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Dispatch of direct load control using dynamic programming
Abstract
An approach based on dynamic programming is presented for the dispatch of direct load control (DLC). The objective is to coordinate DLC strategies with system unit commitment such that the system production cost is minimized. To achieve this goal, the DLC dispatch is first integrated into the unit commitment problem. An optimization technique based on dynamic programming is then developed to reach the optimal DLC dispatch strategy and system generation schedule. To demonstrate the effectiveness of the approach, results from a sample study performed on the Taiwan power system are described.>
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Cited by
Demand side management in smart grid using heuristic optimization
Logenthiran, T., Srinivasan, D., & Shun, T. Z. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2195686 · Cited by 1,229
Callaway, D. S. (2009) · Energy Conversion and Management · doi:10.1016/j.enconman.2008.12.012 · Cited by 759
Demand response in electricity markets: A review
Zhang, Q., & Li, J. (2012) · 2012 9th International Conference on the European Energy Market · doi:10.1109/eem.2012.6254817 · Cited by 84
Metadata
- Authors
- Hsu, Y.-Y., & Su, C.-C.
- Venue
- IEEE Transactions on Power Systems
- Published
- 1991
- Volume
- 6
- Issue
- 3
- Pages
- 1056-1061
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2164889281
- Cited by
- 197
- References
- 0
- FWCI
- 0.92
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Dispatch of direct load control using dynamic programming. https://app.cochat.ai/papers/10.1109/59.119246
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- Hsu, Y.-Y., & Su, C.-C. (1991). Dispatch of direct load control using dynamic programming. IEEE Transactions on Power Systems. https://doi.org/10.1109/59.119246
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
