Integrated Design by Optimization of Electrical Energy Systems
, by Roboam, XavierNote: Supplemental materials are not guaranteed with Rental or Used book purchases.
- ISBN: 9781848213890 | 1848213891
- Cover: Hardcover
- Copyright: 8/20/2012
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.
Xavier Roboam is a Senor Scientist at Laboratory on Plasma and Conversion of Energy, University Paul Sabatier, Toulouse, France
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 |
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