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Sampling stochastic dynamic programming applied to reservoir operation
Author(s) -
Kelman Jerson,
Stedinger Jery R.,
Cooper Lisa A.,
Hsu Eric,
Yuan SunQuan
Publication year - 1990
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr026i003p00447
Subject(s) - dynamic programming , stochastic programming , sampling (signal processing) , inflow , streamflow , hydroelectricity , fork (system call) , computer science , mathematical optimization , hydrology (agriculture) , algorithm , geology , engineering , mathematics , filter (signal processing) , drainage basin , geotechnical engineering , oceanography , cartography , geography , electrical engineering , computer vision , operating system
Most models for reservoir operation optimization have employed either deterministic optimization or stochastic dynamic programming algorithms. This paper develops sampling stochastic dynamic programming (SSDP), a technique that captures the complex temporal and spatial structure of the streamflow process by using a large number of sample streamflow sequences. The best inflow forecast can be included as a hydrologic state variable to improve the reservoir operating policy. A case study using the hydroelectric system on the North Fork of the Feather River in California illustrates the SSDP approach and its performance.