[内容简介]

This book proposes systemic design methodologies applied to electrical energy systems, in particular integrated optimal design with modeling and optimization methods and tools.

It is made up of six chapters dedicated to integrated optimal design. First, the signal processing of mission profiles and system environment variables are discussed. Then, optimization-oriented analytical models, methods and tools (design frameworks) are proposed. A “multi-level optimization” smartly coupling several optimization processes is the subject of one chapter. Finally, a technico-economic optimization especially dedicated to electrical grids completes the book.

The aim of this book is to summarize design methodologies based in particular on a systemic viewpoint, by considering the system as a whole. These methods and tools are proposed by the most important French research laboratories, which have many scientific partnerships with other European and international research institutions. Scientists and engineers in the field of electrical engineering, especially teachers/researchers because of the focus on methodological issues, will find this book extremely useful, as will PhD and Masters students in this field.

[目录]

Preface xi

Chapter 1 Mission and Environmental Data Processing Amine Jaafar Bruno Sareni Xavier Roboam 1

1.1 Introduction 1

1.2 Considerations of the mission and environmental variables 3

1.2.1 Mission representation through a nominal operating point 4

1.2.2 Extraction of a "sizing" temporal chronogram 4

1.2.3 Representation of an environmental variable or mission resulting from statistical analysis 5

1.3 New approach for the characterization of a "representative mission" 6

1.3.1 Characterization indicators of the mission and environmental variables 7

1.3.2 Mission and environmental variables at the heart of the system: an eminently systemic bidirectional coupling 13

1.4 Classification of missions and environmental variables 16

1.4.1 Classification without a priori assumption on the number of classes 17

1.4.2 Mission classification for hybrid railway systems 18

1.5 Synthesis of mission and environmental variable profiles 21

1.5.1 Mission or environmental variable synthesis process 21

1.5.2 Elementary patterns for profile generation 23

1.5.3 Application to the compacting of a wind speed profile 24

1.6 From classification to simultaneous design by optimization of a hybrid traction chain 25

1.6.1 Modeling of the hybrid locomotive 27

1.6.2 Optimization model 30

1.6.3 Mission classification 32

1.6.4 Synthesis of representative missions 33

1.6.5 Simultaneous design by optimization 37

1.6.6 Design results comparison 38

1.7 Conclusion 39

1.8 Bibliography 41

Chapter 2 Analytical Sizing Models for Electrical Energy Systems Optimization Christophe Espanet Daniel Depernet Anne-Claire Sautter Zhenwai Wu 45

2.1 Introduction 45

2.2 The problem of modeling for synthesis 46

2.2.1 Modeling for synthesis 46

2.2.2 Analytical and numerical modeling 48

2.3 System decomposition and model structure 55

2.3.1 Advantage of decomposition 56

2.3.2 Application to the example of the hybrid series-parallel traction chain for the hybrid electrical heavy vehicle 58

2.4 General information about the modeling of the various possible components in an electrical energy system 60

2.5 Development of an electrical machine analytical model 61

2.5.1 The various physical fields of the model and the associated methods for solving them 62

2.5.2 Application to the example of a hybrid electrical heavy vehicle: modeling of a magnet surface-mounted synchronous machine 64

2.6 Development of an analytical static converter model 73

2.6.1 The various physical fields of the model and associated resolution methods 73

2.6.2 Application to the example of a hybrid electrical heavy vehicle: modeling of inverters feeding synchronous machines 75

2.7 Development of a mechanical transmission analytical model 82

2.7.1 The various physical fields of the model and associated resolution methods 82

2.7.2 Application to the example of a hybrid electric heavy vehicle: modeling of the Ravigneaux gear set 83

2.8 Development of an analytical energy storage device model 91

2.9 Use of models for the optimum sizing of a system 91

2.9.1 Introduction 91

2.9.2 Consideration of operating cycles 94

2.9.3 Independent component optimization 97

2.9.4 Simultaneous component optimization 100

2.10 Conclusions 102

2.11 Bibliography 103

Chapter 3 Simultaneous Design by Means of Evolutionary Computation Bruno Sareni Xavier Roboam 107

3.1 Simultaneous design of energy systems 107

3.1.1 Introduction to simultaneous design 107

3.1.2 Simultaneous design by. means of optimization 109

3.1.3 Problems relating to simultaneous design using optimization 110

3.2 Evolutionary algorithms and artificial evolution 113

3.2.2 Evolutionary algorithms principle 114

3.2.3 Key points of evolutionary algorithms 115

3.3 Consideration of multiple objectives 119

3.3.1 Pareto optimality 119

3.3.2 Multi-objective optimization methods 120

3.3.3 Multi-objective evolutionary algorithms . 121

3.4 Consideration of design constraints 123

3.4.1 Single objective problem 123

3.4.2 Multi-objective problem 125

3.5 Integration of robustness into the simultaneous design process 126

3.5.1 Robust design 126

3.5.2 Vicinity and uncertainty 127

3.5.3 Characterization of robustness 128

3.6 Example applications 130

3.6.1 Design of a passive wind turbine system 130

3.6.2 Simultaneous design of an autonomous hybrid locomotive 143

3.7 Conclusions 150

3.8 Bibliography 151

Chapter 4 Multi-Level Design Approaches for Electro-Mechanical Systems Optimization Stéphane Brisset Frédéric Gillon Pascal Brochet 155

4.1 Introduction 155

4.2 Multi-level approaches 156

4.3 Optimization using models with different granularities 160

4.3.1 Principle of SM 162

4.3.2 Mathematical example 164

4.3.3 SM variants 166

4.3.4 Safety transformer application 172

4.4 Hierarchical decomposition of an optimization problem 178

4.4.1 Target cascading for optimal design 178

4.4.2 Formulation of the TC method 180

4.4.3 Mathematical example 183

4.4.4 Railway traction engine example 186

4.5 Conclusion 187

4.6 Bibliography 188

Chapter 5 Multi-criteria Design and Optimization Tools Benoit Delinchant Laurence Estrabaud Laurent Gerbaud Frederic wurtz 193

5.1 The CADES framework: example of anew tools approach 194

5.2 The system approach: a break from standard tools 195

5.2.1 Some component definitions 196

5.2.2 From integrated environments to collaborative tool frameworks 197

5.2.3 A centered model canvas: from generation to utilization 198

5.2.4 Some "business" application frameworks 201

5.3 Components ensuring interoperability around a framework 203

5.3.1 Model types: white box, black box 203

5.3.2 Black boxes: positive collaboration and re-use 205

5.3.3 Object, component, and service paradigms 206

5.3.4 ICAr software components: model normalization for sizing 209

5.4 Some calculation modeling formalisms for optimization 210

5.4.1 Analytical formalisms: algebraic and algorithmic 210

5.4.2 Physical models within various formalisms 213

5.4.3 The generation chain 218

5.5 The principles of automatic Jacobian generation 218

5.5.1 The Jacobian: complementary data for the model 218

5.5.2 Derivation of mathematical expressions 219

5.5.3 Algorithm derivation 221

5.5.4 Derivation of specific formulations 222

5.6 Services using models and their Jacobian 223

5.6.1 Sensitivity study 223

5.6.2 Composition of models 224

5.6.3 Optimal design 226

5.7 Applications of CADES in system optimization 227

5.7.1 Overall optimization of a structure 227

5.7.2 Evaluation of the potential of a structure 229

5.7.3 Comparison between structures 230

5.8 Perspectives 231

5.8.1 Towards optimization using dynamic modeling 231

5.8.2 Towards robust design 233

5.8.3 Robust optimization under reliability constraints 234

5.8.4 Towards the Internet 235

5.9 Conclusions 238

5.10 Bibliography 239

Chapter 6 Technico-economic Optimization of Energy Networks Guillaume Sandou Philippe Dessante Marc Petit Henri Borsenberger 247

6.1 Introduction 247

6.2 Energy network modeling 249

6.2.1 Context 249

6.2.2 Notations 249

6.2.3 Objective function 250

6.2.4 Constraints 251

6.2.5 Expression of the problem and eventual linear reformulation 253

6.2.6 Position of the problem processed relative to the problem of energy network management 254

6.3 Resolution of the energy network optimization problem for a deterministic case 255

6.3.1 State of the art 255

6.3.2 Resolution by dynamic programming and Lagrangian relaxation 257

6.3.3 Resolution by genetic algorithm 262

6.4 Introduction to uncertainty consideration 266

6.4.1 Consideration of uncertainties 266

6.4.2 Recourse notion 267

6.5 Consideration of uncertainties on consumer demand 269

6.5.1 Safety margin 269

6.5.2 Scenario tree uncertainty modeling 269

6.5.3 Resolution by dynamic programming and Lagrangian relaxation 270

6.5.4 Conclusion 272

6.6 Consideration of uncertainties over production costs 273

6.6.1 Introduction 273

6.6.2 Mathematical formulation 274

6.6.3 Resolution 275

6.6.4 Example 277

6.7 From optimization to control 279

6.7.1 The predictive approach principle 279

6.7.2 Example 279

6.8 Conclusions 280

6.9 Bibliography 281

List of Authors 287

Index 291