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Evaluating Gas Network Capacities [Minkštas viršelis]

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  • Formatas: Paperback, 379 pages, aukštis x plotis x storis: 229x152x23 mm, weight: 800 g
  • Serija: MOS-SIAM Series on Optimization
  • Išleidimo metai: 30-Mar-2015
  • Leidėjas: Society for Industrial & Applied Mathematics,U.S.
  • ISBN-10: 1611973686
  • ISBN-13: 9781611973686
Kitos knygos pagal šią temą:
Evaluating Gas Network CapacitiesDidesnis paveikslėlis
  • Formatas: Paperback, 379 pages, aukštis x plotis x storis: 229x152x23 mm, weight: 800 g
  • Serija: MOS-SIAM Series on Optimization
  • Išleidimo metai: 30-Mar-2015
  • Leidėjas: Society for Industrial & Applied Mathematics,U.S.
  • ISBN-10: 1611973686
  • ISBN-13: 9781611973686
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781611973686.html
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.
Foreword xi
Preface xiii
I Fundamentals
1(84)
1 Introduction
3(14)
T. Koch
M. E. Pfetsch
J. Rovekamp
1.1 Mathematical characteristics and challenges
3(1)
1.2 Stationarity
4(1)
1.3 Basic tasks
5(1)
1.4 State of the art and new methodology
6(1)
1.5 Fundamentals of gas transmission
6(5)
1.6 Transport networks
11(6)
2 Physical and technical fundamentals of gas networks
17(28)
A. Fugenschuh
B. Geißler
R. Gollmer
A. Morsi
M. E. Pfetsch
J. Rovekamp
M. Schmidt
K. Spreckelsen
M. C. Steinbach
2.1 Gas transport
18(1)
2.2 Gas properties
19(4)
2.3 Gas network elements
23(14)
2.4 Gas network structures
37(5)
2.5 Gas network representation
42(3)
3 Regulatory rules for gas markets in Germany and other European countries
45(20)
U. Gotzes
N. Heinecke
B. Hiller
J. Rovekamp
T. Koch
3.1 Overview of gas market regulation in Europe and Germany
46(2)
3.2 Current rules for using gas transmission networks
48(8)
3.3 Current rules for determining capacities
56(6)
3.4 Challenges for gas transmission system operators
62(2)
3.5 Summary and outlook
64(1)
4 State of the art in evaluating gas network capacities
65(20)
D. Bargmann
M. Ebbers
N. Heinecke
T. Koch
V. Kuhl
A. Pelzer
M. E. Pfetsch
J. Rovekamp
K. Spreckelsen
4.1 Background for capacity evaluation and simulation
67(1)
4.2 Generation of scenarios
68(10)
4.3 Network control options in simulation
78(3)
4.4 Simulation
81(2)
4.5 Interpretation of calculation results
83(1)
4.6 Conclusions
84(1)
II Validation of nominations
85(186)
5 Mathematical optimization for evaluating gas network capacities
87(16)
L. Schewe
T. Koch
A. Martin
M. E. Pfetsch
5.1 The building blocks of our hierarchy
88(6)
5.2 Abstract problem statement
94(2)
5.3 Additional modeling considerations
96(2)
5.4 Pre- and postprocessing
98(2)
5.5 Overview of the literature
100(1)
5.6 Overview of our approaches
101(2)
6 The MILP-relaxation approach
103(20)
B. Geißler
A. Martin
A. Morsi
L. Schewe
6.1 An MINLP model for the validation of nominations
103(11)
6.2 An MILP relaxation of the MINLP model
114(9)
7 The specialized MINLP approach
123(22)
J. Humpola
A. Fugenschuh
B. Hiller
T. Koch
T. Lehmann
R. Lenz
R. Schwarz
J. Schweiger
7.1 Passive pipe networks
124(6)
7.2 From passive pipe networks to gas networks with active devices
130(4)
7.3 Element modeling
134(9)
7.4 Conclusion
143(2)
8 The reduced NLP heuristic
145(18)
R. Gollmer
R. Schultz
C. Stangl
8.1 Reduction of variables
146(4)
8.2 Constraints for active elements
150(4)
8.3 Objective function
154(1)
8.4 Summary of the model
155(1)
8.5 Heuristics to fix binary decisions
156(6)
8.6 Conclusion
162(1)
9 An MPEC based heuristic
163(18)
M. Schmidt
M. C. Steinbach
B. M. Willert
9.1 Model
165(10)
9.2 MPEC regularization
175(1)
9.3 Solution technique: A two-stage approach
176(5)
10 The precise NLP model
181(30)
M. Schmidt
M. C. Steinbach
B. M. Willert
10.1 Component models
182(25)
10.2 Objective functions
207(1)
10.3 Relaxations
208(1)
10.4 A concrete validation model
209(2)
11 What does "feasible" mean?
211(22)
I. Joormann
M. Schmidt
M. C. Steinbach
B. M. Willert
11.1 Feasible network operation
211(2)
11.2 Availability and accuracy of model data
213(1)
11.3 How "feasible" are solutions of our models?
214(2)
11.4 NLP validation vs. network simulation
216(5)
11.5 The interpretation of ValNLP solutions
221(3)
11.6 Analyzing infeasibility in a first stage model
224(9)
12 Computational results for validation of nominations
233(38)
B. Hiller
J. Humpola
T. Lehmann
R. Lenz
A. Morsi
M. E. Pfetsch
L. Schewe
M. Schmidt
R. Schwarz
J. Schweiger
C. Stangl
B. M. Willert
12.1 Introduction
233(3)
12.2 Results for the MILP-relaxation approach
236(6)
12.3 Results for the specialized MINLP approach
242(7)
12.4 Results for the reduced NLP heuristic
249(3)
12.5 Results for the MPEC based heuristic
252(8)
12.6 Results for the validation NLP
260(4)
12.7 Comparison of the decision approaches and combined solver
264(7)
III Verification of booked capacities
271(54)
13 Empirical observations and statistical analysis of gas demand data
273(18)
H. Heitsch
R. Henrion
H. Leovey
R. Mirkov
A. Moller
W. Romisch
I. Wegner-Specht
13.1 Descriptive data analysis and hypothesis testing
274(5)
13.2 Reference temperature and temperature intervals
279(1)
13.3 Univariate distribution fitting
280(3)
13.4 Multivariate distribution fitting
283(3)
13.5 Forecasting gas flow demand for low temperatures
286(5)
14 Methods for verifying booked capacities
291(26)
B. Hiller
C. Hayn
H. Heitsch
R. Henrion
H. Leovey
A. Moller
W. Romisch
14.1 Motivation and outline of the approach
292(3)
14.2 Sampling statistical load scenarios for verifying booked capacities
295(6)
14.3 Generating quantiles for verifying booked capacities
301(2)
14.4 Modeling capacity contracts
303(2)
14.5 An adversarial heuristic for generating booking-compliant nominations
305(3)
14.6 Methods to verify booked capacities
308(2)
14.7 Computational results for verifications of booked capacities
310(4)
14.8 Conclusions
314(3)
15 Perspectives
317(8)
C. Hayn
J. Humpola
T. Koch
L. Schewe
J. Schweiger
K. Spreckelsen
15.1 Physical models and transient effects
317(1)
15.2 Modeling flow situations
318(1)
15.3 Determining maximal capacities
319(1)
15.4 Extending the network
320(2)
15.5 Making it work in practice
322(1)
15.6 Outlook
323(2)
A Background on gas market regulation
325(6)
J. Rovekamp
A.1 Legislative power, authorities, and organizations
325(2)
A.2 Chronology of European and German gas market regulation
327(3)
A.3 Ongoing and future activities
330(1)
Acronyms 331(2)
Glossary 333(6)
Regulation and gas business literature 339(6)
Bibliography 345(16)
Index 361
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 postdoctoral 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 Pfetsch was a postdoctoral researcher at Zuse Institute Berlin from 2002 until he completed 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 postdoctoral researcher at Friedrich-Alexander Universitat Erlangen-Nurnberg. His research interests include mixed-integer (nonlinear) optimization with an emphasis on problems in networks.