Investment in Electricity Generation and Transmission: Decision Making under Uncertainty

389 Pages·2016·7.805 MB·English

by  Antonio J. Conejo, Luis Baringo Morales, S. Jalal Kazempour, Afzal S. Siddiqui (auth.)

Antonio J. Conejo · Luis Baringo
S. Jalal Kazempour · Afzal S. Siddiqui
Investment
in Electricity
Generation and
Transmission
Decision Making under Uncertainty
Investment in Electricity Generation
and Transmission
Antonio J. Conejo Luis Baringo
(cid:129)
S. Jalal Kazempour Afzal S. Siddiqui
(cid:129)
Investment in Electricity
Generation and Transmission
Decision Making under Uncertainty
123
AntonioJ.Conejo Afzal S.Siddiqui
Departments ofIntegrated Systems Department ofStatistical Science
Engineering, Electrical andComputer University CollegeLondon
Engineering London
TheOhio State University UK
Columbus, OH
and
USA
Stockholm University
LuisBaringo Stockholm
E.T.S.I.Industriales Sweden
Universidad deCastilla—LaMancha
CiudadReal
Spain
S. JalalKazempour
Department ofElectrical Engineering
Technical University of Denmark
Kongens Lyngby
Denmark
ISBN978-3-319-29499-5 ISBN978-3-319-29501-5 (eBook)
DOI 10.1007/978-3-319-29501-5
LibraryofCongressControlNumber:2016936287
©SpringerInternationalPublishingSwitzerland2016
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To Olaia, Mireia, and Núria
To María and Carmen
To Azita
To my parents, Shamim and Saeed, for taking
risks
Preface
Rigorousanalysisisessentialformakinginformeddecisionsabouttheconstruction
of new electricity generation plants or transmission lines. This book provides a
number of relevant models that constitute a framework for such investment
analysis.
Since electricity generation and transmission assets last for many years,
investment decisions are challenging because they involve high costs, are often
made under high uncertainty,andgenerallyrequirerisk control on cost variability.
Moreover, given the required investment level, wrong decisions may prove catas-
trophic from a financial viewpoint. Also, investment decisions are dynamic in
nature since they are made by practitioners throughout a long-term planning
horizonasuncertaintyunfolds.Asaresultoftheuncertaintyinvolved,therequired
risk control, and the dynamic nature of investment decisions, appropriate mathe-
matical models are complex and often need tailored solution techniques.
Within a market framework, distinct agents with different and often conflicting
objectives are generally involved in making investment decisions, and thus a
multiobjective equilibrium approach is desirable. Moreover, market power is
common in electricity markets, and its representation entails nontrivial modeling
features.Consequently,theresultinggame-theoreticmodelsarecomplexenoughto
require carefully crafted solution techniques.
By focusing on the application of the state-of-the-art mathematical tools for
decision-making,thisbookaimstoconveytheprinciplesofinvestmentanalysisin
theelectricityindustrytostudentsandpractitionersalike.Initially,asocialplanning
viewpoint is adopted, and generation expansion, transmission expansion, and
generation plus transmission expansion problems are considered. Subsequently,
a market perspective is taken, and generation investment equilibria are analyzed.
Thisbookconsistsofsevenchaptersandfiveappendices.Chapter1providesan
introduction to both electricity transmission and electricity generation expansion
planning problems, emphasizing their long-term nature, the high degree of uncer-
tainty involved, and the market framework in which electricity is produced,
transported, distributed, and supplied in most parts of the world. Next, the chapter
vii
viii Preface
describes specific decision-making problems involving electricity transmission
and/orgenerationfacilitiesandintroducesthecomputationaltoolsneededtotackle
these problems. It concludes by stating the scope of the book.
Chapter2isdevotedtoelectricitytransmissionexpansionplanningadoptingthe
viewpointofasocialplanner.Therationaleforthisperspectiveisthattheelectricity
transmission network is undoubtedly a natural monopoly. A simple deterministic
model is first introduced to clarify the elements of this important decision-making
problem,followedbyadetailedrobustadaptivemodeltocopewithuncertaintyina
secure yet economical manner.
Chapter 3 considers generation expansion planning also from a social planning
viewpoint. The optimal outcome is intended to guide private investment in gen-
eration facilities. The chapter describes a number of increasingly complex models.
The first model involves a single decision point, includes no network representa-
tion, and is deterministic. Subsequently, the single decision point is substituted by
multiple decision points, the network is represented, and short-term (demand and
renewable production variability within one year) and long-term (changes in
demand and investment/operational costs across years) uncertainties are incorpo-
rated via stochastic programming.
Chapter 4 similarly adopts a social planning viewpoint and considers the joint
generation and transmission expansion planning problem. Addressing the expan-
sion of generation and transmission facilities together yields a transmission–gen-
eration coordinated solution that is optimal. The transmission component of this
solutionistobebuiltbythetransmissionoperator,whileitsgenerationcomponent
serves as a guide for private investment in generation facilities. The chapter
introduces models of increasing complexity: first, a deterministic single
decision-point model, followed by a deterministic multiple decision-point model.
Uncertaintyisthenintroducedviastochasticprogramming,andfinally,riskcontrol
(on total cost variability) is incorporated into this stochastic model.
Chapter 5 considers a market viewpoint and describes the decision-making
problem of a private investor seeking to build electricity generation facilities from
which tosell its output inthemarketfor a profit.We assume that this investor has
the capability to alter market outcomes, i.e., it has market power. This requires a
complementarity or bilevel model, which generally entails high modeling and
computational complexity. A single decision-point model is first introduced, fol-
lowed by a multiple decision-point model. Short-term uncertainty pertaining to
demand levels and renewable (solar- and wind-based) production is then intro-
duced,butforsimplicity,long-termuncertainty(changeininvestment/fuelcostand
demand growth) is not considered. The chapter concludes by discussing compu-
tational techniques to tackle this type of large-scale complementarity problem.
Chapter 6 likewise takes a market viewpoint and considers a number of private
investors competing in building power plants and in selling their generated elec-
tricity in the market for a profit. We assume that these investors are able to exert
market power. The chapter describes models to identify the equilibria that are
eventually reached by these competing investors. For simplicity, short-term
uncertainty is represented, but long-term uncertainty is not. A number of solution
Preface ix
methodologies to tackle these resulting equilibrium problems are discussed.
Identifyingequilibriaisalsoofinteresttotheindustryregulatorinordertoascertain
ways to improve market design and market rules.
Chapter 7 describes the real options methodology for identifying the timing,
sizing, and technological characteristics of a specific investment project in gener-
ation or transmission facilities. The uncertainty unfolding over time is carefully
represented to enable sequential decision analysis comprising features such as
operational flexibility, modularity, and capacity choice. Risk control via utility
functions is naturally embedded in the analysis.
Appendix A reviews the fundamentals of engineering economics. Appendix B
provides an introduction to optimization under uncertainty. Appendix C reviews
complementarity analysis, including equilibrium and hierarchical (bilevel) prob-
lems. Appendix D introduces the fundamentals of risk management. Appendix E
provides an introduction to dynamic programming.
Thematerialinthisbookcanbearrangedindifferentwaystoaddresstheneeds
ofgraduateteachinginaone-semestercourse.Chapters1–4andAppendicesA,B,
and D constitute the core of a capacity expansion planning course with no market
focus. Chapters 1, 5, and 6 and Appendices A, B, C, and D include fundamental
materialforamarket-focusedcapacityexpansionplanningcourse.Chapters1and7
and Appendices A, D, and E constitute the basis for a real options course.
Thebookprovidesanappropriateblendoftheoreticalbackgroundandpractical
applications. This feature makes the book of interest to practitioners as well as to
researchersandstudentsinengineering,operationsresearch,andbusiness.Practical
applications are developed up to working algorithms (coded in the GAMS envi-
ronment) that can be readily used.
Reading this book provides a comprehensive understanding of current invest-
ment problems in electric energy systems, including the formulation of
decision-making models for both generation and transmission expansion planning,
the familiarization with efficient solution algorithms for such decision-making
models, and insights into these investment problems through the detailed analysis
of numerous illustrative examples.
This book opens the door to analyzing investment decisions in electricity gen-
eration andtransmissionfacilities using themostadvanced models available. Such
modelsareexplainedinatutorialandsimplemannerwithillustrationsprovidedby
many worked examples. Hence, the concepts and insights can be accessible to
practitioners and students.
To conclude, we would like to thank our colleagues and students for insightful
observations, pertinent corrections, and helpful comments.
December 2015 Antonio J. Conejo
Luis Baringo
S. Jalal Kazempour
Afzal S. Siddiqui
Contents
1 Investment in Generation and Transmission Facilities. . . . . . . . . . . 1
1.1 Long-Term Decision Making Under Uncertainty . . . . . . . . . . . . 1
1.2 Electricity Markets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Transmission Expansion Planning . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Generation Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5 Generation and Transmission Expansion Planning . . . . . . . . . . . 12
1.6 Investment Valuation and Timing . . . . . . . . . . . . . . . . . . . . . . 14
1.7 What We Do and What We Do Not Do. . . . . . . . . . . . . . . . . . 15
1.8 End-of-Chapter Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2 Transmission Expansion Planning . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 Deterministic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2.1 Notation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.2 MINLP Model Formulation. . . . . . . . . . . . . . . . . . . . . 26
2.2.3 Linearization of Products of Binary
and Continuous Variables. . . . . . . . . . . . . . . . . . . . . . 32
2.2.4 MILP Model Formulation. . . . . . . . . . . . . . . . . . . . . . 32
2.3 Robust Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.3.1 Adaptive Robust Optimization Formulation. . . . . . . . . . 39
2.3.2 Definition of Uncertainty Sets . . . . . . . . . . . . . . . . . . . 40
2.3.3 Feasibility of Operating Decision Variables. . . . . . . . . . 41
2.3.4 Detailed Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.3.5 Solution Procedure. . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.5 End-of-Chapter Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.6 GAMS Code. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
xi
xii Contents
3 Generation Expansion Planning. . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.2 Problem Description. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.2.1 Notation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.2.2 Aim and Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 65
3.2.3 Time Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.2.4 Operating Conditions . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.2.5 Uncertainty Characterization . . . . . . . . . . . . . . . . . . . . 68
3.2.6 Modeling of the Transmission Network . . . . . . . . . . . . 69
3.2.7 Complementarity Model. . . . . . . . . . . . . . . . . . . . . . . 69
3.3 Deterministic Single-Node Static GEP . . . . . . . . . . . . . . . . . . . 70
3.3.1 Complementarity Model. . . . . . . . . . . . . . . . . . . . . . . 71
3.3.2 Equivalent NLP Formulation. . . . . . . . . . . . . . . . . . . . 74
3.3.3 Equivalent MILP Formulation. . . . . . . . . . . . . . . . . . . 76
3.3.4 Meaning of Dual Variables λ . . . . . . . . . . . . . . . . . . . 79
o
3.4 Deterministic Single-Node Dynamic GEP. . . . . . . . . . . . . . . . . 80
3.5 Deterministic Network-Constrained Static GEP . . . . . . . . . . . . . 83
3.5.1 Complementarity Model. . . . . . . . . . . . . . . . . . . . . . . 84
3.5.2 Equivalent MILP Formulation. . . . . . . . . . . . . . . . . . . 87
3.5.3 Meaning of Dual Variables λ . . . . . . . . . . . . . . . . . . 91
no
3.6 Stochastic Single-Node GEP. . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.6.1 Static Model Formulation . . . . . . . . . . . . . . . . . . . . . . 92
3.6.2 Dynamic Model Formulation. . . . . . . . . . . . . . . . . . . . 96
3.7 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
3.8 End-of-Chapter Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
3.9 GAMS Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
4 Generation and Transmission Expansion Planning . . . . . . . . . . . . . 115
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.2 Problem Description. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
4.2.1 Notation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
4.2.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
4.2.3 Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
4.3 Deterministic Static G&TEP . . . . . . . . . . . . . . . . . . . . . . . . . . 120
4.3.1 MINLP Formulation. . . . . . . . . . . . . . . . . . . . . . . . . . 120
4.3.2 MILP Formulation. . . . . . . . . . . . . . . . . . . . . . . . . . . 124
4.4 Deterministic Dynamic G&TEP. . . . . . . . . . . . . . . . . . . . . . . . 125
4.5 Stochastic G&TEP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
4.5.1 Static Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
4.5.2 Dynamic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 137
4.6 Stochastic Dynamic Risk-Constrained G&TEP . . . . . . . . . . . . . 152
4.6.1 Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
4.7 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 161