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arXiv 2023年

作者： Trebbien, Julius Pütz, Sebastian Schäfer, Benjamin Nygård, Heidi S. Gorjão, Leonardo Rydin Witthaut, Dirk Forschungszentrum Jülich Jülich Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Faculty of Science and Technology Norwegian University of Life Sciences Ås Norway

Accurate forecasts of electricity prices are crucial for the management of electric power systems and the development of smart applications. European electricity prices have risen substantially and became highly volatile after the Russian invasion of Ukraine, challenging established forecasting methods. Here, we present a Long Short-Term Memory (LSTM) model for the German-Luxembourg day-ahead electricity prices addressing these challenges. The recurrent structure of the LSTM allows the model to adapt to trends, while the joint prediction of both mean and standard deviation enables a probabilistic prediction. Using a physics-inspired approach – superstatistics – to derive an explanation for the statistics of prices, we show that the LSTM model faithfully reproduces both prices and their volatility. Copyright © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Paving the Ways for Vehicles-in-the-loop: Wireless Integration of Autonomous Vehicles into Mixed Reality Proving Grounds and Test Areas

Paving the Ways for Vehicles-in-the-loop: Wireless Integrati...

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International Conference on Intelligent Transportation

作者： Marc René Zofka Tobias Fleck Philip Schörner Marc Heinrich J. Marius Zöllner Department of Technical Cognitive Systems FZI Research Center for Information Technology Karlsruhe Germany Institute of Applied Informatics and Formal Description Methods (AIFB) KIT Karlsruhe Institute of Technology Karlsruhe Germany

Proving grounds and test areas equipped with smart infrastructure might provide a solution to bridge the gap between laboratory testbeds and real-life testing by challenging the autonomous vehicle with critical scenarios in a risk-less way. Whereas actual research focuses on the infrastructure-sided support of automated and assisted driving, we focus on the integration of real experimental vehicles with simulated, virtual obstacles and traffic participants on the side of smart infrastructure of proving grounds and test areas. In the present work, we propose an innovative testsystem protocol as the missing link to integrate real physical vehicles with virtual obstacles. We derive general requirements and a universally valid testsystem protocol. Due to the technological developments and distribution of the wireless communication protocol stack IEEE 802.11p / ETSI ITS-G5, we focus the evaluation of its suitability for this. Finally, we highlight further emerging research questions.

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Data-driven Agent-based Modeling: Experimenting with the Schelling’s Model

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Procedia Computer Science 2024年 238卷 298-305页

作者： Ruhollah Jamali Wannes Vermeiren Sanja Lazarova-Molnar Maersk Mc-Kinney Moller Institute University of Southern Denmark Campusvej 55 5230 Odense Denmark Faculty of Engineering Technology Catholic University of Leuven Gebroeders De Smetstraat 1 9000 Ghent Belgium Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Kaiserstr. 89 76133 Karlsruhe Germany

Agent-based modeling and simulation is a practical computational technique for studying complex systems of autonomous agents in various disciplines. Agent-based models facilitate the study of emergent phenomena by simulating heterogeneous agents and their flexible behaviors and interactions. However, developing an agent-based model of a complex system is often time-consuming and vulnerable to the modeler’s biases. Addressing this challenge requires a paradigm shift from knowledge-driven modeling to data-driven modeling. In this research, we initiate and experiment with automating the process of composing agent-based models by developing data-driven model extraction. To achieve this objective, we conduct experiments employing different variations of Schelling’s segregation model, a well-known agent-based model, each featuring different parameter sets and complexity levels.

关键词： Agent-based modeling Schelling Model Data-driven modeling Data analysis

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Real-time Pricing Mechanism for V2G Using Distributed Bilevel Optimization

Real-time Pricing Mechanism for V2G Using Distributed Bileve...

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IEEE Conference on Decision and Control

作者： Jiawei You Xinliang Dai Yuning Jiang Haoyu Yin Yuanming Shi Colin N. Jones School of Information Science and Technology ShanghaiTech University China EPFL Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Automatic Control Laboratory EPFL Switzerland Department of Electrical and Systems Engineering Washington University in St. Louis USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper explores the impact of the burgeoning electric vehicle (EV) presence on distribution grid operations, highlighting the challenges they present to conventional pricing strategies due to their dual role as power consumers and suppliers, coupled with their energy storage capabilities. We propose an advanced real-time pricing model for the electricity market, employing a novel distributed bilevel optimization framework. This framework distinguishes between the distribution system operator (DSO) at the upper level and the EVs at the lower level, each aiming to optimize profit margins. The optimization includes power flow constraints at the upper level to ensure efficient operation within safe system limits, while model predictive control (MPC) is used to optimize lower-level EV responses. Additionally, we provide a rigorous convergence analysis of the proposed bilevel optimization method. Detailed convergence studies and simulation results demonstrate the effectiveness and superiority of the proposed algorithm.

关键词： Vehicle-to-grid Simulation Pricing Prediction algorithms Real-time systems Robustness Convergence Load flow Energy storage Predictive control

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On Optimal Grid Partitioning for Distributed Optimization of Reactive Power Dispatch

On Optimal Grid Partitioning for Distributed Optimization of...

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Innovative Smart Grid Technologies (ISGT)

作者： Alexander Murray Michael Kyesswa Hüseyin Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781728188973

ISBN: (纸本)9781728188980

Distributed optimization has been shown to be one promising method for tackling reactive power dispatch, however the performance of distributed algorithms is known to be dependent on how the given problem is partitioned. The question of how to optimally partition a power grid for use in distributed optimization remains open in the literature. In the present paper, we test partitions generated by the graph partitioned KaFFPa, METIS, and spectral clustering using five edge-weighting metrics. The standard IEEE 14, 30, and 57 bus models are used as benchmark case studies and the Augmented Lagrangian Alternating Direction Inexact Newton algorithm is used as the distributed optimization algorithm. It is shown that performance varies drastically depending on which partitioner and weighting is used. Overall, KaFFPa with weightings given by the Y-bus matrix yields the best results.

关键词： Measurement Reactive power Heuristic algorithms Clustering algorithms Partitioning algorithms Optimization Convergence

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EasyMLServe: Easy Deployment of REST Machine Learning Services

arXiv

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arXiv 2022年

作者： Neumann, Oliver Schilling, Marcel Reischl, Markus Mikut, Ralf Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and developers have to implement complete solutions which are often installed locally and include Graphical User Interfaces (GUIs). Distributing software to various users on-site has several problems. Therefore, we propose a concept to deploy software in the cloud. There are several frameworks available based on Representational State Transfer (REST) which can be used to implement cloud-based machine learning services. However, machine learning services for scientific users have special requirements that state-of-the-art REST frameworks do not cover completely. We contribute an EasyMLServe software framework to deploy machine learning services in the cloud using REST interfaces and generic local or web-based GUIs. Furthermore, we apply our framework on two real-world applications, i. e., energy time-series forecasting and cell instance segmentation. The EasyMLServe framework and the use cases are available on GitHub. © 2022, CC BY.

关键词： Machine learning

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Data analytics in the electricity sector – A quantitative and qualitative literature review

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Energy and AI 2020年第1期1卷 145-167页

作者： Frederik vom Scheidt Hana Medinova Nicole Ludwig Bent Richter Philipp Staudt Christof Weinhardt Institute for Information Systems and Marketing Karlsruhe Institute of TechnologyKaiserstr.8976133 KarlsruheGermany Institute for Automation and Applied Informatics Karlsruhe Institute of TechnologyHermann-von-Helmholtz-Platz 176344 Eggenstein-LeopoldshafenGermany

The rapid transformation of the electricity sector increases both the opportunities and the need for Data *** recent years,various new methods and fields of application have been *** research is growing and becoming more diverse and specialized,it is essential to integrate and structure the fragmented body of scientific *** therefore conduct a systematic review of studies concerned with developing and applying Data Analytics methods in the context of the electricity value ***,we provide a quantitative high-level overview of the status quo of Data Analytics research,and show historical literature growth,leading countries in the field and the most intensive international ***,we qualitatively review over 200 high-impact studies to present an in-depth analysis of the most prominent applications of Data Analytics in each of the electricity sector’s areas:generation,trading,transmission,distribution,and *** each area,we review the state-of-the-art Data Analytics applications and *** addition,we discuss used data sets,feature selection methods,benchmark methods,evaluation metrics,and model complexity and run *** the findings from the different areas,we identify best practices and what researchers in one area can learn from other ***,we highlight potential for future research.

关键词： Data analytics Electricity Machine learning Generation Price Transmission Distribution Consumption

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Comparison of reliability functions of simple mixed four-element systems 31

Comparison of reliability functions of simple mixed four-ele...

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31st Joint Seminar on the Development of Materials Science in Research and Education, DMSRE 2022

作者： Labašová, E. Slovak University of Technology in Bratislava Faculty of Materials Science and Technology in Trnava Institute of Applied Informatics Automation and Mechatronics Jána Bottu 2781/25 Trnava917 24 Slovakia

The paper deals with the comparison of reliability function, mean time to failure, and hazard rate function for simple mixed systems. Simple systems are those that can be divided into a sequence of series and/or parallel subsystems. The paper compares systems consisting of four subsystems;a total of seven systems are compared. A system with four parallel arranged subsystems is also included in the paper, for which the mean time to failure has the largest value. A system with four series-arranged subsystems is also included in the paper, for which the mean time to failure has the smallest value. The MATLAB program was used for the solution. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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LQR and Pole Placement Controllers with Integral Action Design Technique and Performance Analysis for Control of Temperature Field in Continuous Casting Process

LQR and Pole Placement Controllers with Integral Action Desi...

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International Carpathian Control Conference (ICCC)

作者： Sohaibullah Zarghoon Cyril Belavý Institute of Automation Measurement and Applied Informatics Faculty of Mechanical Engineering Solvak University of Technology Bratislava Slovakia

This paper presents the design of Linear Quadratic Regulator and Pole Placement controllers with integral action using MATLAB and Simulink for controlling the temperature field in the secondary cooling zone of a continuous casting process. The secondary cooling zone in continuous casting represents a distributed parameter system. During steel production, the type of steel produced changes continuously, necessitating a control system that can respond quickly and flexibly. We compared the performance of the Linear Quadratic Regulator and Pole Placement control techniques for the temperature field in the secondary cooling zone by analyzing the system’s response. The simulation results demonstrate that the Linear Quadratic Regulator technique outperforms the Pole Placement technique for the specified system.

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Learning Temporal Truth Tables of Dynamic Fault Trees from Time Series Data on Faults

Learning Temporal Truth Tables of Dynamic Fault Trees from T...

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International Conference on System Reliability and Science (ICSRS)

作者： Parisa Niloofar Sanja Lazarova-Molnar Mœ rsk Mc-Kinney MØ ller Institute University of Southern Denmark Odense Denmark Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany

Classical fault tree analysis (FTA) can be used to analyze and assess combinations of failures of basic components that lead to system failures within a given system. Classical FTA is however unable to handle how temporal sequences of faults lead to system failures. This is an important issue in complex dynamic systems. To model complex systems where the behavior of components or events depends on the system's state or time, dynamic gates are used. Therefore, we extend our work on learning static repairable fault trees from time series data and propose a data-driven algorithm to extract dynamic gates from time series data of faults. Our proposed algorithm captures sequences of events by converting a time-stamped truth table into temporal gates, which allows learning dynamic gates from data.

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