  We are concerned with the simulation and optimization of large-scale gas
pipeline systems in an error-controlled environment. The gas flow dynamics is
locally approximated by sufficiently accurate physical models taken from a
hierarchy of decreasing complexity and varying over time. Feasible work regions
of compressor stations consisting of several turbo compressors are included by
semiconvex approximations of aggregated characteristic fields. A discrete
adjoint approach within a first-discretize-then-optimize strategy is proposed
and a sequential quadratic programming with an active set strategy is applied
to solve the nonlinear constrained optimization problems resulting from a
validation of nominations. The method proposed here accelerates the computation
of near-term forecasts of sudden changes in the gas management and allows for
an economic control of intra-day gas flow schedules in large networks. Case
studies for real gas pipeline systems show the remarkable performance of the
new method.
