SOCRATES: A system for scheduling hydroelectric generation under uncertainty (original) (raw)

References

  1. R.S. Barr, K. Farhangian and J.L. Kennington, Networks with side constraints: An LU factorization update, Ann. Soc. Log. Eng. 1(1986)66–85.
    Google Scholar
  2. E.M.L. Beale, On minimizing a convex function subject to linear inequalities, J. Roy. Statist. Soc. 17B(1955)173–184.
    Google Scholar
  3. D.P. Bertsekas,Dynamic Programming and Stochastic Control (Academic Press, New York, 1976).
    Google Scholar
  4. J.R. Birge, Decomposition and partitioning methods for multistage stochastic linear programs, Oper. Res. 33(1985)989–1007.
    Google Scholar
  5. J.R. Birge and F.V. Louveaux, A multicut algorithm for two-stage stochastic linear programs, Euro. J. Oper. Res. 34(1989)384–392.
    Article Google Scholar
  6. R.L. Bras, R. Buchanan and K.C. Curry, Real time adaptive closed loop control of reservoirs with the High Aswan Dam as a case study, Water Resources Res. 19(1983)33–52.
    Google Scholar
  7. G.B. Dantzig, Linear programming under uncertainty, Manag. Sci. 1(1955)197–206.
    Google Scholar
  8. G.B. Dantzig and P.W. Glynn, Parallel processors for planning under uncertainty, Ann. Oper. Res. 22(1990)1–21.
    Article Google Scholar
  9. D.J. Druce, Incorporating daily flood control objective into a monthly stochastic dynamic programming model for a hydroelectric complex, Water Resources Res. 26(1990)5–11.
    Article Google Scholar
  10. Y. Ermoliev and R.J-B Wets (eds.),Numerical Techniques for Stochastic Optimization (Springer, Berlin/New York, 1988).
    Google Scholar
  11. L.A.F.M. Ferreira, T. Andersson, C.F. Imparato, T.E. Miller, C.K. Pang, A. Svoboda and A.F. Vojdani, Short-term resource scheduling in multi-area hydrothermal power systems, Electr. Power Energy Syst, 11(1988)200–212.
    Article Google Scholar
  12. Pacific Gas and Electric Company, 1991 Financial and Statistical Report, San Francisco, CA (1992).
  13. H.I. Gassman, MSLiP: A computer code for the multistage stochastic linear programming problem, Math. Progr. 47(1990)407–423.
    Article Google Scholar
  14. A.P. Georgakakos, Extended linear quadratic Gaussian (ELQG) control: Further extensions, Water Resources Res. 25(1989)191–201.
    Google Scholar
  15. A.P. Georgakakos, Computer-aided management of the Savannah River System,4th Operations Management Workshop, American Society of Civil Engineers (March 1992).
  16. J.C. Grygier and J.R. Stedinger, Algorithms for optimizing hydropower system operation, Water Resources Res. 21(1985)1–10.
    Google Scholar
  17. E.R. Hooper, A.P. Georgakakos and D.P. Lettenmaier, Optimal stochastic operation of Salt River Project, Arizona, J. Water Resource Planning Manag. 117(1991)566–587.
    Google Scholar
  18. W-C. Huang, R. Harbo and J.J. Bogardi, Testing stochastic dynamic programming models conditioned on observed or forecasted inflows, J. Water Resource Planning Manag. 117(1991)28–36.
    Google Scholar
  19. Y. Ikura and G. Gross, Efficient large-scale hydro system scheduling with forced spill conditions, IEEE Trans. Power App. Syst. PAS-103(1984)3502–3520.
    Google Scholar
  20. Y. Ikura, G. Gross and G.S. Hall, PG&E's state-of-the-art scheduling tool for hydro systems, Interfaces 16(1986)65–82.
    Article Google Scholar
  21. G. Infanger, Monte Carlo (importance) sampling within a Benders decomposition algorithm for stochastic linear programs, extended version: Including results of large-scale problems, Technical Report SOL 91-6, Systems Optimization Laboratory, Department of Operations Research, Stanford University (1991), to be published in Ann. Oper. Res.
  22. J.M. Jacobs, Advanced basis insertion for repeated optimization of networks with side constraints, Technical Report, Pacific Gas and Electric Company (1992).
  23. S.A. Johnson, J.R. Stedinger, C.A. Shoemaker, Y. Li and J.A. Tejada-Guibert, Numerical solution of continuous-state dynamic programs using linear and spline interpolation, Oper. Res. 41(1993)484–500.
    Google Scholar
  24. M. Karamouz and H.V. Vasiliadis, Bayesian stochastic optimization of reservoir operation using uncertain forecasts, Water Resources Res. 28(1992)1221–1232.
    Article Google Scholar
  25. J. Kelman, J.R. Stedinger, L.A. Cooper, E. Hsu and S.Q. Yuan, Sampling stochastic dynamic programming applied to reservoir operation, Water Resources Res. 26(1990)447–454.
    Article Google Scholar
  26. J.L. Kennington and R.V. Helgason,Algorithms for Network Programming (Wiley, New York, 1980).
    Google Scholar
  27. J.L. Kennington and A. Whisman,Netside user's guide, Technical Report 86-OR-01, Southern Methodist University (1988).
  28. P.H. Kirshen, Extended experience with a short term hydro scheduling model in New England, saving a threatened resource — in search of solutions, in:Proc. Water Resource Sessions, ASCE Water Forum '92, ed. M. Karamouz, Baltimore, MD (American Society of Civil Engineers, 1992).
  29. R. Krzysztofowicz, Optimal water supply based on seasonal runoff forecasts, Water Resources Res. 22(1986)313–321.
    Google Scholar
  30. J. Martinec, Snowmelt-runoff model for stream flow forecasts, Nordic Hydrology 6(1975)145–154.
    Google Scholar
  31. J. Martinec, A. Rango and R. Roberts, User's Manual for the Snowmelt Runoff Model (SRM), Version 3.2, Technical Report HL-17, USDA/ARS Hydrology Laboratory, Beltsville, MD (1992).
    Google Scholar
  32. N.R. Mizyed, J.C. Loftis and D.G. Fontane, Operation of large multireservoir systems using optimal-control theory, J. Water Resource Planning Manag. 118(1992)371–387.
    Google Scholar
  33. D.P. Morton, An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling, Technical Report NPSOR-94-001, Department of Operations Research, Naval Postgraduate School (1994).
  34. M.V.F. Periera and L.M.V.G. Pinto, Stochastic optimization of a multireservoir hydroelectric system — a decomposition approach, Water Resources Res. 21(1985)779–792.
    Google Scholar
  35. M.V.F. Periera and L.M.V.G. Pinto, Multi-stage stochastic optimization applied to energy planning, Math. Progr. 52(1991)359–375.
    Article MathSciNet Google Scholar
  36. D. Randall and B.J. McCrodden, Modeling the Savannah River System for improved operations,4th Operations Management Workshop (American Society of Civil Engineers, 1992).
  37. R.T. Rockafellar and R.J-B Wets, Scenarios and policy aggregation in optimization under uncertainty, Math. Oper. Res. 16(1991)119–147.
    Article Google Scholar
  38. T.A. Røtting and A. Gjelsvik, Stochastic dual dynamic programming for seasonal scheduling in the Norwegian power system, IEEE Trans. Power Syst. 7(1992)273–279.
    Article Google Scholar
  39. S.A. Soliman and G.S. Christensen, Application of functional analysis to optimization of a variable head multireservoir power system for long-term regulation, Water Resources Res. 22(1986)852–858.
    Google Scholar
  40. J.R. Stedinger, J.C. Grygier and H. Yin, Seasonal streamflow forecasts based upon regression, in:Proc. 3rd Water Resources Operations Management Workshop: Computerized Decision Support Systems for Water Managers, ed. J.W. Labadie, L.E. Brazil, I. Corbu and L.E. Johnson, New York (American Society of Civil Engineers, 1990).
  41. J.R. Stedinger, B.F. Sule and D.P. Loucks, Stochastic dynamic programming models for reservoir operation optimization, Water Resources Res. 20(1984)1499–1505.
    Google Scholar
  42. J.R. Stedinger, J.A. Tejada-Guibert and S.A. Johnson, Performance of hydropower systems with optimal operating policies employing different hydrologic information,4th Operations Management Workshop (American Society of Civial Engineers, 1992).
  43. J.R. Stedinger, H. Yin and J.C. Grygier, Disaggregation models of seasonal streamflow forecasts, Technical Report, Department of Environmental Engineering, Cornell University (1987).
  44. C. Stover, Using real-time forecasts to improve flood control operations,4th Operations Management Workshop (American Society of Civil Engineers, 1992).
  45. T. Trezos and W.W-G. Yeh, Use of stochastic dynamic programming for reservoir management, Water Resources Res. 23(1987)983–996.
    Google Scholar
  46. R.M. Van Slyke and R.J-B Wets, L-shaped linear programs with applications to optimal control and stochastic programming, SIAM J. Appl. Math. 17(1969)638–663.
    Article Google Scholar
  47. R.J. Wittrock, Advances in a nested decomposition algorithm for solving saircase linear programs, Technical Report SOL 83-2, Systems Optimization Laboratory, Department of Operations Research, Stanford University (1983).
  48. S. Yakowitz, Dynamic programming applications in water resources, Water Resources Res. 18(1982)673–696.
    Google Scholar

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