Systemic Design Methodologies for Electrical Energy Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 543

Veröffentlichungsjahr: 2012

Beschreibung

This book proposes systemic design methodologies applied to electrical energy systems, in particular analysis and system management, modeling and sizing tools. It includes 8 chapters: after an introduction to the systemic approach (history, basics & fundamental issues, index terms) for designing energy systems, this book presents two different graphical formalisms especially dedicated to multidisciplinary devices modeling, synthesis and analysis: Bond Graph and COG/EMR. Other systemic analysis approaches for quality and stability of systems, as well as for safety and robustness analysis tools are also proposed. One chapter is dedicated to energy management and another is focused on Monte Carlo algorithms for electrical systems and networks sizing. 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.

Leseprobe

Table of Contents

Preface

Chapter 1: Introduction to Systemic Design

1.1. The system and the science of systems

1.2. The model and the science of systems

1.3. Energy systems: specific and shared properties

1.4. Systemic design of energy systems

1.5. Conclusion: what are the objectives for an integrated design of energy conversion systems?

1.6. Glossary of systemic design

1.7. Bibliography

Chapter 2: The Bond Graph Formalism for an Energetic and Dynamic Approach of the Analysis and Synthesis of Multiphysical Systems

2.1. Summary of basic principles and elements of the formalism

2.2. The bond graph: an “interdisciplinary formalism”

2.3. The bond graph, tool of system analysis

2.4. Design of systems by inversion of bond graph models

2.5. Bibliography

Chapter 3: Graphic Formalisms for the Control of Multi-Physical Energetic Systems: COG and EMR

3.1. Introduction

3.2. Which approach should be used for the control of an energetic system?

3.3. The causal ordering graph

3.4. Energetic Macroscopic Representation

3.5. Complementarity of the approaches and extensions

3.6. Bibliography

Chapter 4: The Robustness: A New Approach for the Integration of Energetic Systems

4.1. Introduction

4.2. Control design of electrical systems

4.3. Application to an on-board generation system

4.4. Conclusion

4.5. Bibliography

Chapter 5: Quality and Stability of Embedded Power DC Networks

5.1. Introduction

5.2. Production of DC networks: the quality of the distributed energy

5.3. Characterization of the input impedances/admittances of equipment

5.4. Analysis of asymptotic stability via methods, based on impedance specifications

5.5. Analysis of asymptotic stability via the Routh–Hurwitz criterion

5.6. Analysis tools for asymptotic global stability – dynamic behavior of an HVDC network subject to large-signal disturbances

5.7. Conclusion to the chapter

5.8. Bibliography

Chapter 6: Energy Management in Hybrid Electrical Systems with Storage

6.1. Introduction to energy hybridization via the example of hybrid automobiles

6.2. Energy management in electric junction hybrid systems with electric energy storage

6.3. Indicators, criteria and data for the design of hybrid systems

6.4. Examples in various application areas

6.5. Conclusion for energy management in hybrid systems

6.6. Bibliography

Chapter 7: Stochastic Approach Applied to the Sizing of Energy Chains and Power Systems

7.1. Introduction

7.2. Standard principle of the power report

7.3. Stochastic approach

7.4. Modeling of the loads

7.5. Simulation of the power flows

7.6. Probabilistic and dynamic approach

7.7. Conclusion

7.8. Bibliography

Chapter 8: Probabilistic Approach for Reliability of Power Systems

8.1. Contextual elements

8.2. Basic concepts of the Monte Carlo simulation

8.3. Variance reduction

8.4. Illustrative example

8.5. Conclusion

8.6. Bibliography

List of Authors

Index

First published 2012 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

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© ISTE Ltd 2012

The rights of Xavier Roboam to be identified as the author of this work have been asserted byhim in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2012947346

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A CIP record for this book is available from the British Library

ISBN 978-1-84821-388-3

Preface

The increasingly competitive field of system design is driving designers to produce systems that are increasingly powerful and complex, integrating a large number of elements belonging to various often strongly coupled physical energy fields. The analysis, synthesis and management methods presented in this book clearly contribute to the optimization of energy systems. They are supplemented by approaches specifically orientated toward integrated design by optimization of electrical energy systems, which is the subject of the book Integrated Design by Optimization of Electrical Energy Systems, by the same editor and published by ISTE and John Wiley & Sons.

This book is divided into eight chapters as follows:

Chapter 1 is entitled “Introduction to Systemic Design”. This introductory chapter presents the history and foundations of the systemic approach. A glossary defines key terms and concepts inherent in this vision.

Chapter 2 is entitled “The Bond Graph Formalism for an Energetic and Dynamic Approach of the Analysis and Synthesis of Multi-Physical Systems”. The essential concepts of the bond graph are summarized in this chapter, highlighting its ability to model multi-physical systems and their energy exchanges. The interdomain transformations between electricity and closely related domains (magnetic, mechanical, chemical, hydraulic, photonics, etc.) are shown. Based on the concepts of causality and bicausality in bond graphs, an introduction to systemic analysis (structural analysis, model reduction, etc.), synthesis and sizing is also given.

Chapter 3 is entitled “Graphic Formalisms for the Control of Multi-Physical Energetic Systems: COG and EMR”. Two other graphic formalisms, in addition to the above, are presented here; they are specifically oriented toward the synthesis of control structures for energy systems. The causal ordering graphs (COGs) consist of a basic functional description of elementary systems, taking into account the physical causality of subsystems that can deduce the control structure by inverting the model. Energetic macroscopic representation (EMR) carries out the functional description of more complex systems, graphically highlighting the energy properties of the subsystems and their interactions.

Chapter 4 is entitled “The Robustness: A New Approach for the Integration of Energetic Systems”. Robustness is inherent to the device’s ability to operate in nominal conditions, including in an uncertain environment. An original approach, based on robustness by µ-analysis, is proposed here to analyze and design integrated energy systems with particular focus on the control performances and system stability. The analysis strategy is illustrated in a case study concerning the sizing of a high voltage direct current (HVDC) power channel for an aeronautical electrical network; this analysis is carried out with respect to dynamic criteria.

Chapter 5 is entitled “Quality and Stability of Embedded Power Systems DC Networks”. A review of the methods for quality and stability is proposed here. After a summary of the main existing standards for the DC embedded systems, a quality analysis method based on the causal analysis of interactions is described before elaborating on several analysis techniques for asymptotic and global stability; analysis criteria such as the impedance specification (Middlebrook) and the Routh–Hurwitz criterion are discussed. The development of analytical models for impedance characterization of the main power structures (power converters, variable speed motor drives) is also discussed. These approaches, specifically dedicated to DC embedded systems, are applicable in many fields such as aeronautical and space industries, ship systems and ground transportation systems.

Chapter 6 is entitled “Energy Management in Hybrid Electrical Systems with Storage”. First, some references on the latest energy management strategies for multisource systems hybridized with storage devices are discussed. Then, the authors focus on frequency-based management strategies, ensuring power sharing between storage components and sources. This power sharing is itself based on the allocation of a specific frequency range for each component, this frequency range being based on the power and energy density (Ragone diagram) of each component. These strategies are illustrated by typical case studies, particularly on stand-alone systems for the distributed generation of electric power, ground transportation and aeronautical embedded systems.

Chapter 7 is entitled “Stochastic Approach Applied to the Sizing of Energy Chains and Power Systems”. Although systems are increasingly optimized in terms of performance, conventional techniques for sizing of the electrical networks are usually not adapted to changes in the power of the loads during operation. The authors thus propose here a forecasting method for the power flows, based on probabilistic load models. A Monte Carlo algorithm then allows the designers to estimate the probability density functions for the power levels and their durations of occurrence. Illustrations of an embedded aeronautical electrical network can allow us to analyze the applicability of this approach.

Chapter 8 is entitled “Probabilistic Approach for Reliability of Energy Systems”. The methodology proposed in this chapter aims to estimate reliability indexes for energy systems, in particular for distributed electric grids. This method is based on stochastic simulation via the Monte Carlo algorithm. It is an effective methodology for simulating some phases of the lifecycle of a grid with component defects.

Key aspects covered in Integrated Design by Optimization of Electrical Energy Systems also published by ISTE

Chapter 1 deals with the coupling between the system, its environment, and the mission to be accomplished. It firstly proposes innovative approaches, enabling the representation of mission profiles or environmental variables (habitat, boundary conditions). The authors then propose classification and synthesis methods for profile processing. These approaches are of interest further along the design process and make use of optimization algorithms. Profile, notably mission classification, helps designers to segment the range of products designed. It may be based on “clustering” techniques. For the synthesis process, the idea is to present pertinent profiles with regards to the design criteria and constraints. Similarly, environmental and system mission profile information needs to be compacted where possible to facilitate processing within the context of optimization, which imposes a high number of iterations on the device models and environmental variables. These different approaches are illustrated using some typical examples, such as the design of an electric-diesel hybrid locomotive, including an electrochemical storage.

Chapter 2 deals with the sizing model, which is an essential aspect of design and optimization. According to Edgar Morin, one of the pioneers of the systemic approach introduced in Systemic Design Methodologies for Electrical Energy Systems, “the intelligibility of the complex occurs through modeling.” However, while the word “model” can be used in many different ways, a design model, and more specifically referring to design by optimization, presents a number of specificities that the authors provide by more specifically insisting on analytical models that are well suited to the systemic context. Some examples of design models dedicated to electrical engineering, i.e. machines, electronic power converters, and related areas (such as mechanical transmission) are detailed. The different physical concepts that need to be jointly represented in order to be compatible with the design objectives are presented. The example of the optimization of a thermo-electric hybrid heavy vehicle is proposed by way of illustration.

The three main “pillars” of system design, namely, architecture, sizing and management are intimately linked. Thus, the sizing of an energy system cannot be carried out without thorough knowledge of the way in which the power flows between sources, storage and loads combined within an architecture. Chapter 3, therefore, presents the “simultaneous design approach”, which is an eminently complex process, as different stages of the design process are coupled (integrated), stages that are often sequential for purposes of simplification. The use of optimization techniques is an effective way to enable such integration. This chapter explains how an optimization problem is raised; these problems are often multi-criteria and are nearly always under constraints. Amongst the various optimization methods, evolutionary algorithms are very well suited to solving highly heterogeneous problems with mixed variables (continuous and discrete). The hybrid locomotive example from Chapter 1 is used again in order to illustrate how the design problem is posed and resolved.

How do we handle the complexity of the system design process, particularly through optimization, given its multi-physical and multi-tasking context? Chapter 4 provides part of the answer to this question, with the aim of defining an effective approach to design by optimization. Two points are dealt with more precisely: complexity linked to multiple levels of model granularity (description detail), with techniques such as “space mapping” enabling us to pass from an accurate level of modeling to one that, although more “basic”, is more “efficient” in terms of computation time. Secondly, complexity arising from different viewpoints and optimization levels: it would be unwise to optimize everything within one and the same loop, in order to enable simultaneous understanding of basic physical component behaviors, up to more “complicated” (in terms of size) and “complex” (in terms of interactions) systems. The design by optimization approach is therefore “multi-loop” and methods such as “target cascading” bring about tangible elements in order to move between levels.

Chapter 5 provides a vision of future tools for design by analysis and optimization, by illustrating the concrete case of the CADES framework. These tools, which use an architecture based on software components and cooperative modules, are armed to respond to model capitalization, reutilization, and interoperability problems in a vision system. Some automatic generation methods, which transform high-level or “professional formalisms” (such as electric circuits and three-dimensional representation) into executable programming code are associated with this. In this context, the authors have defined a software component standard called ICAr, which is used for sizing by optimization. Having the Jacobian of the model available is a considerable asset in sensitivity analysis and in the implementation of gradient optimization algorithms. We thus show how it is possible to formally produce this Jacobian precisely and systematically. These components are also destined to be put together to form a more general system. Some sample applications of the CADES framework are provided, such as the optimization of an electromagnetic structure (transformer).

Chapter 6, “Technico-economic optimization of electrical energy networks”, completes this book and concerns the optimum management of electrical networks. This optimization is found within the opening of energy markets, leading to a strong level of competition, which is forcing producers to optimize the management of production plants. The emergence of new technologies, combined with the growth in computation power has enabled the management of production installations to be improved. This chapter presents the modeling approach for this type of system, which must integrate the uncertainties linked to the unfamiliarity or simplification of the model with a view to its optimization, or the uncertainties stemming from the provisional nature and planning of the system operation (such as real consumer demand and economic fluctuations). Optimization of network management can be carried out using a deterministic linear programming model, or by using genetic algorithms. It can also be conducted on models that take uncertainties into account in order to propose more robust solutions. Problems corresponding to the approach are those relating to the assignment of units: several simple examples enable us to understand the various approaches and to judge their relevance.

Chapter 1

Introduction to Systemic Design(1)

This book focuses on “energy conversion systems”, especially on electric energy, which is most of the time combined with other forms of energy using different conversion mechanisms. If “energy” gives them very specific properties that we develop in this chapter and that we explore in the methods introduced in this book, their properties are more generally those of “systems” in the sense of “systems theory” or the “science of systems”. This is especially observable as systems of conversion of energy are highly heterogeneous in several respects: they show indeed, in the same way as most modern technological artifacts, energetic elements, but also automatisms to manage the energy flows, software for data processing and even human operators directly involved in their operation. The objective of this chapter is to introduce and specify the concepts and terms associated with “systems or devices for the conversion of energy” and with the associated “methodologies of systemic design”, in relation to the science of systems as they are considered and discussed in this book. One of the permanent difficulties of an interdisciplinary dialog is the polysemy1 of identical terms with different meanings depending on the cultural origin and the specialty of the speakers. Therefore, in this chapter, we specify as much as possible the meaning that we give to terms that may have several meanings, not to lay down a prescriptive definition but simply to define the exact range within this book. In particular every notion qualified as “systemic” means that it is considered in the sense of “systems theory” or the “science of systems”.

Energy is a unifying concept of physics and even a universal concept of science. It allows us to describe phenomena that seem very different that are observed in all fields of nature and considered as manifestations of different forms of “energy”. Thus, many difficult problems are solved efficiently, and even smartly, by wisely using the properties of energy: the methods introduced in this book. The fundamental properties of energy can only be fully appreciated when associated with entropy, through a combined and modern reading of the first and second principles of thermodynamics. Beyond the pure scientific context, these properties shed new light on the current energy challenges and the concrete issues of our societies in the context of sustainable development [BAL 01a, DIN 07].

In this chapter, after briefly defining the general notion of “system” and of the “science of systems” or “systems theory”, as considered in the book, we develop these concepts with a historical analysis of their improvement until the modern period. Then, modeling and simulation being crucial operations in systemic approaches, we introduce the main and specific properties of systemic models. We then introduce the properties that systems of energy conversion, as being a particular class of systems, inherit, specifically from the properties of energy after recalling and analyzing the latter, as well as the properties of entropy that are just as important. Having considered these general and specifically targeting problems in the following chapters, we finally introduce the systemic design of technological devices of conversion of energy as we perceive them today.

REMARKS 1.1.– In this chapter, only the principal concepts are developed. The chapter also includes a “glossary of the science of systems”, defining the terms used in this book. Indeed, with each definition calling for different terms that need to be defined in the context of the science of systems, the presentation of the glossary in alphabetic order seems most suitable. Glossary terms are indicated in underlined italics in order to distinguish them from other italic texts that are used for emphasis in the text. Moreover, the bibliography, in addition to the references presented in the text, together make up a backup, which was especially used to write this chapter.

1.1. The system and the science of systems

The term “system” comes from the Greek word “systêma”, which means “ensemble”. We first define it as an “ensemble of components in dynamic interaction making up an organized whole”. This short definition already brings out some fundamental properties of systems, which also establish the “science of systems” and “systems theory” in their different declensions. To specify them, we need to define an ensemble of notions and associated terms for which the meanings and the definitions are themselves interrelated. In addition to the following developments, we refer to the “glossary of systems theory” at the end of the chapter.

1.1.1. First notions of systems and systems theory

1.1.1.1. Notion of system

The system makes up an entity that seems identifiable in its environment by its boundaries and its properties. In general, it is neither isolated nor closed, but open; therefore, it is able to exchange energy (work or heat) and/or matter and/or information with its environment. Thus, it also exchanges entropy with its environment. Electromagnetic actuators, a living cell, a car, a city, are thereby defined and identifiable as systems, open to their environment. We can see in Chapter 1 of [ROB 12] the importance of a suitable and compact representation of the environment upstream of the systemic design process2.

L. Von Bertalanffy, founder of the first “general systems theory”, defined the system as a “complex of elements in interaction”. This definition is apparently very simple but is, in fact, very typical. Indeed, with no interactions or associations, there is no system. The properties of a system not only result from the components that constitute it, but also and especially from the bidirectional relationships or interactions between these components and with the environment of the system. These components can be heterogeneous, material or immaterial. From combining all these (possible nonlinear) interactions, the system shows properties at an ensemble level and does not appear at the component level when considered individually. It is then said that there is an emergence of properties, properties that are only seen at the level of a considered system as a whole and which make up an expression of its complexity. It is in this sense that the system seems like “an organized whole”, which “is greater than the sum of its components” by its own emergent properties, which do not result from a simple addition operation of its component properties. Similarly, part of the system that shows specific properties while being in the system may lose them once separated from it. This part, as well as the system, will lose the properties made up by their interactions. This emergence property, integral to the system, introduces a particular difficulty for the possible operational definition of subsystems or for the presentation of a system at different scales to ease its study (level of resolution, granularity and modularity): to accept this difficulty is crucial for the study of systems. We will eventually separate strong couplings from weak couplings between subsystems, i.e. having or not an effect on the emergence of these new properties at a system level. Weak couplings allow, depending on a particular hypothesis, a suitable partition of the system with the emergence of properties that characterize the considered system.

These first properties of the “system”, as we have introduced them above, mean that their study as a “system” cannot be carried out by an approach of decomposition-reconstruction, a “reductionist” approach, but by a so-called “holistic” global approach. This aspect is characteristic of the systemic approach. Consequently, the system is made intelligible by the simultaneous identification of the elements (objects or components) that make it up and especially of their interactions (bidirectional relations) following a conjunctive logic: this approach makes up the “science of systems” [LEM 95]. More than the simple addition of components, the system then emerges as composed of organs, which fulfill their functions and participate in its organization: this is exactly what gives the properties of being a system. The special description of an organ by its function, rather than its physical composition, introduces and evolves the notion of finality of the organ or of its system. This “finalist” aspect has encouraged some reservations about the scientific legitimacy of the systematic approach for 40 years and is still controversial. However, this difficulty is solvable nowadays within the large framework of theories of complexity: we will come back to this later.

The organization in its entirety, by its identified properties, can also be perceived as the finality of the identified system in its environment. This finality can be certified (intentionally) or interpreted (from the observation) and can evolve. Indeed, the exchanges that the system makes with its environment by being open enable it to maintain and develop its organization, which leads to its evolution. The environment thereby influences this evolution by the constraints that it has on the system. These constraints are expressed by boundary conditions of the system. Sometimes, we may also consider a co-evolution of the system combined with its environment. The influence becomes interaction and a part of the environment has to be integrated in the system. This co-evolution is not uncommon. It occurs when the “sources” in the environment cannot be considered as an “infinite view of the system”. This is the well-known case of the evolution of life and the atmosphere on Earth enriched in oxygen by photosynthesis. The consequences of human activity, more particularly in terms of energy, on the availability of natural resources or the climatic system can be comparable. At a lower scale, in the continuity of a co-evolution of the road infrastructure with automobiles [ROS 95], an increase in electrical vehicles in a world full of petrol vehicles would eventually modify the traffic conditions, and consequently the profiles considered to build new optimized vehicles. In addition, new electrical vehicles or hybrids “connected” to the power grid while parking and put in place for a dynamical management by their electrical storage capacity following the concept of “Vehicle to Grid” (V2G) will enhance the co-evolution of infrastructures toward new smart grids and vehicles (electrical architecture, energy management), which will be seen as “vehicles” and “organs of storage”.

The system is therefore identified by “what it does” (what it accomplishes in its environment), rather than by “its apparent constitution” (what it is or what it seems to be). A living cell, an automobile, and an actuator are easily identifiable, despite their wide range of internal constitution: they are then indeed considered and designed by their global function in the first place.

These few elements lay down the fundamental properties of systems and allow us to define a “general system” regrouping the four following concepts: finality, environment, functions and evolution [LEM 95]. However, it is all about specifying and completing them in order to enable an operational use in technological design. Indeed, the study of a system, considered as such with these specific properties, requires a specific approach, itself defined by the “science of systems”.

1.1.1.2. The science of systems

The “science of systems” is said to be a branch of science that defines the “system” as a special object of study. Generally, the systemic approach is defined as a methodology that allows the organization of knowledge for a more efficient action [ROS 95]. It considers the system as organized, depending on the finality. “An ensemble of elements in dynamic interaction, making up a whole in evolution in its environment, organized depending on finality.” Its first finality is the maintenance of its structure [ROS 95]. In accordance with the first property stated above, the system forms an inseparable whole in which its emergent properties are linked to its organization.

We have seen that the system, identified in its environment by what it does, shows an organization, its organs fulfilling different functions. This functional aspect, with a finalist character, implies that the science of systems is a synthetic character and introduces an approach adapted to the design of systems satisfying given requirements, which means performing a group of missions in an environment. The requirements define first the functionalities of the system to be built. This approach, the methods for which will be introduced in the following chapters, is called “systemic design”. But the science of systems can be targeted to understand and/or to predict the behavior of an existing system, be it natural or artificial. This approach can be described as a “systemic analysis”. However, this expression needs to be considered carefully because the analysis is traditionally the processing of parts separated by a “reductionist” decomposition. This expression is therefore sometimes controversial in systems theory because it is a source of confusion. Let us specify that systemic analysis consists of a functional analysis, identifying the organs by their functions, regardless of their constitution (for example, an actuator or a filter). The two approaches, analytic or synthetic, seem complementary as it can be seen in several chapters of [ROB 12], focused on design methods. A systemic analysis proves to be essential in order to successfully complete the systemic design, the feasibility of a direct synthesis of results often remaining (see sections 1.4.2). Most of the chapters of this book, are therefore focused toward illusory systemic analysis, whether this concerns graphic formalisms (Chapters 2 and 3) which participate in comprehension (to the understandability of the complex), or tools that allow us to characterize the system in terms of constraints of robustness, (Chapter 4), of quality and of stability (Chapter 5) or reliability (Chapter 8).

To understand the system, i.e. to build its understandability, the systemic approach carries out an approach of synthesis and does not look for fundamental components by analysis. To do this we need to construct a global representation of a system called a “systemic model”, which reliably conveys its properties as a system within the framework of the modeling objectives. The specific properties of the systemic model are introduced below. Beforehand, in order to better specify the concepts of the science of systems and of the methodological approaches referred to in the context of science, it is useful to analyze the evolutions of the scientific thinking that promoted its emergence as science in its entirety during the 20th Century.

1.1.2. A brief history of systems theory and the science of systems

The biologist L. Von Bertalanffy was the first, in the 1920s, to express the “organism” aspect of human beings, which was later developed by him into the first general systems theory [ENC 95]. Living beings are, indeed, a natural example of systems theory, directly observable in terms of their complex systems forming an organized whole and well identifiable in their environment. Following this, life sciences logically contributed to the development of systemic theories and methods. However, the complexity of natural ecosystems and living organisms have formed many difficulties in freeing some system principles; these principles were finally easier to highlight in artifacts, i.e. societal organizations or man-made complex technological achievements, which are thus qualified as artificial. It was thus the combination of life sciences and artificial sciences (engineering) that contributed to the development of systems theories and methods [SIM 96].

A first, systems theory was formed with the introduction of the cybernetic theory of Norman Wiener (1948), particularly bringing a better understanding of controlled homeostasis phenomena, and also opened the door to the realization of automatic technological systems that were increasingly efficient due to the parallel improvement of electronics. It achieved, with the concept of the “black box”, the grouping of the Active Environment and Finality (teleology) concepts. It brings up the particular apparent opposition between “continuation of an aim” and “causality” a concept that will be discussed in the following.

Systems theory was later improved by the introduction of structuralism, which appeared first in linguistics and anthropology, then spreading to several other fields. It brought together the concepts of Operation and Transformation (or Evolution).

Depending on the structuralist paradigm, the structure covers two aspects: a spatial aspect (form and architecture) and a functional aspect (functions and metabolism). This structure is transformed during operation (Evolution). This mix of form and function is also called the “total structure”. The simultaneous study of form and function is, for example, very often used in molecular biology and forms one of the features of the systematic approach in this scientific field [DEL 95]. We will discover another application in the definition of “simultaneous or integrated design” of energetic systems introduced at the end of this chapter and in Chapter 3 of [ROB 12].

Modern systems theory is able to bring together four concepts which also define the “general system”: Finality, Environment, Functions and Evolution [LEM 95]. During its development, this new science has, however, produced strong oppositions, linked to various fundamental concepts; these concepts seemed to contradict the fundamental concepts of modern science according to Galileo and Descartes, especially in regard to finality and causality.

First, systems theory is based on a holistic approach, considering the system as an inseparable whole, which led it for a while to contradict the so-called reductionist Cartesian approach which is extremely well known and has been used in the modern sciences since Descartes. On the basis of the research of fundamental elementary components, for which the combination of elementary properties allows the explanation of the properties of all superior levels of the ensemble, and going further into the research of elementary particles (molecules, atoms, quarks and bosons) and of fundamental interactions, the reductionist approach has, indeed, built the principal theories of modern physics. Its great success in the understanding of natural and artificial physics phenomena since the 17th Century has logically favored it among scientists. The holistic approach appeared to contradict the second and third (reductionist) precepts laid down in 1637 by Descartes in his “Discourse on the Method”. In addition, the emergence of the properties at the system level seemed slightly mysterious in some respects, apparently not particularly compatible with a good scientific approach and contradicting the third and fourth precepts (non-closure of the model). However, the reductionist approach does not bring a simple understanding of the problem due to property changes due to its transition from an atomic level to a molecular level: the macroscopic properties of water are very different from the properties of the molecular hydrogen or oxygen considered separately and are not easily deduced from it: there is, in fact, something new. Similarly, the identical elementary phenomena of the formation of droplets and crystals lead to different clouds (stratus, cumulus and cirrus) that we interpret depending on the current meteorology and that to come. So many examples, assuming that the reductionist approach is correct, show that it is insufficient “in order to understand everything”. The synthetic approach of the systems theory suggests a more subjective approach than the analytical approach, therefore giving it an operational character more efficient for the action.

Secondly, we have already highlighted that the notion of organization introduces the purpose or finality question. This notion brings up that of the “final cause” as defined by Aristotle and contradicts the physical temporal causality of interactions. It recalls an incompatible projection in the future (teleology) with the linear unidirectional concept of the time used by science and philosophy since Galileo. This ability of teleology remains logically preserved only for living and thinking beings, able to project it is then about “reason” (announced as finality) rather than “final cause”.

The study of natural ecosystems, whose existence involve multiple “homeostasis”, and cybernetic theory have actually allowed us to clarify this apparent opposition by formalizing the structures of control which ensure (by feedback) the maintenance of an objective without breaking the causality (interpreted as finality). In addition, we will see that the finality must be taken into account in the systemic model and in the systemic approach of simultaneous design, without breaking the physical causality of interactions.

Regarding the interactions, knowing that data transmission presents a maximal velocity since Einstein’s work on relativity, the physical causality of interactions is such that the cause comes before the effect that it triggers in any Galilean referential. This fundamental scientific principle satisfies the idea that “the causal determinism is the first principle of understandability of the world”. However, statistical physics and quantum physics have strongly changed the notion of cause connected to an observed effect. Multiple works have been devoted, by philosophers as well as scientists, to this difficult problem of “causality”, which is still a much debated topic [KLE 04]. The concept of causality had to be rebuilt to conformed to contemporary science. By simply referring to the properties of a unidirectional linear time, the principle of causality stipulates that we can always draw up a chronology between causally linked events. However, a modern and more recent definition not directly linked to time is given in [KIS 06]: “two events c and e are related as cause and effect if and only if there is at least one physical quantity P, subject to a conservation law, exemplified in c and e, of which a determined quantity is transferred between c and e”. This definition, which fits physics and biology, relies on the properties of transfer of energy. It is then very relevant within the framework of this book. This clear representation of causality will be a privilege to some systemic models such as those described by bond graphs (BG), IGC or EMR which we will analyze in Chapters 2 and 3, respectively.

Despite the obstacles, this recent period, increasingly facing the design and control of complex systems, has, on the one hand, shown the limits of previous reductionist approaches and, on the other hand, allowed a more operational knowledge for the general concepts of the science of systems. The science of complexity is what keeps these two together within a general framework by clarifying the processes of self-organization, especially in the dissipative structures defined by Prigogine [PRI 97]. The complementarity of the reductionist approach, on the one hand, and the systemic approach, on the other hand, is nowadays more precise. The science of systems is thus a recent science, which comes from previous thinkers. Economists and sociologists, facing the increasing complexity of the artificial human societal organizations, as well as the science of the living and ecologists, have led to the development of its concepts (C.L. Strauss, J. De Rosnay, J.L. Le Moigne, P. De Lattre and E. Morin). At the same time, more and more hightech and complex systems developed in the 20th Century have justified this approach by their design and optimization (G. Kron, H.M. Paynter, H.A. Simon and J.P. Meinadier). According to Joel de Rosnay, the computer and all the enormous new possibilities that it brought along to conduct simulations have formed a key factor of this recent evolution. All fields of knowledge and science have, each with their own path depending on their culture and needs, appropriated this evolution of thinking at the end of the 20th Century through different schools. Ecology since 1960 and the problems encountered by the sustainable development since 1990 have been particularly affected. Therefore, we can confirm that a real “system paradigm” and, more generally, a “paradigm of complexity” have emerged in the second half of the 20th Century on which various approaches of knowledge are based on today.

1.1.3. The science of systems and artifacts

1.1.3.1. Interactions, finality of artifacts

We can distinguish “natural” physical or living (ecosystems) systems from artifacts, systems that are called artificial because they are man-made. They include not only technological systems but also societies and everything that forms them (judicial systems, economical systems, political systems and diverse organizations). Multiple studies carried out in all fields have allowed us to present the principle of a general science of systems, which is recalled above. We are particularly interested in the applications of the design of technological artifacts to process energy.

We have seen that interactions are the base of properties of the system. These interactions, in practical terms, appear at different levels:

– Interactions between the elements of a system, no matter whether their nature is identical or different. Interactions between elements, depending on different fields, push us to wonder whether the system is heterogeneous and whether the multidisciplinary or interdisciplinary, or even transdisciplinary, approaches are necessary. From these couplings between elements, heterogeneous or not, emerge new properties that only exist at the system level, where the system is considered as a whole. And from there appear the notions of organs and functions, which has already been discussed.

– Interactions with the environment, by exchanges of energy and entropy, and/or of matter and/or of data, essential exchanges for the system to exist at a given time (sustainability, maintenance and evolution). As long as the environment has not been modified by the system, these interactions resolve into constraints applied by the environment and specified by the boundary conditions of the system.

The system then appears like an “ensemble of elements organized according to a purpose in dynamic interactions between each other and with an environment where it evolves” [ROS 75]. The finality is the essential purpose of an artificial organization thought up by human beings: this finality is therefore explicitly “stated” and well listed requirements define it. However, using a systemic approach, we can also allocate finality to a natural organization. This finality is implicit in order to fulfill the physical causality; it is only “interpreted” by the “systems modeler”. For artifacts (technological or societal systems), we will also talk about “mission”, and this mission is assigned by human. The mission can group together several sub-missions, for example different profiles of traffic for a vehicle in order to drive in the city and between towns. Therefore, the global mission of the system, listed in the specifications during design, depends on its “finality” [AST 03]. This mission completely belongs to the requirements, which gives all the features of the system. Consequently, for an artifact, this mission needs to be inserted into the process of systemic design and thereby into the systemic model itself while being developed. As we will see, this operation requires the use of the concept of bi-causality, in order to simultaneously take the physical causality and finality into account. For example, the traffic profiles must be incorporated into the systemic design model of a hybrid electric vehicle [CHA 99, JAA 11] (see Chapter 1 of [ROB 12]).

1.1.3.2. Evolution of artifacts

The notion of evolution is essentially associated with the sciences of the living, allowing the sustainability of life despite the changes in the environment, occurring by general mechanisms initially stated by Darwin. But this notion is also valid for an artifact in terms of several meanings.

On the one hand, the adaptation of an artifact is particularly preferred because of the presence of management, control software, which allows operations to be controlled, manipulated or set up depending on the constraints. We may also underline the frequent “improvements of software” (by updating the “firmware”), which allow us to update the functions of the system over time. This is the case for the example of aircraft: the cell shows a long period of operational life and allows several “retrofit” operations due especially to the evolution of software. On the other hand, we can also consider that the system evolves first during the design phase, especially throughout its systemic model and simulation, which are used more often to predict its behavior and to confirm its appropriate results to the requirements (virtual prototyping), then with the burn-in of (real) prototypes before the final product that may itself also use reconfigurable and damaged operating modes. Finally, there is the aging of materials from the assembly to the recycling.

The objective of the design phase is to end up with an optimized solution. The methods, models and tools explained in [ROB 12] follow this way of thinking. The comparison with biological evolution is more relevant today with the development of methods using algorithms of evolutionary optimization (these algorithms are described in Chapter 4 of [ROB 12]). They build on the general principles of natural evolution and sexual selection, nowadays identified as universal, a century and a half after Darwin and Mendel stated, independently and in complementary ways, the foundations of these principles. Besides, we can recall that the observation of the effects of artificial selection carried out by breeders and horticulturists formed for Darwin a crucial groundwork of reflection for the development of his theory of evolution and natural selection; similarly, the engineering of artifacts has brought an essential contribution for the development of the science of systems of which, in reciprocity, it now takes advantage of in order to make more efficient systems.

Furthermore, since sustainable development is a major issue due to the limited amount of natural primary resources, we must also take into account the essential recycling that we are forced to consider from the design stage due to environmental concerns [MEA 04, MIL 03]. Therefore, after the notion of “lifecycle assessment” of the product from the beginning of its design until its recycling at the end of its lifecycle, we must now follow an “eco-design”, a complete and modern approach that allows us to consider the whole lifecycle of the system.

Let us also recall that, in some cases, it is necessary to consider the coupled co-evolution between the system and the environment. For example, the conditions imposed by the traffic in an urban environment will undoubtedly be modified by the increase of electric vehicles, not only due to the management of energy but also due to the behavior of different drivers.

Finally, with regard to the previous example and to artifacts, human beings, seen as manufacturers and agents, are placed in some systems that become composite (heterogeneous) ensembles of organized individuals, material and software, so that their interworking may allow us, in a given environment, to fulfill missions matching the required finality (J.P. Meinadier, H.A. Simon and J.L. Le Moigne). It is especially the case for very large systems, such as airports and institutions. To the extreme, models of economic and financial markets take into account, in addition to economic laws, the knowledge of human cognitive behavior, for example, when faced with a financial risk.

Humanity co-evolves with two environments in interaction: a natural environment (ecosphere, biosphere) that humanity influences strongly and an artificial environment that it creates and develops (technosphere). In this evolution, we observe a double convergence of natural sciences and sciences of the artificial for the design of optimized technologic systems (engineering and design). On the one hand, this convergence aims to take advantage of natural examples within the framework of the observed and copied solutions; these solutions depend on structures or even components: organizations in networks, bio-inspired components or even bio-components. On the other hand, there is a convergence in the methods used for the design. These methods are directly inspired by mechanisms of evolution that appear active in nature: the optimization is performed by evolutionary algorithms, swarms of particles and multiagents.

1.2. The model and the science of systems

The science of systems enables not only the understanding and/or prediction of the behavior of an existing natural or artificial system, but also the design of a system with given requirements. For this, the science of systems provides a relevant virtual representation, called the “systemic model”, which brings up its properties during simulations. We generally distinguish several types of models in science: of knowledge, phenomenological, empiric or behavioral, of conduct, etc. with regard to a more general and complete notion of model, we may refer to the article entitled “Model” from the Encyclopedia Universalis [ENC 95]. We introduce in this book the principal properties of systemic models.

Generally, within a scientific framework, simulation consists of carrying out tests on a model by aiming to fulfill three objectives: understanding, designing and acting [KLE 03]. Modeling and simulation form fundamental operations of the systemic approach which can end up in confusion between the system and its model (the system is virtually born at the first phases of its evolution through its model). Validation no longer relies on a causal explanation due to the reductionist analysis, but depends on the relevant operation of the ensemble obtained by simulation and evaluated using specific results criteria. This is why the progress in computer science over recent decades has really led to its rich display. This is, in addition, what Joël de Rosnay expressed in 1995 [ROS 95] by explaining that the computer, the “macroscope”, this virtual tool that he imagined in the 1970s to process complexity [ROS 75], became an operational reality. Using data processing skills and simulation using a model, it offers a new vision of reality, which was previously inaccessible to our senses and to the resulting measurement instruments. The model is, however, incomplete. First, the status itself of reality has changed due to the disclosures set by quantum physics, which draws fundamental limitations for the observation and the notion of a certain reality. Second, the instrumentation, taking advantage of the improvement in IT, has gone past our natural perception, by introducing more and more virtual visualization for the past 30 years for entities usually measured through sophisticated data processing, especially imaging: scans, magnetic resonance imaging (MRI), false color imaging, etc. Between theory and experience, which are the basis of a meticulous scientific approach, vitality is now defined by a new link between the abstract and practical [SER 03], therefore giving to simulation an important status in the scientific field, and also making the activities of everyday life trivial (medical checkups, weather forecasting, travel planning by GPS, etc.) In this context, the systemic model, on which systemic simulation is based, must be characterized by its principal properties.

A complex system is, in essence, difficult to understand: it is difficult to understand its operation in order to control and design it. The main objective of systemic modeling is to give sense to the system: we generally model it to build its understandability. Weaker than an objective reality, we are interested in an accessible and useful reality by using a pragmatic approach: the model is the synthesis of all types of observations made on relationships between components and their behavior in an environment, which includes a modeler. Therefore, during systemic modeling, we must determine the level of description in comparison to the given problem: “What does the system do?” or more generally, “What is interesting to us in what it does?” Independently of “How is it made?” Even though the required knowledge for this last question may be useful in answering the previous questions, the model shows what is perceived in reality according to the objectives of the study of the modeler – designer in relation to the observable (analysis) and the desired (design) finality of the considered system in its environment. In this sense, the finality (mission) and the constraints imposed by the environment must be incorporated into the model itself [AST 03]. Following the example of all modeling, systemic modeling takes on an arbitrary aspect that needs to be underlined and assumed. Assuming this aspect is also the ensurance of correctly using the model, within the limitations of its validity domain. By representing a perceived reality, the systemic model allows the running of a simulation of the system with the three following objectives: to understand, to design and to act. During the analysis phase, in order to understand and to explain, it shows a vision of reality, based on observations and knowledge. Modeling can also fulfill the pragmatic research of an operational result in a given case, in order to design (synthesis) an optimal (optimization) solution and to predict its properties and its achievements.

An important quality of systemic models that aims at globality is thus the “good” level of description or granularity: a level at which a property or an organization is clearly identified and that we consider relevant to understand the model and to use it. Systemic models belong to the “phenomenological” category describing reality at the level where it occurs as an effect rather than at the level where it occurs as a cause.

We then have to determine the level of granularity at which the order that we are interested in is occurring the most accurately. Too much detail, even though seeming more precise and having or providing better knowledge, could damage the calculation time during simulation and might even take away the understandability of the system, such as for example when looking too closely at a pointillist painting. The good level must present an appropriately defined structure, for example an energetic structure. Ideally, the systemic model must enable changes of a granularity level in space and/or time; it is the equivalent of a zoom effect, naturally keeping the systemic character of the model. Energetic systems such as electrical vehicles, including for example power electronics converters, obtain commutation phenomena at the microsecond scale, mechanical phenomena of several minutes, thermal phenomena of several hours and an aging over so many years involved, which is the equivalent of more than 10 orders of magnitude. This multiscaled character of models forms an issue that needs a lot of work. The multileveled approach for the optimized design explained in Chapter 4 of [ROB 12] is a good illustration of this activity. Similarly, some techniques, called “model reduction”, are discussed in Chapter 2 of this book.

The spatial filtering temporal or even statistical effect goes along with the change of the granularity level. This is what allows us to identify an organ and its function in its organization, independently of its more detailed constitution. H.A. Simon states a separation of the constants of time as general in the management of the flow between organs in organizations [SIM 95]. This is what distinguishes the discreet signals of control of power electronics converter switches from their global function of processing electrical energy. As an example, the same M.P.P.T. function in the low or middle frequency range can be fulfilled either by dedicated high frequency static converters or supported by another organ as well.

At this stage, it is important to highlight that the laws of thermodynamics, which rule at the macroscopic scale, have allowed us to understand and handle energy conservations at the macroscopic level. First, purely phenomenological, they were established by empirical observations during the development of the industrial era; they were later clarified by statistical mechanics, which ensured a rigorous and efficient transition from the microscopic level to the macroscopic level [NGÔ 08, BAL 92]. Underlying the macroscopic laws of “smooth” thermodynamics that involve a small amount of parameters, there is the strong disorganized and random motion of billions of billions of molecules that are characterized individually by a certain amount of unknown variables. With regard to this, the establishment of thermodynamics looks similar to a systemic approach of modeling. The random and statistical character of multiple phenomena and the resulting macroscopic organization has nowadays led to several studies that are based on statistical tools. Chapters 7 and 8 show this aspect within the framework of essential questions on a design basis and on the safety of systems.

The model can also be modular and completed by successive approximations. Moreover, different sub-models individually linked to a useful aspect of reality may be complementary because, in given conditions, it would be illusory to obtain a unique model for several reasons: ways of representation or power and the length of calculation. We need therefore to ensure their coherent cooperative operation in respect to their limitations of validity in an adapted simulation environment. The emergence, in particular, increases this difficulty. This is why the unifying multifield formalisms, as such in Chapter 2, are especially interesting.

The practice of modeling that has spread to all fields in science from physics to the social and aesthetic sciences, by way of biology, promotes more than ever the principal role of figures and signs in the institution of knowledge: the model therefore brings a figurative basis to the concept. It is particularly illustrated by graphical formalisms of energetic modeling, especially those introduced and discussed in this book such as BG, GIC and EMR. For a specialist, representations in bond graphs BG [PAY 61, KAR 00] or Energetic Macroscopic Representation (REM) [BOU 03] of a system are disclosed by a direct reading of the energetic structure of the modeled system, i.e. it synthetically displays the energetic relations between different parts of the system, with different levels of possible granularity, as will be shown in Chapters 2 and 3. An operational character of the model is to be added; it takes its entire dimension with the simulation and the visualization that allows us to observe on graphs (or even on synthetically animated images) with the operation, the behavior or the evolution of the system and to make its organization understandable.

To these general properties shared by all systemic models are added the specific properties of energy conversion systems.

1.3. Energy systems: specific and shared properties

1.3.1. Energy and its properties

First, energy conversion systems naturally inherit energy properties, and also properties from the entropy that governs their evolution.

We have recalled in the general introduction that energy is a unifying abstract concept of physics that allows us to describe various phenomena observed in all fields of nature and which are considered as having as many different forms of representations as energy.

Furthermore, the fundamental properties of energy associated with entropy allow us to highlight the current issues and some concrete problems of our societies concerning sustainable development [BAL 01a, DIN 07]. The sector of energy is, in fact, a key sector of economic, industrial and domestic activities. Energy is, therefore, a common notion and is present in most day-to-day actions. Electrical engineering fully depends on this sector, and electricity, which is a synonym of development, forms a major energetic vector of the post-industrial era. Electricity is also essential for electronics and for data processing, which covers all fields of activity.

The word “energy” comes from the Greek word “energeia”, which means “force in action”. Well known in appearance, the concept of energy is, however, difficult to define using a few words, as shown with the multiple definitions given in dictionaries:

1) The magnitude characterizing a system and expressing its capacity to modify the state of other systems with which it is in interaction. (Dictionary 1)

2) Each mode which can represent such systems: mechanical, electric, magnetic, chemical, thermal, nuclear energy. (Dictionary 2)

3) The capacity of a system to modify a state, to produce a work which leads to motion, to electromagnetic radiation or to heat (Wikipedia).

The appropriation of the concept is made by its properties and by multiple phenomena where the energy interferes during interactions. We usually distinguish several forms of energy at the macroscopic level, as listed in the definition above of dictionary 2. A certain link has been precisely established between disciplinary fields of physics and different forms of energy. Let us specify that matter is also a form of condensed energy, as revealed in nuclear reactions. We also consider the radiant or light energy (photons) resulting from electromagnetic coupling and for which the propagation does not require any medium. Well defined mathematically, energy is represented by a scalar quantity and measured in joules (J). Energy has the essential property of staying constant in time for an isolated system. The term “energy” was introduced by Thomas Young in 1807, and after several studies of all disciplines, it was H.L.F. von Helmholtz who stated the generality of the conservation of energy under different forms in 1847, whose rigorous mathematical formulation is given by analytical mechanics, especially Hamiltonian mechanics [BAL 00]. The passing from one form of energy to another is called energy conversion. The energy is therefore precisely “the physical entity” which is conserved during phenomena (that follows these conversions under different forms). Other types of conservation may occur in the conversion process: for example the kinetic impulsion or electric charge. More fundamentally, this conservation of energy symbolizes the invariance of laws of nature in time, an axiom which constitutes a direct consequence of the theorem of Emmy Noether [LAN 94]. The link between time and energy is confirmed in quantum mechanics where these two quantities seem to be conjugated and are the basis of one of Heisenberg’s uncertainly principles [HEI 30].

However, within the framework of systems and conversions that we are interested in within this book, thermodynamics is what defines the best and most useful properties, through its “first principle” and “second principle” and their implications while considered as combined [PRI 05, BOR 05, DIN 07].

Thermodynamics defines and distinguishes between several types of systems, especially in terms of energy exchanges with their environment:

– Isolated systems which do not exchange either matter or energy with their environment.

– Closed systems which can only exchange heat (a form of energy) with their environment.

– Open systems that can exchange all forms of energy with their environment: work, heat and matter. Energy conversion systems belong mainly to this last category.

The first principle of thermodynamics states that the total energy E of an isolated system stays constant in time.

The second principle of thermodynamics defines another function of state, as fundamental as energy: entropy S, which brings an essential explanation to irreversibility. Entropy is also measured by a scalar quantity and in Joules over Kelvin [JK−1]; the second principle expresses that this quantity (for its non-conservative part) can only increase while the isolated system is evolving (see sections 1.2 and 1.3.2). Even though energy has been a central concept of traditional thermodynamics, it is by the expression of entropy according to its conservative variables (energy, number of moles) that the thermodynamics characterizes an equilibrium or non-equilibrium system in its modern formulation [PRI 05]. With non-equilibrium thermodynamics, the entropy associated with a system allows us to describe its evolution and to particularly distinguish the quality of mobilized energy during a conversion process.

It is using the simultaneous combination of these two previous principles, operated by exegetic theory, that we can really define a conversion process within the framework of thermodynamics (thermodynamic yields) [BOR 05, DIN 07]. However, we will not discuss exergy in the following, but only the energy and entropy because they are used more commonly in electrical engineering.