Hedging Strategy and Electricity Contract Engineering - IFOR

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120 Power portfolio optimization Inflow Portfolio optimization Demand Production portfolio Contract portfolio Availability Marginal costs Strike Exercise flexibility Fixed costs Flexibility Interaction Volume flexibility Interruptability Contract engineering & Portfolio optimization Optimal dispatch strategy Engineering thinking Portfolio optimization Optimal contract portfolio Financial thinking Fuel prices Spot prices Fig. 6.1: Schematic figure of portfolio optimization. max E lÛ x Ü Y Ý xÙmÚ n xÝ s.t. ÞOßàÛ C. (6.1) We will in the coming chapters show that (6.1) actually can capture also the other aspects, in terms of handling the production side and simultaneously optimize the contract and production portfolio, and in terms of managing the complex electricity contracts. Until just a few years ago linear programming was thought to be unable to cope with uncertainty in power optimization. The quote from Ku [Ku95] illustrates this: . . . it [LP] is unable to deal with uncertainty without relying on numerous assumptions, approximations and post-LP analysis We will however later in this chapter describe how linear programming in an efficient way can be used to capture the risk aspect in power portfolio optimization. In Figure 6.1 a power portfolio optimization is illustrated, where the interaction between the production portfolio and the contract portfolio is stressed. Some important characteristics of the two portfolios are highlighted; availability, marginal costs, fixed costs and flexibility for the production portfolio and strike
6.4 Modeling of plants and their operational flexibility 121 price, exercise flexibility, volume flexibility and interruptability for the contract portfolio. The marginal cost of a plant, as already mentioned, corresponds to the strike price of an option, and the availability of a plant corresponds to the interruptability of an OTC contract. Before the production portfolio is optimized we identify the contracts corresponding to each plant, which we call contract engineering. By doing this we can more easily compare the production and the contract portfolio and derive the optimal dispatch strategy for the different plant types. The power portfolio optimization problem is very tractable from an academic point of view, since the engineering skills and ways of thinking are needed to understand the production portfolio, whereas financial thinking and skills are needed for the understanding of the contract portfolio. The return of a power portfolio is affected by four major sources of uncertainty; spot price, demand in swing options creating volume risk, inflow into water dams adding production output uncertainty and fuel prices for thermal plants causing production cost uncertainty. The importance of the fuel price effect on the profit and loss should not be underestimated. For example, PowerGen [plc92] attributes almost 70% of their operating costs to fuel. Further fuel prices are volatile, in 1974 oil prices quadrupled and doubled in 1979. Derivatives of oil, like natural gas, followed a similar pattern. In general we will assume that the time, over which we study profit and risk corresponds to K periods, where the length of each period is given by the length of the spot contract, typically one hour. 6.4. Modeling of plants and their operational flexibility For some plant types it is trivial to find the dispatch strategy making full use of the operational flexibility, for other plants it is however a complex task. Before we investigate this strategy for the most challenging plant, the hydro storage plant in depth, we as an introduction shortly discuss the strategy corresponding to the other plant types.
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Diss. ETH No. 14727 Hedging Strateg
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Abstract This thesis studies risk m
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Zusammenfassung Die vorliegende Sch
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Contents Acknowledgements ¡ ¢ ¡
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Contents xi 5.1 Overview . . . . .
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List of Figures 2.1 Illustration of
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List of Figures xv 7.6 Marginal val
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List of Tables 2.1 Spot market bid.
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Chapter 1 Introduction 1.1. Motivat
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1.3 Structure of the thesis 5 hedge
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Chapter 2 The electricity market 2.
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2.1 Overview 9 Generation ¥ Distri
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2.2 Liberalization of the electrici
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duction cost Marginal pro © 2.3 Su
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2.4 Transmission costs 15 10 Peak l
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2.4 Transmission costs 17 destinati
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2.5 Demand 19 2.5. Demand Compared
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2.6 Special characteristics of elec
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2.7 Electricity contracts 23 tions.
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0& K $ 0& K K % + $ 2.7 Electricity
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2.7 Electricity contracts 27 2.7.2.
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04 04 2.7 Electricity contracts 29
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2.8 Power exchanges and pricing mec
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2.8 Power exchanges and pricing mec
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S 0 eH aIKJ 2.9 Price dynamic 35 To
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2.9 Price dynamic 37 400 2000 350 1
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2.9 Price dynamic 39 simple sinus f
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2.9 Price dynamic 41 1800 1800 1600
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2.10 Summary 43 To deal with the un
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Chapter 3 Risk management 3.1. Over
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3.3 The risks in the electricity ma
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3.3 The risks in the electricity ma
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3.4 Traditional risk management mod
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l H xg yLihkjml f 3.5 The need for
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3.5 The need for a new risk measure
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R S x GaRKV is convex and continuou
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R P 3.6 Multi period risk managemen
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“ “ “ 3.7 Valuation models 67
Page 85 and 86: 3.7 Valuation models 69 storability
Page 87 and 88: 3.7 Valuation models 71 by introduc
Page 89 and 90: “ 3.7 Valuation models 73 formula
Page 91 and 92: Chapter 4 Contract engineering 4.1.
Page 93 and 94: 4.2 Gas turbine 77 2000 1800 1600 1
Page 95 and 96: 4.2 Gas turbine 79 220 200 Capped l
Page 97 and 98: 4.3 Hydro storage plant 81 facilita
Page 99 and 100: ¢ 4.3 Hydro storage plant 83 I k L
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Page 103 and 104: 4.4 Coal plant and oil plant 87 4.4
Page 105 and 106: 4.4 Coal plant and oil plant 89 cor
Page 107 and 108: 4.5 Nuclear plant 91 of 1000 MW. Ov
Page 109 and 110: 4.7 All production plants 93 possib
Page 111 and 112: 4.8 Transmission lines 95 Unit type
Page 113 and 114: 4.9 Real option theory 97 The scien
Page 115 and 116: 4.10 Value of different production
Page 117 and 118: 4.12 Summary 101 like the capped sp
Page 119 and 120: Chapter 5 Hedging strategies 5.1. O
Page 121 and 122: È È È È È 5.2 Traditional hedg
Page 123 and 124: É È É È Ì 5.2 Traditional hedg
Page 125 and 126: 5.3 Relevance for electricity hedgi
Page 127 and 128: 5.4 Best Hedge 111 that the expecte
Page 129 and 130: 5.4 Best Hedge 113 that they alread
Page 131 and 132: Chapter 6 Power portfolio optimizat
Page 133 and 134: 6.3 Power portfolio optimization in
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Page 139 and 140: 6.4 Modeling of plants and their op
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Page 143 and 144: 6.4 Modeling of plants and their op
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Page 147 and 148: õ õ 6.5 Analysis of optimal dispa
Page 149 and 150: õ Ü r á 6.5 Analysis of optimal
Page 151 and 152: ¢ é á 6.5 Analysis of optimal di
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Page 157 and 158: õ õ õ á á 6.5 Analysis of opti
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Page 161 and 162: è á 6.6 Power portfolio optimizat
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Page 173 and 174: ö 6.6 Power portfolio optimization
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Page 181 and 182: î yú1 î é yú1 á ¢ 6.6 Power
Page 183 and 184: 6.7 Summary 167 opportunity set wou
Page 185 and 186: Chapter 7 Case study In this chapte
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171 3200 140 3000 2800 120 2600 240
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S7 D7 min 8 0 if S i or D p otherwi
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175 Production Spot price Demand [C
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7.1 Efficient frontier 177 lG x 6 9
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7.2 Spot position 179 of the old po
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7.3 Hedging value of water 181 The
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7.4 Value decomposition 183 expecte
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MWh CHF/ P 7.4 Value decomposition
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7.6 Summary 187 2500 2000 Dispatch
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7.6 Summary 189 We have quantified
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Chapter 8 Conclusions The special c
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a4q V a4q G 1 V H a4q 6 Appendix A
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8 _ 195 Proof The results follow im
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g b4i d G9:7j b4 j G 1 9j7j H b4 j
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Ax 3 V AxK1 G 1 V H AxK2 V b 1 G 1
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Bibliography [ABA98] [AD80] [ADEH97
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Bibliography 203 [BS73] [BS98] [BT0
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Bibliography 205 [GR92] H. Gravelle
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Bibliography 207 [Mer76] R. C. Mert
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Bibliography 209 [SC89] [Sch90] [Sc
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Curriculum Vitae Personal Data Name
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