The optimal solution, as well as the objective of stochastic programming problems vary with the underlying probability measure. This paper addresses stability with respect to the underlying probability measure and stability of the objective. The techniques presented are employed to make problems numerically tractable, which are formulated by involving numerous scenarios, or even by involving a continuous probability measure. The results justify clustering techniques, which significantly reduce computation times while guaranteeing a desired approximation quality. The second part of the paper highlights Newton’s method to solve the reduced stochastic recourse problems. The techniques presented exploit the particular structure of the recourse function of the stochastic optimization problem. The tools are finally demonstrated on a benchmark problem, which is taken from electrical power flows. graph of the transmission network consisting of buses B and transmission lines L buses i, k , … the set of transmission lines linking buses: (i, k) ∈ L if a transmission line connects i and k generators (PV bus) demand, or load bus (PQ bus) net power injected at bus i. The superscripts g indicates power generated at bus i, while d relates to demand net reactive power injected at bus i complex power (measured in watt) impedance (measured in ohm). Rik is the resistance of transmission asset linking the buses i and k, Xik the reactance admittance (measured in siemens). Gik is the conductance, Bik the susceptance voltage magnitude at bus i (measured in volt) difference of voltage angles at buses i and j voltage angle (phase) at bus i (measured in radian) imaginary unit, j 2 = − 1 
Nonlinear stochastic programming–With a case study in continuous switching