Integrated Design by Optimization of Electrical Energy Systems E-Book

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

Seitenzahl: 356

Veröffentlichungsjahr: 2012

Beschreibung

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.

Leseprobe

Table of Contents

Preface

Chapter 1. Mission and Environmental Data Processing

1.1. Introduction

1.2. Considerations of the mission and environmental variables

1.3. New approach for the characterization of a “representative mission”

1.4. Classification of missions and environmental variables

1.5. Synthesis of mission and environmental variable profiles

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

1.7. Conclusion

1.8. Bibliography

Chapter 2. Analytical Sizing Models for Electrical Energy Systems Optimization

2.1. Introduction

2.2. The problem of modeling for synthesis

2.3. System decomposition and model structure

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

2.5. Development of an electrical machine analytical model

2.6. Development of an analytical static converter model

2.7. Development of a mechanical transmission analytical model

2.8. Development of an analytical energy storage device model

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

2.10. Conclusions

2.11. Bibliography

Chapter 3. Simultaneous Design by Means of Evolutionary Computation

3.1. Simultaneous design of energy systems

3.2. Evolutionary algorithms and artificial evolution

3.3. Consideration of multiple objectives

3.4. Consideration of design constraints

3.5. Integration of robustness into the simultaneous design process

3.6. Example applications

3.7. Conclusions

3.8. Bibliography

Chapter 4. Multi-Level Design Approaches for Electro-Mechanical Systems Optimization

4.1. Introduction

4.2. Multi-level approaches

4.3. Optimization using models with different granularities

4.4. Hierarchical decomposition of an optimization problem

4.5. Conclusion

4.6. Bibliography

Chapter 5. Multi-criteria Design and Optimization Tools

5.1. The CADES framework: example of a new tools approach

5.2. The system approach: a break from standard tools

5.3. Components ensuring interoperability around a framework

5.4. Some calculation modeling formalisms for optimization

5.5. The principles of automatic Jacobian generation

5.6. Services using models and their Jacobian

5.7. Applications of CADES in system optimization

5.8. Perspectives

5.9. Conclusions

5.10. Bibliography

Chapter 6. Technico-economic Optimization of Energy Networks

6.1. Introduction

6.2. Energy network modeling

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

6.4. Introduction to uncertainty consideration

6.5. Consideration of uncertainties on consumer demand

6.6. Consideration of uncertainties over production costs

6.7. From optimization to control

6.8. Conclusions

6.9. 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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John Wiley & Sons, Inc. 111 River Street Hoboken, NJ 07030 USA

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© ISTE Ltd 2012

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

British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN: 978-1-84821-389-0

Preface

The increasingly competitive field of system design is driving designers to produce ever more efficient systems, minimizing investment, and ownership costs. The analysis, synthesis, and management methods presented in the book Systemic Design Methodologies for Electrical Energy Systems by the same editor and published by ISTE, and John Wiley and Sons, clearly contribute to the optimization of energy systems. However, the techniques, algorithms, and optimization tools explained in this book enable us to elucidate performance, as the number of inter-element and interdomain couplings and interactions between the system and its mission and environment complicate the designer’s task. The process of design by optimization, which consists of coupling a model to an optimization algorithm using software, is thus most useful. Multiple criteria, traditionally optimized for energy systems, involve geometry (mass or volume), energy efficiency (loss, consumption, pollution), and dynamic performance. These criteria are optimized under different constraints related to quality (harmonic content, electromagnetic compatibility (EMC)), stability, and technological consistency (thermal, magnetic, etc.). The inherent costs obviously need to be considered and enable coupling of various highly heterogeneous points of view: optimization thus becomes technico-economic.

Even while intensive research and development in this area continues, we see now how systems analysis through system simulation has matured, with the development of some particularly effective tools and solvers, such as Matlab/Simulink©, Saber©, Simplorer©, Modelica/Dymola© and VHDLAMS. The use of virtual prototyping has thus become commonplace in industry to accelerate design cycles and minimize costs. The aeronautics industry is a particularly typical example of this, with the European MOET Airbus, as leader and systems provider, imposed the delivery of multiple levels of analysis modeling (“functional, behavioral”) on its 61 academic and industrial partners, in parallel with the equipment developed, in order to enable systems characteristic “electrical network” studies. Thus, while the last decade was notable for an “increase in power” of systems analysis by simulation, we can be sure that the current decade should see the advent of design by optimization; hence providing justification for this book to which it is entirely dedicated.

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.

Recap of the key points discussed in Systemic Design Methodologies of Electrical Energy Systems: Analysis, Synthesis, and Management also published by ISTE

Chapter 1 “Introduction to the systemic approach to design”: this introductory chapter presents the history and basis of the systemic approach. A lexicon defines the main terms and concepts inherent in this vision.

Chapter 2 “Bond graph formalism, for an energetic and dynamic approach to the analysis and synthesis of multi-physical systems”: the essential concepts of the bond graph are summarized, with attention paid to its capacity for modeling of multi-physical systems and their energy exchanges. The inter-domain transformations between electricity and related domains (magnetic, mechanical, chemical, hydraulic, photonic, etc.) are represented. From the concepts of bond graph causality and bi-causality, an introduction to systems analysis (such as structural analysis and model reduction), synthesis and sizing is finally proposed.

Chapter 3 “Graphical formalisms for multi-physical energy systems: from COG to EMR”: two other graphical formalisms, which complement the formalism above, are presented; they are specifically oriented towards the synthesis of control structures for energy systems. Causal ordering graphs (COG) consist of a functional description of elementary systems, taking into account the physical causality of sub-systems, which enable the control structure to be deduced through inversion of the model. Energetic macroscopic representation (EMR) is used for the functional description of more complex systems, graphically emphasizing the energy properties of sub-systems and their interactions.

Chapter 4 “Robustness: a new approach for integrating energy systems”: robustness is inherent in the capacity of devices that need to function under rated conditions, including within an uncertain environment. An original approach, based on robustness using μ-analysis, is proposed in order to analyze and design integrated energy systems with a particular focus on control performance and system stability. The analysis strategy is illustrated by a case study linked to the sizing of an “HVDC” power channel for an electrical network for aircraft; this analysis is carried out with reference to dynamic criteria.

Chapter 5 “Quality and stability of direct current networks”: a review of quality and stability methods is proposed in this chapter. After a summary of the standard principles in place for DC networks, a quality analysis method based on the causal analysis of interactions is described, before we develop a number of analysis techniques for asymptotic and general stability: analysis criteria such as impedance specification (Middlebrook) and the Routh Hurwitz criterion are presented. The development of analytical models in order to characterize impedance of the main power structures (power converters and motor drives) is also proposed. These approaches, which are specifically dedicated to DC networks, are applicable across many domains, such as aeronautics and space, shipping networks, and ground transport systems.

Chapter 6 “Energy management strategies for multisource systems, including storage”: this chapter begins with innovative energy management strategies for multisource systems hybridized by storage devices. Then, the authors focus on frequency based management strategies, which ensure power sharing between sources and storage devices. This power sharing is, itself, based on the attribution of a specific frequency range to each constituent, this frequency range being based on the energy and power density (Ragone diagram) of each constituent. These strategies are illustrated using typical case studies, particularly for autonomous systems for the decentralized electricity generation, ground transportation and embedded aeronautical networks.

Chapter 7 “Stochastic approach applied to the sizing of energy systems and networks”: whereas systems must be increasingly optimized in terms of performance, traditional electrical network sizing techniques are usually not suited to variations in the power of loads during operation. The authors therefore propose a method for forecasting power flow, based on probabilistic load models. A Monte Carlo algorithm thus allows designers to estimate density and probability functions on the power networks and their duration of occurrence. Some illustrations for an electrical network for aircraft enable the applicability of this approach to be analyzed.

Chapter 8 “Stochastic approach applied to safety in energy systems and networks”: the proposed methodology aims to estimate security indices for energy systems, particularly for distributed electrical grids. This method is based on a stochastic simulation using the Monte Carlo algorithm. It contains a methodology that is effective for simulating certain phases of the lifecycle of a network containing constituent defects.

Chapter 1

Mission and Environmental Data Processing1

1.1. Introduction

Energy systems design, particularly electrical, is nowadays increasingly influenced by social issues linked to energy economy policies and reducing the impact on the environment. To this end, numerous technical demands are added, such as volume and mass, lifetime, reliability (see Chapter 8, [ROB 12]), quality (susceptibility, harmonic pollution), stability (see Chapter 5 [ROB 12]), and recyclability. Being strongly linked to cost criteria, these demands require design to be tackled according to a technicoeconomic approach. Thus, the end of the 20th Century was marked by a notable evolution towards a more complete evaluation of costs over the whole of the lifecycle of the system (production, maintenance, and usage costs, even dismantling/recycling costs). Faced with these new considerations, designers are called upon to consider the environment in which their systems will evolve in more detail. Fundamentally, it is imperative to evolve toward a systems design approach from the outset; thus enabling an understanding of the coupling between system constituents and facilitation and the integration of the utility of the device and environmental variables.

This heterogeneous and complex set of requirements is pushing designers towards a simultaneous design approach, which truly integrates systems design, as indicated in this introductory chapter (see also Chapter 1 of [ROB 12]). Simultaneous design is an approach that considers the system as a whole, where the fundamental questions of system architecture, sizing and management are integrated (see ). As we will see in of this book, simultaneous design can be handled using optimization techniques, which is where multiple levels of difficulty are jointly associated with the design problem.