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Investment in Electricity Generation and Transmission: Decision Making under Uncertainty 1st ed. 2016 [Hardback]

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  • Format: Hardback, 384 pages, height x width: 235x155 mm, weight: 7214 g, 10 Illustrations, color; 79 Illustrations, black and white; XIV, 384 p. 89 illus., 10 illus. in color., 1 Hardback
  • Pub. Date: 22-Jun-2016
  • Publisher: Springer International Publishing AG
  • ISBN-10: 3319294997
  • ISBN-13: 9783319294995
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Investment in Electricity Generation and Transmission: Decision Making under Uncertainty 1st ed. 2016Larger Image
  • Format: Hardback, 384 pages, height x width: 235x155 mm, weight: 7214 g, 10 Illustrations, color; 79 Illustrations, black and white; XIV, 384 p. 89 illus., 10 illus. in color., 1 Hardback
  • Pub. Date: 22-Jun-2016
  • Publisher: Springer International Publishing AG
  • ISBN-10: 3319294997
  • ISBN-13: 9783319294995
Other books in subject:
Permanent link: https://www.kriso.ee/db/9783319294995.html
Keywords:
This book provides an in-depth analysis of investment problems pertaining to electric energy infrastructure, including both generation and transmission facilities. The analysis encompasses decision-making tools for expansion planning, reinforcement, and the selection and timing of investment options. In this regard, the book provides an up-to-date description of analytical tools to address challenging investment questions such as:

How can we expand and/or reinforce our aging electricity transmission infrastructure?

How can we expand the transmission network of a given region to integrate significant amounts of renewable generation?

How can we expand generation facilities to achieve a low-carbon electricity production system?

How can we expand the generation system while ensuring appropriate levels of flexibility to accommodate both demand-related and production-related uncertainties?

How can we chooseamong alternative production facilities?

What is the right time to invest in a given production or transmission facility?

Written in a tutorial style and modular format, the book includes a wealth of illustrative examples to facilitate comprehension. It is intended for advanced undergraduate and graduate students in the fields of electric energy systems, operations research, management science, and economics. Practitioners in the electric energy sector will also benefit from the concepts and techniques presented here.
1 Investment in Generation and Transmission Facilities
1(20)
1.1 Long-Term Decision Making Under Uncertainty
1(3)
1.2 Electricity Markets
4(3)
1.3 Transmission Expansion Planning
7(2)
1.4 Generation Investment
9(3)
1.5 Generation and Transmission Expansion Planning
12(2)
1.6 Investment Valuation and Timing
14(1)
1.7 What We Do and What We Do Not Do
15(1)
1.8 End-of-Chapter Exercises
16(5)
References
17(4)
2 Transmission Expansion Planning
21(40)
2.1 Introduction
21(3)
2.2 Deterministic Approach
24(14)
2.2.1 Notation
25(1)
2.2.2 MINLP Model Formulation
26(6)
2.2.3 Linearization of Products of Binary and Continuous Variables
32(1)
2.2.4 MILP Model Formulation
32(6)
2.3 Robust Approach
38(15)
2.3.1 Adaptive Robust Optimization Formulation
39(1)
2.3.2 Definition of Uncertainty Sets
40(1)
2.3.3 Feasibility of Operating Decision Variables
41(1)
2.3.4 Detailed Formulation
41(2)
2.3.5 Solution Procedure
43(10)
2.4 Summary
53(1)
2.5 End-of-Chapter Exercises
53(3)
2.6 GAMS Code
56(5)
References
58(3)
3 Generation Expansion Planning
61(54)
3.1 Introduction
61(2)
3.2 Problem Description
63(7)
3.2.1 Notation
63(2)
3.2.2 Aim and Assumptions
65(1)
3.2.3 Time Framework
65(2)
3.2.4 Operating Conditions
67(1)
3.2.5 Uncertainty Characterization
68(1)
3.2.6 Modeling of the Transmission Network
69(1)
3.2.7 Complementarity Model
69(1)
3.3 Deterministic Single-Node Static GEP
70(10)
3.3.1 Complementarity Model
71(3)
3.3.2 Equivalent NLP Formulation
74(2)
3.3.3 Equivalent MILP Formulation
76(3)
3.3.4 Meaning of Dual Variables λ0
79(1)
3.4 Deterministic Single-Node Dynamic GEP
80(3)
3.5 Deterministic Network-Constrained Static GEP
83(8)
3.5.1 Complementarity Model
84(3)
3.5.2 Equivalent MILP Formulation
87(4)
3.5.3 Meaning of Dual Variables λn0
91(1)
3.6 Stochastic Single-Node GEP
91(15)
3.6.1 Static Model Formulation
92(4)
3.6.2 Dynamic Model Formulation
96(10)
3.7 Summary and Conclusions
106(1)
3.8 End-of-Chapter Exercises
106(2)
3.9 GAMS Codes
108(7)
References
113(2)
4 Generation and Transmission Expansion Planning
115(54)
4.1 Introduction
115(2)
4.2 Problem Description
117(3)
4.2.1 Notation
117(2)
4.2.2 Approach
119(1)
4.2.3 Risk Management
119(1)
4.3 Deterministic Static G&TEP
120(5)
4.3.1 MINLP Formulation
120(4)
4.3.2 MILP Formulation
124(1)
4.4 Deterministic Dynamic G&TEP
125(6)
4.5 Stochastic G&TEP
131(21)
4.5.1 Static Approach
132(5)
4.5.2 Dynamic Approach
137(15)
4.6 Stochastic Dynamic Risk-Constrained G&TEP
152(9)
4.6.1 Formulation
153(8)
4.7 Summary and Conclusions
161(1)
4.8 End-of-Chapter Exercises
162(2)
4.9 GAMS Code
164(5)
References
166(3)
5 Investment in Production Capacity
169(60)
5.1 Introduction
169(5)
5.1.1 Electricity Pool
170(1)
5.1.2 Network Representation
170(1)
5.1.3 Static and Dynamic Investment Models
171(1)
5.1.4 Operating Conditions: Demand Level and Stochastic Production
171(1)
5.1.5 Uncertainty
172(1)
5.1.6 Bilevel Model
173(1)
5.1.7 Alternative Solution Approaches
174(1)
5.2 Static Production Capacity Investment Model
174(16)
5.3 Dynamic Production Capacity Investment Model
190(8)
5.4 Direct Solution Approach
198(11)
5.4.1 MPEC
198(4)
5.4.2 MPEC Linearization
202(3)
5.4.3 Numerical Results
205(4)
5.5 Benders Solution Approach
209(7)
5.5.1 Complicating Variables
209(2)
5.5.2 Convexity Analysis
211(1)
5.5.3 Functioning of Benders Decomposition
212(1)
5.5.4 The Benders Algorithm
213(3)
5.6 Summary
216(1)
5.7 End-of-Chapter Exercises
217(3)
5.8 GAMS Code
220(9)
References
226(3)
6 Investment Equilibria
229(40)
6.1 Introduction
229(1)
6.2 Solution Approach
230(2)
6.3 Modeling Features and Assumptions
232(1)
6.4 Single-Producer Problem
233(12)
6.4.1 MPEC
240(5)
6.5 Multiple-Producer Problem: EPEC
245(8)
6.5.1 EPEC Solution
245(5)
6.5.2 Searching for Multiple Solutions
250(1)
6.5.3 Ex-Post Algorithm for Detecting Nash Equilibria
251(1)
6.5.4 Numerical Results
252(1)
6.6 Summary
253(2)
6.7 End-of-Chapter Exercises
255(1)
6.8 GAMS Code
255(14)
References
266(3)
7 Deciding on Alternative Investments: A Real Options Approach
269(58)
7.1 Assumptions and the Need for Dynamic Programming
269(5)
7.2 Optimal Timing Versus Now-or-Never Net Present Value Approaches
274(9)
7.3 Operational Flexibility
283(7)
7.4 Modularity and Capacity Expansion
290(7)
7.5 Continuous Capacity Sizing
297(5)
7.6 Mutually Exclusive Technologies
302(6)
7.7 Risk Aversion
308(6)
7.8 Summary and Extensions
314(3)
7.9 End-of-Chapter Exercises
317(1)
7.10 MATLAB Codes
318(9)
References
323(4)
Appendix A Engineering Economics 327(10)
Appendix B Optimization Under Uncertainty 337(10)
Appendix C Complementarity 347(14)
Appendix D Risk Management 361(10)
Appendix E Dynamic Programming 371(10)
Index 381
Antonio J. Conejo, professor at Ohio State University, received his MS from MIT and his PhD from the Royal Institute of Technology, Sweden. He has published over 165 papers in SCI journals and has authored or coauthored books published by Springer, John Wiley, McGraw-Hill, and CRC. An IEEE Fellow, he has been the principal investigator in many research projects financed by public agencies and the power industry and has supervised 19 PhD theses.





Luis Baringo, assistant professor at the University of Castilla-La Mancha, Ciudad Real, Spain, received his Industrial Engineering degree and his PhD in Electrical Engineering from the University of Castilla-La Mancha, Spain, in 2009 and 2013, respectively. In 2014, he was a postdoctoral researcher at the Power Systems Laboratory, ETH Zurich, Switzerland.





S. Jalal Kazempour, postdoctoral fellow at the Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark, received his BS from the University of Tabriz, Iran in 2006, his MS from Tarbiat Modares University, Tehran, Iran in 2009, and his PhD from the University of Castilla-La Mancha, Ciudad Real, Spain in 2013, all in the field of electrical engineering. In 2014, he was a postdoctoral fellow at the Whiting School of Engineering, Johns Hopkins University, Baltimore, USA.

Afzal S. Siddiqui, Senior Lecturer in the Department of Statistical Science at University College London, received the B.S. from Columbia University and the M.S. and Ph.D. from the University of California at Berkeley (all in industrial engineering and operations research). His research interests are in decision making under uncertainty in the energy sector. Besides having participated in several externally funded projects, he has coordinated an EU project on risk management and energy efficiency in public buildings. He also holds visiting positions at Stockholm University and Aalto University.