HyStEPs - Hybrid Storage for Efficient Processes
The HyStEPs - Hybrid storage for efficient processes project is part of the energy flagship region NEFI for the decarbonization of industry. It researches efficient thermal storage technologies for industrial processes. The motivation for new innovative solutions is, on the one hand, the increase in the share of renewable energy sources in the power grid and, on the other hand, progressive decarbonization. Both factors will require a drastic increase in the capacity of thermal energy storage in existing industrial plants and industrial processes in the future.
A widespread and proven storage technology is the Ruths steam storage (RSS). The goal of HyStEPs is to increase the storage capacity of existing steam storage devices by attaching latent heat thermal energy storages (LHTES). The LHTES modules surrounding the RSS are filled phase change material (PCM):
The PCM stores energy during phase transition at almost constant temperature. Due to the complex energy-temperature dependency and low thermal conductivity of PCM, the state of charge cannot be measured directly. To optimally implement the LHTES into industrial processes, it is crucial to know in detail the locally distributed state of the storage (temperature distribution, molten zones) and thus the state of charge (SoC). Our institute has developed powerful, accurate observer algorithms that can estimate these internal distributed states optimally, robustly, and in real time using typical measurements and a detailed physical model, enabling effective and flexible management of complex thermal systems.
The developed observer is able to handle complex nonlinear distributed-parameter systems, such as those encountered in the present phase change problem. The method is based on a high-order but real-time capable finite element model for the PCM, which is used to predict future states. This prediction model is successively linearized and then efficiently reduced via balanced truncation to obtain an observer model with dominant modes only. The observer model system matrices serve as Jacobians in an extended Kalman filter, and a state update of the prediction model is computed according to the difference between the measurements and the predicted model outputs. Thereby the observer combines temperature and volume measurements.
Interested in more information?
Watch our project video on this exciting topic!
Pernsteiner, Dominik, Alexander Schirrer, Lukas Kasper, René Hofmann, and Stefan Jakubek. "Data-based model reduction for phase change problems with convective heat transfer., opens an external URL in a new window" Applied Thermal Engineering 184 (2021): 116228.
Pernsteiner, Dominik, Alexander Schirrer, Lukas Kasper, René Hofmann, and Stefan Jakubek. "State estimation concept for a nonlinear melting/solidification problem of a latent heat thermal energy storage., opens an external URL in a new window" Computers & Chemical Engineering 153 (2021): 107444.
Kasper, Lukas, Dominik Pernsteiner, Martin Koller, Alexander Schirrer, Stefan Jakubek, and René Hofmann. "Numerical studies on the influence of natural convection under inclination on optimal aluminium proportions and fin spacings in a rectangular aluminium finned latent-heat thermal energy storage., opens an external URL in a new window" Applied Thermal Engineering 190 (2021): 116448.
Pernsteiner, Dominik, Lukas Kasper, Alexander Schirrer, Rene Hofmann, and Stefan Jakubek. "Co-simulation methodology of a hybrid latent-heat thermal energy storage unit., opens an external URL in a new window" Applied Thermal Engineering 178 (2020): 115495.
- September 2018 - August 2021