Digital Twins for Flexibility Service Provision from Industrial Energy Systems

Author(s):  
Yue Zhou ◽  
Pengfei Su ◽  
Jianzhong Wu ◽  
Wenqiang Sun ◽  
Xiandong Xu ◽  
...  
2020 ◽  
Vol 10 (24) ◽  
pp. 8903
Author(s):  
Gernot Steindl ◽  
Martin Stagl ◽  
Lukas Kasper ◽  
Wolfgang Kastner ◽  
Rene Hofmann

Digital Twins have been in the focus of research in recent years, trying to achieve the vision of Industry 4.0. In the domain of industrial energy systems, they are applied to facilitate a flexible and optimized operation. With the help of Digital Twins, the industry can participate even stronger in the ongoing renewable energy transition. Current Digital Twin implementations are often application-specific solutions without general architectural concepts and their structures and namings differ, although the basic concepts are quite similar. For this reason, we analyzed concepts, architectures, and frameworks for Digital Twins in the literature to develop a technology-independent Generic Digital Twin Architecture (GDTA), which is aligned with the information technology layers of the Reference Architecture Model Industry 4.0 (RAMI4.0). This alignment facilitates a common naming and understanding of the proposed architectural structure. A proof-of-concept shows the application of Semantic Web technologies for instantiating the proposed GDTA for a use case of a Packed-Bed Thermal Energy Storage (PBTES).


2021 ◽  
Author(s):  
Carles Ribas Tugores ◽  
Gerald Birngruber ◽  
Jürgen Fluch ◽  
Angelika Swatek ◽  
Gerald Schweiger

2020 ◽  
Vol 110 (01-02) ◽  
pp. 12-17
Author(s):  
Niklas Panten ◽  
Heiko Ranzau ◽  
Thomas Kohne ◽  
Daniel Moog ◽  
Eberhard Abele ◽  
...  

Die optimierte Betriebsweise von industriellen Energiesystemen ist eine Schlüsseltechnologie, um signifikante Kosteneinsparpotenziale durch Steigerung der Energieeffizienz und -flexibilität zu heben. Weil dabei eine Vielzahl dynamischer und stochastischer Einflüsse berücksichtigt werden müssen, spielt die Simulation des Energiesystems eine entscheidende Rolle. Zur Evaluierung unterschiedlicher Betriebsoptimierungsverfahren wird ein simulationsgestütztes Framework vorgestellt, welches bei KI (Künstliche Intelligenz)-Algorithmen unter anderem für das Anlernen mit synthetischen Daten verwendet werden kann.   The optimized operation of industrial energy systems is a key technology to unlock significant cost savings by increasing energy efficiency and flexibility. Since a variety of dynamic and stochastic influences must be considered, the simulation of the energy system plays a decisive role. A simulation-based framework is presented for evaluating various operational optimization methods, which can also be used for learning based on synthetic data with AI (artificial intelligence) algorithms.


2021 ◽  
Author(s):  
Sabri Deniz ◽  
Ulf Christian Müller ◽  
Ivo Steiner ◽  
Thomas Sergi

Abstract The Covid-19 pandemic has changed the university education, with most teaching moved off campus and students learning online or remote at home, but a cornerstone of undergraduate engineering education has been a big challenge, namely the laboratory classes. As the engineering and education communities continue to adapt to the realities of a global pandemic, it is important to recognize the importance of the laboratory-based courses. In order to address to this situation, an ambitious approach is taken to recreate the laboratory experience entirely online with the help of the digital twins of the fluid mechanics, thermodynamics, and turbomachinery laboratory experiments. Laboratory based undergraduate courses such as EFPLAB1, EFPLAB2 (Energy; Fluid and Process Laboratory 1 & 2) and EFPENG (Energy; Fluid and Process Engineering) are important parts of the “mechanical engineering” and “energy systems engineering” curricula of the Lucerne University of Applied Sciences (HSLU) in Switzerland. Each course mentioned above include six different laboratory experiments about fluid mechanics, thermodynamics, turbomachinery, energy efficiency, and energy systems, including mass- and energy flow balances in energy systems. During the Covid-19 pandemic, it was necessary to adapt to the new environment of remote learning courses and modify the laboratory experiments so that they can be carried out online. The approach was developing digital twins of each laboratory experiment with web applications and providing an environment together with supporting videos and interactive problems so that the laboratory experiments can be carried out remotely. A digital twin is a digital representation of a physical system, e.g., the test rig. It may contain a collection of various digital models with related physical equations and solutions, information related to the operation of the test rig, including 2D or 3D models, process models, sensor data records, and documentation. Ideally, all quantities and attributes that could be measured or observed from the real experiment should be accessible from its digital twin. The digital twin not only reproduces the experimental setup in the laboratory but also helps to improve the knowledge related to the theory and concepts of the laboratory experiments. One major advantage of the digital twin is that the number and range of the parameters, which can be manipulated or varied, is larger in comparison to the actual testing in the laboratory. This paper explains the development of the digital twins (web applications) of the laboratory experiments and provides information about the selected experiments such as potential vortex, linear momentum equation, diffuser flow, radial compressor, fuel cell, and pump test rig with the measurement of pump characteristics. A remote or distance learning has many hurdles, one of the largest being how to teach hands-on laboratory courses outside of an actual laboratory. The experience at the Lucerne University of Applied Sciences showed that teaching online labs using the digital twins of the laboratory experiments can work and the students can take part in remote laboratories that meet the learning outcomes and objectives as well as engage in scientific inquiry from a distance.


Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2780 ◽  
Author(s):  
Aida Sa ◽  
Patrik Thollander ◽  
Enrico Cagno ◽  
Majid Rafiee

With regard to increased sustainability, managers not only need to know WHAT is needed for their company to improve, but also HOW to do so in detail is equally important. Energy management (EnM) is a pillar to the transformation of industrial energy systems towards enhanced energy efficiency and increased sustainability. One way to develop more and improve EnM both practically and theoretically is to shed light on how the combination of techniques and operation can contribute to successful EnM. This paper, therefore, through investigation of 10 Swedish foundries aims to present the structure of the energy strategy and associated practices at first; second, to assess industry’s EnM program and maturity level; and third, to identify and understand the nature of energy efficiency promoting factors within studied cases.


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