Integrated risk management of hydro power scheduling and contract management

Mo, Birger; Gjelsvik, Anders; Grundt, A.

doi:10.1109/59.918289

Cited by 103 publications

(64 citation statements)

References 2 publications

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“…During the course of the algorithm, when cuts are added, we record a set of cuts for each state in the Markov chain instead of just one for the stage. A similar approach has been described by [14] in the context of modelling uncertainty in the objective function (e.g. from electricity prices) as well as in the constraint right-hand sides.…”

Section: Markov Process In the Sddp Algorithmmentioning

confidence: 99%

Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion

Philpott

Matos

2012

European Journal of Operational Research

176

115

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We consider the incorporation of a time-consistent coherent risk measure into a multi-stage stochastic programming model, so that the model can be solved using a SDDP-type algorithm. We describe the implementation of this algorithm, and study the solutions it gives for an application of hydro-thermal scheduling in the New Zealand electricity system. The performance of policies using this risk measure at di¤erent levels of risk aversion is compared with the risk-neutral policy.

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Section: Markov Process In the Sddp Algorithmmentioning

confidence: 99%

Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion

Philpott

Matos

2012

European Journal of Operational Research

176

115

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“…Another explanation is that the objective function used in the Driva Kraftverk's production planning is such that the optimal production strategy is not a bang-bang strategy. The current industry practice seems to be towards using risk aversive objectives which have different target levels for earnings and where different target levels are progressively penalized (see, e.g., Fleten et al 2002 andMo et al 2001). A nonlinear power production as a function of the water level (η depending on the water level) also gives production strategies which are not bang-bang.…”

Section: Earnings and The Realized Productionmentioning

confidence: 99%

“…Many times hydropower production models have also a "legacy" term in the objective function giving the value of the reservoir at the end of the planning period as a function of water level ( ) x τ (see, e.g., Fleten et al 2002 andMo et al 2001). Due to a technical reason that will be clarified after Lemma 1 we omit this term in our model.…”

Section: U T I X T T U T X X T T U T X Tmentioning

confidence: 99%

“…In their models the price is deterministic and the demand is stochastic. Stochastic programming for deregulated electricity markets is studied, for example, in Gjelsvik and Wallace (1996), Fosso et al (1999), Mo et al (2001), and Fleten et al (2002). A good survey of the stochastic programming models, both in regulated and deregulated electricity markets, is given in Wallace and Fleten (2002).…”

Section: Introductionmentioning

confidence: 99%

“…The information from the financial markets is important because the value of the production cash flows can be at least partly represented in terms of the electricity derivatives. Mo et al (2001) and Fleten et al (2002) study hydropower production planning when the possibility to trade forwards and options is included. In these studies the expected spot price process is first adjusted so that it is consistent with the forward curve.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hydropower with Financial Information*

Näsäkkälä

Keppo

2008

Applied Mathematical Finance

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We consider a single utility company's long-and medium-term hydropower planning. The uncertainties are from the electricity forward curve and a random inflow. We give a simple and intuitive parameterization for the optimal production strategy. The accuracy of the parameterization is analyzed by comparing its expected cash flows to the corresponding upper bound.In our testing case we compare our method with the realized production strategy of a Norwegian hydropower producer during winters 1997-2003. During these years the parameterization gives earnings that are within 2.6% from the theoretical upper bound.Further, the results illustrate that a significant part of the actual production strategy can be explained with our method. This suggests that the information from electricity derivative markets is used in the production optimization.

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Natural gas price uncertainty and the cost‐effectiveness of hedging against low hydropower revenues caused by drought

Kern

Characklis

Foster

2015

Water Resources Research

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Prolonged periods of low reservoir inflows (droughts) significantly reduce a hydropower producer's ability to generate both electricity and revenues. Given the capital intensive nature of the electric power industry, this can impact hydropower producers' ability to pay down outstanding debt, leading to credit rating downgrades, higher interests rates on new debt, and ultimately, greater infrastructure costs. One potential tool for reducing the financial exposure of hydropower producers to drought is hydrologic index insurance, in particular, contracts structured to payout when streamflows drop below a specified level. An ongoing challenge in developing this type of insurance, however, is minimizing contracts' ''basis risk,'' that is, the degree to which contract payouts deviate in timing and/or amount from actual damages experienced by policyholders. In this paper, we show that consideration of year-to-year changes in the value of hydropower (i.e., the cost of replacing it with an alternative energy source during droughts) is critical to reducing contract basis risk. In particular, we find that volatility in the price of natural gas, a key driver of peak electricity prices, can significantly degrade the performance of index insurance unless contracts are designed to explicitly consider natural gas prices when determining payouts. Results show that a combined index whose value is derived from both seasonal streamflows and the spot price of natural gas yields contracts that exhibit both lower basis risk and greater effectiveness in terms of reducing financial exposure.

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Integrated risk management of hydro power scheduling and contract management

Cited by 103 publications

References 2 publications

Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion

Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion

Hydropower with Financial Information*

Natural gas price uncertainty and the cost‐effectiveness of hedging against low hydropower revenues caused by drought

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