Rebennack Handbook of Power Systems I

1. Auflage 2010
ISBN: 978-3-642-02493-1
Verlag: Springer
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 494 Seiten

Energy is one of the world`s most challenging problems, and power systems are an important aspect of energy related issues. This handbook contains state-of-the-art contributions on power systems modeling and optimization. The book is separated into two volumes with six sections, which cover the most important areas of energy systems. The first volume covers the topics operations planning and expansion planning while the second volume focuses on transmission and distribution modeling, forecasting in energy, energy auctions and markets, as well as risk management. The contributions are authored by recognized specialists in their fields and consist in either state-of-the-art reviews or examinations of state-of-the-art developments. The articles are not purely theoretical, but instead also discuss specific applications in power systems.

Autoren/Hrsg.

Rebennack, Steffen

Inhaltsverzeichnis

1;Handbook of Power Systems I;3
1.1;Preface of Volume I;7
1.2;Contents of Volume I;11
1.3;Contents of Volume II;15
1.4;Contributors;17
1.5;Part I Operation Planning;21
1.5.1;Constructive Dual DP for Reservoir Optimization;22
1.5.1.1;1 Introduction;22
1.5.1.2;2 Background;23
1.5.1.3;3 A Deterministic Single-Reservoir CDDP Algorithm;26
1.5.1.4;4 Alternative Precomputations for Intra-period Optimization;31
1.5.1.5;5 Guideline Augmentation vs. Demand Curve Addition;32
1.5.1.6;6 Dealing with Uncertainty;36
1.5.1.7;7 Efficient Simulation Using CDDP Precomputations;40
1.5.1.8;8 Adding Reservoirs;41
1.5.1.9;9 Current Models: RAGE/DUBLIN and ECON BID;44
1.5.1.10;10 Conclusion;49
1.5.1.11;References;49
1.5.2;Long- and Medium-term Operations Planning and Stochastic Modelling in Hydro-dominated Power Systems Based on Stochastic Dual Dynamic Programming;52
1.5.2.1;1 Introduction;52
1.5.2.2;2 Basic Power System Model;54
1.5.2.2.1;2.1 Introduction;54
1.5.2.2.2;2.2 Power Station Model;54
1.5.2.2.3;2.3 Reservoir and Inflow;55
1.5.2.2.4;2.4 Power Balance and Objective Function;56
1.5.2.2.5;2.5 Price Modelling;58
1.5.2.2.6;2.6 Overall Local Model;60
1.5.2.3;3 Solution Method for the Local Model;60
1.5.2.3.1;3.1 Overview;60
1.5.2.3.2;3.2 Solution by a Dynamic Programming Approach;61
1.5.2.4;4 Extensions to the Local Model;64
1.5.2.4.1;4.1 Head Variations in Medium-term Scheduling;64
1.5.2.4.2;4.2 Risk Control;65
1.5.2.4.3;4.3 Use of the Results from the Local Model;66
1.5.2.5;5 A Numerical Example;66
1.5.2.6;6 A Global Scheduling Model;67
1.5.2.7;7 More on Stochastic Inflow Modelling;68
1.5.2.8;8 Computational Issues;71
1.5.2.9;9 Discussion;72
1.5.2.10;10 Conclusion;73
1.5.2.11;References;73
1.5.3;Dynamic Management of Hydropower-Irrigation Systems;75
1.5.3.1;1 Introduction;75
1.5.3.2;2 Stochastic Dual Dynamic Programming;77
1.5.3.3;3 SDDP Model with Irrigation Benefits;81
1.5.3.4;4 SDDP Model of the Euphrates River in Turkey and Syria;86
1.5.3.5;5 Analysis of Allocation Policies;88
1.5.3.6;6 Conclusion;91
1.5.3.7;References;92
1.5.4;Latest Improvements of EDF Mid-term Power Generation Management;94
1.5.4.1;1 Introduction;94
1.5.4.2;2 EDF Mid-term Power Generation Management;95
1.5.4.2.1;2.1 EDF Generation Units;95
1.5.4.2.2;2.2 Mid-term Power Generation Management Purposes;95
1.5.4.2.3;2.3 Mid-term Power Generation Management Tools;96
1.5.4.2.4;2.4 Mid-term Power Generation Management Toolsas an Approximated Dynamic Programming Method;97
1.5.4.3;3 The Simulator;97
1.5.4.3.1;3.1 The Former Simulator and Its Limitations;97
1.5.4.3.2;3.2 Horizon of Simulation;98
1.5.4.3.3;3.3 The Simulator Modeling;98
1.5.4.3.4;3.4 MIP Resolution;99
1.5.4.3.4.1;3.4.1 Modeling;99
1.5.4.3.4.2;3.4.2 Comparison Between Solvers;103
1.5.4.3.4.3;3.4.3 Some Results;103
1.5.4.3.5;3.5 Heuristics;103
1.5.4.3.5.1;3.5.1 First Heuristic H1;104
1.5.4.3.5.2;3.5.2 Second Heuristic H2;107
1.5.4.3.6;3.6 Calculation Duration Comparison;108
1.5.4.4;4 Conclusion;109
1.5.4.4.1;Further Reading;110
1.5.4.4.1.1;EDF Modelling System;110
1.5.4.4.1.2;Other References;110
1.5.5;Large Scale Integration of Wind Power Generation;112
1.5.5.1;1 Wind Intermittence;112
1.5.5.2;2 Impact in the Power System;114
1.5.5.3;3 Options for Managing Intermittency;116
1.5.5.3.1;3.1 Wind Power Forecasting;118
1.5.5.3.2;3.2 Aggregation and Distribution;120
1.5.5.3.3;3.3 Interconnection with Other Grids;124
1.5.5.3.4;3.4 Power Plants Providing Reserve;124
1.5.5.3.5;3.5 Curtailment of Wind Farms;125
1.5.5.3.6;3.6 Distributed Generation;125
1.5.5.3.7;3.7 Complementarity Between Renewable Sources;125
1.5.5.3.8;3.8 Demand-Side Management;128
1.5.5.3.9;3.9 Demand Response;129
1.5.5.3.10;3.10 Energy Storage;131
1.5.5.4;4 Solutions Adopted in the Existent Markets;133
1.5.5.5;5 Conclusion;135
1.5.5.6;References;135
1.5.6;Optimization Models in the Natural Gas Industry;137
1.5.6.1;1 Introduction;137
1.5.6.2;2 Optimization in Gas Production (Recovery);139
1.5.6.2.1;2.1 Production Scheduling Considering Well Placement;139
1.5.6.2.1.1;2.1.1 Mixed Integer Linear Programming Formulation;139
1.5.6.2.1.2;2.1.2 Nonlinear Programming Formulation;141
1.5.6.2.2;2.2 Total Gas Recovery Maximization: An Optimal Control Formulation;142
1.5.6.3;3 Natural Gas Pipeline Network Optimization;144
1.5.6.3.1;3.1 Compressor Station Allocation Problem Considering Pipeline Configurations;145
1.5.6.3.2;3.2 Least Gas Purchase Problem and Optimal Dimensioning of Gas Pipelines;147
1.5.6.3.3;3.3 Minimum Fuel Consumption Problem;150
1.5.6.4;4 Natural Gas Market Models;152
1.5.6.4.1;4.1 Reallocation Problem in a Regulated Natural Gas Market;152
1.5.6.4.2;4.2 Deregulated Natural Gas Market Models;154
1.5.6.4.3;4.3 Optimization in the Energy System Combining Natural Gas System and Electricity System;157
1.5.6.4.3.1;4.3.1 Electricity System Reliability Study using Natural Gas Transmission Network Modeling;157
1.5.6.4.3.2;4.3.2 Optimization in Natural Gas Contracts;159
1.5.6.5;5 Conclusion;161
1.5.6.6;References;162
1.5.7;Integrated Electricity–Gas Operations Planning in Long-term Hydroscheduling Based on Stochastic Models;165
1.5.7.1;1 Introduction;166
1.5.7.2;2 Overview of Electricity and Gas Sectors;167
1.5.7.3;3 Electricity–Natural Gas Integration Issues;170
1.5.7.4;4 Probabilistic Evaluation of Gas-Fired Plant Schedules;170
1.5.7.4.1;4.1 Stochastic Hydrothermal Scheduling;171
1.5.7.4.1.1;4.1.1 Objective Function;172
1.5.7.4.1.2;4.1.2 Water Balance Equations;172
1.5.7.4.1.3;4.1.3 Bounds on Storage, Turbined Volumes, and Thermal Generation Variables;173
1.5.7.4.1.4;4.1.4 Load Balance Equation;173
1.5.7.4.2;4.2 Probabilistic Gas Scheduling Model;173
1.5.7.4.2.1;4.2.1 Gas Production and Flow Limits;174
1.5.7.4.2.2;4.2.2 Gas Balance Equations;174
1.5.7.4.2.3;4.2.3 Objective Function;175
1.5.7.4.3;4.3 Case Study;175
1.5.7.5;5 Integrated Electricity–Gas Modeling in HydroScheduling Models;178
1.5.7.5.1;5.1 Gas Pipeline Equations;178
1.5.7.5.2;5.2 Case Study;179
1.5.7.6;6 Conclusions;181
1.5.7.7;References;181
1.5.8;Recent Progress in Two-stage Mixed-integer Stochastic Programming with Applications to Power Production Planning;192
1.5.8.1;1 Introduction;192
1.5.8.2;2 Models and Structural Properties;193
1.5.8.3;3 Stability;197
1.5.8.4;4 Scenario Reduction;199
1.5.8.5;5 Decomposition Algorithms;203
1.5.8.5.1;5.1 Convexification of the Expected Recourse Function;207
1.5.8.5.2;5.2 Convexification of the Value Function;208
1.5.8.5.2.1;5.2.1 Solving the Master Problem;209
1.5.8.5.2.2;5.2.2 Convexification of Disjunctive Cuts;210
1.5.8.5.2.3;5.2.3 Approximation of (u, t) by Linear Optimality Cuts ;210
1.5.8.5.2.4;5.2.4 Approximation of (u, t) by Lift-and-Project;211
1.5.8.5.2.5;5.2.5 Approximation of (u, t) by Branch-and-Bound ;212
1.5.8.5.2.6;5.2.6 Full Algorithm;213
1.5.8.5.2.7;5.2.7 Extension to Multistage Problems;214
1.5.8.5.3;5.3 Scenario Decomposition;215
1.5.8.6;6 Application to Stochastic Thermal Unit Commitment;216
1.5.8.7;7 Conclusions;220
1.5.8.8;References;221
1.5.9;Dealing With Load and Generation Cost Uncertainties in Power System Operation Studies: A Fuzzy Approach;224
1.5.9.1;1 Introduction;224
1.5.9.2;2 Uncertainty Modeling in Power System Studies;226
1.5.9.3;3 Fuzzy Set Basics;228
1.5.9.4;4 Fuzzy Optimal Power Flow;229
1.5.9.5;5 New Fuzzy Optimal Power Flow Model;231
1.5.9.5.1;5.1 General Aspects;231
1.5.9.5.2;5.2 Integration of Load Uncertainties;233
1.5.9.5.3;5.3 Integration of Generation Cost Uncertainties;236
1.5.9.5.4;5.4 Simultaneous Integration of Cost and Load Uncertainties;236
1.5.9.5.5;5.5 Integration of Active Losses;237
1.5.9.5.6;5.6 Computation of Nodal Marginal Prices;238
1.5.9.5.7;5.7 Final Remarks;239
1.5.9.6;6 Case Study;240
1.5.9.6.1;6.1 Data;240
1.5.9.6.2;6.2 Results Considering Only Load Uncertainties;241
1.5.9.6.3;6.3 Results Considering Only Generation Cost Uncertainties;243
1.5.9.6.4;6.4 Results Considering Load and Generation Cost Uncertainties;244
1.5.9.7;7 Conclusions;246
1.5.9.8;References;247
1.5.10;OBDD-Based Load Shedding Algorithm for Power Systems;249
1.5.10.1;1 Introduction;249
1.5.10.2;2 Literature Review;250
1.5.10.3;3 Preliminary;251
1.5.10.3.1;3.1 Boolean Expressions and Their OBDDs;251
1.5.10.3.2;3.2 Signed Integers and Their OBDD Vectors;252
1.5.10.3.3;3.3 Key Constraints;255
1.5.10.4;4 The Load Shedding Problem(LSP);255
1.5.10.5;5 Solution of LSP Based on OBDD;257
1.5.10.5.1;5.1 Boolean Expression for the Power Balance Constraint (PBC);258
1.5.10.5.2;5.2 Boolean Expression for the Priority Constraint (PRC);260
1.5.10.6;6 NP-Hardness of the Problem;260
1.5.10.7;7 Case Study;262
1.5.10.8;8 Conclusions;265
1.5.10.9;References;266
1.5.11;Solution to Short-term Unit Commitment Problem;268
1.5.11.1;1 Introduction;270
1.5.11.2;2 Generating Units;272
1.5.11.2.1;2.1 Thermal Units;272
1.5.11.2.1.1;2.1.1 Steam Turbine Unit;272
1.5.11.2.1.2;2.1.2 Gas Turbine Unit;273
1.5.11.2.2;2.2 Hydro Units;274
1.5.11.3;3 Operating Constraints;274
1.5.11.3.1;3.1 System Constraints;274
1.5.11.3.2;3.2 Unit Constraints;274
1.5.11.4;4 Objective Function and Constraints of Unit Commitment Problem;275
1.5.11.5;5 Lagrangian Relaxation Approach;277
1.5.11.5.1;5.1 Lagrangian Dual Problem;277
1.5.11.5.2;5.2 Solution of the Dual Problem;279
1.5.11.5.3;5.3 Solving Thermal Subproblems;280
1.5.11.5.3.1;5.3.1 Steam Turbine Unit Without Ramp Rate Constraints;280
1.5.11.5.3.2;5.3.2 Steam Turbine Unit with Ramp Rate Constraints;283
1.5.11.5.3.3;5.3.3 Gas Turbine Unit;286
1.5.11.5.4;5.4 Solving Hydro Subproblems;287
1.5.11.6;6 Solution Methodology;287
1.5.11.6.1;6.1 Variable Metric Method for Dual Optimization;287
1.5.11.6.2;6.2 Linear Interpolation Method for Suboptimal Feasible Solution;289
1.5.11.6.3;6.3 Development of the Refinement Algorithm;290
1.5.11.6.4;6.4 Unit Commitment Expert System;292
1.5.11.7;7 Hydrothermal Scheduling;293
1.5.11.8;8 Numerical Results;298
1.5.11.9;9 Conclusions;304
1.5.11.10;References;305
1.5.12;A Systems Approach for the Optimal Retrofitting of Utility Networks Under Demand and Market Uncertainties;306
1.5.12.1;1 Introduction;306
1.5.12.2;2 Problem Description;308
1.5.12.3;3 The Stochastic Programming Based Approach;309
1.5.12.3.1;3.1 The Stochastic Programming Framework;309
1.5.12.3.2;3.2 Realisation of the Proposed Approach;310
1.5.12.4;4 Case Study;312
1.5.12.4.1;4.1 The Targeted Utility System;312
1.5.12.4.2;4.2 The Retrofit Problem Specification and Solution Choices;313
1.5.12.5;5 Results and Discussion;314
1.5.12.5.1;5.1 Optimal Retrofit Design Resulting from the Proposed Approach;315
1.5.12.5.2;5.2 Comparison with Other Designs;315
1.5.12.6;6 Conclusions and Future Work;317
1.5.12.7;References;318
1.5.13;Co-Optimization of Energy and Ancillary Service Markets;320
1.5.13.1;1 Introduction;320
1.5.13.2;2 Basic Concepts;322
1.5.13.3;3 Formulation;325
1.5.13.3.1;3.1 Energy Market Formulation;325
1.5.13.3.2;3.2 Defining Requirements;326
1.5.13.3.3;3.3 Defining Participant Offers;327
1.5.13.3.4;3.4 Defining Performance Limits18;328
1.5.13.4;4 Economic Interpretation;332
1.5.13.5;5 Multi-zone Formulations;335
1.5.13.6;6 Multi-period Formulations;337
1.5.13.7;7 Conclusions;339
1.5.13.8;References;339
1.6;Part II Expansion Planning;341
1.6.1;Investment Decisions Under Uncertainty Using Stochastic Dynamic Programming: A Case Study of Wind Power;342
1.6.1.1;1 Introduction;342
1.6.1.2;2 Real Options On Wind Power;344
1.6.1.3;3 Uncertainty;347
1.6.1.3.1;3.0.1 Market Prices;347
1.6.1.3.2;3.0.2 Investment Costs and Subsidies;348
1.6.1.4;4 Value of Wind Power Investments;349
1.6.1.5;5 Conclusion;350
1.6.1.6;References;351
1.6.2;The Integration of Social Concerns into Electricity Power Planning: A Combined Delphi and AHP Approach;353
1.6.2.1;1 Introduction;353
1.6.2.2;2 Energy and Sustainable Development;354
1.6.2.2.1;2.1 Sustainable Energy Planning;356
1.6.2.2.2;2.2 Importance of the Social Dimension;357
1.6.2.3;3 Methodology;358
1.6.2.3.1;3.1 Suitability of the AHP Approach;359
1.6.2.3.2;3.2 Suitability of the Delphi Approach;360
1.6.2.4;4 Implementation of the Proposed Methodology;361
1.6.2.4.1;4.1 Selection of Options (Electricity Generation Technologies);361
1.6.2.4.2;4.2 Selection of Criteria;362
1.6.2.4.3;4.3 Hierarchical Structure Formulation;364
1.6.2.4.4;4.4 Delphi Implementation;366
1.6.2.4.5;4.5 Determination of Weights for the Electricity Generation Options;366
1.6.2.4.6;4.6 Social Impact of Future Electricity Generation Scenarios;369
1.6.2.5;5 Conclusions;371
1.6.2.6;References;372
1.6.3;Transmission Network Expansion Planning Under Deliberate Outages;375
1.6.3.1;1 Introduction;375
1.6.3.2;2 Traditional Transmission Network Expansion Planning;376
1.6.3.3;3 Vulnerability-Constrained Transmission Expansion Planning;378
1.6.3.4;4 Decision Framework, Uncertainty Characterization, and Risk Modeling;379
1.6.3.4.1;4.1 Decision-Making Process;379
1.6.3.4.2;4.2 Scenario Generation Procedure;381
1.6.3.4.3;4.3 Risk Modeling;383
1.6.3.5;5 Formulation;384
1.6.3.5.1;5.1 Risk-Neutral Approach;384
1.6.3.5.2;5.2 Risk-Averse Approach;386
1.6.3.5.3;5.3 Computational Issues;388
1.6.3.6;6 Numerical Results;389
1.6.3.6.1;6.1 Risk-Neutral Analysis;390
1.6.3.6.2;6.2 Risk-based Analysis;391
1.6.3.7;7 Conclusions;393
1.6.3.8;References;397
1.6.4;Long-term and Expansion Planning for Electrical Networks Considering Uncertainties;400
1.6.4.1;1 Introduction;400
1.6.4.2;2 Planning of Transmission and Distribution Networks;402
1.6.4.2.1;2.1 Uncertainties of Network Planning;402
1.6.4.2.1.1;2.1.1 Technical Uncertainties;403
1.6.4.2.1.2;2.1.2 Economical Uncertainties;403
1.6.4.2.1.3;2.1.3 Regulatory Uncertainties;404
1.6.4.2.2;2.2 Technical Boundary Conditions of Network Planning;404
1.6.4.2.3;2.3 Long-term Planning;406
1.6.4.2.4;2.4 Expansion Planning;407
1.6.4.3;3 Algorithms for Long-term and Expansion Planning;409
1.6.4.3.1;3.1 Genetic Algorithms;410
1.6.4.3.2;3.2 Ant Colony Optimization;412
1.6.4.3.3;3.3 Comparison of Algorithms;414
1.6.4.4;4 Practical Application of Network Optimization Algorithms;415
1.6.4.5;5 Conclusion;416
1.6.4.6;References;416
1.6.5;Differential Evolution Solution to Transmission Expansion Planning Problem;418
1.6.5.1;1 Introduction;418
1.6.5.2;2 Problem Formulation;420
1.6.5.2.1;2.1 Overall TEP Problem;420
1.6.5.2.2;2.2 Reference Network Subproblem;422
1.6.5.3;3 Solution of Reference Network Subproblem;423
1.6.5.4;4 Simple Differential Evolution;424
1.6.5.4.1;4.1 Initialization;424
1.6.5.4.2;4.2 Mutation;424
1.6.5.4.3;4.3 Crossover;425
1.6.5.4.4;4.4 Selection;425
1.6.5.5;5 Improved Differential Evolution;425
1.6.5.5.1;5.1 Scaling Factor F;425
1.6.5.5.2;5.2 Selection Scheme;426
1.6.5.5.3;5.3 Auxiliary Set;426
1.6.5.5.4;5.4 Treatment of Constraints;427
1.6.5.5.5;5.5 Handling of Integer Variables;427
1.6.5.6;6 Overview of the IDE Solution to TEP Problem;427
1.6.5.7;7 Results and Discussion;428
1.6.5.7.1;7.1 Parameter Values for IDE;428
1.6.5.7.2;7.2 Comparison of TEP Methods;428
1.6.5.7.2.1;7.2.1 Case 30;428
1.6.5.7.2.2;7.2.2 Case 57 and Case 118;432
1.6.5.8;8 Conclusions;432
1.6.5.9;References;434
1.6.6;Agent-based Global Energy Management Systems for the Process Industry;437
1.6.6.1;1 Introduction;437
1.6.6.2;2 Conceptual Representation of a Dynamic Management System;439
1.6.6.2.1;2.1 Utility Services Negotiation;439
1.6.6.2.2;2.2 Utility System Optimisation;440
1.6.6.3;3 Mathematical Formulations, Optimisation Models, and Integration;441
1.6.6.3.1;3.1 Level I: Tactical Level;442
1.6.6.3.1.1;3.1.1 Problem Statement;442
1.6.6.3.1.2;3.1.2 Problem Representation;442
1.6.6.3.1.3;3.1.3 Mathematical Formulation;445
1.6.6.3.2;3.2 Level II: Strategic Level;447
1.6.6.3.2.1;3.2.1 Superstructure Development;447
1.6.6.3.2.2;3.2.2 Optimisation Model;449
1.6.6.4;4 An Agent-enabled Realisation;450
1.6.6.5;5 Illustrative Examples;453
1.6.6.5.1;5.1 Case Study One;454
1.6.6.5.2;5.2 Case Study Two;455
1.6.6.6;6 Conclusions;457
1.6.6.7;References;457
1.6.7;Optimal Planning of Distributed Generation via Nonlinear Optimization and Genetic Algorithms;459
1.6.7.1;1 Introduction;459
1.6.7.2;2 Distributed Generation;461
1.6.7.3;3 DG Location and Sizing Issues;462
1.6.7.4;4 Problem Formulation;464
1.6.7.4.1;4.1 The Objective Function;465
1.6.7.4.2;4.2 Operational Constraints;466
1.6.7.5;5 Nonlinear Optimization Approach;467
1.6.7.6;6 Genetic Algorithms Approach;470
1.6.7.7;7 Case Study;474
1.6.7.7.1;7.1 Nonlinear Optimization Algorithm;474
1.6.7.7.2;7.2 Genetic Algorithm;475
1.6.7.7.2.1;7.2.1 Selection Mechanism;476
1.6.7.7.2.2;7.2.2 Population Size;477
1.6.7.7.2.3;7.2.3 Crossover Fraction;477
1.6.7.7.3;7.3 Solution Analysis;481
1.6.7.8;8 Conclusions;486
1.6.7.9;References;488
1.7;Index;491