Kallrath / Pardalos / Rebennack Optimization in the Energy Industry

2009
ISBN: 978-3-540-88965-6
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 534 Seiten

Reihe: Energy Systems

Autoren/Hrsg.

Kallrath, Josef

Pardalos, Panos M.

Rebennack, Steffen

Scheidt, Max

Inhaltsverzeichnis

1;Preface;6
2;Contents;10
3;List of Contributors;13
4;Conventions and Abbreviations;19
5;Part I Challenges and Perspectives of Optimization in the Energy Industry;20
5.1;1 Current and Future Challenges for Production Planning Systems;21
5.1.1;1.1 Introduction;21
5.1.2;1.2 Production Planning – History and Present;22
5.1.3;1.3 The Coming Challenge: Handling Uncertainty;24
5.1.4;1.4 Requirements for Future Production Planning Systems;27
5.1.5;1.5 Conclusion;32
5.1.6;References;33
5.2;2 The Earth Warming Problem: Practical Modeling in Industrial Enterprises;34
5.2.1;2.1 Introduction;34
5.2.2;2.2 Management: What Changes will Affect the Planning Work?;35
5.2.3;2.3 Modeling: How to Make a Practical Model for the Earth Warming Problem?;36
5.2.4;2.4 Problems When Applying to Real World;39
5.2.5;2.5 Conclusion;40
5.2.6;References;40
6;Part II Deterministic Methods;41
6.1;3 Trading Hubs Construction for Electricity Markets;42
6.1.1;3.1 Introduction;42
6.1.2;3.2 Hedging in the Electricity Markets and Hubs Usage;44
6.1.3;3.3 Problem Formulations;51
6.1.4;3.4 Heuristics for Construction of Given Number of Hubs;57
6.1.5;3.5 Solving the Single Hub Selection Problem;62
6.1.6;3.6 Conclusion;67
6.1.7;References;67
6.1.8;Appendix;69
6.2;4 A Decision Support System to Analyze the Influence of Distributed Generation in Energy Distribution Networks;71
6.2.1;4.1 Introduction;71
6.2.2;4.2 Methodology;73
6.2.3;4.3 Simulation Study;76
6.2.4;4.4 Computational Results;78
6.2.5;4.5 Conclusions;87
6.2.6;References;88
6.3;5 New Effective Methods of Mathematical Programming and Their Applications to Energy Problems;90
6.3.1;5.1 Introduction;90
6.3.2;5.2 Polynomial-Time Algorithms in Convex Programming;91
6.3.3;5.3 Solution of Energy Problems by Polynomial-Time Algorithms;120
6.3.4;5.4 Conclusion;138
6.3.5;References;139
6.4;6 Improving Combustion Performance by Online Learning;142
6.4.1;6.1 Introduction;142
6.4.2;6.2 High Dimensional Combustion Data Streams;145
6.4.3;6.3 Virtual Age of a Boiler;146
6.4.4;6.4 Stream Clustering;147
6.4.5;6.5 Determining the Best Centroid;150
6.4.6;6.6 Industrial Case Study;151
6.4.7;6.7 Conclusion;157
6.4.8;References;157
6.5;7 Critical States of Nuclear Power Plant Reactors and Bilinear Modeling;160
6.5.1;7.1 Introduction;160
6.5.2;7.2 System-Theoretical Description of Nuclear Reactor Dynamics;162
6.5.3;7.3 Bilinear Logic-Dynamical Models;163
6.5.4;7.4 Versal Models of Critical States;165
6.5.5;7.5 Bilinear Model of the Thermal-Hydraulic Systems;170
6.5.6;7.6 Bilinear Simulation of Reactor Core Accidents;172
6.5.7;7.7 Conclusions;174
6.5.8;References;175
6.6;8 Mixed-Integer Optimization for Polygeneration Energy Systems Design;177
6.6.1;8.1 An Overview of Polygeneration Energy Systems;177
6.6.2;8.2 Studies and Existing Problems;181
6.6.3;8.3 Superstructure Representation;182
6.6.4;8.4 Mathematical Model;185
6.6.5;8.5 A Polygeneration Plant for Electricity and Methanol – A Case Study;193
6.6.6;8.6 Conclusions;197
6.6.7;References;198
6.6.8;Appendix A – Nomenclature;199
6.7;9 Optimization of the Design and Partial-Load Operation of Power Plants Using Mixed-Integer Nonlinear Programming;202
6.7.1;9.1 Introduction;202
6.7.2;9.2 Model of a Cogeneration Power Plant;204
6.7.3;9.3 Solution of the MINLP;212
6.7.4;9.4 Optimization Results;218
6.7.5;9.5 Conclusions;224
6.7.6;References;225
6.8;10 Optimally Running a Biomass-Based Energy Production Process;230
6.8.1;10.1 Introduction;230
6.8.2;10.2 Modeling the Production Process;231
6.8.3;10.3 A Real-World Application;235
6.8.4;10.4 Model Improvements;238
6.8.5;10.5 Conclusion;240
6.8.6;References;241
6.9;11 Mathematical Modeling of Batch, Single Stage, Leach Bed Anaerobic Digestion of Organic Fraction of Municipal Solid Waste;242
6.9.1;11.1 Introduction;243
6.9.2;11.2 Characteristics of Municipal Solid Waste;245
6.9.3;11.3 Metabolic Processes in Anaerobic Digestion;247
6.9.4;11.4 Model Description;249
6.9.5;11.5 Selection of Parameters;259
6.9.6;11.6 Model Implementation and Simulation;264
6.9.7;11.7 Model Validation;266
6.9.8;11.8 Model Application;275
6.9.9;11.9 Conclusions;277
6.9.10;References;278
6.9.11;Appendix;281
6.10;12 Spatially Differentiated Trade of Permits for Multipollutant Electric Power Supply Chains;285
6.10.1;12.1 Introduction;285
6.10.2;12.2 The Electric Power Supply Chain Network Model with Multipollutant Tradable Permits;287
6.10.3;12.3 Algorithm and Examples;298
6.10.4;12.4 Summary and Conclusions;301
6.10.5;References;302
6.11;13 Applications of TRUST-TECH Methodology in Optimal Power Flow of Power Systems;305
6.11.1;13.1 Introduction;305
6.11.2;13.2 Optimal Power Flow;308
6.11.3;13.3 Overview of TRUST-TECH Methodology;309
6.11.4;13.4 Computational and Analytical Basis;312
6.11.5;13.5 Active-Set Quotient Gradient System;315
6.11.6;13.6 Stage II – IPM;318
6.11.7;13.7 Numerical Studies;320
6.11.8;13.8 Concluding Remarks;323
6.11.9;References;324
7;Part III Stochastic Programming: Methods and Applications;327
7.1;14 Scenario Tree Approximation and Risk Aversion Strategies for Stochastic Optimization of Electricity Production and Trading;328
7.1.1;14.1 Introduction;328
7.1.2;14.2 Mathematical Framework;330
7.1.3;14.3 Stability of Multistage Problems;331
7.1.4;14.4 Construction of Scenario Trees;335
7.1.5;14.5 Polyhedral Risk Functionals;340
7.1.6;14.6 Case Study;345
7.1.7;14.7 Conclusion;351
7.1.8;References;351
7.2;15 Optimization of Dispersed Energy Supply – Stochastic Programming with Recombining Scenario Trees;354
7.2.1;15.1 Introduction;354
7.2.2;15.2 Model Description;355
7.2.3;15.3 Decomposition Using Recombining Scenario Trees;359
7.2.4;15.4 Case Study;365
7.2.5;15.5 Numerical Results;365
7.2.6;15.6 Conclusions and Outlook;369
7.2.7;References;370
7.3;16 Stochastic Model of the German Electricity System;372
7.3.1;16.1 Introduction;372
7.3.2;16.2 Model;373
7.3.3;16.3 Scenarios;377
7.3.4;16.4 Conclusion and Outlook;391
7.3.5;References;392
7.4;17 Optimization of Risk Management Problems in Generation and Trading Planning;393
7.4.1;17.1 Introduction and Motivation;394
7.4.2;17.2 Analysis and Modeling;395
7.4.3;17.3 Optimization Method;402
7.4.4;17.4 Exemplary Results;408
7.4.5;17.5 Conclusions;412
7.4.6;References;413
7.5;18 Optimization Methods Application to Optimal Power Flow in Electric Power Systems;415
7.5.1;18.1 Introduction;415
7.5.2;18.2 Overview of Optimal Power Flow;416
7.5.3;18.3 Stochastic Methods for OPF;422
7.5.4;18.4 Numerical Application;432
7.5.5;18.5 Concluding Remarks;438
7.5.6;References;439
7.6;19 WILMAR: A Stochastic Programming Tool to Analyze the Large-Scale Integration of Wind Energy;443
7.6.1;19.1 Introduction;443
7.6.2;19.2 Existing Modeling Approaches;445
7.6.3;19.3 Markets and Unit Commitment;445
7.6.4;19.4 Key Model Equations;446
7.6.5;19.5 Key Model Features;453
7.6.6;19.6 Application;457
7.6.7;19.7 Final Remarks;461
7.6.8;References;461
7.6.9;Appendix: Symbols Used;463
8;Part IV Stochastic Programming in Pricing;465
8.1;20 Clean Valuation with Regard to EU Emission Trading;466
8.1.1;20.1 Introduction;466
8.1.2;20.2 Market Developments and Observations;468
8.1.3;20.3 Clean Valuation in a Multicommodity Context;471
8.1.4;20.4 Modeling Investment Planning and Power Generation;477
8.1.5;20.5 Conclusions;486
8.1.6;References;487
8.2;21 Efficient Stochastic Programming Techniques for Electricity Swing Options;489
8.2.1;21.1 Introduction;489
8.2.2;21.2 General Valuation Problem;491
8.2.3;21.3 Concrete Valuation Problem;497
8.2.4;21.4 Computational Experiments;500
8.2.5;21.5 Computational Results;501
8.2.6;21.6 Discussion;505
8.2.7;21.7 Conclusion;508
8.2.8;References;508
8.3;22 Delta-Hedging a Hydropower Plant Using Stochastic Programming;511
8.3.1;22.1 Introduction;511
8.3.2;22.2 The Nordic Power Market;512
8.3.3;22.3 Hedging of Power Production;514
8.3.4;22.4 Production Models – Theory and Implementation;516
8.3.5;22.5 Results;522
8.3.6;22.6 Discussion;525
8.3.7;22.7 Conclusion;526
8.3.8;References;527
9;Index;529