Design optimization and long-term control of borehole heat exchanger fields in heterogeneous ground under descriptive and predictive uncertainty

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Ground source heat pump systems are standard applications of low-enthalpy geothermal energy utilization. For supply of greater energy demands, large scale systems with multiple borehole heat exchangers (BHEs) are applied that access substantial volumes of the shallow ground. As these technologies are operated for decades, and ground heat transport processes are very slow, extra caution has to be taken for avoiding long-term degradation of both the ground and of system performance, which can hardly be restored in the short-run. The descriptive uncertainty regarding the conditions in the ground is accompanied by the predictive uncertainty in the energy demands that are often only roughly known and highly variable in time. Still, efficient concepts that quantify these uncertainties are lacking. This project presents a new approach to address uncertainty and it goes one step further: The goal is to find optimal system solutions even when relevant conditions are unsecure. With the start of operation, the temperature evolution in the ground can easily be monitored in the circulating heat carrier fluid at the outlet of the BHEs. This offers a precious insight in the true ground conditions, which are commonly highly uncertain in the planning phase. The insight can be directly exploited for tuning the system´s operation scheme. In contrast, choosing a deterministic, off-the shelf approach for design and static control of BHE fields has limited chance of tapping the full technological potential. In this project, we suggest a novel optimization and control procedure that explicitly accounts for descriptive and predictive uncertainty, and that is intended to mitigate the impact of unknown conditions while optimizing the performance of ground source heat pump systems with multiple BHEs for the full life cycle. In fact, here critical parametric uncertainty is rarely considered in an explicit way, neither in theory nor in practice. For efficient uncertainty-based simulation, a versatile line-source based modeling framework is developed that accounts for layered ground heterogeneity, groundwater flow and nonuniform ground heat flux. This is cast into an optimization and control procedure, which continuously learns during operation and ideally adapts heat exchange in the borehole field by individual control of each BHE. Different procedural variants based on model-predictive control, Bayesian learning as well as multi-model ensemble concepts are compared and ideal formulations are derived. Development and validation are carried out within a virtual reality framework, to simulate a hypothetical perfectly known “true case”, with hidden features that are learnt during the course of application. The theoretical, computer-based developments will be the basis for reliable implementation in the field. This will be carried out in the last phase of the project, where the model-based learning procedure will be validated at a BHE field site monitored at high resolution.