Références
- Ackooij, W., Lopez, I. D., Frangioni, A., Lacalandra, F., & Tahanan, M. (2018). Large-scale unit commitment under uncertainty: An updated literature survey. Annals of Operations Research, 271(1), 1–13. https://doi.org/10.1007/s10479-018-3003-z
- Ahmed, S. (2010). Two‐stage stochastic integer programming: A brief introduction. In J. J. Cochran, L. A. Cox, P. Keskinocak, J. P. Kharoufeh & J.C. Smith (Eds.), Wiley encyclopedia of operations research and management science. London.
- Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47–55. https://doi.org/10.1038/nature14956
- Bellier, J., Bontron, G., & Zin, I. (2017). Using meteorological analogues for reordering postprocessed precipitation ensembles in hydrological forecasting. Water Resources Research, 53(12), 10085–10107. https://doi.org/10.1002/2017WR021245
- Bellier, J., Zin, I., & Bontron, G. (2018). Generating coherent ensemble forecasts after hydrological postprocessing: Adaptation of ECC-based methods. Water Resources Research, 54(8), 5741–5762. https://doi.org/10.1029/2018WR022601
- Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust optimization. Princeton university press.
- Bompart, P., Bontron, G., Celie, S., & Haond, M. (2009). Une chaîne opérationnelle de prévision hydrométéorologique pour les besoins de la production hydroélectrique de la CNR. La Houille Blanche, 95(5), 54–60. https://doi.org/10.1051/lhb/2009056
- Chang, G. W., Aganagic, M., Waight, J. G., Medina, J., Burton, T., Reeves, S., & Christoforidis, M. (2001). Experiences with mixed integer linear programming based approaches on short-term hydro scheduling. IEEE Transactions on Power Systems, 16(4), 743–749. https://doi.org/10.1109/59.962421
- Corporation, G. D. (2020). GAMS documentation. https://www.gams.com
- Gneiting, T., Raftery, A. E., Westveld Ill, A. H., & Goldman, T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review, 133(5), 1098–1118. https://doi.org/10.1175/MWR2904.1
- Gneiting, T., Balabdoui, F., & Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society, 69B(2), 243–268. https://doi.org/10.1111/j.1467-9868.2007.00587.x
- Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477), 359–378. https://doi.org/10.1198/016214506000001437
- Growe-Kuska, N., Heitsch, H., & Romisch, W. (2003). Scenario reduction and scenario tree construction for power management problems. In IEEE Bologna Power Tech Conference Proceedings (Vol. 3). IEEE (Institute of Electrical and Electronics Engineers), pp. 7. https://doi.org/10.1109/PTC.2003.1304379
- Haneveld, W. K., Van der Vlerk, M. H., & Romeijnders, W. (2019). Stochastic programming: Modeling decision problems under uncertainty. Springer Nature.
- Heitsch, H., & Römisch, W. (2003). Scenario reduction algorithms in stochastic programming. Computational Optimization and Applications, 24(2/3), 187–206. https://doi.org/10.1023/A:1021805924152
- Hersbach, H. (2000). Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting, 15(5), 559–570. https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2
- IBM Studio, I. C. (2016). CPLEX user’s manual. https://www.ibm.com/docs/en/SSSA5P_12.8.0/ilog.odms.studio.help/pdf/usrcplex.pdf
- Kall, P., & Mayer, J. (2011). Stochastic linear programming: Models, theory, and computation. Springer.
- King, A. J., & Wallace, S. W. (2012). Modeling with stochastic programming. Springer Science & Business Media.
- Labadie, J. W. (2004). Optimal operation of multireservoir systems: State-of-the-art review. Journal of Water Resources Planning and Management, 130(2), 93–111. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:2(93)
- Matheson, J. E., & Winkler, R. L. (1976). Scoring rules for continuous probability distributions. Management Science, 22(10), 1087–1096. https://doi.org/10.1287/mnsc.22.10.1087
- Mesfin, G., & Shuhaimi, M. (2010). A probabilistic optimization approach for a gas processing plant under uncertain feed conditions and product requirements. International Journal of Chemical and Molecular Engineering, 4(2), 192–197. https://scholar.google.fr/citations?view_op=view_citation&hl=fr&user=5JswSQkAAAAJ&citation_for_view=5JswSQkAAAAJ:qjMakFHDy7sC
- Osorio, G. J., Matias, J. C., & Catalao, J. P. (2013). A review of short-term hydro scheduling tools. In 48th International Universities’ Power Engineering Conference (UPEC). IEEE (Institute of Electrical and Electronics Engineers), (pp. 1–6). https://doi.org/10.1109/UPEC.2013.6714906
- Piron, V., Bontron, G., & Pochat, M. (2015). Operating a hydropower cascade to optimize energy management. Hydropower & Dams, 22(5). Operating a hydropower cascade to optimize energy management | Hydropower & Dams International (hydropower-dams.com)
- Raftery, A. E., Gneiting, T., Balabdaoui, F., & Polakowski, M. (2005). Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 133(5), 1155–1174. https://doi.org/10.1175/MWR2906.1
- Schefzik, R., Thorarinsdottir, T. L., & Gneiting, T. (2013). Uncertainty quantification in complex simulation models using ensemble copula coupling. Statistical Sciences, 28(4), 616–640. https://doi.org/10.1214/13-STS443
- Shapiro, A., Dentcheva, D., & Ruszczyński, A. (2014). Lectures on stochastic programming: Modeling and theory. Society for Industrial and Applied Mathematics.
- Tahanan, M., van Ackooij, W., Frangioni, A., & Lacalandra, F. (2015). Large-scale unit commitment under uncertainty. 4OR: A Quarterly Journal of Operations Research, 2(13), 115–171. https://doi.org/10.1007/s10288-014-0279-y
- Wu, H., Shahidehpour, M., Li, Z., & Tian, W. (2014). Chance-constrained day-ahead scheduling in stochastic power system operation. IEEE Transactions on Power Systems, 29(4), 1583–1591. https://doi.org/10.1109/TPWRS.2013.2296438