000085612 001__ 85612
000085612 005__ 20190316233734.0
000085612 02470 $$2ISI$$a000247080004161
000085612 037__ $$aREP_WORK
000085612 245__ $$aBenchmarking of hydroelectric stochastic risk management models using financial indicators
000085612 269__ $$a2005
000085612 260__ $$c2005
000085612 336__ $$aWorking Papers
000085612 500__ $$aArticle à publier dans IEEE PES 2006 General Meeting Proceedings et présenté à l'IEEE Power Engineering Society General Meeting, Montréal, June 18-22 2006. CDM Working Papers Series
000085612 520__ $$aThe objective of this paper is to present the operating and hedging analysis of a hydroelectric system in a non-hydro dominated market using a specifically-developed tool for operating and contracting decisions. Hydropower companies are likely to face stochastic inflows, spot prices, and forward prices, during their operation. The objective of the tool is to maximize expected revenues from spot and forward market trading, considering suitable indicators of the company risk aversion. We benchmark the implemented risk indicator of required Minimum Revenues in the optimization tool using financial risk indicators, such as Value at Risk, Conditional Value at Risk, and the Risk Premium of a Utility function. This portfolio management problem, which includes physical and financial assets, is formulated as a stochastic revenue maximization problem under a specified risk aversion constraint. The company risk aversion is apprehended by penalizing reservoir operation and derivative instruments contracting decisions policies that lead to financial performances that are violating the required Minimum Revenues at the end of a predefined profit period. A hybrid Stochastic Dynamic Programming (SDP) / Stochastic Dual Dynamic Programming (SDDP) formulation is adopted to solve this large-scale optimization problem.
000085612 6531_ $$aCvaR
000085612 6531_ $$adynamic programming
000085612 6531_ $$aelectricity financial risk indicators
000085612 6531_ $$ahydropower
000085612 6531_ $$aportfolio management
000085612 6531_ $$astochastic optimization
000085612 6531_ $$aUtility function and VaR
000085612 700__ $$aIliadis, Niko A.
000085612 700__ $$aPereira, Mario V. F.
000085612 700__ $$aGranville, Sergio
000085612 700__ $$0241371$$aFinger, Matthias$$g141278
000085612 700__ $$aHaldi, Pierre-André
000085612 700__ $$aBarroso, L.-A.
000085612 8564_ $$s249078$$uhttps://infoscience.epfl.ch/record/85612/files/v7%20pes_2006_EPFL.pdf$$zn/a
000085612 909C0 $$0252203$$pMIR$$xU12065
000085612 909CO $$ooai:infoscience.tind.io:85612$$pworking$$pCDM$$qGLOBAL_SET
000085612 937__ $$aMIR-WORKING-2006-003
000085612 937__ $$aMIR-REPORT-2006-003
000085612 973__ $$aEPFL$$sPUBLISHED
000085612 980__ $$aWORKING