ZO.RRO II

Project description:

In the ZO.RRO 2 project, the Department of Energy Use Optimization is working with partners from industry and research to contribute to the decarbonization of industrial processes in Thuringia. The department is mainly active in the work packages (WP) "Method development for energy production planning" and "Controllability of industrial consumers".

WP 2: "Development of methods for energy production planning"

The aim of this research work is to exploit flexibility in the production process for the financial benefit of the participating companies. Uncertainties that can arise, for example, due to deviations in the production plan or forecasting errors in local energy generation are taken into account. A high level of self-consumption of locally generated energy is achieved through suitable optimization processes. It is planned that flexible electricity prices can also be taken into account in the optimization. Finally, the results will be tested and illustrated using a demonstrator.

Focal points:

• Use of stochastic optimization
• Scheduling for energy management systems
• Reduction of operating costs through average optimized planning
• Cooperation with industrial partners

WP 5: "Controllability of industrial consumers"

An important aspect of the transformation to a sustainable energy industry is increasing the efficiency and flexibility of existing energy systems. Energy management systems are increasingly being used for this purpose. Unlike rule-based control approaches, they are able to plan the use of energy systems in a system in advance and estimate cost risks. A prerequisite for these functions is the realistic modeling of the energy system under consideration. However, precise modeling is associated with considerable effort due to the great diversity of energy systems. In order to reduce the time required for modeling, possibilities are being sought to derive the required information, such as the operating point-dependent efficiency, directly from measurements during normal operation.

Focus areas:

• Semi-automated energy system modeling
• Data-driven parameter identification
• Reduction of time and costs for energy system modeling
• Cross-sectoral energy system modeling and energy management systems