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Physics-inspired machine learning for power grid frequency modelling

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arXiv 2022年

作者： Kruse, Johannes Cramer, Eike Schäfer, Benjamin Witthaut, Dirk Jülich52428 Germany Institute for Theoretical Physics University of Cologne Köln50937 Germany Jülich52428 Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a non-autonomous system whose parameters vary strongly with time. However, the external driving factors are usually only available on coarse scales and the actual dependencies of the dynamic system parameters are generally unknown. Here, we propose a physics-inspired machine learning model that bridges the gap between large-scale drivers and short-term dynamics of the power system. Integrating stochastic differential equations and artificial neural networks, we construct a probabilistic model of the power grid frequency dynamics in Continental Europe. Its probabilistic prediction outperforms the daily average profile, which is an important benchmark. Using the integrated model, we identify and explain the parameters of the dynamical system from the data, which reveals their strong time-dependence and their relation to external drivers such as wind power feed-in and fast generation ramps. Finally, we generate synthetic time series from the model, which successfully reproduce central characteristics of the grid frequency such as their heavy-tailed distribution. All in all, our work emphasises the importance of modelling power system dynamics as a stochastic non-autonomous system with both intrinsic dynamics and external drivers. Copyright © 2022, The Authors. All rights reserved.

关键词： Wind power

Model Predictive Control of Distributed Energy Resources in Residential Buildings Considering Forecast Uncertainties

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SSRN

SSRN 2023年

作者： Langner, Felix Wang, Weimin Frahm, Moritz Hagenmeyer, Veit Energy Production and Infrastructure Center University of North Carolina at Charlotte CharlotteNC28223 United States Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Department of Engineering Technology University of North Carolina at Charlotte CharlotteNC28223 United States

Forecast uncertainties pose a considerable challenge to model predictive controllers (MPCs) in buildings. Numerous possibilities for considering forecast uncertainties in MPCs are available, but an in-depth comparison between those is still lacking. The present paper compares two main approaches to consider uncertainties: robust and stochastic MPC. They are benchmarked against a deterministic MPC and an MPC that perfectly predicts the future. The MPCs utilize a holistic building model to reflect modern smart homes, including photovoltaic power generation and storage, thermally controlled loads, and smart appliances. Real-world data is used to identify the thermal building model. The performance of the various controllers is investigated under three different uncertainty scenarios. The results show that the stochastic MPC performs superior to the robust and deterministic MPC. However, the deterministic MPC yields sufficiently satisfying results for our holistic building model and it is easier to be implemented. © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

Applying evolutionary algorithms successfully a guide gained from real-world applications

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arXiv 2021年

作者： Jakob, Wilfried Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Metaheuristics (MHs) in general and Evolutionary Algorithms (EAs) in particular are well known tools for successful optimization of difficult problems. But when is their application meaningful and how does one approach such a project as a novice? How do you avoid beginner's mistakes or use the design possibilities of a metaheuristic search as efficiently as possible? This paper tries to give answers to these questions based on 30 years of research and application of the Evolutionary Algorithm GLEAM and its memetic extension HyGLEAM. Most of the experience gathered and discussed here can also be applied to the use of other metaheuristics such as ant algorithms or particle swarm optimization. This paper addresses users with basic knowledge of MHs in general and EAs in particular who want to apply them in an optimization project. For this purpose, a number of questions that arise in the course of such a project are addressed. At the end, some non-technical project management issues are discussed, whose importance for project success is often underestimated. © 2021, CC BY-SA.

关键词： Global optimization

Concepts for automated machine learning in smart grid applications

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arXiv 2021年

作者： Meisenbacher, Stefan Pinter, Janik Martin, Tim Hagenmeyer, Veit Mikut, Ralf Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Adaptive test case selection for DNN-based perception functions

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Adaptive test case selection for DNN-based perception functi...

IEEE International Symposium on Systems Engineering (ISSE)

作者： Johannes Bernhard Thomas Schulik Mark Schutera Eric Sax Institute for Information Processing Technologies Karlsruhe Institue of Technology Germany Research and Development ZF Friedrichshafen AG Institute for Automation and Applied Informatics Karlsruhe Institue of Technology Germany

The progress in deep learning methods has bolstered the development of automated vehicles during the last decade. However, the deployment of deep learning methods in safety-critical applications raised questions on their safety. Like other vehicle components, a testing process has to prove the reliability of perception systems. Scalability issues arise when using real-world data to validate perception algorithms due to the immense amount of sensor data that needs to be tested. Simulation tools can complement this testing process, as they can fabricate synthetic data based on variable specifications of test cases. While simulative tools can produce a vast amount of test data, at some point, the testing process is limited by the available computing resources. Identifying test specifications that pose risks to the perception algorithms is crucial for efficiently utilizing these computing resources and estimating functional reliability. We present a pipeline for adaptive test case selection to expose the faults of a deep learning system using synthetic image data generated by a simulation framework. We apply our concept to a state-of-the-art object detector and implement multiple adaptive sampling strategies to demonstrate their ability of early fault detection. Our experiments show that our pipeline can achieve a 95% coverage of system faults while reducing the number of test executions by 90%.

关键词： Deep learning Adaptive systems Computational modeling Scalability Pipelines Tools Data models

A New Julia-Based Parallel Time-Domain Simulation Algorithm for Analysis of Power System Dynamics

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A New Julia-Based Parallel Time-Domain Simulation Algorithm ...

International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Michael Kyesswa Philipp Schmurr Hüseyin K. Çakmak Uwe Kühnapfel Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781728173436

ISBN: (纸本)9781728173443

The present paper describes a new parallel time-domain simulation algorithm using a high performance computing environment - Julia - for the analysis of power system dynamics in large networks. The parallel algorithm adapts a parallel-in-space decomposition scheme to a previously sequential algorithm in order to develop a new parallelizable numerical solution of the power system equations. The parallel-in-space decomposition is based on the block bordered diagonal form, which reformulates the network admittance matrix into sub-blocks that can be solved in parallel. For the optimal spatial decomposition of the network, a new extended graph partitioning strategy is developed for load balancing and minimizing the communication between subnetworks. The new parallel simulation algorithm is tested using standard test networks of varying complexity. The simulation results are compared to those obtained from a sequential implementation in order to validate the solution accuracy and to determine the performance improvement in terms of computational speedup. Test simulations are conducted using the ForHLR II supercomputing cluster and show a huge potential in computational speedup with increasing network complexity.

关键词： Mathematical model Computational modeling Power system stability Power system dynamics Heuristic algorithms Partitioning algorithms Analytical models

Dynamic Modeling of Wind and Solar Power Generation with Grid Support for Large-Scale Integration in Power Systems

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Dynamic Modeling of Wind and Solar Power Generation with Gri...

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

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

ISBN: (数字)9781728171005

ISBN: (纸本)9781728171012

The large-scale integration of variable and unpredictable renewable energy sources into the current networks necessitates considerable adjustments in operation and analysis strategies in order to ensure stability of power systems. The present paper describes the dynamic modeling and integration of solar PV and wind power generation systems into an open source toolbox for power system transient stability analysis. The main contribution is the development of high-level functions to model converter interfaces to the electrical network in reference to standard grid operation codes. The models and control functions are tested using a real network representing the transmission grid of the Baden-Württemberg state in Germany in order to analyze the inverter functions necessary for grid stability support. From the simulation results, it is observed that the developed control functions of the interfacing converters can equip renewable energy generators with equivalent features to synchronous machines from a functional point of view.

关键词： Analytical models Power system dynamics Stability criteria Power system stability Wind power generation Standards Load modeling

Integrating battery aging in the optimization for bidirectional charging of electric vehicles

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arXiv 2020年

作者： Schwenk, Karl Meisenbacher, Stefan Briegel, Benjamin Harr, Tim Hagenmeyer, Veit Mikut, Ralf Mercedes-Benz AG Sindelfingen Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

Smart charging of Electric Vehicles (EVs) reduces operating costs, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enables efficient power grid support. Today, however, uncoordinated charging, e.g. governed by users' habits, is still the norm. Thus, the impact of upcoming smart charging applications is mostly unexplored. We aim to estimate the expenses inherent with smart charging, e.g. battery aging costs, and give suggestions for further research. Using typical on-board sensor data we concisely model and validate an EV battery. We then integrate the battery model into a realistic smart charging use case and compare it with measurements of real EV charging. The results show that i) the temperature dependence of battery aging requires precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs' operating costs by approx. 30%, and iii) the profitability of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g. energy arbitrage, depends on battery aging costs and the electricity price spread. © 2020, CC BY.

关键词： Neural networks

NFDI4Energy Task Area 4: FAIR Data for Energy System Research

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NFDI4Energy Task Area 4: FAIR Data for Energy System Researc...

2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Wein, Amanda Reinkensmeier, Jan Weidlich, Anke Lilliestam, Johan Hagenmeyer, Veit Richter, Mascha Auer, Sören Nieße, Astrid Lehnhoff, Sebastian OFFIS E.V. RandD Division Energy Escherweg 2 Oldenburg26121 Germany Friedrichstr. 39 Freiburg79110 Germany Friedrich-Alexander-Universität Erlangen-Nörnberg Lehrstuhl for Sustainability Transition Policy Lange Gasse 20 Nörnberg90403 Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Kaiserstr. 89 Karlsruhe76131 Germany Reiner Lemoine Institut GGmbH Research Unit Transformation of Energy Systems Rudower Chaussee 12 Berlin12489 Germany Technische Informationsbibliothek Data Science and Digital Libraries Research Group Welfengarten 1 B Hannover30167 Germany Carl von Ossietzky Universität Oldenburg Department for Informatik Ammerländer Heerstr. 114-118 Oldenburg26129 Germany

ISBN: (纸本)9783885797319

The NFDI4Energy consortium will create a research data infrastructure for energy system research, emphasizing the openness and FAIRness of data and models in this research domain. Within the consortium, Task Area 4 focuses on the development of resources and services that will provide a semantic layer for the overall platform built by NFDI4Energy. The team of this Task Area will produce artifacts including a domain ontology, metadata standards, a knowledge graph, a Persistent Identifier service, and integration infrastructure to join these artifacts to the NFDI4Energy platform. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Metadata