Permanently increasing requirements in power supply necessitate efficient control of electric power systems. An emerging subject of importance is optimization. Papers on modelling aspects of unit commitment and optimal power flow provide the introduction to power systems control and to its associated problem statement. Due to the nature of the underlying optimization problems recent developments in advanced and well established mathematical programming methodologies are presented, illustrating in which way dynamic, separable, continuous and stochastic features might be exploited. In completing the various methodologies a number of presentations have stated experiences with optimization packages currently used for unit commitment and optimal power flow calculations. This work represents a state-of-the-art of mathematical programming methodologies, unit commitment, optimal power flow and their applications in power system control.

Stochastic Programming offers models and methods for decision problems wheresome of the data are uncertain. These models have features and structural properties which are preferably exploited by SP methods within the solution process. This work contributes to the methodology for two-stagemodels. In these models the objective function is given as an integral, whose integrand depends on a random vector, on its probability measure and on a decision. The main results of this work have been derived with the intention to ease these difficulties: After investigating duality relations for convex optimization problems with supply/demand and prices being treated as parameters, a stability criterion is stated and proves subdifferentiability of the value function. This criterion is employed for proving the existence of bilinear functions, which minorize/majorize the integrand. Additionally, these minorants/majorants support the integrand on generalized barycenters of simplicial faces of specially shaped polytopes and amount to an approach which is denoted barycentric approximation scheme.