Investment in Electricity Generation and Transmission: Decision Making under Uncertainty PDF
Preview Investment in Electricity Generation and Transmission: Decision Making under Uncertainty
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 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor foranyerrorsoromissionsthatmayhavebeenmade. Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAGSwitzerland 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
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