Hostname: page-component-89b8bd64d-72crv Total loading time: 0 Render date: 2026-05-05T06:08:04.751Z Has data issue: false hasContentIssue false
  • English
  • Français

USE OF THE PAR(p) MODEL IN THE STOCHASTIC DUAL DYNAMIC PROGRAMMING OPTIMIZATION SCHEME USED IN THE OPERATION PLANNING OF THE BRAZILIAN HYDROPOWER SYSTEM

Published online by Cambridge University Press:  12 December 2005

M. E. P. Maceira  and
J. M. Damázio
M. E. P. Maceira
Affiliation:
Electric Power Research Center, CEPEL, Rio de Janeiro, Brazil, and, Rio de Janeiro State University, UERJ, Rio de Janeiro, Brazil, E-mail: elvira@cepel.br; damazio@cepel.br
J. M. Damázio
Affiliation:
Electric Power Research Center, CEPEL, Rio de Janeiro, Brazil, and, Rio de Janeiro State University, UERJ, Rio de Janeiro, Brazil, E-mail: elvira@cepel.br; damazio@cepel.br
Get access Rights & Permissions [Opens in a new window]

Abstract

In September 2000, the Brazilian system dispatch and spot prices were calculated twice, using different inflow forecasts for that month, as in the last 5 days of August the inflows to the reservoirs in the South and Southeast regions changed 200%. The first run used a smaller forecasted energy inflow and the second used a higher energy inflow. Contrary to expectations, the spot price in the second run, with the higher energy inflow, was higher than the one found in the first run. This paper describes the problem, presents the special features of the PAR(p) model that allow the described behavior, and shows the solution taken to avoid the problem.

Information

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 20 , Issue 1 , January 2006 , pp. 143 - 156
Copyright
© 2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Box, G.E.P. & Jenkins, G.M. (1970). Time Series Analysis: Forecasting and Control. Littleton, CO: Holden-Day.
Maceira, M.E.P. & Bezerra, C.V. (1997). Stochastic streamflow model for hydroelectric systems. In 5th Probabilistic Methods Applied to Power Systems—PMAPS-1997.
Maceira, M.E.P., Mercio, C.B., Gorenstin, B.G., Cunha, S.H.F., Suanno, C., Sacramento, M.C., & Kligerman, A.S. (1998). Energy evaluation of the North/Northeastern and South/Southeastern interconnection with NEWAVE model. In VI Symposium of Specialists in Electric Operational and Expansion Planning—SEPOPE.
Maceira, M.E.P., Terry, L.A., Damazio, J.M., Costa, F.S., & Melo, A.C.G. (2002). Chain of optimization models for setting the energy dispatch and spot price in the Brazilian system. In Power System Computation Conference—PSCC'02.
Noakes, D.J., McLeod, A.I., & Hipel, K.W. (1985). Forecasting monthly riverflow times series. International Journal of Forecasting 1: 179–190.Google Scholar
Pereira, M.V.F. (1989). Optimal stochastic operations scheduling of large hydroelectric systems. International Journal of Electric Power and Energy Systems 11(3): 161–169.Google Scholar
Pereira, M.V.F. & Pinto, L.M.V.G. (1985). Stochastic optimization of a multireservoir hydroelectric system: A decomposition approach. Water Resources Research 21(6): 779–792.Google Scholar
PSR Consultoria. (2000). Analysis of spot price projection for September and October of 2000. Technical report, Draft 2. PSR Consultoria, Rio de Janeiro.
Salas, J.D., Delleur, J.W., Yevjevich, V., & Lane, W.L. (1980). Applied Modeling of Hydroeletric Series. Water Resources Publications, San Francisco.
Thompstone, R.M., Hipel, K.W., & Mcleod, A.I. (1984). Forecasting quarter-monthly riverflow. Technical report TR-84-07, Department of Statistical & Actuarial Sciences, University of Western Ontario, London, Ontario, Canada.