Most energy-intensive industrial processes possess an intrinsic energy flexibility that can be utilized to provide auxiliary services to the electricity grid when supply and demand are unbalanced. Therefore, their electricity consumption can be regulated up and down based on the actual unbalance in the grid. In addition to the provision of auxiliary services, these processes also produce large amounts of waste heat that can be captured and reused in district heating systems. Hence, the successfully integration of energy-intensive industrial processes into our national energy system plays a significant role in the green energy transition.
Although there is huge energy flexibility potential in industrial processes, many potentials are not realized. Furthermore, there is currently a hesitance from industry to adopt flexible production due to lack of knowledge about any inconveniences or unforeseen consequences it may cause to the production flow.
Therefore, this project aims to develop a Digital Twin platform that will provide decision makers with actionable insights through an integrated dashboard. The developed Digital Twins can capture the inherent energy flexibility in a production system, leading to increased incentive and knowledge about industrial sector-coupling possibilities. The integrated dashboard can show the results and overview of sector-coupling possibilities. Furthermore, it can provide recommendations to the facility and production managers with the consideration of cost, CO2 emission, and production quality.
This Digital Twin platform will build upon the IoT/Edge computing and Cloud-based software platform from Inuatek A/S, and the project ‘Datadrevet energiovervågning og optimering af industrielle processer’ funded by Energy Cluster Denmark in 2022.
The project will facilitate energy-intensive industries to realize their energy flexibility potentials through industry 4.0 technologies using IoT, Big Data, AI, cloud computing, and Digital Twins as integrated parts of their production system (A Digital Twin is a digital counterpart to a real system that can predict the system's behavior under various circumstances).
The project will aid in quickly and efficiently establishing Digital Twins of production facilities for sector coupling by developing a Digital Twin framework. With the use of the Digital Twin framework developed in this project, the industry can perform what-if scenarios to investigate how activation of process flexibility may propagate through the production, thereby minimizing the risk of bottlenecks, missed deadlines, and low-quality products.
[1]Energistyrelsen (2020) Data, tabeller, statistikker og kort Energistatistik 2019. https://ens.dk/sites/ens.dk/files/Statistik/energistatistik2019_dk-webtilg.pdf