Hydro storage system optimization is becoming one of the most challengingtasks in Energy Finance. While currently the state-of-the-art of the commercialsoftware in the industry implements mainly linear models, we would like tointroduce risk aversion and a generic utility function. At the same time, weaim to develop and implement a computational efficient algorithm, which is notaffected by the curse of dimensionality and does not utilize subjectiveheuristics to prevent it. For the short term power market we propose asimultaneous solution for both dispatch and bidding problems. Following the Blomvall and Lindberg (2002) interior point model, we set up astochastic multiperiod optimization procedure by means of a "bushy" recombiningtree that provides fast computational results. Inequality constraints arepacked into the objective function by the logarithmic barrier approach and theutility function is approximated by its second order Taylor polynomial. Theoptimal solution for the original problem is obtained as a diagonal sequencewhere the first diagonal dimension is the parameter controlling the logarithmicpenalty and the second is the parameter for the Newton step in the constructionof the approximated solution. Optimal intraday electricity trading and watervalues for hydro assets as shadow prices are computed. The algorithm isimplemented in Mathematica.
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