An aggregate stochastic dynamic programming model of multireservoir systems
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A new method is presented in which the operating policy for a reservoir is determined by solving a stochastic dynamic programming model consisting of that reservoir and a two-dimensional representation of the rest of the system.Abstract:
We present a new method of determining an operating policy for a multireservoir system in which the operating policy for a reservoir is determined by solving a stochastic dynamic programming model consisting of that reservoir and a two-dimensional representation of the rest of the system The method is practical for systems with many reservoirs because the time required to determine an operating policy only increases quadratically with the number of reservoirs in the system and because the operating policy for a reservoir is a function of few variables We apply the method to examples of multireservoir systems with between 3 and 17 reservoirs and show that the operating policies determined are very close to optimalread more
Citations
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References
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Optimal operation of multireservoir power systems with stochastic inflows
TL;DR: This paper presents and compares two possible manipulation methods for solving the optimization of the weekly operating policy of multireservoir hydroelectric power systems and shows that the suboptimal global feedback operating policy gives better results than the optimal local feedback Operating policy.
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Numerical solution of continuous-state dynamic programs using linear and spline interpolation
Sharon A. Johnson,Christine A. Shoemaker,Ying Li,José Alberto Tejada-Guibert,Jery R. Stedinger +4 more
TL;DR: This paper demonstrates that the computational effort required to develop numerical solutions to continuous-state dynamic programs can be reduced significantly when cubic piecewise polynomial functions, rather than tensor product linear interpolants, are used to approximate the value function.
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A decomposition method for the long-term scheduling of reservoirs in series
TL;DR: In this paper, the authors present a method for determining the weekly operating policy of a power system of n reservoirs in series; the method takes into account the stochasticity of the river flows.
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Learning disaggregation technique for the operation of long‐term hydroelectric power systems
TL;DR: In this paper, a nonlinear disaggregation technique for the operation of multireservoir systems is described, where the disaggregation is done by training a neural network to give, for an aggregated storage level, the storage level of each reservoir of the system.
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Nested Benders decomposition and dynamic programming for reservoir optimisation
TL;DR: This paper presents a computational comparison of nested Benders decomposition and dynamic programming (DP) for stochastic optimisation problems arising from the optimisation of hydro-electric generation from hydraulically linked reservoirs.