Refine
Year of publication
Document Type
- ZIB-Report (12)
- Article (3)
- In Proceedings (1)
- Habilitation (1)
Is part of the Bibliography
- no (17)
Keywords
Institute
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.
Expected recourse functions in linear two-stage stochastic programs with mixed-integer second stage are approximated by estimating the underlying probability distribution via empirical measures. Under mild conditions, almost sure uniform convergence of the empirical means to the original expected recourse function is established.
We present an algorithm for solving stochastic integer programming problems with recourse, based on a dual decomposition scheme and Lagrangian relaxation. The approach can be applied to multi-stage problems with mixed-integer variables in each time stage. %We outline a branch-and-bound algorithm for obtaining primal feasible and %possibly optimal solutions. Numerical experience is presented for some two-stage test problems.
Preprocessing in two-stage stochastic programming is considered from the viewpoint of Fourier-Motzkin elimination. Although of exponential complexity in general, Fourier-Motzkin elimination is shown to provide valuable insights into specific topics such as solving integer recourse stochastic programs or verifying stability conditions. Test runs with the computer code PORTA [1994] are reported.
Integrals of optimal values of random optimization problems depending on a finite dimensional parameter are approximated by using empirical distributions instead of the original measure. Under fairly broad conditions, it is proved that uniform convergence of empirical approximations of the right hand sides of the constraints implies uniform convergence of the optimal values in the linear and convex case.
Integer stochastic linear programming is considered from the viewpoint of discontinuous optimization. After reviewing solution approaches via mollifier subgradients and decomposition we outline how to base a solution method on efficient pointwise calculation of the objective employing computer algebra.
Strong Convexity in Stochastic Programs with Complete Recourse II: Partially Random Right-Hand Side
(1995)
We establish a verifiable sufficient condition for strong convexity of the expected recourse as a function of the tender variable in a two-stage stochastic program with linear recourse. Generalizing a former result where all components of the second-stage right-hand side vector were random we treat the case where only a subvector of the right-hand side is random. As prerequisite, a refined analysis of the polyhedral complex of lineality regions of the second-stage value function is carried out. The sufficient condition for strong convexity allows to widen the class of recourse models for which certain quantitative results on stability and asymptotic convergence of optimal solutions are valid.
In this paper we present a framework for solving stochastic programs with complete integer recourse and discretely distributed right-hand side vector, using Gröbner basis methods from computational algebra to solve the numerous second-stage integer programs. Using structural properties of the integer expected recourse function, we prove that under mild conditions an optimal solution is contained in a finite set. Furthermore, we present a basic scheme to enumerate this set and suggest possible improvements to economize on the number of function evaluations needed.
A Two-Stage Stochastic Program for Unit Commitment Under Uncertainty in a Hydro-Thermal Power System
(1998)
We develop a two-stage stochastic programming model with integer first-stage and mixed-integer recourse for solving the unit commitment problem in power generation in the presence of uncertainty of load profiles. The solution methodology rests on a novel scenario decomposition method for stochastic integer programming. This method combines Lagrangian relaxation of non-anticipativity constraints with branch-and-bound. It can be seen as a decomposition algorithm for large-scale mixed-integer linear programs with block-angular structure. With realistic data from a German utility we validate our model and carry out test runs. Sizes of these problems go up to 20.000 integer and 150.000 continuous variables together with up to 180.000 constraints.