Integrated Design by Optimization of Electrical Energy Systems

by Roboam, Xavier

• ISBN13: 9781848213890
• ISBN10: 1848213891
• Edition: 1st
• Format: Hardcover
• Copyright: 2012-08-20
• Publisher: Wiley-ISTE

Summary

This book presents the vision of French academics about systemic design methodologies applied to electrical energy systems. It is especially dedicated to discussion of analysis and system management, as well as modeling and sizing tools.

Author Biography

Xavier Roboam is a Senor Scientist at Laboratory on Plasma and Conversion of Energy, University Paul Sabatier, Toulouse, France

Table of Contents

Preface	p. xi
Mission and Environmental Data Processing	p. 1
Introduction	p. 1
Considerations of the mission and environmental variables	p. 3
Mission representation through a nominal operating point	p. 4
Extraction of a "sizing" temporal chronogram	p. 4
Representation of an environmental variable or mission resulting from statistical analysis	p. 5
New approach for the characterization of a "representative mission"	p. 6
Characterization indicators of the mission and environmental variables	p. 7
Mission and environmental variables at the heart of the system: an eminently systemic bidirectional coupling	p. 13
Classification of missions and environmental variables	p. 16
Classification without a priori assumption on the number of classes	p. 17
Mission classification for hybrid railway systems	p. 18
Synthesis of mission and environmental variable profiles	p. 21
Mission or environmental variable synthesis process	p. 21
Elementary patterns for profile generation	p. 23
Application to the compacting of a wind speed profile	p. 24
From classification to simultaneous design by optimization of a hybrid traction chain	p. 25
Modeling of the hybrid locomotive	p. 27
Optimization model	p. 30
Mission classification	p. 32
Synthesis of representative missions	p. 33
Simultaneous design by optimization	p. 37
Design results comparison	p. 38
Conclusion	p. 39
Bibliography	p. 41
Analytical Sizing Models for Electrical Energy Systems Optimization	p. 45
Introduction	p. 45
The problem of modeling for synthesis	p. 46
Modeling for synthesis	p. 46
Analytical and numerical modeling	p. 48
System decomposition and model structure	p. 55
Advantage of decomposition	p. 56
Application to the example of the hybrid series-parallel traction chain for the hybrid electrical heavy vehicle	p. 58
General information about the modeling of the various possible components in an electrical energy system	p. 60
Development of an electrical machine analytical model	p. 61
The various physical fields of the model and the associated methods for solving them	p. 62
Application to the example of a hybrid electrical heavy vehicle: modeling of a magnet surface-mounted synchronous machine	p. 64
Development of an analytical static converter model	p. 73
The various physical fields of the model and associated resolution methods	p. 73
Application to the example of a hybrid electrical heavy vehicle: modeling of inverters feeding synchronous machines	p. 75
Development of a mechanical transmission analytical model	p. 82
The various physical fields of the model and associated resolution methods	p. 82
Application to the example of a hybrid electric heavy vehicle: modeling of the Ravigneaux gear set	p. 83
Development of an analytical energy storage device model	p. 91
Use of models for the optimum sizing of a system	p. 91
Introduction	p. 91
Consideration of operating cycles	p. 94
Independent component optimization	p. 97
Simultaneous component optimization	p. 100
Conclusions	p. 102
Bibliography	p. 103
Simultaneous Design by Means of Evolutionary Computation	p. 107
Simultaneous design of energy systems	p. 107
Introduction to simultaneous design	p. 107
Simultaneous design by. means of optimization	p. 109
Problems relating to simultaneous design using optimization	p. 110
Evolutionary algorithms and artificial evolution	p. 113
Evolutionary algorithms principle	p. 114
Key points of evolutionary algorithms	p. 115
Consideration of multiple objectives	p. 119
Pareto optimality	p. 119
Multi-objective optimization methods	p. 120
Multi-objective evolutionary algorithms	p. .
Consideration of design constraints	p. 123
Single objective problem	p. 123
Multi-objective problem	p. 125
Integration of robustness into the simultaneous design process	p. 126
Robust design	p. 126
Vicinity and uncertainty	p. 127
Characterization of robustness	p. 128
Example applications	p. 130
Design of a passive wind turbine system	p. 130
Simultaneous design of an autonomous hybrid locomotive	p. 143
Conclusions	p. 150
Bibliography	p. 151
Multi-Level Design Approaches for Electro-Mechanical Systems Optimization	p. 155
Introduction	p. 155
Multi-level approaches	p. 156
Optimization using models with different granularities	p. 160
Principle of SM	p. 162
Mathematical example	p. 164
SM variants	p. 166
Safety transformer application	p. 172
Hierarchical decomposition of an optimization problem	p. 178
Target cascading for optimal design	p. 178
Formulation of the TC method	p. 180
Mathematical example	p. 183
Railway traction engine example	p. 186
Conclusion	p. 187
Bibliography	p. 188
Multi-criteria Design and Optimization Tools	p. 193
The CADES framework: example of anew tools approach	p. 194
The system approach: a break from standard tools	p. 195
Some component definitions	p. 196
From integrated environments to collaborative tool frameworks	p. 197
A centered model canvas: from generation to utilization	p. 198
Some "business" application frameworks	p. 201
Components ensuring interoperability around a framework	p. 203
Model types: white box, black box	p. 203
Black boxes: positive collaboration and re-use	p. 205
Object, component, and service paradigms	p. 206
ICAr software components: model normalization for sizing	p. 209
Some calculation modeling formalisms for optimization	p. 210
Analytical formalisms: algebraic and algorithmic	p. 210
Physical models within various formalisms	p. 213
The generation chain	p. 218
The principles of automatic Jacobian generation	p. 218
The Jacobian: complementary data for the model	p. 218
Derivation of mathematical expressions	p. 219
Algorithm derivation	p. 221
Derivation of specific formulations	p. 222
Services using models and their Jacobian	p. 223
Sensitivity study	p. 223
Composition of models	p. 224
Optimal design	p. 226
Applications of CADES in system optimization	p. 227
Overall optimization of a structure	p. 227
Evaluation of the potential of a structure	p. 229
Comparison between structures	p. 230
Perspectives	p. 231
Towards optimization using dynamic modeling	p. 231
Towards robust design	p. 233
Robust optimization under reliability constraints	p. 234
Towards the Internet	p. 235
Conclusions	p. 238
Bibliography	p. 239
Technico-economic Optimization of Energy Networks	p. 247
Introduction	p. 247
Energy network modeling	p. 249
Context	p. 249
Notations	p. 249
Objective function	p. 250
Constraints	p. 251
Expression of the problem and eventual linear reformulation	p. 253
Position of the problem processed relative to the problem of energy network management	p. 254
Resolution of the energy network optimization problem for a deterministic case	p. 255
State of the art	p. 255
Resolution by dynamic programming and Lagrangian relaxation	p. 257
Resolution by genetic algorithm	p. 262
Introduction to uncertainty consideration	p. 266
Consideration of uncertainties	p. 266
Recourse notion	p. 267
Consideration of uncertainties on consumer demand	p. 269
Safety margin	p. 269
Scenario tree uncertainty modeling	p. 269
Resolution by dynamic programming and Lagrangian relaxation	p. 270
Conclusion	p. 272
Consideration of uncertainties over production costs	p. 273
Introduction	p. 273
Mathematical formulation	p. 274
Resolution	p. 275
Example	p. 277
From optimization to control	p. 279
The predictive approach principle	p. 279
Example	p. 279
Conclusions	p. 280
Bibliography	p. 281
List of Authors	p. 287
Index	p. 291
Table of Contents provided by Ingram. All Rights Reserved.