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PIDE: Photovoltaic integration dynamics and efficiency for autonomous control on power distribution grids

Energy informatics 2025年第1期8卷 1-25页

作者： Demirel, Gökhan Fernengel, Natascha Grafenhorst, Simon Förderer, Kevin Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

With a focus on larger rooftop or utility-scale solar systems, there is a lack of research on the potential impact of mini photovoltaic (MPV) systems, often referred to as balcony power plants. This work analyzes the impact of varying concentrations of MPV systems, on the stability and control of low-voltage (LV) grids. We offer a comprehensive technical assessment of MPV within a distribution grid and quantify their effects on power quality, losses, transformer loading, and the performance of other inverter-based voltage-regulation devices. For this purpose, this paper introduces the open-source Python-based framework PIDE (Photovoltaic Integration Dynamics and Efficiency), a tool for simulating the integration of distributed energy resources (DER)s and evaluating their impact on autonomous reactive power control in the distribution grid. Our case studies include a one-year sensitivity analysis based on Monte Carlo simulations, compare distributed and decentralized DER control strategies, and demonstrate the role of autonomous inverters in providing ancillary services. With the growing use of battery energy storage (BES) systems in LV grids for these services, the need for adaptable DER control strategies becomes increasingly evident. Our results show that high MPV penetration increases mean transformer load by up to 3%, line load by 2.5% and total power losses by around 17%. © The Author(s) 2025.

关键词： Sensitivity analysis

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Mask R-CNN Outperforms U-Net in Instance Segmentation for Overlapping Cells

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Current Directions in Biomedical Engineering 2023年第1期9卷 335-338页

作者： Rettenberger, Luca Rieken Münke, Friedrich Bruch, Roman Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

U-Net is the go-to approach for biomedical segmentation applications. However, it is not designed to segment overlapping objects, a challenge Mask R-CNN has shown to have great potential in. Yet, Mask R-CNN receives little attention in biomedicine. Hence, we evaluate both approaches on a publicly available biomedical dataset. We find that Mask RCNN outperforms U-Net in segmenting overlapping cells and achieves comparable performance if they do not intersect. Our study provides valuable decision support to practitioners in selecting an appropriate method when solving instance segmentation tasks using deep learning, as well as important insights into enhancing the accuracy of such approaches in biomedical image analysis. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Computer vision

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Drone-Based and 5G-Connected Multisensor Platform for Forest Monitoring 12

Drone-Based and 5G-Connected Multisensor Platform for Forest...

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12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Asen, Sebastian Has, Carola Poeschl, Rainer Kunze, Stefan Institute for Applied Informatics Deggendorf Institute of Technology Freyung Germany

ISBN: (纸本)9798350350982

The paper presents an unmanned aerial vehicle (UAV)-based multisensor platform for forest monitoring. The system is intended to provide information on the health status of individual trees and for forest inventory purposes. Therefore, a concept and design of the UAV based sensor system consisting of light detection and ranging (LiDAR), a hyperspectral and an RGB-camera is shown. The monitoring system uses a 5G Network to transmit the image data live, during the flight to the server backend. Two-dimensional georeferencing is already carried out on the drone so that a preview of the data can be displayed live in a web application. The processing of initial data as well as future steps and improvements are also presented in the paper. © 2024 IEEE.

关键词： Drones

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Leveraging Big Data Technologies for Practical Radio Frequency Fingerprinting Applications

Leveraging Big Data Technologies for Practical Radio Frequen...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kunze, Stefan Dorner, Wolfgang Deggendorf Institute of Technology Institute for Applied Informatics Freyung Germany

ISBN: (纸本)9798350362480

Radio frequency fingerprinting is an emerging research field that made a big leap with the adoption of deep learning, and practicability issues became more of a focus, lately. Nevertheless, further research is necessary to adopt practical, real-world solutions. Big data technologies allow for enhanced collection, processing and analysis of vast amounts of data and are well established in many fields by now. In this paper, current challenges in the field as well as various considerations about how big data approaches can benefit radio frequency fingerprinting on the way to real-world applications are presented. © 2024 IEEE.

关键词： Big data

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A fuzzy logic approach for economic energy-efficient heat pump operation in thermal building control

A fuzzy logic approach for economic energy-efficient heat pu...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Kovačević, Jovana Süß, Anne-Christin Çakmak, Hüseyin K. Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany

ISBN: (纸本)9789531842976

Advanced thermal control has great potential to decrease energy consumption in buildings. Thereby, encountering the balance between thermal comfort, and electricity load reduction is a challenge. For this, advanced control systems need to be developed that can respond to external signals such as electricity prices and are robust to different types of buildings to be widely utilized. In this paper, we propose and investigate two fuzzy control systems, an economic-energy-efficient controller and a comfort controller. The comfort controller focuses on thermal comfort, whereas the economic-energy-efficient controller is attentive to electricity prices and the energy efficiency of the heat pump. For the simulation, we use a reduced-order building model identified by measurements of a real-world building. Furthermore, we compare the proposed controllers with a hysteresis controller as a lower benchmark control strategy. Simulation results reveal that an economic-energy-efficient controller can reduce electricity consumption by 14.92% and costs by 18.41 % when compared to a benchmark controller and with mean thermal discomfort of 0.216 °C over an evaluation period of two months. The comfort controller has slightly smaller costs and electricity consumption than the hysteresis one, but less zone temperature fluctuations, enhancing the quality of controlled zone temperature and occupants' comfort. © 2024 IEEE.

关键词： Fuzzy control

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Fallback Communication System for Drones in Medical Applications 5

Fallback Communication System for Drones in Medical Applicat...

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5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Khan, Asad Saeed Kunze, Stefan Poeschl, Rainer Deggendorf Institute of Technology Institute for Applied Informatics Freyung Germany

ISBN: (纸本)9798350352863

The project KIMoNo is a research project that focuses on the implementation of transporting medical products and samples using a vertical take-off and landing unmanned aerial vehicle (VTOL UAV) for patients in rural and remote areas. The cellular network coverage issue in remote areas limits UAVs to operate only in urban areas. To ensure effective communication between UAVs and a ground team even in remote areas, a fallback communication system is implemented in this paper. A web-based monitoring system is also designed that will allow authorized third parties (e.g. doctors, lab employees) to monitor the status of the UAVs during missions. © 2024 IEEE.

关键词： Drones

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Radio Frequency Fingerprinting: A Conceptual Study for Addressing Real-World Challenges 2

Radio Frequency Fingerprinting: A Conceptual Study for Addre...

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Kunze, Stefan Dorner, Wolfgang Institute for Applied Informatics Deggendorf Institute of Technology Freyung Germany

ISBN: (纸本)9798331530099

While radio frequency fingerprinting has attracted a lot of research interest, especially since the adoption of deep learning based approaches. However, the practicability of these approaches in real-world applications remains the greatest challenge. In this paper, a conceptual study for addressing these real-world challenges is presented. Potential applications and requirements of practical radio frequency fingerprinting systems are proposed. Concepts for addressing individual practicability issues individually are reviewed, and a concept for addressing them simultaneously is derived. Finally, conclusions from the study are drawn, and a look at the future work in the field of radio frequency fingerprinting is presented. © 2024 IEEE.

关键词： Deep learning

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Gossen’s first law in the modeling for demand side management: a thorough heat pump case study with deep learning based partial time series data generation

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Energy informatics 2024年第1期7卷 47页

作者： Li, Chang Brecher, Gina Kovačević, Jovana Çakmak, Hüseyin K. Förderer, Kevin Matthes, Jörg Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Gossen’s First Law describes the law of diminishing marginal utility. This paper aims to further verify the proposed hypothesis that Gossen’s First Law also holds in the modeling for Demand Side Management (DSM) with a thorough heat pump case study. The proposed hypothesis states that in general the complexity-utility relationship in the field of DSM modeling could be represented by a diminishing marginal utility curve. On the other hand, in data based modeling, when utilizing a large dataset for validation, the data integrity is critical to the reliability of the results. However, the absence of partial time series data may occur during the measurement due to missing sensors or IT related issues. In this work, an extensive real-world open dataset of a ground source heat pump is utilized for the case study. In the raw data, one key variable namely the flow rate is missing. Thus, three different algorithms based on machine learning and deep learning architectures namely Random Forest (RF), Long Short-Term Memory (LSTM) and Transformer are applied to predict the flow rate by utilizing an open loop forecasting. The raw data are first pre-processed with a time interval of one hour and then used for training, validation and forecast. Furthermore, a modified persistence model as the baseline is also defined. The predicted flow rate using LSTM yields the lowest error of 7.47% nMAE and 10.56% nRMSE respectively. The forecast results are then utilized in the following step of modeling of a heat pump use case. With the introduced quantification method for complexity and a modified version for utility, we further verify the proposed hypothesis with a longer time horizon of 7 days. © The Author(s) 2024.

关键词： Forecasting

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Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance 3

Leveraging small language models for Text2SPARQL tasks to im...

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3rd International Workshop on Linked Data-Driven Resilience Research, D2R2 2024

作者： Brei, Felix Frey, Johannes Meyer, Lars-Peter ETi Competence Center @ Institute for Applied Informatics Germany KMI Competence Center @ Institute for Applied Informatics Germany Institute of Computer Science Leipzig University Germany

In this work we will show that language models with less than one billion parameters can be used to translate natural language to SPARQL queries after fine-tuning. Using three different datasets ranging from academic to real world, we identify prerequisites that the training data must fulfill in order for the training to be successful. The goal is to empower users of semantic web technology to use AI assistance with affordable commodity hardware, making them more resilient against external factors. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Computational linguistics

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Vibrational Control for Energy Balancing in Modular Multilevel Converters 65

Vibrational Control for Energy Balancing in Modular Multilev...

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65th IEEE Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2024

作者： Kastner, Adam Groll, Lutz Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350365771

In order to balance the stored energy between the branches of a modular multilevel converter, an output-invisible control allocation strategy is used. A vibrational control approach, modeled via higher-order averaging methods, is applied to provide additional input variables that enable controlling the one-period average of all branch energies at the same time. Stability analysis of the linearized control loop including the moving average filter is performed using a delay model approach, for which careful handling of a removable singularity at zero is necessary. A finite-order discrete-time approximation of the delay system is used for optimization of the controller parameters. The potential of the proposed control scheme is demonstrated in simulation. © 2024 IEEE.

关键词： Delay control systems

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