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W.L. Oliveira (wlocepel.br) Abstract: The midterm operation planning of hydrothermal 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 stagewise 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. Keywords: Scenario Reduction, Stochastic Programming, MidTerm Operation Planning of HydroThermal Systems. Category 1: Stochastic Programming Citation: G.E.P. Box; G.M. Jenkins, Time Series Analysis, Forecasting and Control, HoldenDay, 1994, San Francisco, Third Edition. T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms. 2nd Edition. MIT Press and McGrawHill, 2001, Section 35.3, The setcovering problem, pp.10331038. J. Dupacová; N. GröweKuska; W. Römisch, Scenario reduction in stochastic programming: An approach using probability metrics, Mathematical Programming, 2003, Ser. A 95, 49351. N. GröweKuska; H. Heitsch; W. Römisch, Scenario reduction and scenario tree construction for power management problems, IEEE Bologna Power Tech Proceedings, 2003, (A. Borghetti, C.A. Nucci, M. Paolone eds.), IEEE, pp. 24. D.N. Gujarati, Basic Econometrics, McGrawHill, 2000, 3th ed. H. Heitsch, W. Römisch, Scenarios Reduction Algorithms in Stochastic Programming, Computational optimization an Applications, 2003, 187206. H. Heitsch, W. Römisch, C. Strugarek, Stability of Multistage Stochastic Programs, SIAM J.OPTIM, 2006, 551525. H. Heitsch, W. Römisch, Scenario Tree for Multistage Stochastic Programs, Comput. Manag. Science 6 (2009), 117133. M.E.P. Maceira; C.V. Bezerra, Stochastic Streamflow model for Hydroelectric Systems In: Proceedings of 5th International Conference on Probabilistic Methods Applied to Power Systems, 1997, Vancouver, Canada, Sep., pp. 305310. M.E.P. Maceira; L.A. Terry; J.M. Damazio; F.S. Costa; A.C.G. Melo, Chain of Models for Setting the Energy Dispatch and Spot Price in the Brazilian System, Power System Computation Conference  PSCC'02, 2002, Sevilla, Spain, June 2428. F.J. Massey, The KolmogorovSmirnov Test for Goodness of Fit, Journal of the American Statistical Association, 46 (March 1956), pp 6877. S. Seigel, Nonparametric statistics for the behavioral sciences, New York. McGrawHill Book Company, 1956. Download: [PDF] Entry Submitted: 11/21/2008 Modify/Update this entry  
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