Probabilistic Constrained Optimization on Gas Networks

Next July 27, 2023 our postdoctoral researcher Michael Schuster, will talk on “Probabilistic Constrained Optimization on Gas Networks” at ICSP 2023, the XVI International Conference Stochastic Programming on July 24-28th, 2023.

Abstract. Uncertainty often plays an important role in gas transport and probabilistic constraints are an excellent modeling tool to obtain controls and other quantities that are robust against perturbations in e.g., the boundary data.

We first consider a stationary gas transport model with uncertain boundary data on networks. We provide an efficient way to compute the probability that random boundary data is feasible. In this context feasible means that the pressure corresponding to the random boundary data meets some box constraints at the network junctions.

Further we consider and analyze optimization problems with probabilistic constraints in the stationary
and the dynamic setting where the probabilistic constraints are approximated by the kernel density estimator approach. Additionally we compare the solutions of the probabilistic constrained optimization problems with the solutions of the corresponding deterministic problems.


University of California, Davis
Conference Center
550 Alumni Lane, Davis, CA, 95616

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