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 prob...

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Bibliographic Details
Published in:Optimization methods & software 2010-12, Vol.25 (6), p.917-936
Main Authors: de Oliveira, Welington Luis, Sagastizábal, Claudia, Penna, Débora Dias Jardim, Maceira, Maria Elvira Piñeiro, Damázio, Jorge Machado
Format: Article
Language:English
Subjects:
operation planning of hydro-thermal systems
scenario reduction
Stochastic models
stochastic programming
Studies
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Summary: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 the stochastic process well enough, this work employs two variants of the Scenario Optimal Reduction technique. The first variant applies such a 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 resampling. Both approaches are assessed numerically on hydrological sequences generated for real configurations of the Brazilian power system.
ISSN:1055-6788
1029-4937
DOI:10.1080/10556780903420135