Multi-Year Time-Series-Based Power System Planning with Hybrid Optimization and Supervised Learning Methods

The increasing share of renewable energy sources in the power system necessitates new planning methods for power systems. On the one hand, flexible operational measures must be included in planning. On the other hand, conventional measures have to be considered. In this thesis, a multi-year planning strategy for meshed high voltage (HV) systems is proposed considering operational flexibility as well as conventional planning measures. The defined optimization problem is solved by a hybrid optimization algorithm combining the advantages of heuristic and mathematical programming approaches. A reduction of the high computational effort of time series simulations is achieved by several strategies, which are integrated into the open-source tool pandapower. Furthermore, several machine learning algorithms are compared. The developed hybrid optimization method is a combination of the Iterated Local Search metaheuristic and a linear optimization model. This combination increases convergence while reducing simulation time in comparison to the existing methods. Finally, two case studies show the applicability of the developed planning framework for a real HV power system model.