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Modeling curtailment in germany: How spatial resolution impacts line congestion

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arXiv 2020年

作者： Frysztacki, Martha Brown, Tom Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

This paper investigates the effects of network constraints in energy system models at transmission level on renewable energy generation and curtailment as the network is being spatially aggregated. We seek to reproduce historically measured curtailment in Germany for the years 2013-2018 using an open model of the transmission system, PyPSA-Eur. Our simulations include spatial and temporal considerations, including congestion per line as well as curtailment per control zone and quarter. Results indicate that curtailment at high network resolution is significantly overestimated due to inaccurate allocation of electricity demand and renewable capacities to overloaded sites. However, high congestion rates of the transmission network decrease as the network is clustered to a smaller number of nodes, thus reducing curtailment. A measure to capture errors in the assignment of electricity demand and power plants is defined and hints towards a preferable spatial resolution. Thus, we are able to balance the effects of accurate node assignment and network congestion revealing that a reduced model can capture curtailment from recent historical data. This shows that it is possible to reduce the network to improve computation times and capture the most important effects of network constraints on variable renewable energy feed-in at the same time. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power utilization

The strong effect of network resolution on electricity system models with high shares of wind and solar

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arXiv 2021年

作者： Frysztacki, Martha Maria Hörsch, Jonas Hagenmeyer, Veit Brown, Tom Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Frankfurt Institute for Advanced Studies Ruth-Moufang-Straße 1 Frankfurt am Main60438 Germany

Energy system modellers typically choose a low spatial resolution for their models based on administrative boundaries such as countries, which eases data collection and reduces computation times. However, a low spatial resolution can lead to sub-optimal investment decisions for wind and solar generation. Ignoring power grid bottlenecks within regions tends to underestimate system costs, while combining locations with different wind and solar capacity factors in the same resource class tends to overestimate costs. We investigate these two competing effects in a capacity expansion model for Europe’s power system with a high share of renewables, taking advantage of newly-available high-resolution datasets as well as computational advances. We vary the number of nodes, interpolating between a 37-node model based on country and synchronous zone boundaries, and a 1024-node model based on the location of electricity substations. If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with better capacity factors and thus reduces system costs by up to 10% compared to a low resolution model. This results in a big swing from offshore to onshore wind investment. However, if we introduce grid bottlenecks by raising the network resolution, costs increase by up to 23% as generation has to be sourced more locally at sites with worse capacity factors. These effects are most pronounced in scenarios where grid expansion is limited, for example, by low local acceptance. We show that allowing grid expansion mitigates some of the effects of the low grid resolution, and lowers overall costs by around 16%. © 2021, CC BY.

关键词： Investments

Semantic CIM2Matpower: Ontology-Based Network Topology Processor and Converter for CIM Node-Breaker Models 7

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Semantic CIM2Matpower: Ontology-Based Network Topology Proce...

7th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2019

作者： Schumilin, Artem Cakmak, Huseyin Kemal Stucky, Karl-Uwe Duepmeier, Clemens Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

ISBN: (纸本)9781728124407

Smart electricity grids are key to a successful transition towards a future sustainable energy system. Network topology processing (NTP) is an important and frequently reoccurring task in most power grid analysis and simulation applications' including power flow calculations, short circuit and contingency analysis. NTP is applied to a detailed node-break-er representation of the power grid, as provided by the IEC Common Information Model (CIM). It is performed in order to obtain a reduced bus-branch representation, which serves as input to downstream analysis and simulation applications. The present paper introduces a novel approach to NTP that utilises a semantic knowledge base for network model management and exploits semantic reasoning over the formal CIM ontology and instance data. The proposed system is tested on publicly available ENTSO-E data, validated by converting the generated bus-branch model to Matpower and running power flow calculations in a power grid modelling and simulation framework. © 2019 IEEE.

关键词： Ontology

Label assistant: A workflow for assisted data annotation in image segmentation tasks

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arXiv 2021年

作者： Schilling, Marcel P. Rettenberger, Luca Münke, Friedrich Cui, Haijun Popova, Anna A. Levkin, Pavel A. Mikut, Ralf Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Institute of Biological and Chemical Systems Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in complex image processing scenarios since traditional computer vision approaches are expensive to develop or reach their limits due to complex relations. However, a common criticism is the need for large annotated datasets to determine robust parameters. Annotating images by human experts is time-consuming, burdensome, and expensive. Thus, support is needed to simplify annotation, increase user efficiency, and annotation quality. In this paper, we propose a generic workflow to assist the annotation process and discuss methods on an abstract level. Thereby, we review the possibilities of focusing on promising samples, image pre-processing, pre-labeling, label inspection, or post-processing of annotations. In addition, we present an implementation of the proposal by means of a developed flexible and extendable software prototype nested in hybrid touchscreen/laptop device. © 2021, CC BY.

关键词： Computer vision

Probabilistic solar power forecasting: Long short-term memory network vs simpler approaches

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arXiv 2021年

作者： Sharma, Vinayak Ordiano, Jorge Ángel González Mikut, Ralf Cali, Umit University of North Carolina at Charlotte United States Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Colorado State University United States Norwegian University of Science and Technology Norway

The high penetration of volatile renewable energy sources such as solar, make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic forecasts are an example of these methods, as they assist energy planners in their decision-making process by providing them with information about the uncertainty of future power generation. Currently, there is a trend towards the use of deep learning probabilistic forecasting methods. However, the point at which the more complex deep learning methods should be preferred over more simple approaches is not yet clear. Therefore, the current article presents a simple comparison between a long short-term memory neural network and other more simple approaches. The comparison consists of training and comparing models able to provide one-day-ahead probabilistic forecasts for a solar power system. Moreover, the current paper makes use of an open source dataset provided during the Global Energy Forecasting Competition of 2014 (GEFCom14). © 2021, CC BY.

关键词： Long short-term memory

Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Grids Using ALADIN

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arXiv 2022年

作者： Zhai, Junyi Dai, Xinliang Jiang, Yuning Xue, Ying Hagenmeyer, Veit Jones, Colin N. Zhang, Xiao-Ping Qingdao China City & Energy Research Institute Suzhou China Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Automatic Control Laboratory EPFL Switzerland School of Electric Power Engineering South China University of Technology Guangzhou China Department of Electronic Electrical and Systems Engineering University of Birmingham Birmingham United Kingdom

The increasing application of voltage source converter (VSC) based high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing transmission network. This poses significant challenges in dealing with the resulting optimal power flow (OPF) problem. In this paper, a recently proposed nonconvex distributed optimization algorithm—Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN), is tailored to solve the nonconvex AC/DC OPF problem for emerging voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid systems. The proposed scheme decomposes this AC/DC hybrid OPF problem and handles it in a fully distributed way. Compared to the existing state-of-art Alternating Direction Method of Multipliers (ADMM), which is in general, not applicable for nonconvex problems, ALADIN has a theoretical convergence guarantee. Applying these two approaches to VSC-MTDC coupled with an IEEE benchmark AC power system illustrates that the tailored ALADIN outperforms ADMM in convergence speed and numerical robustness. Copyright © 2022, The Authors. All rights reserved.

关键词： Constrained optimization

Impact of nonplanar 3d printing on surface roughness and build time in fused filament fabrication 7th

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Impact of nonplanar 3d printing on surface roughness and bui...

7th KES International Conference on Sustainable Design and Manufacturing, KES-SDM 2020

作者： Elkaseer, Ahmed Müller, Tobias Rabsch, Dominik Scholz, Steffen G. Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Faculty of Engineering Port Said University Port Fuad42526 Egypt Future Manufacturing Research Institute College of Engineering Swansea University Bay Campus Crymlyn Burrows SwanseaSA1 8EN United Kingdom Hermann-Von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

ISBN: (纸本)9789811581304

Nonplanar 3D printing is a recently emerged approach to increase surface quality and part strength in additive manufacturing. In this paper, the impact of a nonplanar printing method utilized for the fused filament fabrication (FFF) technique on the resultant surface roughness of printed parts is presented. In particular, the influence of different inclination angles and part orientation on the obtainable surface quality was investigated by comparing a traditional planar printing strategy with a nonplanar finishing solution. A pyramidal geometry was utilized to assess the effect of inclination angle as well as part orientation and relative printhead movement on the surface characteristics. The results show a decrease in obtainable surface roughness for inclination angles up to 25°, while higher angles cause rougher surfaces when compared with results of planar 3D printing strategy. This can be explained by the way the nonplanar nozzle movements interact with previously deposited filament strands by deforming them due to the size of the nozzle and geometry of the 3D printed part. As a consequence, solutions for an improved nonplanar printing technique using Delta FFF printers are suggested that will be investigated in the future work. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Fabrication

Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Grids Using ALADIN

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Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Gri...

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

作者： Junyi Zhai Xinliang Dai Yuning Jiang Ying Xue Veit Hagenmeyer Colin N. Jones Xiao-Ping Zhang College of New Energy China University of Petroleum (UPC) China Karlsruhe Institute of Technology (KIT) Institute for Automation and Applied Informatics Germany Automatic Control Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Switzerland School of Electric Power Engineering South China University of Technology (SCUT) China Department of Electronic Electrical and Systems Engineering University of Birmingham (UBir) Birmingham United Kingdom

The increasing application of voltage source converter (VSC) based high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing transmission network. This poses significant challenges in dealing with the resulting optimal power flow (OPF) problem. In this paper, a recently proposed nonconvex distributed optimization algorithm — Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN), is tailored to solve the nonconvex AC/DC OPF problem for emerging voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid systems. The proposed scheme decomposes this AC/DC hybrid OPF problem and handles it in a fully distributed way. Compared to the existing state-of-art Alternating Direction Method of Multipliers (ADMM), which is in general, not applicable for nonconvex problems, ALADIN has a theoretical convergence guarantee. Applying these two approaches to VSC-MTDC coupled with an IEEE benchmark AC power system illustrates that the tailored ALADIN outperforms ADMM in convergence speed and numerical robustness.

关键词：

Comparing Time Series Classification And Forecasting To Automatically Detect Distributed Generation

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Comparing Time Series Classification And Forecasting To Auto...

Power Tech Conference

作者： Aleksandr Petrusev Rebecca Bauer Rémy Rigo-Mariani Vincent Debusschere Patrick Reignier Nouredine Hadjsaid CNRS Grenoble INP G2Elab Univ. Grenoble Alpes Grenoble France CNRS Grenoble INP LIG Univ. Grenoble Alpes Grenoble France Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

This paper introduces tools for the automatic detection of "hidden" behind-the-meter solar generation in case where there is no monitoring or connection agreement contract with the system operator. The objective is to reach the highest precision while discriminating the nodes with and without solar generation. The proposed methods are based on exogeneous information (smart meter and temperature data) and artificial intelligence techniques consisting of neural networks as well as analytical classification algorithms. A wide range of models differing in size, architecture and number of parameters has been investigated, and the best performing ones are presented in the article. The first method involves time series classification (TSC), and the second involves time series forecasting (TSF). Open-access consumption data were used for the training of the neural networks. The implemented solutions were tested across all the nodes of the simulated electrical grid and the sensitivity of the tools was analyzed with regard to the level of PV penetration. One of the proposed tools is able to detect up to 100% of new PV installation, depending on the exogenous conditions.

关键词： Training Temperature distribution Time series analysis Neural networks Tools Smart meters Classification algorithms