Zukunftslabor
ENERGIE

The coupling of photovoltaic-thermal (PV-T) collectors with heat pumps improves both the electrical and thermal performance of the heat pumps. The main objective of this study is to investigate the potential efficiency improvements resulting from the combined operation of an air PV-T system with an air source heat pump (ASHP). We simulate the said combined operation of the system for meeting building thermal demands (space heating and domestic hot water production), using models developed for this study. Different configurations of the PV-T collector system (from 2 to 12 panels in series) are simulated, and the configuration with 4 panels in series is identified as the most suitable one in this context. This configuration of the PV-T ASHP system has better electrical performance as compared to the baseline case of only a photovoltaic (PV) ASHP system with the same configuration, with a 6.61% increase in the seasonal COP of the heat pump. The presented simulation methodology builds the base for further investigation of other types of PV-T collector systems and heat pumps.

As in all disciplines, increasing the FAIRness (findability, accessibility, interoperability, and reusability) of research data and software is a goal in energy research. In order to achieve this, it is important to identify the most appropriate ontology for the description of research data and software. However, despite the importance of this task, it still presents a significant challenge. While there are some comparisons of ontologies, a gap exists in assessing their usefulness according to ontology metadata. This paper fills this gap by defining 21 criteria sorted into four categories to help researchers choose ontologies in the energy domain. The criteria are used to compare eight ontologies for energy research to showcase their use and analyze the ontologies. The analysis reveals the Open Energy Ontology (OEO) as the top-ranked ontology. This underscores the importance of metadata comparison in ontology selection and highlights the benefits of incorporating metadata criteria into ontology terminology services to support researchers.

The integration of renewable energy sources and the increasing demands from the electrification of transportation and heating introduce significant variability and uncertainty to distribution grids. This variability can lead to operational challenges, including voltage range violations and instability in power supply. To address these challenges, it is crucial to develop robust methodologies that quantify uncertainties and evaluate their impacts on grid parameters, thereby informing decision-making processes to enhance the reliability and efficiency of Active Distribution Networks (ADNs).

Leveraging the enhanced measurement and data acquisition capabilities from the smart meter rollout in Germany and the network-oriented control regulations outlined in §14a of the Energy Industry Act (EnWG), this research presents a configurable framework for quantifying uncertainties and measuring their impacts on grid operational parameters. This framework supports risk assessments to identify potential violations of permissible limits for these grid parameters, aiding critical grid operation decisions.

The framework employs open-source data and libraries to ensure transparency, reproducibility, and accessibility, facilitating comprehensive grid simulations. Utilizing a real residential district in Lower Saxony as a model, the grid includes 49 buildings equipped with PV systems, EV charging stations, and battery storage systems. Key components of the framework include libraries such as pandapower for low-voltage grid simulation, PVlib for PV generation modelling, emobpy for EV charging profiles, and weather data from the German Weather Service. The primary source of uncertainty in this study is the error in Global Horizontal Irradiance (GHI) affecting PV generation, though the framework is adaptable to other forecasted parameters.

The methodology involves calculating forecast errors, performing statistical analysis, and generating random samples to model PV generation at each bus. These samples are integrated into a quasi-dynamic grid simulation to evaluate the sensitivity of voltage range violations to the identified uncertainties. Monte Carlo simulations are used throughout this process to provide a probabilistic assessment of potential grid stability issues, supporting informed decisions on power injection and grid management based on voltage sensitivity analysis. By adjusting the uncertainty variables, this framework facilitates the investigation of various factors to determine their impact on grid stability. To isolate the effects of the forecast variable under investigation, other variables are set to their actual values. This method identifies critical issues and pinpoints the most vulnerable buses in the grid, enhancing the understanding of grid dynamics under varying conditions and aiding in the development of targeted risk mitigation strategies.

Future work aims to enhance the reliability and efficiency of ADNs by integrating Uncertainty Quantification with advanced control strategies. This includes developing dynamic mechanisms for proactively detecting and mitigating limit violations in key grid parameters through sensitivity analysis and optimized power injection. The goal is to improve grid stability and operation while providing valuable insights into uncertainty propagation within grid systems.