CHARACTERISTICS OF BASIN INFLOWS A STATISTICAL ANALYSIS FOR LONG-TERM/MID-TERM HYDROTHERMAL SCHEDULING

Abstract

The presented paper focuses on the characteristics of reservoir inflows and the appropriate inflow model for long-term/mid-term hydrothermal scheduling. The goal was to find the type of distribution that best fits the observed series of monthly and weekly average inflows in most cases for a model which considers the inflows as independent random variables without time correlation. Also, the objective was to explore the correlation between the inflows during time periods (for weekly and monthly intervals, respectively), and to investigate whether the more complex model of reservoir inflow as a dependent random variable is advisable for optimal long-term/mid-term hydrothermal scheduling. Differences in the characteristics of monthly and weekly inflows, which have been noticed during the analysis, are discussed. Numerical results are presented.

Dates

  • Submission Date2013-11-28
  • Revision Date2014-03-29
  • Acceptance Date2014-05-05
  • Online Date2014-09-06

DOI Reference

10.2298/TSCI1403799S

References

  1. Yu, Z., A New Long-Term Hydro Production Scheduling Method for Maximizing the Profit of Hydroelectric Systems, IEEE Transactions on Power Systems, 13 (1998), 1, pp. 66-71
  2. Ikura, Y., Gross, G., Efficient Large-Scale Hydro System Scheduling with Forced Spill Conditions, IEEE Transactions on Power Apparatus and Systems, 103 (1984), 12, pp. 3502-3520
  3. Ngundam, J. M., et al., Optimal Scheduling of Large-Scale Hydrothermal Power Systems Using the Lagrangian Relaxation Technique, Electrical Power and Energy Systems, 22 (2000), 4, pp. 237-245
  4. Ferrero, R. W., et al., A Dynamic Programming Two-Stage Algorithm for Long-Term Hydrothermal Scheduling of Multireservoir Systems, IEEE Transactions on Power Systems, 3 (1998), 4, pp. 1534- 1540
  5. Sutlovic, E., et al., A Method for the Long-Term Scheduling of Hydrothermal Power System with Multiple User Reservoirs, Thermal Science, 11 (2007), 3, pp. 75-83
  6. Siqueira, T. G., et al., Stochastic Dynamic Programming for Long-term Hydrothermal Scheduling Considering Different Streamflow Models, Proceedings, 9th International Conference on Probabilistic Methods Applied to Power Systems, Stockholm, Sweden, 2006
  7. Pereira, M., et al., Long-Term Hydro Scheduling Based on Stochastic Models, Proceedings, International Conference on Electrical Power Systems Operation and Management, Zurich, Switzerland, 1998
  8. Gjelsvik, A., et al., Long- and Medium-Term Operations Planning and Stochastic Modelling in Hydro- Dominated Power Systems Based on Stochastic Dual Dynamic Programming, in: Handbook of Power Systems I (Ed. P. Pardalos et al.), Energy Systems, Springer, 2010, pp. 33-55
  9. Loucks, D. P., et al., Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications, UNESCO, Paris, 2005
  10. Wurbs, R. A., Modeling and Analysis of Reservoir System Operations, Prentice Hall PTR, New York, USA, 1996
  11. Pavlić, I., Statistical theory and application (in Croatian), Technical book, Zagreb, Croatia, 1988
  12. ***, Engineering Statistics, NIST/SEMATECH e-Handbook of Statistical Methods, www.itl.nist.gov/div898/handbook/, 2/02/2013
  13. Yue, S., et al., A Review of Bivariate Gamma Distributions for Hydrological Application, Journal of Hydrology, 246 (2001), 1, pp. 1-18
  14. Al-Fawzan, M. A., Methods for Estimating the Parameters of the Weibull Distribution, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia, 2000
  15. Yue, S., The Bivariate Lognormal Distribution to Model a Multivariate Flood Episode, Hydrological Processes, 14 (2000), 14, pp. 2575-2588
  16. Thomopoulos, N. T., Johnson, A. C., Some Measures on the Standard Bivariate Lognormal Distribution, Proceedings, Decision Sciences Institute Annual Meeting, Boston, Mass., USA, 2004
  17. McMahon, T. A, Diaz Arenas, A., Methods of Computation of Low Streamflow - a Contribution to the International Hydrological Programme, UNESCO Report, 1982
  18. Žugaj, R., Hydrology (in Croatian), University of Zagreb, Zagreb, Croatia, 2000
  19. Bonacci, O., Karst Hydrology with Special Reference to the Dinaric Karst, Springer-Verlag, Berlin, Germany, 1987
  20. Wiche, J. G., Vecchia, V. A., Lake-Lavel Frequency Analysis for Devils Lake, N. D., USA, Geological Survey Water-Supply paper; 2469, US Department of the Interior, Washington D. C., USA, 1996
  21. Gupta, V. L., Fordham, J. W., Multisite Streamflow Simulation of Truckee River, Nevada, Proceedings, Symposium on statistical hydrology, Tuscon, Ariz., USA, 1971, pp. 26-46
  22. Wang, Y., Guo, S., Chen, H., Comparative Study of Monthly Inflow Prediction Methods for the Three Gorges Reservoir, Stochastic Environmental Research and Risk Assessment, 28 (2014), 3, pp. 555-570
  23. Luna, I., et al., Fuzzy Inference Systems for Synthetic Monthly Inflow Time Series Generation, EUSFLAT-LFA 2011, Proceedings, The 7th Conference of the European Society for Fuzzy Logic and Technology, Aix-Les-Bains, France, 2011, pp. 1060-1065
  24. McMahon, T. A., et al., Global Streamflows - Part 1: Characteristics of Annual Streamflows, Journal of Hydrology, 347 (2007), 3-4, pp. 243-259
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