Momoh Adaptive Stochastic Optimization Techniques with Applications

Erscheinungsjahr 2015
ISBN: 978-1-4398-2979-0
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 442 Seiten

This book presents new trends in optimization methods that can be used to handle the stochastic, predictive nature of large-scale system problems in power and energy. The author provides decision tools and techniques for heuristic optimization and adaptive dynamic programming. He also reviews the latest research in optimization techniques derived from static optimization, decision support tools, and heuristic and adaptive dynamic programming for handling problems with stochastic, predictive, and adaptive behavior. In addition to easy-to-follow algorithms and illustrative engineering examples, the author also includes benchmark problems from power systems using state-of-the-art optimization.

Zielgruppe

Senior undergraduate and graduate students, decision-makers, researchers, and scholars who are engaged in management science, risk assessment, uncertainty decision-making, planning, pricing and large-scale planning and operations.

Autoren/Hrsg.

Momoh, James A.

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Technik Allgemein Technik: Allgemeines
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze

Inhaltsverzeichnis

Part 1: Classical Optimization Techniques

Static Optimization Overview

Definition

Applications of Static Optimization

Constraints and Limitation of Static Optimization Techniques

Tools/Solution Techniques

Dynamic Optimization Techniques and Optimal Control

Definition

Strengths and Limitations of Dynamic Optimization Techniques

Functional Optimization or Dynamic Programming (DP)

Optimal Control

Pontryagin Minimum Principle

Decision Analysis Tools

Concepts and Definitions for Decision Analysis

Decision Analysis (DA)

Analytical Hierarchical Programming (ARP)

Analytical Network Process (ANP)

Cost/Benefit Analysis (CBA)

Risk Assessment

Game Theory

Intelligent System

Expert Systems

Fuzzy Logic Systems

Artificial Neural Networks

Genetic Algorithm

Evolutionary Programming/Heuristic Optimization

Particle Swann Optimization

Ant Colony Optimization

Tabu Search

Annealing Method

Pareto Multiples Optimization

Adaptive Dynamic Programming (ADP)

Overview

Strengths and Limitations of ADP

Variants of ADP

Implementation Approach

ADP Formulation



Part 2: Applications to Power Systems

Introduction to Power System Applications

Overview of Power System Applications

Analysis of Possible Optimization Techniques

OPF

Formulation

Variants

Challenges

Solution Techniques

Design

Vulnerability

Stability

Real Time Assessment

Limitations

Framework for Design

Scheduling

Formulation

Algorithm for Multiple Objectives

Tools / Proposed Approaches

Pricing

Formulation

Static vs. Dynamic Applications

Tools / Proposed Approaches

Unit Commitment

Formulation

Variants: Static vs. Dynamic Applications

Algorithm and Computational Strategy

Control & Voltage/ VAR regulation

Formulation

Variants

Limitations

Algorithms and Computational Strategy

Smart Grid and Adaptive Dynamic Stochastic Optimization Application

Evaluation of stochastic optimization for smart grid design

Implementation system

Über Autor(innen)

James A. Momoh is a Professor of Electrical and Computer Engineering and the Director of the Center for Energy Systems and Control at Howard University, Washington, D.C.