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Adaptive stochastic optimization techniques with applications

By: Momoh, James A.
Publisher: Boca Raton CRC Press 2016Description: xxii, 414 p.ISBN: 9781439829783.Subject(s): Stochastic processes | Point processes | Gaussian processesDDC classification: 519.23 Summary: Adaptive Stochastic Optimization Techniques with Applications provides a single, convenient source for state-of-the-art information on optimization techniques used to solve problems with adaptive, dynamic, and stochastic features. Presenting modern advances in static and dynamic optimization, decision analysis, intelligent systems, evolutionary programming, heuristic optimization, stochastic and adaptive dynamic programming, and adaptive critics, this book: Evaluates optimization methods for handling operational planning, Voltage/VAr, control coordination, vulnerability, reliability, resilience, and reconfiguration issues Includes mathematical formulations, algorithms for implementation, illustrative engineering examples, and case studies from actual power systems Discusses the limitations of current optimization techniques in meeting the challenges of smart electric grids Adaptive Stochastic Optimization Techniques with Applications describes cutting-edge optimization methods used to address large-scale system problems applicable to power, energy, communications, transportation, and economics. (https://www.crcpress.com/Adaptive-Stochastic-Optimization-Techniques-with-Applications/Momoh/9781439829783)
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Table of Contents:

Chapter 1 Introduction

• Introduction
• Intelligent Systems and Adaptive Dynamic Programming Techniques
• Outline
• References
• Suggested Readings


SECTION I: CLASSICAL OPTIMIZATION TECHNIQUES


Chapter 2 Static Optimization Techniques

• Introduction
• Definition
• Applications of Static Optimization
• Constraints and Limitation of Static Optimization Techniques
• Solution Techniques
• Conclusion
• Problem Set
• References
• Suggested Readings

Chapter 3 Dynamic Optimization Techniques and Optimal Control

• Introduction
• Definitions of Dynamic Programming
• Dynamic Programming Formulations
• Optimal Control
• Pontryagin’s Minimum Principle
• Illustrative Examples
• Conclusions
• Problem Set
• References
• Suggested Readings

Chapter 4 Decision Analysis Tools

• Introduction
• Classification of Decision Analysis
• Decision Analysis Techniques Based on Probability Methods
• Analytical Hierarchical Programming (AHP)
• Analytical Network Process (ANP)
• Cost-Benefit Analysis
• Risk Assessment Strategy for Decision Support
• Game Theory
• Illustrative Examples
• Conclusion
• Problem Set
• References
• Suggested Readings

Chapter 3 Intelligent Systems

• Introduction
• Expert Systems
• Fuzzy Logic Systems
• Artificial Neural Networks
• Genetic Algorithm
• Application of Intelligent System to Power System
• Illustrative Examples
• Conclusion
• Problem Set
• References
• Suggested Readings

Chapter 6 Evolutionary Programming and Heuristic Optimization

• Introduction
• Particle Swarm Optimization
• Ant Colony Optimization
• Genetic Algorithm
• Annealing Method
• Pareto Multiples Optimization
• Tabu Search Optimization Method
• Conclusion
• References
• Suggested Readings

Chapter 7 Stochastic and Adaptive Dynamic Programming Fundamentals

• Overview
• Introduction to Stochastic Programming
• Stochastic Programming Variants
• Definition of ADP
• ADP Formulation
• Illustrative Examples
• Conclusion
• Problem Set
• References
• Suggested Readings


SECTION-II APPLICATIONS TO POWER SYSTEMS


Chapter 8 Introduction to Power System Applications

• Overview of Power System Optimization Models
• Overview of Power System Applications

Chapter 9 Optimal Power Flow

• Introduction
• History of Optimum Power Flow (OPF) Computation
• OPF Problem Formulations and Computation
• Methods Used in OPF
• Cases
• Conclusion
• Problem Set
• References
• Suggested Readings

Chapter 10 Vulnerability Assessment

• Introduction
• Generalized Model for Vulnerability Assessment
• Methods Used in Vulnerability Assessment
• Vulnerability Assessment Challenges
• Cases
• Conclusion
• Problem Set
• References
• Suggested Readings

Chapter 11 Voltage/VAr

• Introduction
• History of Voltage/VAr Control
• Models and Formulation
• Methods Used in Voltage/VAr
• Cases
• Conclusion
• Problem Set
• References
• Suggested Readings

Chapter 12 Unit Commitment

• Introduction
• History of Unit Commitment Optimization
• Objective Function
• A Simple Merit Order Scheme
• Methods for Unit Commitment
• Challenges Facing Unit Commitment Optimization
• Cases
• Conclusion
• Problem Set
• References
• Suggested Readings

Chapter 13 Control Coordination

• Introduction
• Control Strategy
• Coordinated Control Design
• Problem Definition and Formulation
• Methods Used in Control Coordination
• Cases
• Conclusion
• Problem Set
• References
• Suggested Readings

Chapter 14 Reliability and Reconfiguration

• Introduction
• Reliability
• Reconfiguration
• Optimization of Reliability and Reconfiguration
• Cases
• Conclusion
• Problem Set
• References
• Suggested Readings

Chapter 15 Smart Grid and Adaptive Dynamic Stochastic Optimization

• Introduction
• Power Grid Generation Level in Smart Grid
• Bulk Power System Automation of Smart Grid at Transmission Level
• Distribution System of the Power Grid
• End User/Appliance Level of the Smart Grid
• Design Smart Grid Using Advanced Optimization and Control Techniques
• Applications for Dynamic Stochastic Optimum Power Flow (DSOPF)
• DSOPF Application to Smart Grid
• Computational Challenges for the Development of Smart Grid
• Cases
• Conclusion
• References
• Suggested Readings

Chapter 16 Epilogue

• Design of Optimal Future Grid with Different Distributed Energy Resources with the Capability for Sustainability, Economies of Scale, and Resilient to Different Attacks
• Storage and Energy Management under Uncertainties
• Transmission Challenges and Optimization for Smart Grid
• Next-Generation Distribution Grid
• Conclusion





Adaptive Stochastic Optimization Techniques with Applications provides a single, convenient source for state-of-the-art information on optimization techniques used to solve problems with adaptive, dynamic, and stochastic features. Presenting modern advances in static and dynamic optimization, decision analysis, intelligent systems, evolutionary programming, heuristic optimization, stochastic and adaptive dynamic programming, and adaptive critics, this book:

Evaluates optimization methods for handling operational planning, Voltage/VAr, control coordination, vulnerability, reliability, resilience, and reconfiguration issues
Includes mathematical formulations, algorithms for implementation, illustrative engineering examples, and case studies from actual power systems
Discusses the limitations of current optimization techniques in meeting the challenges of smart electric grids

Adaptive Stochastic Optimization Techniques with Applications describes cutting-edge optimization methods used to address large-scale system problems applicable to power, energy, communications, transportation, and economics.


(https://www.crcpress.com/Adaptive-Stochastic-Optimization-Techniques-with-Applications/Momoh/9781439829783)

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