Benchmarking of hydroelectric stochastic risk management models using financial indicators

The objective of this paper is to present the operating and hedging analysis of a hydroelectric system in a non-hydro dominated market using a specifically-developed tool for operating and contracting decisions. Hydropower companies are likely to face stochastic inflows, spot prices, and forward pri...

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Bibliographic Details
Published in2006 IEEE Power Engineering Society General Meeting p. 8 pp.
Main Authors Iliadis, N.A., Perira, V.F., Granville, S., Finger, M., Haldi, P.-A., Barroso, L.-A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Asset management
CvaR
Dynamic programming
electricity financial risk indicators
Financial management
Forward contracts
Hydroelectric power generation
hydropower
Instruments
portfolio management
Portfolios
Reservoirs
Risk management
stochastic optimization
Stochastic processes
Utility function and VaR
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Summary:The objective of this paper is to present the operating and hedging analysis of a hydroelectric system in a non-hydro dominated market using a specifically-developed tool for operating and contracting decisions. Hydropower companies are likely to face stochastic inflows, spot prices, and forward prices, during their operation. The objective of the tool is to maximize expected revenues from spot and forward market trading, considering suitable indicators of the company risk aversion. We benchmark the implemented risk indicator of required minimum revenues in the optimization tool using financial risk indicators, such as value at risk, conditional value at risk, and the risk premium of a utility function. This portfolio management problem, which includes physical and financial assets, is formulated as a stochastic revenue maximization problem under a specified risk aversion constraint. The company risk aversion is apprehended by penalizing reservoir operation and derivative instruments contracting decisions policies that lead to financial performances that are violating the required minimum revenues at the end of a predefined profit period. A hybrid stochastic dynamic programming (SDP)/stochastic dual dynamic programming (SDDP) formulation is adopted to solve this large-scale optimization problem
ISBN:1424404932
9781424404933
ISSN:1932-5517
DOI:10.1109/PES.2006.1709283