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Impact of Renewable Energy Expansion to the Balancing Energy Demand of Differential Balancing Groups
(2018)
The research addresses the present situation on the German electricity market caused by variable renewable energy sources. The increasing number of households with photovoltaic and battery storage systems and their impacts require special attention. These systems change the traditional electricity customer from the sole electricity consumer to an electricity producer and consumer in one person. These so-called prosumers differ in their actual electricity demand from the initially estimated electricity demand with standard load profile. This discrepancy results in deviations within a differential balancing group. Thereby, the main finding of this research is a significant higher balancing energy demand with the expansion of photovoltaic and battery storage systems. Hence, the standard load profile is particularly not suitable for differential balancing groups with a high penetration of solar systems and still less suitable for groups with solar and battery storage systems
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 traff.
Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current methods. With our systematic literature review, we want to close the gap between the three disciplines (1) assessment of security of electricity supply, (2) artificial intelligence, and (3) design of experiments. For this, we conduct a large-scale quantitative review on selected fields of application and methods and make a synthesis that relates the different disciplines to each other. Among other findings, we identify metamodeling of complex security of electricity supply models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities as promising fields of application that have not sufficiently been covered, yet. We end with deriving a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of security of electricity supply.