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A New Method for Unit Commitment at Electricite De France
Abstract
Unit commitment is a complex problem which, until now and for real size systems, has been solved using heuristic methods only. This paper proposes a new Lagrangian relaxation method, which produces ε-optimal solutions. The flexibility of the algorithm is such that numerous developments can be envisaged, such as simultaneous management of pumping units, probabilistic determination of the spinning reserve, etc.
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Citations
Cited by
Genetic algorithm solution to unit commitment problem
Madraswala, H. S., & Deshpande, A. S. (2016) · 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) · doi:10.1109/icpeices.2016.7853075 · Cited by 963
Factoring the elasticity of demand in electricity prices
Kirschen, D. S., Strbac, G., Cumperayot, P., & de Paiva Mendes, D. (2000) · IEEE Transactions on Power Systems · doi:10.1109/59.867149 · Cited by 616
Optimal response of a thermal unit to an electricity spot market
Arroyo, J. M., & Conejo, A. J. (2000) · IEEE Transactions on Power Systems · doi:10.1109/59.871739 · Cited by 536
Metadata
- Authors
- Merlin, A., & Sandrin, P.
- Venue
- IEEE Transactions on Power Apparatus and Systems
- Published
- 1983-05
- Volume
- PAS-102
- Issue
- 5
- Pages
- 1218-1225
- ISSN
- 0018-9510 (ISSN-L)
- OpenAlex
- W2142339771
- Cited by
- 419
- References
- 0
- FWCI
- 4.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A New Method for Unit Commitment at Electricite De France. https://app.cochat.ai/papers/10.1109/tpas.1983.318063
- The paper
- Merlin, A., & Sandrin, P. (1983). A New Method for Unit Commitment at Electricite De France. IEEE Transactions on Power Apparatus and Systems. https://doi.org/10.1109/tpas.1983.318063
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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.
