Approximate stochastic dynamic programming for hydroelectric production planning
•A novel approximate stochastic dynamic programming approach is presented.•The state space is partitioned into simplices.•The value function is evaluated over the vertices of the simplices.•Bounds on the true value function are used to refine the partition.•The methodology is experimented with simul...
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| Published in: | European journal of operational research Vol. 262; no. 2; pp. 586 - 601 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
16.10.2017
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| Subjects: | |
| ISSN: | 0377-2217, 1872-6860 |
| Online Access: | Get full text |
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| Summary: | •A novel approximate stochastic dynamic programming approach is presented.•The state space is partitioned into simplices.•The value function is evaluated over the vertices of the simplices.•Bounds on the true value function are used to refine the partition.•The methodology is experimented with simulated data and on a real hydropower system.
This paper presents a novel approach for approximate stochastic dynamic programming (ASDP) over a continuous state space when the optimization phase has a near-convex structure. The approach entails a simplicial partitioning of the state space. Bounds on the true value function are used to refine the partition. We also provide analytic formulae for the computation of the expectation of the value function in the “uni-basin” case where natural inflows are strongly correlated. The approach is experimented on several configurations of hydro-energy systems. It is also tested against actual industrial data. |
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| ISSN: | 0377-2217 1872-6860 |
| DOI: | 10.1016/j.ejor.2017.03.050 |