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Adaptive Stochastic Optimization Techniques with Applications de James A. Momoh

Adaptive Stochastic Optimization Techniques with Applications

Autor James A. Momoh
en Limba Engleză Hardback – 24 noi 2015
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.
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Specificații

ISBN-13: 9781439829783
ISBN-10: 1439829780
Pagini: 442
Ilustrații: 85 black & white illustrations, 42 black & white tables
Dimensiuni: 156 x 234 x 31 mm
Greutate: 0.75 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press

Public țintă

Professional Practice & Development

Cuprins

Introduction. Static Optimization Techniques. Dynamic Optimization Techniques and Optimal Control. Decision Analysis Tools. Intelligent Systems. Evolutionary Programming and Heuristic Optimization. Stochastic and Adaptive Dynamic Programming Fundamentals. Introduction to Power System Applications. Optimal Power Flow. Vulnerability Assessment. Voltage/VAr. Unit Commitment. Control Coordination. Reliability and Reconfiguration. Smart Grid and Adaptive Dynamic Stochastic Optimization. Epilogue.

Notă biografică

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.

Recenzii

"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)

Descriere

This book describes state-of-the-art optimization techniques used to solve problems with adaptive, dynamic, and stochastic features. It presents modern advances in static and dynamic optimization, decision analysis, intelligent systems, evolutionary programming, heuristic optimization, stochastic and adaptive dynamic programming, and adaptive critics. It evaluates optimization methods for handling operational planning, Voltage/VAr, control coordination, vulnerability, reliability, resilience, and reconfiguration issues, providing mathematical formulations, algorithms for implementation, examples, and case studies. It also discusses the limitations of current optimization techniques in meeting the challenges of smart electric grids.