Managing uncertainty and variability in power injections has become a major concern for system operators due to increasing levels of fluctuating renewable energy connected the grid. This work addresses this via joint chance-constrained formulation DC optimal flow (OPF) problem, which satisfies all constraints jointly with pre-determined probability. The few existing approaches solving OPF probl...

In this paper, we use an input oriented chance-constrained DEA model withrandom inputs and outputs. A super-eciency model with chance constraintsis used for ranking. However, for convenience in calculations a non-linear deterministicequivalent model is obtained to solve the models. The non-linearmodel is converted into a model with quadratic constraints to solve the nonlineardeterministic model...

We examine the convexity and tractability of the two-sided linear chance constraint model under Gaussian uncertainty. We show that these constraints can be applied directly to model a larger class of nonlinear chance constraints as well as provide a reasonable approximation for a challenging class of quadratic chance constraints of direct interest for applications in power systems. With a view ...

Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. The only complete solution approach to date — scenario-based stochastic constraint programming — compiles SCSPs down into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space req...

A natural way to handle optimization problem with data affected by stochastic uncertainty is to pass to a chance constrained version of the problem, where candidate solutions should satisfy the randomly perturbed constraints with probability at least 1− . While being attractive from modeling viewpoint, chance constrained problems “as they are” are, in general, computationally intractable. In th...

Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. The only solution approach to date compiles down SCSPs into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space requirements and weak constraint propagation. This paper tries to...

The question posed in the title of this section goes beyond general-type theoretical considerations — this is mainly a modeling issue that should be resolved on the basis of application-driven considerations. There is however a special case where this question makes sense and can, to some extent, be answered — this is the case where our goal is not to build an uncertainty model “from scratch,” ...