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Stochastic inflow modeling for hydropower scheduling problems
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DOI：10.1016/j.ejor.2015.05.022.png)
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We introduce a new stochastic model for inflow time series that is designed with the requirements of hydropower scheduling problems in mind. The model is an iterated function system: it models inflow as continuous, but the random innovation at each time step has a discrete distribution. With this inflow model, hydro-scheduling problems can be solved by the stochastic dual dynamic programming (SDDP) algorithm exactly as posed, without the additional sampling error introduced by sample average approximations. The model is fitted to univariate inflow time series by quantile regression. We consider various goodness-of-fit metrics for the new model and some alternatives to it, including performance in an actual hydro-scheduling problem. The numerical data used are for inflows to New Zealand hydropower reservoirs. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
Keyword:
OR in energy
Hydro-thermal scheduling
Stochastic dual dynamic programming
Time series
Quantile regression
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6
论文数:
2.2W
被引数:
6.4W
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