Optimal Scenario Tree Reduction for Stochastic Streamflows in Power Generation Planning Problems

The mid-term operation planning of hydro-thermal power systems needs a large number of synthetic sequences to represent accurately stochastic streamflows. These sequences are generated by a periodic autoregressive model. If the number of synthetic sequences is too big, the optimization planning problem may be too difficult to solve. To select a small set of sequences representing well enough the stochastic process, this work employs two variants of the Scenario Optimal Reduction technique. The first variant applies such technique at the last stage of a tree defined a priori for the whole planning horizon while the second variant combines a stage-wise reduction, preserving the periodic autoregressive structure, with re-sampling. Both approaches are assessed numerically on hydrological sequences generated for real configurations of the Brazilian power system.


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