Systemic Design Methodologies for Electrical Energy Systems

Xavier Roboam is full researcher as Director of Research at CNRS, Toulouse, France.

• Chapter 1. Introduction to Systemic Design 1Stéphan ASTIER, Alain BOUSCAYROL and Xavier ROBOAM1.1. The system and the science of systems 21.1.1. First notions of systems and systems theory 31.1.2. A brief history of systems theory and the science of systems 61.1.3. The science of systems and artifacts 91.2. The model and the science of systems 121.3. Energy systems: specific and shared properties 151.3.1. Energy and its properties 151.3.2. Entropy and quality of energy 191.3.3. Consequences for energy systems 241.4. Systemic design of energy systems 261.4.1. The context of systemic design in technology 261.4.2. The design process: toward an integrated design 281.5. Conclusion: what are the objectives for an integrated design of energy conversion systems? 321.6. Glossary of systemic design 331.7. Bibliography 36Chapter 2. The Bond Graph Formalism for an Energetic and Dynamic Approach of the Analysis and Synthesis of Multiphysical Systems 39Xavier ROBOAM, Eric BIDEAUX, Genevieve DAUPHIN-TANGUY, Bruno SARENI and Stéphan ASTIER2.1. Summary of basic principles and elements of the formalism 412.1.1. Basic elements 412.1.2. The elementary phenomena 422.1.3. The causality in bond graphs 452.2. The bond graph: an “interdisciplinary formalism” 462.2.1. “Electro-electrical” conversion 472.2.2. Electromechanical conversion 512.2.3. Electrochemical conversion 522.2.4. Example of a causal multiphysical model: the EHA actuator 552.3. The bond graph, tool of system analysis 562.3.1. Analysis of models properties 562.3.2. Linear time invariant models 582.3.3. Simplification of models 612.4. Design of systems by inversion of bond graph models 692.4.1. Inverse problems associated with the design approach 702.4.2. Inversion of systems modeled by bond graph 722.4.3. Example of application to design problems 782.5. Bibliography 84Chapter 3. Graphic Formalisms for the Control of Multi-Physical Energetic Systems: COG and EMR 89Alain BOUSCAYROL, Jean Paul HAUTIER and Betty LEMAIRE-SEMAIL3.1. Introduction 893.2. Which approach should be used for the control of an energetic system? 903.2.1. Control of an energetic system 903.2.2. Different approaches to the control of a system 913.2.3. Modeling and control of an energetic system 923.2.4. Toward the use of graphic formalisms of representation 933.3. The causal ordering graph 953.3.1. Description by COG 953.3.2. Structure of control by inversion of the COG 1003.3.3. Elementary example: control of a DC drive 1053.4. Energetic Macroscopic Representation 1073.4.1. Description by EMR 1083.4.2. Structure of control by inversion of an EMR 1113.4.3. Elementary example: control of an electrical vehicle 1143.5. Complementarity of the approaches and extensions 1163.5.1. Differences and complementarities 1173.5.2. Example: control of a paper band winder/unwinder 1173.5.3. Other applications and extensions 1193.6. Bibliography 120Chapter 4. The Robustness: A New Approach for the Integration of Energetic Systems 125Nicolas RETIÈRE, Delphine RIU, Mathieu SAUTREUIL and Olivier SENAME4.1. Introduction 1254.2. Control design of electrical systems 1264.2.1. The control design is an issue of integration 1264.2.2. The nominal control synthesis 1304.2.3. The analysis of robustness 1354.3. Application to an on-board generation system 1414.3.1. Presentation of a nominal system 1414.3.2. Modeling and dynamical analysis of the nominal system 1414.3.3. Analysis of the robustness 1474.4. Conclusion 1554.5. Bibliography 155Chapter 5. Quality and Stability of Embedded Power DC Networks 159Hubert PIQUET, Nicolas ROUX, Babak NAHID-MOBARAKEH, Serge PIERFEDERICI, Pierre MAGNE and Jérôme FAUCHER5.1. Introduction 1595.1.1. Challenges to quality optimization 1605.1.2. The difficulty of stability 1615.2. Production of DC networks: the quality of the distributed energy 1655.2.1. Combined and specialized electrical architectures 1655.2.2. AC/DC converters 1675.2.3. Studying AC/DC interactions 1675.2.4. Simplified modeling of the HVDC network 1695.2.5. Methods of causal analysis of AC/DC interactions 1705.3. Characterization of the input impedances/admittances of equipment 1725.3.1. Analytical characterization of the input impedance of systems in electrical engineering 1735.3.2. Experimental and simulation characterization 1875.4. Analysis of asymptotic stability via methods, based on impedance specifications 1905.4.1. Introduction 1905.4.2. Principles: the case of a two-body cascading system 1915.5. Analysis of asymptotic stability via the Routh–Hurwitz criterion 2065.5.1. Overview of the Routh–Hurwitz criterion 2065.5.2. Example, design charts 2075.5.3. Analysis of network architectures with regard to their stability 2105.6. Analysis tools for asymptotic global stability – dynamic behavior of an HVDC network subject to large-signal disturbances 2155.6.1. Introduction 2155.6.2. Analysis tools for large signal stability 2165.6.3. Conclusion 2195.7. Conclusion to the chapter 2195.8. Bibliography 220Chapter 6. Energy Management in Hybrid Electrical Systems with Storage 223Christophe TURPIN, Stéphan ASTIER, Xavier ROBOAM, Bruno SARENI and Hubert PIQUET6.1. Introduction to energy hybridization via the example of hybrid automobiles 2246.1.1. General information on the architectures of hybrid automobiles 2246.1.2. Parallel architecture: summation of the mechanical powers 2256.1.3. Series architecture: summation of the electric powers 2266.1.4. Series–parallel architecture 2286.2. Energy management in electric junction hybrid systems with electric energy storage 2296.2.1. Storage, essential properties, power invertibility, losses 2296.2.2. Electric junction hybrid systems, electric node 2336.2.3. Generic hybrid system with an electric node containing storage, energy flow management 2346.2.4. Strategy for frequency splitting of power via active filtering 2366.2.5. Electric node and energy degrees of freedom 2396.2.6. Overview of energy management in electric-junction multisource hybrid systems with storage: energy management strategy 2426.3. Indicators, criteria and data for the design of hybrid systems 2456.3.1. Properties of storage units for hybridization 2456.3.2. Mission properties, energy indicators 2476.4. Examples in various application areas 2506.4.1. Example 1. Simple hybridization: emergency generator for an aircraft based on a wind turbine hybridized by supercapacitors 2506.4.2. Example 2. Simple hybridization: emergency generator for an aircraft based on a fuel cell hybridized with supercapacitors 2566.4.3. Example 3. Double hybridization: power train of a locomotive based on a combustion engine hybridized by batteries and supercapacitors 2666.4.4. Example 4. Double hybridization: smoothing of photovoltaic generation via an electrolyzer–fuel cell tandem (H2 /O2 battery) and a lead acid battery 2756.5. Conclusion for energy management in hybrid systems 2816.6. Bibliography 283Chapter 7. Stochastic Approach Applied to the Sizing of Energy Chains and Power Systems 287Patrick GUÉRIN, Geoffroy ROBLOT and Laurence MIÈGEVILLE7.1. Introduction 2877.2. Standard principle of the power report 2897.2.1. Maximum current 2907.2.2. Load factor Ku 2907.2.3. Diversity factor Ks 2917.2.4. Enhancement factor Ka 2927.2.5. Application 2927.3. Stochastic approach 2947.3.1. Observation 2947.3.2. Principle of the stochastic approach 2957.4. Modeling of the loads 2977.4.1. Different types of loads 2987.4.2. Modeling using a specification 2997.4.3. Modeling using experimental readings 3017.5. Simulation of the power flows 3027.5.1. Analytical method 3027.5.2. Monte Carlo method 3047.5.3. Application to an “on-board” power system 3067.6. Probabilistic and dynamic approach 3127.6.1. Modeling of the loads or associated electrical quantities 3127.6.2. Simulation of the power flows 3167.6.3. Application to the embedded network 3177.7. Conclusion 3197.8. Bibliography 321Chapter 8. Probabilistic Approach for Reliability of Power Systems 325Yvon BÉSANGER and Jean-Pierre ROGNON8.1. Contextual elements 3258.2. Basic concepts of the Monte Carlo simulation 3318.2.1. Monte Carlo method 3318.2.2. Simulation 3318.2.3. Basic statistical concepts and definitions 3318.2.4. Monte Carlo simulation 3338.3. Variance reduction 3408.3.1. Justification and principles 3408.3.2. Comparative study of the variance reduction methods 3428.4. Illustrative example 3638.5. Conclusion 3678.6. Bibliography 368List of Authors 371Index 373

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