Abstract
The medium term hydropower scheduling (MTHS) problem involves an attempt to determine, for each time stage of the planning period, the amount of generation at each hydro plant which will maximize the expected future benefits throughout the planning period, while respecting plant operational constraints. Besides, it is important to emphasize that this decision-making has been done based mainly on inflow earliness knowledge. To perform the forecast of a determinate basin, it is possible to use some intelligent computational approaches. In this paper one considers the Dynamic Programming (DP) with the inflows given by their average values, thus turning the problem into a deterministic one which the solution can be obtained by deterministic DP (DDP). The performance of the DDP technique in the MTHS problem was assessed by simulation using the ensemble prediction models. Features and sensitivities of these models are discussed.
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References
Pereira, M.V.F.: Optimal Scheduling of Hydrothermal System – An Overview. In: IFAC Symposium on Planning and Operation of Electric Energy Systems, Rio de Janeiro, pp. 1–9 (1985)
Araripe Neto, T.A., Cotia, C.B., Pereira, M.V.F., Kelman, J.: Comparison of Stochastic and Deterministic Approches in Hydrothermal Generation Scheduling. In: IFAC Symposium on Planning and Operaion of Electric Energy Systems, Rio de Janeiro, Brazil (1985)
Arvanitidis, N.V., Rosing, J.: Composite representation of a multireservoir hydroelectric power system. IEEE Transactions on Power Apparatus and Systems PAS-89, 319–326 (1970)
Dagli, C.H., Miles, J.F.: Determining Operating Policies for a Water Resources System. Journal of Hydrology 47, 297–306 (1980)
Bellman, R.E.: Dynamic Programming. Princeton University Press, Princeton (1957)
Bertsekas, D.P.: Dynamic Programming: Deterministic and Stochastic Models. Academic Press, London (1987)
Stedinger, J.R., Sule, B.F., Loucks, D.P.: Stochastic Dynamic Programming Models for Reservoir Operation Optimization. Water Resources Research 20(11), 1499–1505 (1984)
Martinez, L., Soares, S.: Comparison between Closed-Loop and Partial Open-Loop Feedback Control Policies in Long Term Hydrothermal Scheduling. IEEE Transactions on Power Systems 17(2) (2002)
Perrone, M.P.: Improving regression estimates: Averaging methods for variance reduction with extensions to general convex measure optimization. PhD Thesis, Brown University (1993)
Reid, D.J.: Combining three estimates of gross domestic product. Economics 35, 431–444 (1968)
Bates, J.M., Granger, G.W.J.: The combination of forecasts. Operations Research Quaterly 20, 451–468 (1969)
Kang, B.H.: Unstable weights in the combination of forecasts. Management Science 32, 683–695 (1986)
Baxt, W.G.: Improving the accuracy of an artificial neural network using multiple differently trained networks. Neural Computation 4(5), 135–144 (1992)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Hansen, L., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)
Sharkey, A. (ed.): Combining artificial neural nets: Ensemble and modular multi-net systems. Springer, London (1999)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Koza, J.R.: Survey of genetic algorithms and genetic programming. In: Proceedings of the Wescon 95 - Conference Record: Microelectronics, Communications Technology, Producing Quality Products, Mobile and Portable Power, Emerging Technologies, San Francisco, CA, November 7–9. IEEE, New York (1995)
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de Siqueira, T.G., Salgado, R.M. (2011). An Intelligent Approach for Medium Term Hydropower Scheduling Using Ensemble Model. In: Wan, X. (eds) Electrical Power Systems and Computers. Lecture Notes in Electrical Engineering, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21747-0_43
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DOI: https://doi.org/10.1007/978-3-642-21747-0_43
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