Adaptive Stochastic Optimization Techniques with Applications: 1st Edition (Hardback) book cover

Adaptive Stochastic Optimization Techniques with Applications

1st Edition

By James A. Momoh

CRC Press

414 pages | 85 B/W Illus.

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pub: 2015-11-24
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Description

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.

Reviews

"The book serves as a pioneering work for addressing many of the computational challenges, speci cally, the power system optimization problems with adaptive dynamic stochastic and predictive characteristics." - I. M. Stancu-Minasian (Bucure?sti)

Table of Contents

Introduction

Intelligent Systems and Adaptive Dynamic Programming Techniques

Outline

References

Suggested Readings

CLASSICAL OPTIMIZATION TECHNIQUES

Static Optimization Techniques

Introduction

Definition

Applications of Static Optimization

Constraints and Limitation of Static Optimization Techniques

Solution Techniques

Conclusion

Problem Set

References

Suggested Readings

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

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

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

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

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

APPLICATIONS TO POWER SYSTEMS

Introduction to Power System Applications

Overview of Power System Optimization Models

Overview of Power System Applications

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

Vulnerability Assessment

Introduction

Generalized Model for Vulnerability Assessment

Methods Used in Vulnerability Assessment

Vulnerability Assessment Challenges

Cases

Conclusion

Problem Set

References

Suggested Readings

Voltage/VAr

Introduction

History of Voltage/VAr Control

Models and Formulation

Methods Used in Voltage/VAr

Cases

Conclusion

Problem Set

References

Suggested Readings

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

Control Coordination

Introduction

Control Strategy

Coordinated Control Design

Problem Definition and Formulation

Methods Used in Control Coordination

Cases

Conclusion

Problem Set

References

Suggested Readings

Reliability and Reconfiguration

Introduction

Reliability

Reconfiguration

Optimization of Reliability and Reconfiguration

Cases

Conclusion

Problem Set

References

Suggested Readings

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

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

About the Author

James A. Momoh is a professor at Howard University and the director of the Centre for Energy Systems and Control (CESaC) at Howard University. He is well known for his achievements in engineering education and his extensive research in optimization, power systems, and smart grids/micro grids. He is a distinguished fellow of the Nigerian Society of Engineers (NSE), a fellow of the Institute of Electrical and Electronics Engineers (IEEE), a fellow of the Nigerian Academy of Engineering (NAE), and a fellow member of the Nigerian Academy of Science (NAS). He served as program director at the National Science Foundation (NSF) from 2001-2004 and as Electrical and Computer Engineering (EECE) Department chair at Howard University for 11 years. He holds a PhD from Howard University, an MSEE from Carnegie Mellon University, and an MS in systems engineering from the University of Pennsylvania. He is a recipient of numerous awards, including the coveted 1987 NSF Presidential Young Investigator award. Dr. Momoh has published several technical papers and bestselling textbooks on power systems, optimization, and smart grids.

Subject Categories

BISAC Subject Codes/Headings:
BUS049000
BUSINESS & ECONOMICS / Operations Research
TEC009000
TECHNOLOGY & ENGINEERING / Engineering (General)
TEC031020
TECHNOLOGY & ENGINEERING / Power Resources / Electrical