Overview

Abstract

Power generation based on renewable energy sources plays an important role in the development of a sustainable and environmentally friendly generation of energy, motivated by the finite nature of fossil energy sources and environmental pollution. In particular, wind energy is considered to be most promising to provide a substantial part of the electrical energy supply. But due to the fluctuating behavior of power production from renewable energies, especially caused by wind power production, new challenges are posed to the structure of power generation systems. In this context, we approach the question of how energy storages and flexible generation units may contribute to decouple fluctuating supply and demand, yielding a sustainable and cost efficient energy production. To this end, the problem is formulated as an optimization model including combinatorial, nonlinear, and stochastic aspects. By approximating the nonlinearities, we receive a stochastic multistage mixed-integer program. The aim of this thesis is the development of a solution algorithm which is capable to solve test instances sufficiently large to provide reliable results. This is accomplished by developing a decomposition approach based on splitting the corresponding scenario tree, enhanced by mixed integer programming techniques, such as primal methods and cutting plane generation.