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E-raamat: Electrical Power Unit Commitment: Deterministic and Two-Stage Stochastic Programming Models and Algorithms

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  • Formaat: EPUB+DRM
  • Sari: SpringerBriefs in Energy
  • Ilmumisaeg: 13-Jan-2017
  • Kirjastus: Springer-Verlag New York Inc.
  • Keel: eng
  • ISBN-13: 9781493967681
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Electrical Power Unit Commitment: Deterministic and Two-Stage Stochastic Programming Models and AlgorithmsSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: SpringerBriefs in Energy
  • Ilmumisaeg: 13-Jan-2017
  • Kirjastus: Springer-Verlag New York Inc.
  • Keel: eng
  • ISBN-13: 9781493967681
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This volume in the SpringerBriefs in Energy series offers a systematic review of unit commitment (UC) problems in electrical power generation. It updates texts written in the late 1990s and early 2000s by including the fundamentals of both UC and state-of-the-art modeling as well as solution algorithms and highlighting stochastic models and mixed-integer programming techniques.





The UC problems are mostly formulated as mixed-integer linear programs, although there are many variants. A number of algorithms have been developed for, or applied to, UC problems, including dynamic programming, Lagrangian relaxation, general mixed-integer programming algorithms, and Benders decomposition. In addition the book discusses the recent trends in solving UC problems, especially stochastic programming models, and advanced techniques to handle large numbers of integer- decision variables due to scenario propagation

Arvustused

A short, carefully written and accessible text, despite the mathematical complexity, the reader is provided with a comprehensive view of the problem allowing to quickly reach know-how in the handling of various models and algorithms aiming to formulate and reach solutions for it. Obviously interesting for both academics and practitioners in energy production and planning domains. ( Manuel Alberto M. Ferreira, Acta Scientiae et Intellectus, Vol. 4 (04), 2018)

1 Introduction
1(10)
References
8(3)
2 Deterministic Unit Commitment Models and Algorithms
11(38)
2.1 Introduction
11(1)
2.2 Objective Function
12(2)
2.3 Constraints
14(7)
2.3.1 Unit Commitment Constraints
14(1)
2.3.2 Thermal Generation Constraints
15(1)
2.3.3 Operating Reserve Constraints
16(2)
2.3.4 Transmission Constraints
18(1)
2.3.5 Emission Constraints
19(1)
2.3.6 Unserved Energy Constraint
20(1)
2.3.7 Reactive Power Constraints
20(1)
2.4 Case Studies
21(9)
2.4.1 Case 1: Joint Energy and Ancillary Service Optimization
22(3)
2.4.2 Case 2: SCUC with Transmission Contingency
25(5)
2.5 Solution Approaches for Deterministic Unit Commitment
30(15)
2.5.1 Priority List
30(1)
2.5.2 Dynamic Programming
31(1)
2.5.3 Mixed Integer Linear Programming
32(2)
2.5.4 Lagrangian Relaxation
34(5)
2.5.5 Benders' Decomposition
39(6)
2.6 Summary
45(4)
References
45(4)
3 Two-Stage Stochastic Programming Models and Algorithms
49(38)
3.1 Introduction
49(1)
3.2 Two-Stage Stochastic Unit Commitment Modeling
50(4)
3.2.1 Problem Formulation
52(2)
3.3 SUC with Demand Respond
54(4)
3.3.1 Demand Respond
55(3)
3.4 SUC with Energy Storage
58(6)
3.4.1 Energy Storage
59(1)
3.4.2 Case 3: Two-Stage Stochastic Unit Commitment with Energy Storage and Wind Power Generation
60(4)
3.5 Two-Stage Stochastic Unit Commitment Problem Decomposition
64(4)
3.5.1 Benders' Cut
65(1)
3.5.2 The Implementation of Benders' Decomposition
66(2)
3.6 SUC with Real-Time Rescheduling
68(4)
3.6.1 Problem Formulation
68(2)
3.6.2 A Decomposition Algorithm
70(2)
3.7 SUC with Contingency Management
72(5)
3.7.1 Generating Unit Outage
73(2)
3.7.2 Transmission Outage
75(2)
3.8 Two-Stage SUC with Risk Constraints
77(7)
3.8.1 Value at Risk
77(3)
3.8.2 Conditional Value at Risk
80(1)
3.8.3 Case 4: Two-Stage Stochastic Unit Commitment with CVaR Risk Constraints
81(3)
3.9 Summary
84(3)
References
85(2)
Appendix A Nomenclature 87(4)
Appendix B Renewable Energy Scenario Generation 91
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