Second-Order Stochastic Dominance Constraints Induced by Mixed-Integer Linear Recourse

We introduce stochastic integer programs with dominance constraints induced by mixed-integer linear recourse. Closedness of the constraint set mapping with respect to perturbations of the underlying probability measure is derived. For discrete probability measures, large-scale, block-structured,mixed-integer linear programming equivalents to the dominance constrained stochastic programs areidentified. For these models, a decomposition algorithm is proposed. Computational tests withinstances from power optimization and Sudoku puzzling conclude the paper

Risk modeling via stochastic dominance in power systems with dispersed generation

We propose a new approach to risk modeling in power optimization employing the concept of stochastic dominance. This leads to new classes of large-scale block-structured mixed-integer linear programs for which we present decomposition algorithms. The new methodology is applied to stochastic optimization problems related to operation and investment planning in a power system with dispersed generation

A hybrid approach for high precision prediction of gas flows

10.1007/s12667-021-00466-4Energy System