. Future Research Directions
    6. Conclusion
    7. Appendix 1: Key Terms, Concepts, And Equations
  19. Chapter 12: Solving Fuzzy Optimization Problems of Uncertain Technological Coefficients with Genetic Algorithms and Hybrid Genetic Algorithms Pattern Search Approaches
    1. Abstract
    2. Introduction
    3. Fuzzy Membership Function
    4. Genetic Algorithms For Production Planning Problems
    5. Computational Results
    6. Conclusion
  20. About the Contributors
  21. Index