Talk and Poster @ FUSED 2024

The talk and poster were presented at the conference “FUSED: From Underground to Surface: Energy provision and Distribution” organized by the Research Department of Subsurface Modeling and Engineering (RDSME) from Ruhr University Bochum. We would like to thank the organizing committee for having the opportunity to present our latest research at FUSED 2024.
Talk info: Aquifer thermal energy storages (ATES) are groundwater saturated aquifers that store thermal energy in form of heated or cooled groundwater. Combining two ATES for heat and cold, one can harness excess heat and cold from summer and winter, respectively, to support the building’s heating, ventilation, and air conditioning (HVAC) technology. To avoid the use of fossil fuel-based HVAC technology and to maximize the green use of ATES, a dynamical operation of ATES throughout the year is beneficial. Operating ATES systems in an optimal fashion can be achieved using model predictive control (MPC). In fact, MPC allows to recurringly compute optimal control actions using model-based predictions of the ATES’ behavior. However, to accurately predict the extracted groundwater temperatures, the model should ideally reflect temperature profiles around the boreholes. Unfortunately, measurements of the current temperatures are typically only available right at the boreholes. Hence, meaningful predictions require to estimate the remaining current state of the ATES system. In control, this is often realized by model-based observers. Still, observing the state of an ATES system is non-trivial since the model is typically hybrid (i.e, containing both discrete and continuous states or inputs) and since the estimation of temperatures far from the boreholes is (numerically) challenging. We show how to overcome these issues by exploiting the specific structure of the hybrid model and observing only relevant temperatures near the boreholes using an optimization-based approach.
Poster info: Borehole thermal energy storages (BTES) use (a network of) boreholes as heat exchangers to store thermal energy in the ground. The boreholes usually contain U-shaped pipes that transfer thermal energy by fluid circulation. BTES are primarily employed to store heat from summer for heating buildings in winter and vice versa (i.e, storing cold in winter for cooling in summer). The integration of BTES into the building’s heating and cooling technology is complex and requires advanced control methods, such as model predictive control (MPC). MPC allows to recurringly compute optimal control actions using model-based predictions of the behavior of dynamical systems. This work presents a novel modeling and MPC approach for a building with a BTES system and heat pump, where the MPC is used to optimize the efficiency, energy balance, and indoor climate. For the MPC’s BTES model, we leverage g-functions for the thermal response of the ground and the multipole method for the energy transport inside the borehole. Both models rely on the ”pygfunction” library by Cimmino (2018). The heat pump and building models are based on first principle laws to reduce the numerical effort for solving the resulting optimal control problem. A concluding numerical study compares different borehole (network) designs and operational scenarios. Our approach enables the efficient use of seasonal energy storages and geothermal heat flow for sustainable building heating and cooling, with the potential of cost and energy savings in real-world applications.

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