The increasing share of volatile, renewable energies, such as wind and solar power, leads to challenges in the stabilization of power grids and requires more flexibility in future energy systems. This article addresses the flexibilization of the consumer side and presents a simulation-based method for the technical and economic investigation of energy flexibility measures in industrial steam supply systems. The marketing of three different energy-flexibility measures—bivalence, inherent energy storage and adjusting process parameters—both at the spot market and at the balancing power market, are investigated from a technical as well as an economic point of view. Furthermore, the simulation-based methodology also considers pressure and temperature fluctuation induced by energy-flexibility measures. First, different energy-flexibility measures for industrial steam supply systems are introduced. Then, the physical modeling of the steam generation, distribution, and consumption as well as measure-specific control strategies will be discussed. Finally, the methodology is applied to a steam supply system of a chemical company. It is shown that the investigated industrial steam supply system shows energy-flexibility potentials up to 10 MW at peak and an annual average of 5.6 MW, which highly depend on consumer behavior and flexibility requirements.

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 transformation of energy-intensive industries towards greenhouse gas neutrality leads to increasing complexity of industrial energy supply systems. This affects particularly thermal energy systems due to waste heat utilization measures as well as the integration of renewable energy sources and further storage capacities. This complexity is also reflected in the control strategies of such systems, which makes the development of dynamic simulation models for testing them a research field of growing interest. The ThermalSystemsControlLibrary is a novel Modelica library, which aims at standardized modeling of industrial energy supply systems for control strategy development. Based on a generic data model, all components cover physical as well as control modeling and are particularly suitable for testing supervisory control strategies within external frameworks using the FMI standard. The library is validated for an exemplary use case of an industrial energy supply system comparing two different supervisory control strategies.

Deep Reinforcement Learning (DRL) can optimize the operating strategies of industrial energy supply systems (IESS), enhancing energy efficiency and flexibility while reducing costs. This work conducts and evaluates a real-world application of Proximal Policy Optimization (PPO), a widely used DRL algorithm, on the complex ETA Research Factory’s supply system. Our approach involves five steps: system boundary definition and data accumulation, modelling and validation, DRL implementation, simulation-based evaluation and finally, application of the DRL-based controller on the real system. In simulation experiments, the controller demonstrates a 43% reduction in operational costs with respect to the conventional controller while maintaining operational stability within safety limits, thus proving to be a cost-effective and reliable solution. During real-world application, the DRL-based controller upheld safety and robustness. Future research should aim at establishing comparability under various environmental conditions and closing the gap between simulation and reality.