TY - JOUR A1 - Gonzalez Grandon, Tatiana A1 - Heitsch, Holger A1 - Henrion, Rene T1 - A joint model of probabilistic/robust constraints for gas transport management in stationary networks JF - Computational Management Science N2 - We present a novel mathematical algorithm to assist gas network operators in managing uncertainty, while increasing reliability of transmission and supply. As a result, we solve an optimization problem with a joint probabilistic constraint over an infinite system of random inequalities. Such models arise in the presence of uncertain parameters having partially stochastic and partially non-stochastic character. The application that drives this new approach is a stationary network with uncertain demand (which are stochastic due to the possibility of fitting statistical distributions based on historical measurements) and with uncertain roughness coefficients in the pipes (which are uncertain but non-stochastic due to a lack of attainable measurements). We study the sensitivity of local uncertainties in the roughness coefficients and their impact on a highly reliable network operation. In particular, we are going to answer the question, what is the maximum uncertainty that is allowed (shaping a 'maximal' uncertainty set) around nominal roughness coefficients, such that random demands in a stationary gas network can be satisfied at given high probability level for no matter which realization of true roughness coefficients within the uncertainty set. One ends up with a constraint, which is probabilistic with respect to the load of gas and robust with respect to the roughness coefficients. We demonstrate how such constraints can be dealt with in the framework of the so-called spheric-radial decomposition of multivariate Gaussian distributions. The numerical solution of a corresponding optimization problem is illustrated. The results might assist the network operator with the implementation of cost-intensive roughness measurements. KW - chance constraint KW - robust constraint KW - uncertainty set KW - spheric-radial decomposition Y1 - 2017 U6 - https://doi.org/10.1007/s10287-017-0284-7 VL - 14 SP - 443 EP - 460 ER - TY - JOUR A1 - van Ackooij, Wim A1 - Henrion, Rene T1 - (Sub-) Gradient formulae for probability functions of random inequality systems under Gaussian distribution JF - SIAM/ASA J. Uncertainty Quantification N2 - We consider probability functions of parameter-dependent random inequality systems under Gaussian distribution. As a main result, we provide an upper estimate for the Clarke subdifferential of such probability functions without imposing compactness conditions. A constraint qualification ensuring continuous differentiability is formulated. Explicit formulae are derived from the general result in case of linear random inequality systems. In the case of a constant coefficient matrix an upper estimate for even the smaller Mordukhovich subdifferential is proven. Y1 - 2017 U6 - https://doi.org/10.1137/16M1061308 VL - 5 SP - 63 EP - 87 ER - TY - JOUR A1 - Guigues, Vincent A1 - Henrion, Rene T1 - Joint dynamic probabilistic constraints with projected linear decision rules JF - Optimization Methods and Software N2 - We consider multistage stochastic linear optimization problems combining joint dynamic probabilistic constraints with hard constraints. We develop a method for projecting decision rules onto hard constraints of wait-and-see type. We establish the relation between the original (infinite dimensional) problem and approximating problems working with projections from different subclasses of decision policies. Considering the subclass of linear decision rules and a generalized linear model for the underlying stochastic process with noises that are Gaussian or truncated Gaussian, we show that the value and gradient of the objective and constraint functions of the approximating problems can be computed analytically. Y1 - 2016 U6 - https://doi.org/10.1080/10556788.2016.1233972 VL - 32 SP - 1006 EP - 1032 ER - TY - JOUR A1 - Diniz, Andre Luiz A1 - Henrion, Rene T1 - On probabilistic constraints with multivariate truncated Gaussian and lognormal distributions JF - Energy Systems N2 - Many engineering problems with uncertain data, notably arising in power management, can be formulated as optimization problems subject to probabilistic constraints. While dealing with such constraints under continuous distributions of the underlying random parameter remains a difficult task in general both from the numerical and theoretical point of view, quite some progress has been made in the special case of multivariate Gaussian distributions. These are not perfectly adequate, however, in many circumstances, in particular not, when modeling uncertain inflows to hydro reservoirs or uncertain demands in gas networks. Interesting alternatives are offered by truncations of multivariate Gaussian distributions to polyhedra or by multivariate lognormal distributions. The paper discusses the applicability of such distributions in the context of a simple joint linear probabilistic constraint putting the emphasis on the numerical approximation of probabilities and their gradients (w.r.t. decisions to be optimized) as well as on the convexity of the set of feasible decisions. Y1 - 2016 U6 - https://doi.org/10.1007/s12667-015-0180-6 VL - 8 SP - 149 EP - 167 ER -