Prof. H.-J. Lüthi WS Budapest , 1 Hedging strategy and operational flexibility in the electricity market Characteristics of the electricity market Non-storability Transmission constraints Very complex contracts Physical production

Prof. H.-J. Lüthi WS Budapest , 2 European Energy Exchange Profit in 2002 (for FPD) : 5'127 €/MWh Profit in 2003 (for FPD) : -15'434 €/MWh

Prof. H.-J. Lüthi WS Budapest , 3 Introduction Focus of the Study Risk management in the electricity market Interaction between physical production and contracts Operational flexibility as hedging tool

Prof. H.-J. Lüthi WS Budapest , 4 Hydro plant and Options xsxs EsEs IsIs LsLs In each period we have the option to produce Payoff Electricity price K = marginal cost of production K

Prof. H.-J. Lüthi WS Budapest , 5 If we produce today the possibility to produce tomorrow will be affected In each period we have the option to produce if E s > 0 Time (hourly buckets) Max capacity, 500MW Min capacity, -50MW A series of interdependent options Storage almost empty High spot prices Low spot prices

Prof. H.-J. Lüthi WS Budapest , 6 Portfolio optimization Production portfolio Engineering thinking Marginal costs Fixed costs Flexibility Availability Contract engineering & Portfolio optimization Optimal dispatch strategy Interaction Inflow (I) Fuel prices Demand (D) Spot price (S) Contract portfolio Financial thinking Exercise flexibility Interruptability Strike Volume uncertainty Optimal contract portfolio Portfolio optimization

Prof. H.-J. Lüthi WS Budapest , 7 Maximize expected profit –Given risk constraint (measured as CVaR) Large problems can be handled if X is a polyhedral set –“static model” –Besides production decisions (pump or produce) we model the amount of futures positions to be hold given the written bilateral contracts Optimal (static) portfolio

Prof. H.-J. Lüthi WS Budapest , 8 Case study portfolio Long positionsShort positions Hydro plantsSwing options Future contract Spot contracts

Prof. H.-J. Lüthi WS Budapest , 9 Modeling the Stochastics Jumps Mean reversion Risk measure? Yearly seasonality Daily variations Modeling the Stochastics?

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Prof. H.-J. Lüthi WS Budapest , 11 Portfolio optimization Spot price Inflow Demand Scenarios  j

Prof. H.-J. Lüthi WS Budapest , 12 Notations in Period s xsxs EsEs IsIs LsLs x s Production / Pumping I s Inflow E s Waterlevel L s Spill-over

Prof. H.-J. Lüthi WS Budapest , 13 Modeling of hydro plant Don’t produce when storage empty Don’t pump when storage full Leave water for future production Technical constraint Note: E, I, and L are stochastic variables !!!

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Prof. H.-J. Lüthi WS Budapest , 15 Dynamic Dispatch Dispatch responds to observations of uncertainties –Spot-price S –Aggregated Inflow up to time t: I –Demand Corresponds to an exercise-frontier in American options

Prof. H.-J. Lüthi WS Budapest , 16 Modeling exercise conditions Let the decision variable determine exercise conditions instead of the actual dispatch in each period The dispatch is allowed to react to new information Decision variables Exercise condition

Prof. H.-J. Lüthi WS Budapest , 17 Pure profit maximization  dispatch is a step function Risk averse case  convex combination of step functions The step functions and are given exogenously and the weighting factors and are decision variables Can optimize the complex hydro storage plant with LP Hydro dispatch strategy

Prof. H.-J. Lüthi WS Budapest , 18 Portfolio optimization & hedging strategy Dispatch strategy Tight risk constraint (low C) No risk constraint (high C)

Prof. H.-J. Lüthi WS Budapest , 19 Hedging strategy Uncertain demand is risky Cannot hedge with standardized contracts Operational flexibility to hedge against volume risk What is the operational flexibility worth?

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Prof. H.-J. Lüthi WS Budapest , 21 Additional Flexibility Slide 1

Prof. H.-J. Lüthi WS Budapest , 22 Additional Flexibility Expected Profit: Constant Slide 2 Volume Risk 24,0 Mio 24, 2 Mio

Prof. H.-J. Lüthi WS Budapest , 23 Guidance on how to dispatch hydro storage plants under risk / return considerations. Not just identify but actually quantify operational flexibility with regard to handle uncertainty. Perceive uncertainty as a challenge to flexibility instead of a threat. Identified an important value driver in hydro storage plants (and flexible plants in general). Achievements