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Evaluating Gas Network Capacities - Softcover

 
9781611973686: Evaluating Gas Network Capacities
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This book addresses a seemingly simple question: Can a certain amount of gas be transported within a pipeline network? The question is difficult, however, when asked in relation to a meshed nationwide gas transportation network and when taking into account technical details and discrete decisions, as well as regulations, contracts, and varying demands involved.

Evaluating Gas Network Capacities provides an introduction to the field of gas transportation planning and discusses in detail the advantages and disadvantages of several mathematical models that address gas transport within the context of the technical and regulatory framework. It shows how to solve the models using sophisticated mathematical optimization algorithms and includes examples of large-scale applications of mathematical optimization to this real-world industrial problem.

Readers will also find a glossary of gas transport terms, tables listing the physical and technical quantities and constants used throughout the book, and a reference list of regulation and gas business literature.

Audience: This book is intended for mathematicians interested in industrial applications. Engineers working in gas transport will also find the book of interest.

Contents: Foreword; Preface; Part I: Fundamentals; Chapter 1: Introduction; Chapter 2: Physical and Technical Fundamentals of Gas Networks; Chapter 3: Regulatory Rules for Gas Markets in Germany and Other European Countries; Chapter 4: State of the art in Evaluating Gas Network Capacities; Part II: Validation of Nominations; Chapter 5: Mathematical Optimization for Evaluating Gas Network Capacities; Chapter 6: The MILP-Relaxation Approach; Chapter 7: The Specialized MINLP Approach; Chapter 8: The Reduced NLP Heuristic; Chapter 9: An MPEC Based Heuristic; Chapter 10: The Precise NLP Model; Chapter 11: What Does Feasible Mean?; Chapter 12: Computational Results for Validation of Nominations; Part III: Verification of Booked Capacities; Chapter 13: Empirical Observations and Statistical Analysis of Gas Demand Data; Chapter 14: Methods for Verifying Booked Capacities; Chapter 15: Perspectives; Appendix: Background on Gas Market Regulation; Acronyms; Glossary; Regulation and Gas Business Literature; Bibliography; Index.

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Book Description:
The question of whether a certain amount of gas can be transported through a pipeline network is the subject of this book. It provides an introduction to gas transportation planning, discusses in detail several mathematical models for gas transport and shows how to solve the models using mathematical optimization algorithms.
About the Author:
Thorsten Koch is a professor of Software and Algorithms for Discrete Optimization at TU Berlin and director of the Scientific Information Department at Zuse Institute Berlin (ZIB). He joined ZIB in 1998, became a member of the DFG research center MATHEON in 2001 and has served as head of the Linear and Nonlinear Integer Programming Group since 2009. He has led joint research projects with industrial partners in the planning of infrastructure networks, chip verification, and integer programming.

Benjamin Hiller is a postdoc researcher at Zuse Institute Berlin. His research interests involve solution methods for large-scale real-world optimization problems, in particular mixed-integer (nonlinear) programming, and column generation. His recent work focuses on optimization problems related to gas transportation networks.

Marc E. Pfetsch was a postdoc researcher at Zuse Institute Berlin from 2002 to 2008, where he finished his habilitation in 2008. That year he was appointed full professor for Mathematical Optimization at TU Braunschweig. Since 2012 he has been full professor for Discrete Optimization at TU Darmstadt. His research interests are integer and mixed-integer nonlinear programming, in particular infeasibility and symmetry handling.

Lars Schewe is a postdoc researcher at Friedrich-Alexander Universität Erlangen-Nürnberg. His research interests include mixed-integer (nonlinear) optimization with an emphasis on problems in networks.

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