For Intelligent and Energy-flexible Production in the Manufacturing Industry

Successful completion of the Delfine project

Research focus: Climate Action Innovation

Intelligent and energy-flexible manufacturing: this is what the Delfine project stands for. It was dedicated to connecting industrial electricity consumers and energy suppliers to enable flexible electricity consumption in manufacturing at dynamic electricity prices.

Demand response (DR) programs enable end consumers to adjust their electricity consumption to the power grid’s load and the electricity market’s price signals to reduce their electricity costs. The extension of these programs in Europe is progressing steadily, though slowly.

The aim of the Delfine project, funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK) over three years, was to find technical solutions for industrial customers from various sectors to participate in price-based and incentive-based DR programs. Among other things, the solutions developed make it possible to investigate the effects of DR programs on the balance between electricity generation and consumption as well as on the development of electricity costs, especially in the manufacturing industry.

Positive developments in these areas are a prerequisite for an increased share of renewable energies in the electricity grid. An end-to-end data network was developed using semantic middleware, stretching from the automated creation of production and demand forecasts over the dynamic structuring of electricity prices to the energy-flexible and intelligent use of production resources.

In the project, the FZI focused on predicting load profiles and energy consumption of industrial production processes and, on this basis, a production planning that optimizes energy costs.

At the final meeting, FZI employees Martin Trat and Mischa Ahrens presented the latest machine learning-based prediction and optimization module results. Suitable methods for various use cases were presented and evaluated. In addition, more advanced machine learning approaches, such as transfer learning, were discussed.

Beyond the project term

Thanks to the holistic approach, business plans emerged from the project for the use of the project results by electricity providers and the manufacturing industry. They ensure sustainable utilization of the project results beyond the project duration.

The progress of the project was discussed in a public working group. Here, the ideal framework was provided by OpenEMS, a community that is promoting the development of an open-source energy management system and is interested in the digitalization of energy generation, storage, and use. The working group organized regular meetings where speakers provided insights into the project. The meetings also provided a space for free exchange between the public and project participants. After the project’s end, the established group continues to meet biweekly to exchange ideas and further develop impulses.

About the project

The project was led by Stadtwerke Trier as the energy supplier and promoted in cooperation with seven other partners from scientific institutions and industry.

The idea for this project is a response to the public call “Digitalization of the energy transition,” which was formulated within the Federal Government’s 7th Energy Research Programme – Innovations for the Energy Transition.