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The Value of Hydrologic Information in Stochastic Dynamic Programming Models of a Multireservoir System
Author(s) -
TejadaGuibert J. Alberto,
Johnson Sharon A.,
Stedinger Jery R.
Publication year - 1995
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 - 10.1029/95wr02172
Subject(s) - maximization , hydrological modelling , state variable , economic shortage , function (biology) , computer science , variable (mathematics) , state (computer science) , value (mathematics) , stochastic programming , dynamic programming , mathematical optimization , utility maximization , bellman equation , operations research , econometrics , mathematics , geology , mathematical economics , biology , mathematical analysis , linguistics , philosophy , physics , algorithm , climatology , evolutionary biology , machine learning , government (linguistics) , thermodynamics
Reservoir operating policies can be derived using stochastic dynamic programming (SDP) with different hydrologic state variables. This paper considers several choices for such hydrologic state variables for SDP models of the Shasta‐Trinity system in northern California, for three different benefit functions. We compare how well SDP models predict their policies will perform, as well as how well these policies performed when simulated. For a benefit function stressing energy maximization, all policies did nearly as well, and the choice of the hydrologic state variable mattered very little. For a benefit function with larger water and firm power targets and severe penalties on corresponding shortages, predicted performance significantly overestimated simulated performance, and policies that employed more complete hydrologic information performed significantly better.