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

描述性和预测不确定性下异质地面埋管换热器场的设计优化和长期控制

• 批准号: 456018213
• 负责人: Professor Dr. Peter Bayer
• 金额: --
• 依托单位: Fachgebiet Angewandte Geologie (Hydro- und Umweltgeologie/Ingenieurgeologie)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/456018213?language=en
• 关键词: Design; optimization; long; term; control

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.

地面源热泵系统是低率地地热能利用的标准应用。为了供应更大的能源需求，应用具有多个井眼热交换器（BHES）的大型系统，可访问大量浅层地面。由于这些技术经营数十年，地面加热过程非常缓慢，因此必须谨慎谨慎，以避免地面和系统性能的长期退化，这在短期内几乎无法恢复。关于地面条件的描述性不确定性是通过能量需求的预测不确定性来实现的，这些需求通常只有大致已知且时间高度变化。尽管如此，还缺乏量化这些不确定性的有效概念。该项目提出了一种解决不确定性的新方法，并且进一步迈进了一步：即使相关条件不安全，目标是找到最佳系统解决方案。随着工作的开始，可以轻松地在循环的热载体流体中轻松监测地面中的温度演化。这为真实的地面条件提供了宝贵的见解，在计划阶段通常非常不确定。可以直接探索洞察力，以调整系统的操作方案。相比之下，选择确定性的，即货架的设计和对BHE领域的静态控制的方法的机会有限。在这个项目中，我们建议一种新颖的优化和控制程序，该过程明确地说明了描述性和预测性不确定性，这旨在减轻未知条件的影响，同时优化具有多个BHES的地面源热泵系统的性能，以在整个生命周期中进行多个BHES。实际上，这里很少以明确的方式考虑关键参数不确定性。为了有效的基于不确定性的模拟，开发了一种基于多功能线的建模框架，该框架解释了分层的地面异质性，地下水流量和不均匀的地面热通量。这被投入到优化和控制过程中，该过程在操作过程中连续学习，理想地通过每个BHE的个人控制来适应钻孔场中的热交换。比较基于模型预测性控制，贝叶斯学习以及多模型集合概念的不同程序变体，并得出了理想的公式。开发和验证是在虚拟现实框架内进行的，以模拟假设的完美已知的“真实情况”，并具有在应用过程中学习的隐藏特征。理论上的基于计算机的开发将是在现场可靠实施的基础。这将在项目的最后阶段进行，在该项目中，将在高分辨率以高分辨率监控的BHE现场网站上验证基于模型的学习过程。