1st Edition

Adaptive Stochastic Optimization Techniques with Applications

By James A. Momoh Copyright 2016
    442 Pages 85 B/W Illustrations
    by CRC Press

    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.

    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

    Biography

    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.

    "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)