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E-grāmata: Integrated Design by Optimization of Electrical Energy Systems

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  • Formāts: PDF+DRM
  • Izdošanas datums: 02-Apr-2013
  • Izdevniecība: ISTE Ltd and John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118588000
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Integrated Design by Optimization of Electrical Energy SystemsPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 02-Apr-2013
  • Izdevniecība: ISTE Ltd and John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118588000
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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.
Preface xi
Chapter 1 Mission and Environmental Data Processing
1(44)
Amine Jaafar
Bruno Sareni
Xavier Roboam
1.1 Introduction
1(2)
1.2 Considerations of the mission and environmental variables
3(3)
1.2.1 Mission representation through a nominal operating point
4(1)
1.2.2 Extraction of a "sizing" temporal chronogram
4(1)
1.2.3 Representation of an environmental variable or mission resulting from statistical analysis
5(1)
1.3 New approach for the characterization of a "representative mission"
6(10)
1.3.1 Characterization indicators of the mission and environmental variables
7(6)
1.3.2 Mission and environmental variables at the heart of the system: an eminently systemic bidirectional coupling
13(3)
1.4 Classification of missions and environmental variables
16(5)
1.4.1 Classification without a priori assumption on the number of classes
17(1)
1.4.2 Mission classification for hybrid railway systems
18(3)
1.5 Synthesis of mission and environmental variable profiles
21(4)
1.5.1 Mission or environmental variable synthesis process
21(2)
1.5.2 Elementary patterns for profile generation
23(1)
1.5.3 Application to the compacting of a wind speed profile
24(1)
1.6 From classification to simultaneous design by optimization of a hybrid traction chain
25(14)
1.6.1 Modeling of the hybrid locomotive
27(3)
1.6.2 Optimization model
30(2)
1.6.3 Mission classification
32(1)
1.6.4 Synthesis of representative missions
33(4)
1.6.5 Simultaneous design by optimization
37(1)
1.6.6 Design results comparison
38(1)
1.7 Conclusion
39(2)
1.8 Bibliography
41(4)
Chapter 2 Analytical Sizing Models for Electrical Energy Systems Optimization
45(62)
Christophe Espanet
Daniel Depernet
Anne-Claire Sautter
Zhenwai Wu
2.1 Introduction
45(1)
2.2 The problem of modeling for synthesis
46(9)
2.2.1 Modeling for synthesis
46(2)
2.2.2 Analytical and numerical modeling
48(7)
2.3 System decomposition and model structure
55(5)
2.3.1 Advantage of decomposition
56(2)
2.3.2 Application to the example of the hybrid series-parallel traction chain for the hybrid electrical heavy vehicle
58(2)
2.4 General information about the modeling of the various possible components in an electrical energy system
60(1)
2.5 Development of an electrical machine analytical model
61(12)
2.5.1 The various physical fields of the model and the associated methods for solving them
62(2)
2.5.2 Application to the example of a hybrid electrical heavy vehicle: modeling of a magnet surface-mounted synchronous machine
64(9)
2.6 Development of an analytical static converter model
73(9)
2.6.1 The various physical fields of the model and associated resolution methods
73(2)
2.6.2 Application to the example of a hybrid electrical heavy vehicle: modeling of inverters feeding synchronous machines
75(7)
2.7 Development of a mechanical transmission analytical model
82(9)
2.7.1 The various physical fields of the model and associated resolution methods
82(1)
2.7.2 Application to the example of a hybrid electric heavy vehicle: modeling of the Ravigneaux gear set
83(8)
2.8 Development of an analytical energy storage device model
91(1)
2.9 Use of models for the optimum sizing of a system
91(11)
2.9.1 Introduction
91(3)
2.9.2 Consideration of operating cycles
94(3)
2.9.3 Independent component optimization
97(3)
2.9.4 Simultaneous component optimization
100(2)
2.10 Conclusions
102(1)
2.11 Bibliography
103(4)
Chapter 3 Simultaneous Design by Means of Evolutionary Computation
107(48)
Bruno Sareni
Xavier Roboam
3.1 Simultaneous design of energy systems
107(6)
3.1.1 Introduction to simultaneous design
107(2)
3.1.2 Simultaneous design by means of optimization
109(1)
3.1.3 Problems relating to simultaneous design using optimization
110(3)
3.2 Evolutionary algorithms and artificial evolution
113(6)
3.2.2 Evolutionary algorithms principle
114(1)
3.2.3 Key points of evolutionary algorithms
115(4)
3.3 Consideration of multiple objectives
119(4)
3.3.1 Pareto optimality
119(1)
3.3.2 Multi-objective optimization methods
120(1)
3.3.3 Multi-objective evolutionary algorithms
121(2)
3.4 Consideration of design constraints
123(3)
3.4.1 Single objective problem
123(2)
3.4.2 Multi-objective problem
125(1)
3.5 Integration of robustness into the simultaneous design process
126(4)
3.5.1 Robust design
126(1)
3.5.2 Vicinity and uncertainty
127(1)
3.5.3 Characterization of robustness
128(2)
3.6 Example applications
130(20)
3.6.1 Design of a passive wind turbine system
130(13)
3.6.2 Simultaneous design of an autonomous hybrid locomotive
143(7)
3.7 Conclusions
150(1)
3.8 Bibliography
151(4)
Chapter 4 Multi-Level Design Approaches for Electro-Mechanical Systems Optimization
155(1)
Stephane Brisset
Frederic Gillon
Pascal Brochet
4.1 Introduction
155(1)
4.2 Multi-level approaches
156(4)
4.3 Optimization using models with different granularities
160(18)
4.3.1 Principle of SM
162(2)
4.3.2 Mathematical example
164(2)
4.3.3 SM variants
166(6)
4.3.4 Safety transformer application
172(6)
4.4 Hierarchical decomposition of an optimization problem
178(9)
4.4.1 Target cascading for optimal design
178(2)
4.4.2 Formulation of the TC method
180(3)
4.4.3 Mathematical example
183(3)
4.4.4 Railway traction engine example
186(1)
4.5 Conclusion
187(1)
4.6 Bibliography
188(5)
Chapter 5 Multi-criteria Design and Optimization Tools
193(1)
Benoit Delinchant
Laurence Estrabaud
Laurent Gerbaud
Frederic Wurtz
5.1 The CADES framework: example of a new tools approach
194(1)
5.2 The system approach: a break from standard tools
195(8)
5.2.1 Some component definitions
196(1)
5.2.2 From integrated environments to collaborative tool frameworks
197(1)
5.2.3 A centered model canvas: from generation to utilization
198(3)
5.2.4 Some "business" application frameworks
201(2)
5.3 Components ensuring interoperability around a framework
203(7)
5.3.1 Model types: white box, black box
203(2)
5.3.2 Black boxes: positive collaboration and re-use
205(1)
5.3.3 Object, component, and service paradigms
206(3)
5.3.4 ICAr software components: model normalization for sizing
209(1)
5.4 Some calculation modeling formalisms for optimization
210(8)
5.4.1 Analytical formalisms: algebraic and algorithmic
210(3)
5.4.2 Physical models within various formalisms
213(5)
5.4.3 The generation chain
218(1)
5.5 The principles of automatic Jacobian generation
218(5)
5.5.1 The Jacobian: complementary data for the model
218(1)
5.5.2 Derivation of mathematical expressions
219(2)
5.5.3 Algorithm derivation
221(1)
5.5.4 Derivation of specific formulations
222(1)
5.6 Services using models and their Jacobian
223(4)
5.6.1 Sensitivity study
223(1)
5.6.2 Composition of models
224(2)
5.6.3 Optimal design
226(1)
5.7 Applications of CADES in system optimization
227(4)
5.7.1 Overall optimization of a structure
227(2)
5.7.2 Evaluation of the potential of a structure
229(1)
5.7.3 Comparison between structures
230(1)
5.8 Perspectives
231(7)
5.8.1 Towards optimization using dynamic modeling
231(2)
5.8.2 Towards robust design
233(1)
5.8.3 Robust optimization under reliability constraints
234(1)
5.8.4 Towards the Internet
235(3)
5.9 Conclusions
238(1)
5.10 Bibliography
239(8)
Chapter 6 Technico-economic Optimization of Energy Networks
247(40)
Guillaume Sandou
Philippe Dessante
Marc Petit
Henri Borsenberger
6.1 Introduction
247(2)
6.2 Energy network modeling
249(6)
6.2.1 Context
249(1)
6.2.2 Notations
249(1)
6.2.3 Objective function
250(1)
6.2.4 Constraints
251(2)
6.2.5 Expression of the problem and eventual linear reformulation
253(1)
6.2.6 Position of the problem processed relative to the problem of energy network management
254(1)
6.3 Resolution of the energy network optimization problem for a deterministic case
255(11)
6.3.1 State of the art
255(2)
6.3.2 Resolution by dynamic programming and Lagrangian relaxation
257(5)
6.3.3 Resolution by genetic algorithm
262(4)
6.4 Introduction to uncertainty consideration
266(3)
6.4.1 Consideration of uncertainties
266(1)
6.4.2 Recourse notion
267(2)
6.5 Consideration of uncertainties on consumer demand
269(4)
6.5.1 Safety margin
269(1)
6.5.2 Scenario tree uncertainty modeling
269(1)
6.5.3 Resolution by dynamic programming and Lagrangian relaxation
270(2)
6.5.4 Conclusion
272(1)
6.6 Consideration of uncertainties over production costs
273(6)
6.6.1 Introduction
273(1)
6.6.2 Mathematical formulation
274(1)
6.6.3 Resolution
275(2)
6.6.4 Example
277(2)
6.7 From optimization to control
279(1)
6.7.1 The predictive approach principle
279(1)
6.7.2 Example
279(1)
6.8 Conclusions
280(1)
6.9 Bibliography
281(6)
List of Authors 287(4)
Index 291
Xavier ROBOAM, Institut National Polytechnique de Toulouse, France.
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