Simulation-Based Evolutionary Dynamic and Stochastic Optimization for Smart Electric Power Systems

The electric power systems research society early identied the necessity of optimization both for planning and operation tasks, where formulations such as the optimal power ow (OPF) problem shape this research domain ever since. At the same time, technological changes to electric power grids challenge new methods, requiring optimization in both dynamic as well as uncertain systems. Optimization tasks for the control of numerous distributed devices fundamentally require scalability aspects. Heuristic optimization methods have evolved as being capable of managing many of those upcoming needs. Simulation optimization with metaheuristics provides a promising fundament for optimization under uncertainty, and offers the basic approach for handling manifold challenging optimization issues within this work. While various aspects of future power grid optimization tasks are being analyzed, simulation-based methods both for static but mainly for dynamic problems are developed. Further on, simulation-based evolutionary policy function approximation is being discussed for dynamic power ow control problems, which is presented both in a generic manner as well as tailored to the electric engineering domain. In a first experimental part, the developed methods are applied both to a static probabilistic planning problem and to a dynamic OPF control task within benchmark systems. Showing that approximate optimal policy-based control yields competitive results compared to reference solutions, it additionally is able to make quick and robust control actions within dynamic and stochastic environments, being scalable to numerous devices. The second experimental part treats optimal load control issues in the smart electric grids context. Electric vehicle (EV) charging control is defined as a generic problem for optimal load control over time. Existing works in the literature are being analyzed while major lacks can be identied that need to be tackled.