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A Knowledge-Based Decision Support System for Micro and Nano Manufacturing Process Chains

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A Knowledge-Based Decision Support System for Micro and Nano...

Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

作者： Tobias Müller Veit Hagenmeyer Andreas Schmidt Steffen Scholz Ahmed Elkaseer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Karlsruher Institut fur Technologie Karlsruhe Baden-WÃ¼rttemberg DE Inst. for Autom. & Appl. Inf. Karlsruhe Inst. of Technol. Eggenstein-Leopoldshafen Germany Germany Faculty of Engineering Port Said Univeristy Egypt

ISBN: (纸本)9781538673843

In modern production environments, decision support systems for flexible and scalable manufacturing of functional components have become a critical issue for economic success, especially for small and medium enterprises. Knowledge-based modelling of process chains has been effectively applied to the manufacturing of macro-scale products. However, modelling and therefore the process planning of manufacturing for micro scale products with tight tolerances and high accuracy proves to be very challenging. This paper proposes a methodology to support developers work in the field of micro manufacturing technologies. In particular, support for decision making process through workflow technologies as well as an example for managing technological processes based on a capability database is presented. The ontology allows for modelling and storage of technical capabilities, supporting product developers during the initial product design phase as well as the set-up of suitable manufacturing chains by taking into account different views (materials, technologies, tools, equipment) on a production process. This enables a highly flexible production system, allowing for a fast exchange of design variants and implementation of new manufacturing modules and techniques.

关键词： Databases Tools automation Process planning Manufacturing processes

Flow-based analysis of storage usage in a low-carbon European electricity scenario

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arXiv 2018年

作者： Tranberg, Bo Schäfer, Mirko Brown, Tom Hörsch, Jonas Greiner, Martin Department of Engineering Aarhus University Aarhus C8000 Denmark Danske Commodities A/S Aarhus C8000 Denmark Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Frankfurt Institute for Advanced Studies Frankfurt am Main60438 Germany

The application of the flow tracing method to power flows in and out of storage units allows to analyse the usage of this technology option in large-scale interconnected electricity systems. We apply this method to a data-driven model of the European electricity network, which uses a techno-economic optimisation to determine generation and storage capacities and dispatch, assuming a 95% reduction of CO2emission compared to 1990 levels. A flow-based analysis of the power inflow into the different storage technologies confirms the intuition that longerterm hydrogen storage is mainly utilised for wind, whereas shortterm battery storage mostly receives inflow from solar power generation. The usage of storage technologies in general shows a local-but-global behaviour: Whereas on average the power outflow from these capacities is predominantly consumed locally inside the same node, when exported it is also transmitted over long distances as a global flexibility option for the entire system. Copyright © 2018, The Authors. All rights reserved.

关键词： Solar power generation

A Generic and Highly Scalable Framework for the automation and Execution of Scientific Data Processing and Simulation Workflows

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A Generic and Highly Scalable Framework for the Automation a...

Working IEEE/IFIP Conference on Software Architecture

作者： Jianlei Liu Eric Braun Clemens Düpmeier Patrick Kuckertz D. Severin Ryberg Martin Robinius Detlef Stolten Veit Hagenmeyer Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Eggenstein-Leopoldshafen Germany Institute of Electrochemical Process Engineering (IEK-3) Forschungszentrum Jiilich. GmbH Jiilich Germany Chair of Fuel Cells Forschungszentrum Jiilich. GmbH Jiilich Germany

ISBN: (数字)9781538663981

ISBN: (纸本)9781538663998

In order to perform complex data processing and co-simulation workflows for research on data driven energy systems, a generic, modular and highly scalable process operation framework is presented in this article. This framework consistently applies web technologies to build up a microservices architecture. It automates the startup, synchronization, and management of scientific data processing and simulation tools (e.g. Python, Matlab, OpenModelica) as part of larger transdisciplinary, multi-domain data processing and co-simulation workflows. It uses container virtualization on the underlying cluster computing environment to control and manage different simulation *** the framework's processing workflow, software executables can be distributed to different nodes on the cluster, easily access data and communicate with other components via communication adapters and a high-performance messaging channel infrastructure. By integrating Apache NiFi, the framework also provides an easy-to-use web user interface to allow users to model, perform and operate workflows for future energy system solutions. As soon as a complex workflow is set up in the process operation framework, researchers can use the workflow without any setup or configuration on their local workstations and without knowing any details of the underlying infrastructure or software environment.

关键词： Computational modeling Containers Data models Virtualization Computer architecture Big Data

Optimal Power Flow Subject to Non-Gaussian Stochastic Uncertainties: An L2-based Approach

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PAMM 2018年第1期18卷

作者： Tillmann Mühlpfordt Timm Faulwasser Veit Hagenmeyer Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Germany

The optimal power flow (OPF) problem—i.e., the task to minimize power system operation costs while maintaining technical and network limitations—is key for the operational planning of power systems. The influx of inherently volatile renewable energy sources calls for methods that allow to consider stochasticity directly in the OPF problem. Modeling uncertainties as second-order continuous random variables, the OPF problem subject to stochastic uncertainties can be posed as an infinite-dimensional L 2 -problem. A tractable and exact reformulation thereof can be obtained using polynomial chaos expansion, under mild assumptions. Polynomial chaos as such is a Hilbert space series expansion for random variables that is frequently employed for uncertainty propagation and uncertainty quantification. Polynomial chaos offers several advantages for OPF subject to stochastic uncertainties. For example, multivariate non-Gaussian uncertainties can be considered straightforwardly. Also, the solutions from polynomial chaos are effectively feedback laws in terms of the realizations of the uncertainty that are determined in a single numerical run.

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Counter-intuitive behaviour of energy system models under CO2caps and prices

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arXiv 2018年

作者： Weber, Juliane Heinrichs, Heidi Ursula Gillessen, Bastian Schumann, Diana Horsch, Jonas Brown, Tom Witthaut, Dirk Forschungszentrum Julich Institute for Energy and Climate Research-Systems Analysis and Technology Evaluation Julich52428 Germany University of Cologne Institute for Theoretical Physics Zulpicher Str. 77 Cologne50937 Germany Frankfurt Institute for Advanced Studies Frankfurtam Main60438 Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Eggenstein76344 Germany

The mitigation of climate change requires a fundamental transition of the energy system. Aordability, reliability and the reduction of greenhouse gas emissions constitute central but often conicting targets for this energy transition. Against this context, we reveal limitations and counter-intuitive results in the model-based optimization of energy systems, which are often applied for policy advice. When system costs are minimized in the presence of a CO2cap, effciency gains free a part of the CO2cap, allowing cheap technologies to replace expensive low-emission technologies. Even more striking results are observed in a setup where emissions are minimized in the presence of a budget constraint. Increasing CO2prices can oust clean, but expensive technologies out of the system, and eventually lead to higher emissions. These effects robustly occur in models of different scope and complexity. Hence, extreme care is necessary in the application of energy system optimization models to avoid misleading policy advice. Copyright © 2018, The Authors. All rights reserved.

关键词： Greenhouse gases

Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable european energy system

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arXiv 2018年

作者： Brown, T. Schlachtberger, D. Kies, A. Schramm, S. Greiner, M. Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz i 76344 Eggenstein-Levpoldshafen Germany Frankfurt Institute for Advanced Studies Ruih-Moufang-Strafie I. Frankfurt Am Main60438 Germany Department of Engineering Aarhus University Aar Hus C8000 Denmark

There are two competing concepts in the literature for the integration of high shares of renewable energy: The coupling of electricity to other energy sectors, such as transport and heating, and the reinforcement of continent-wide transmission networks. In this paper both cross-sector and cross-border integration are considered in the model PyPSA-Eur-Sec-30. the first open, spatially-resolved. temporally-resolved and sector-coupled energy model of Europe. Using a simplified network with one node per country, the cost-optimal system is calculated for a 95% reduction in carbon dioxide emissions compared to 1990. incorporating electricity, transport and heat demand. Flexibility from battery electric vehicles (BEV), power-to-gas units (P2G) and long-term thermal energy storage (LTES) make a significant contribution to the smoothing of variability from wind and solar and to the reduction of total system costs. The cost-minimising integration of BEV pairs well with the daily variations of solar power, while P2G and LTES balance the synoptic and seasonal variations of demand and renewables. In all scenarios, an expansion of cross-border transmission reduces system costs, but the more tightly the energy sectors are coupled, the weaker the benefit of transmission reinforcement becomes. Copyright © 2018, The Authors. All rights reserved.

关键词： Carbon dioxide

Feedback, Dynamics, and Optimal Control in Climate Economics⋆

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arXiv 2018年

作者： Kellett, Christopher M. Weller, Steven R. Faulwasser, Timm Grüne, Lars Semmler, Willi School of Electrical Engineering and Computing University of Newcastle CallaghanNSW2308 Australia Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Mathematisches Institut Universität Bayreuth Bayreuth95440 Germany New School for Social Research New York United States University of Bielefeld Germany International Institute for Applied Systems Analysis Laxenburg Austria

For his work in the economics of climate change, Professor William Nordhaus was a co-recipient of the 2018 Nobel Memorial Prize for Economic Sciences. A core component of the work undertaken by Nordhaus is the Dynamic Integrated model of Climate and Economy, known as the DICE model. The DICE model is a discrete-time model with two control inputs and is primarily used in conjunction with a particular optimal control problem in order to estimate optimal pathways for reducing greenhouse gas emissions. In this paper, we provide a tutorial introduction to the DICE model and we indicate challenges and open problems of potential interest for the systems and control community. Copyright © 2018, The Authors. All rights reserved.

关键词： Economics

The impact of climate change on a cost-optimal highly renewable European electricity network

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arXiv 2018年

作者： Schlott, Markus Kies, Alexander Brown, Tom Schramm, Stefan Greiner, Martin Frankfurt Institute for Advanced Studies Goethe University Frankfurt Ruth-Moufang-Str. 1 Frankfurt am Main60438 Germany Department of Engineering Aarhus University Inge Lehmans Gade 10 Aarhus C8000 Denmark Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

We use three ensemble members of the EURO-CORDEX project and their data on surface wind speeds, solar irradiation as well as water runoff with a spatial resolution of 12 km and a temporal resolution of 3 hours under representative concentration pathway 8.5 (associated with a strong climate change and a temperature increase of 2.6 to 4.8 °C until the end of the century) until 2100 to investigate the impact of climate change on wind, solar and hydro resources and consequently on a highly renewable and cost-optimal European power system. The weather data is transformed into power, different aspects such as capacity factors and correlation lengths are investigated and the resulting implications for the European power system are discussed. In addition, we compare a 30-node model of Europe with historical and climate change-affected data, where investments in generation, transmission and storage facilities are optimised. Differences in capacity factors among European countries are more strongly emphasized at the end of the century compared to historic data. This results in a significantly increased photovoltaic share in the cost-optimal power system. In addition, annual hydro inflow patterns of major hydro producers change considerably. System costs increase by 5% until the end of the century and the impact of climate change on these costs is of similar magnitude as differences between the ensemble members. The results show that including climate affected-weather data in power system simulations of the future has an observeable effect. Copyright © 2018, The Authors. All rights reserved.

关键词： Wind power

Principal Flow Patterns across renewable electricity networks

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arXiv 2018年

作者： Hofmann, Fabian Schäfer, Mirko Brown, Tom Hörsch, Jonas Schramm, Stefan Greiner, Martin Frankfurt Institute for Advanced Studies Ruth-Moufang-Straße 1 Frankfurt am Main60438 Germany University of Freiburg Emmy-Noether-Strasse 2 Freiburg79110 Germany Department of Engineering Aarhus University Inge Lehmanns Gade 10 Aarhus C8000 Denmark Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany