Stochastic Dynamic Programming For Long Term Hydrothermal Scheduling Considering Different Streamflow Models

This paper is concerned with the performance of stochastic dynamic programming for long term hydrothermal scheduling. Different streamflow models progressively more complex have been considered in order to identify the benefits of increasing sophistication of streamflow modeling on the performance of stochastic dynamic programming. The first and simplest model considers the inflows given by their average values; the second model represents the inflows by independent probability distribution functions; and the third model adopts a Markov chain based on a lag-one periodical auto-regressive model. The effects of using different probability distribution functions have been also addressed. Numerical results for a hydrothermal test system composed by a single hydro plant have been obtained by simulation with Brazilian inflow records. © Copyright KTH 2006.Pereira, M.V.F., Optimal Scheduling of Hydrothermal System - An Overview (1985) IFAC Symposium on Planning and Operation of Electric Energy Systems, pp. 1-9. , Rio de Janeiro, ppPereira, M.V., Pinto, L.M.V.G., Optimal Stochastic Operation Scheduling of large hydroelectric Systems (1989) International Journal of Electrical Power and Energy Systems, 11, pp. 161-169Yakowitz, S., Dynamic programming applications in water resources (1982) Water Resources Research, 18 (4), pp. 673-696Arvanitidis, N.V., Rosing, J., Composite representation of a multireservoir hydroelectric power system (1970) IEEE Transactions on Power Apparatus and Systems, PAS-89, pp. 319-326Bellman, R.E., (1957) Dynamic Programming, , Princeton University Press, Princeton, NJBertsekas, D.P., (1976) Dynamic Programming and Stochastic Control, , Academic PressBertsekas, D.P., (1987) Dynamic Programming: Deterministic and Stochastic Models, , Academic PressBox, G., Cox, D., An analysis of transformations (1964) J. R. Statistic Society, Serie B, 26, pp. 211-252Box, G.E.P., Jenkins, G.M., Reinsel, G.C., (1994) Time Series Analysis, Forecasting and Control, , 3rd ed. Prentice Hall, Englewood Cliffs, NJPapoulis, A., (1921) Probability, random variables, and stochastic processes, , 4th edition, McGray-Hill, New York, NYStedinger, J.R., Sule, B.F., Loucks, D.P., Stochastic Dynamic Programming Models for Reservoir Operation Optimization (1984) Water Resources Research, 20 (11), pp. 1499-1505Tehada-Guibert, J.A., Johnson, S.A., Stedinger, J.R., The value of hydrologic information in Stochastic dynamic programming models of a multi-reservoir system (1995) Water Resources Research, 31 (10), pp. 2571-2579Ogwang, T., Gouranga Rao, U.L., A simple algorithm for estimating Box-Cox models (1970) The Statistician, 46 (3), pp. 399-40