A. Sauer’s research while affiliated with Fraunhofer Institute for Manufacturing Engineering and Automation and other places

Abstract: The industrial sector consumes a large amount of electricity, making it an ideal candidate for Demand response (DR)flexibility in modern power systems. How‐ever, current solutions for industrialDRare limited to in‐dividual cases, services and platforms, preventing com‐panies from exploring their complete flexibility potential. Addressing this, we introduce the Energy synchronization platform (ESP), a digital integration platform concept to enable and streamline automated industrial DR. This paper outlines the ESP’s conceptual architecture, components, and operational interactions, highlighting the benefits and challenges faced in a small‐scale demonstrator consisting of three industrial companies

In the energy transition context, the manufacturing industry moves into the spotlight, as it is responsible for significant proportions of global greenhouse gas emissions. The consequent pressure to decarbonize leads to suppliers needing to report and continuously reduce the energy consumption incurred in manufacturing supplied goods. To track the energy footprint of their products, manufacturing companies need to integrate energy data with process and planning data, enabling the tracing of the product-specific energy consumption on the shop floor level. Since manufacturing processes are prone to disturbances such as maintenance, the energy footprint of each product differs. Meanwhile, the demand for energy-efficiently produced products is increasing, supporting the development of a sustainability-focused procurement by OEMs. This paper addresses this development and outlines the technical requirements as well as how companies can identify product-specific energy consumption. Furthermore, a case study is conducted detailing how to determine the product-specific energy footprint.

In order to offer energy flexibility in energy markets in short time slots a fast and efficient processing and analysis of data from shop floor to production planning and control is necessary. To this end and to gain more knowledge, different datasets and sources have to be integrated. This paper proposes a conceptual architecture and a method for profiling energy data of manufacturing systems. This includes datasets from information systems as well as physical sources such as sensors, actuators or machine data. Real-life data often come with quality problems like missing and invalid values, outliers or duplicates. The key concept is to automatically identify the necessary metadata for including the dataset in an environment where further analysis and integration of datasets can take place. Moreover, a web service for profiling and visualizing data is implemented.