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  • 1
    Electronic Resource
    Electronic Resource
    Metric regularity and quantitative stability in stochastic programs with probabilistic constraints (1999)
    Springer
    Mathematical programming 84 (1999), S. 55-88 
    Details
    ISSN: 1436-4646
    Keywords: Key words: stochastic programming – probabilistic constraints – metric regularity – nonsmooth analysis – quadratic growth – quantitative stability – empirical approximation Mathematics Subject Classification (1991): 90C15, 90C30, 49J52
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s10107980016a
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    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Stability in multistage stochastic programming (1995)
    Springer
    Annals of operations research 56 (1995), S. 79-93 
    Details
    ISSN: 1572-9338
    Keywords: Multistage stochastic programs ; optimization in Banach spaces ; stability ; approximation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract Multistage stochastic programs are regarded as mathematical programs in a Banach spaceX of summable functions. Relying on a result for parametric programs in Banach spaces, the paper presents conditions under which linearly constrained convex multistage problems behave stably when the (input) data process is subjected to (small) perturbations. In particular, we show the persistence of optimal solutions, the local Lipschitz continuity of the optimal value and the upper semicontinuity of optimal sets with respect to the weak topology inX. The linear case with deterministic first-stage decisions is studied in more detail.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02031701
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    A simple recourse model for power dispatch under uncertain demand (1995)
    Springer
    Annals of operations research 59 (1995), S. 135-164 
    Details
    ISSN: 1572-9338
    Keywords: Power dispatch under uncertainty ; stochastic programming ; asymptotic stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract Optimal power dispatch under uncertainty of power demand is tackled via a stochastic programming model with simple recourse. The decision variables correspond to generation policies of a system comprising thermal units, pumped storage plants and energy contracts. The paper is a case study to test the kernel estimation method in the context of stochastic programming. Kernel estimates are used to approximate the unknown probability distribution of power demand. General stability results from stochastic programming yield the asymptotic stability of optimal solutions. Kernel estimates lead to favourable numerical properties of the recourse model (no numerical integration, the optimization problem is smooth convex and of moderate dimension). Test runs based on real-life data are reported. We compute the value of the stochastic solution for different problem instances and compare the stochastic programming solution with deterministic solutions involving adjusted demand portions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02031746
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    Paper (German National Licenses)
    Fulltext
  • 4
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    Unknown
    Optimale Blockauswahl bei der Kraftwerkseinsatzplanung (1996)
    Details
    Publication Date: 2014-02-26
    Description: The paper addresses the unit commitment problem in power plant operation planning. For a real power system comprising coal and gas fired thermal as well as pumped storage hydro plants a large-scale mixed integer optimization model for unit commitment is developed. Then primal and dual approaches to solving the optimization problem are presented and results of test runs are reported.
    Keywords: ddc:000
    Language: German
    Type: reportzib , doc-type:preprint
    Format: application/postscript
    Format: application/pdf
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