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.

地源热泵系统是低焓地热能利用的标准应用。为了供应更大的能量需求，应用具有多个埋管换热器（BHE）的大规模系统，其进入大量的浅地层。由于这些技术已经运行了几十年，而且地热输送过程非常缓慢，因此必须格外小心，以避免地面和系统性能的长期退化，这种退化在短期内很难恢复。关于地下条件的描述性不确定性伴随着能源需求的预测性不确定性，这些预测性不确定性往往只是粗略地知道，并且随着时间的推移变化很大。然而，量化这些不确定性的有效概念仍然缺乏。该项目提出了一种解决不确定性的新方法，并且更进一步：目标是即使在相关条件不安全的情况下也能找到最佳系统解决方案。随着操作的开始，可以在BHE的出口处的循环载热流体中容易地监测地下的温度演变。这提供了对真实地面条件的宝贵见解，而这些条件在规划阶段通常是高度不确定的。这种洞察力可以直接用于调整系统的操作方案。相比之下，选择一个确定性的，现成的方法来设计和静态控制的BHE领域有有限的机会，充分挖掘技术潜力。在这个项目中，我们提出了一种新的优化和控制程序，明确占描述性和预测的不确定性，这是为了减轻未知条件的影响，同时优化的性能与多个BHE的地源热泵系统的整个生命周期。事实上，这里的关键参数的不确定性很少考虑在一个明确的方式，无论是在理论上还是在实践中。为了有效的基于不确定性的模拟，一个通用的线源为基础的建模框架，考虑分层地面异质性，地下水流和非均匀地面热通量。这被投入到优化和控制程序中，该程序在操作期间不断学习，并通过对每个BHE的单独控制来理想地适应井眼场中的热交换。基于模型预测控制，贝叶斯学习以及多模型集成的概念不同的程序变量进行比较，并推导出理想的配方。开发和验证是在虚拟现实框架内进行的，以模拟一个假设的完全已知的“真实情况”，并在应用过程中学习隐藏的功能。基于计算机的理论发展将成为在实地可靠实施的基础。这将在项目的最后阶段进行，在该阶段，将在高分辨率监测的BHE现场验证基于模型的学习程序。