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Stochastic dynamic programming for reservoir optimal control: Dense discretization and inflow correlation assumption made possible by parallel computing
Author(s) -
Piccardi Carlo,
SonciniSessa Rodolfo
Publication year - 1991
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 - 10.1029/90wr02766
Subject(s) - inflow , discretization , computer science , dynamic programming , mathematical optimization , stochastic programming , state variable , control variable , exploit , state (computer science) , algorithm , mathematics , mathematical analysis , physics , machine learning , mechanics , thermodynamics , computer security
The solution via dynamic programming (DP) of a reservoir optimal control problem is often computationally prohibitive when the proper description of the inflow process leads to a system model having several state variables and/or when a sufficiently dense state discretization is required to achieve numerical accuracy. Thus, to simplify, the inflow correlation is usually neglected and/or a coarse state discretization is adopted. However, these simplifications may significantly affect the reliability of the solution of the optimization problem. Nowadays, the availability of very powerful computers based on innovative architectures (vector and parallel machines), even in the domain of personal computers (transputer architectures), stimulates the reformulation of the standard dynamic programming algorithm in a form able to exploit these new machine architectures. The reformulated DP algorithm and new machines enable faster and less costly solution of optimization problems involving a system model having two state variables (storage and previous period inflow, then taking into account the inflow correlation) and a number of states (of the order of 10 4 ) such as to guarantee a high numerical accuracy.