Thomas Spiegel

• Germany’s national climate protection policy, the German Energy Transition, is oriented towards the reduction of energy-related greenhouse gas emissions by rapidly expanding the share of renewable energy systems. New challenges arise regarding technical and energy economical integration as installed wind and photovoltaic power capacities increase continuously. This thesis contributes to the topic by introducing a framework for online multi-criteria flexibility allocation in electrical distribution networks with transport capacity limitations. It considers technical aspects and market participant interests that may contradict each other. Local master data is preprocessed using geospatial information of buildings, photovoltaic systems, wind turbines, and the human population within a spatial domain of interest. By combining weather forecast data, this establishes the foundation for predicting power flow in the electrical distribution network and generating grid-beneficial criteria utilizing the smart grid traffic light concept. Furthermore, master data of the German wind and photovoltaic power plant portfolio is combined with weather forecast data to predict ationwide wind and photovoltaic power feed-in and to generate ecological- and market-beneficial criteria. In addition, metamodels are created and employed to forecast balancing group management data, i.e., EPEX SPOT Day-Ahead electricity prices in the bidding zone of Germany and Luxembourg, and imbalance energy demand of difference balancing groups. At last, non-scalarized schedule solutions are estimated to reach a multi-criteria flexibility allocation. The framework applicability is demonstrated by defined scenarios. Results show that publicly available data allows modelling, calibrating, and operating the introduced multi-criteria flexibility allocation framework in a real-world setting.