Algorithms of approximate dynamic programming for hydro scheduling

Iram Parvez; Jianjian Shen

doi:10.1051/e3sconf/202014401001

All issues

Volume 144 (2020)

E3S Web Conf., 144 (2020) 01001

References

Open Access

Issue		E3S Web Conf. Volume 144, 2020 The 2^nd International Symposium on Water Resource and Environmental Management (WREM 2019)


Article Number		01001
Number of page(s)		6
DOI		https://doi.org/10.1051/e3sconf/202014401001
Published online		27 January 2020

Séguin, S., P. Côté, and C. Audet, Self-scheduling short-term unit commitment and loading problem. IEEE Transactions on Power Systems, 2015. 31(1: p. 133-142. [CrossRef] [Google Scholar]
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[1] Séguin, S., P. Côté, and C. Audet, Self-scheduling short-term unit commitment and loading problem. IEEE Transactions on Power Systems, 2015. 31(1: p. 133-142. [CrossRef] [Google Scholar]

[2] Zheng, Q.P., J. Wang, and A.L. Liu, Stochastic optimization for unit commitment—A review. IEEE Transactions on Power Systems, 2014. 30(4: p. 1913-1924. [CrossRef] [Google Scholar]

[3] Sutton, R.S., et al. Fast gradient-descent methods for temporal-difference learning with linear function approximation. in Proceedings of the 26th Annual International Conference on Machine Learning. 2009. ACM. [Google Scholar]

[4] Powell, W.B., Approximate Dynamic Programming: Solving the curses of dimensionality. Vol. 703. 2007: John Wiley & Sons. [CrossRef] [Google Scholar]

[5] Buşoniu, L., et al. Online least-squares policy iteration for reinforcement learning control. in Proceedings of the 2010 American Control Conference. 2010. IEEE. [Google Scholar]

[6] Lazaric, A., M. Ghavamzadeh, and R. Munos, Finite-sample analysis of least-squares policy iteration. Journal of Machine Learning Research, 2012. 13(Oct): p. 3041-3074. [Google Scholar]

[7] Bertsekas, D.P. and J.N. Tsitsiklis, Neuro-dynamic programming. Vol. 5. 1996: Athena Scientific Belmont, MA. Lagoudakis, M.G. and R. Parr, Least-squares policy iteration. Journal of machine learning research, 2003. 4(Dec): p. 1107-1149. [Google Scholar]

[8] Koller, D. and R. Parr. Policy iteration for factored MDPs. in Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence. 2000. Morgan Kaufmann Publishers Inc. [Google Scholar]

[9] Wang, S., et al., Short-term generation scheduling with transmission and environmental constraints using an augmented Lagrangian relaxation. IEEE Transactions on Power Systems, 1995. 10(3: p. 1294-1301. [CrossRef] [Google Scholar]

[10] Frangioni, A., C. Gentile, and F. Lacalandra, Sequential Lagrangian-MILP approaches for unit commitment problems. International Journal of Electrical Power & Energy Systems, 2011. 33(3: p. 585-593. [CrossRef] [Google Scholar]

[11] Virmani, S., et al., Implementation of a Lagrangian relaxation based unit commitment problem. IEEE Transactions on Power Systems, 1989. 4(4: p. 1373-1380. [Google Scholar]