Approximate dynamic programming for storage problems
Publication
, Journal Article
Hannah, LA; Dunson, DB
Published in: Proceedings of the 28th International Conference on Machine Learning Icml 2011
October 7, 2011
Storage problems are an important subclass of stochastic control problems. This paper presents a new method, approximate dynamic programming for storage, to solve storage problems with continuous, convex decision sets. Unlike other solution procedures, ADPS allows math programming to be used to make decisions each time period, even in the presence of large state variables. We test ADPS on the day ahead wind commitment problem with storage. Copyright 2011 by the author(s)/owner(s).
Duke Scholars
Published In
Proceedings of the 28th International Conference on Machine Learning Icml 2011
Publication Date
October 7, 2011
Start / End Page
337 / 344
Citation
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Hannah, L. A., & Dunson, D. B. (2011). Approximate dynamic programming for storage problems. Proceedings of the 28th International Conference on Machine Learning Icml 2011, 337–344.
Hannah, L. A., and D. B. Dunson. “Approximate dynamic programming for storage problems.” Proceedings of the 28th International Conference on Machine Learning Icml 2011, October 7, 2011, 337–44.
Hannah LA, Dunson DB. Approximate dynamic programming for storage problems. Proceedings of the 28th International Conference on Machine Learning Icml 2011. 2011 Oct 7;337–44.
Hannah, L. A., and D. B. Dunson. “Approximate dynamic programming for storage problems.” Proceedings of the 28th International Conference on Machine Learning Icml 2011, Oct. 2011, pp. 337–44.
Hannah LA, Dunson DB. Approximate dynamic programming for storage problems. Proceedings of the 28th International Conference on Machine Learning Icml 2011. 2011 Oct 7;337–344.
Published In
Proceedings of the 28th International Conference on Machine Learning Icml 2011
Publication Date
October 7, 2011
Start / End Page
337 / 344