The increasing share of volatile, renewable energy sources rises the demand for consumers who can shift their electrical power demand in time. In theory, the industrial sector offers great potential here, as it accounts for a large proportion of electricity demand. However, the heterogeneous structure of facilities in factories and the concerns of operators regarding data security and process control often prevent the implementation of demand side management measures in this sector. In order to counteract these obstacles, this paper presents a general mathematical framework for modelling and evaluating different types of inherent energy storages (IES) which typically can be found in industrial production systems. The method can be used to calculate the flexibility potential of the IES in a factory with focus on hysteresis-controlled devices and make the potential visible and usable for power grid stabilization. The method is applied in a typical production line from metalworking industry to provide live monitoring of the current flexibility potential of selected devices.

Despite the growing interest in digital twins, their practical application in manufacturing remains limited, primarily due to the lack of structured, accessible development methods. This paper introduces a systematic six-step methodology for the development and implementation of digital twins in manufacturing, aimed at bridging the gap between theoretical concepts and practical application. The methodology provides detailed guidance for defining objectives, deriving requirements, implementing functionalities, and validating the digital twin through iterative improvements. The effectiveness of the approach is demonstrated through two use cases: a digital twin for demand response in an aqueous parts cleaning machine, highlighting its potential for energy cost reduction and sustainability, and a digital twin for predictive maintenance of a machine tool spindle, showcasing its role in enhancing operational reliability. This methodology offers a practical framework for expanding the adoption of digital twins in manufacturing. By addressing both technical and practical challenges, it paves the way for broader implementation and standardization. Future research could explore its integration with frameworks such as the Asset Administration Shell and Digital Product Passports to further enhance interoperability and sustainability across manufacturing systems.

In the context of the ongoing climate change and increasingly strict climate goals of the European Green Deal, industry faces a growing challenge to decrease its high demand for electrical energy and its greenhouse gas emissions. Demand-Side Integration measures have a great potential to reduce the greenhouse gas emissions of the industrial sector. However, there is still no definition and consistent characterising terms for Industrial Demand-Side Integration. The lack of clarity in concepts and definitions may impose hurdles in the transfer of results and methodologies from research activities and thus, in the implementation of measures in the industry. Furthermore, the economic value of implementing these measures is often unclear but of high relevance to industrial consumers. This paper proposes a comprehensive Industrial Demand-Side Integration definition and a methodology to classify and characterise its measures. The methodology is aimed at helping industrial consumers decide which measures can be implemented in their specific setting and how these measures can be monetised. The methodology is validated by applying it to eight relevant use cases in the ETA Research Factory.

This article proposes a novel framework for the rapid implementation of automation programs in industrial heating and cooling systems. The global push for sustainability necessitates significant infrastructural transformations within these systems, which currently rely heavily on fossil fuels and are responsible for 75% of industrial final energy consumption. Our research highlights the critical role of design patterns and object-oriented programming principles to address the complex integration of additional energy converters and storage into automation programs. By leveraging design patterns, our framework encapsulates the intricacies of various components, such as actuators, sensors, and storage, within a comprehensive object-oriented model that also allows the integration of different control strategies. Qualitatively, this approach enhances the reusability, scalability, and adaptability of automation programs. Therefore, quantitatively, our framework enables a more resilient and efficient energy system. The framework is validated through its application to a complex, cross-linked industrial heating and cooling system at the ETA Research Factory of the Technical University of Darmstadt. Using the developed framework reduces implementation effort significantly due to its consistent and modular structure resulting from the reusable design patterns.

The digital product passport is becoming an essential tool to increase transparency about the sustainability of products and to boost the circular economy in the European Union. With the revised Ecodesign for Sustainable Products Regulation having entered into force in July 2024, the digital product passport will become a necessary extension for many products placed on the European market. Industry practitioners are faced with the challenge of fulfilling content requirements while maintaining interoperability at the same time. Although industry practitioners face challenges in implementing digital product passports, existing research has largely focused on digital product passport systems, often overlooking the crucial aspect of digital product passport data. Work on this data typically falls short of addressing comprehensive interoperability. This research therefore proposes a minimal digital product passport data model that integrates current regulatory requirements while ensuring interoperability through the use of standardised submodel templates of the asset administration shell. We demonstrate that the current regulatory requirements can be met, but foreseeable extensions will require adjustments and therefore close synchronisation between standardisation initiatives and the institutions that further specify regulatory requirements.

To reduce global greenhouse gas emissions, numerous new renewable power plants are installed and integrated in the power grid. Due to the volatile generation of renewable power plants large storage capacity has to be installed and electrical consumer must adapt to periods with more or less electrical generation. Industry, as one of the largest global consumers of electrical energy, can help by adjusting its electricity consumption to renewable production (demand response). Industrial aqueous parts cleaning machines offer a great potential for demand response as they often have inherent energy storage potential and their process can be adapted for energy-flexible operation. Therefore, this paper presents a method for implementing demand response measures to aqueous parts cleaning machines. We first determine the potential for shifting electrical consumption. Then, we adapt the automation program of the machine so that submodules and process steps with high potential can be energy-flexibly controlled. We apply the method to an aqueous parts cleaning machine in batch process at the ETA Research Factory.