Abstract

Due to the increasing demand for space heating and cooling and emphasis on sustainable solutions, ground source heat exchangers become more attractive. However, ground source heat pumps using direct cooling face adoption challenges due to their high upfront investment costs. These costs can be reduced by limiting the required borefield size through combining active and passive cooling regimes with ground source heat pumps. The absence of a widely-available sizing methodology for combined cooling regimes results in suboptimal borefield design. Hence, the full economic potential of combining these systems is not utilised. Therefore, this study introduces an innovative approach for cost-efficient borefield sizing that integrates active and passive cooling during the design phase, aiming for minimal TCO. The methodology is based on a doubly iterative sizing algorithm using an hourly time resolution. Novel to this domain, it relies on Bayesian optimisation to determine the most cost-efficient borefield size. It shows that load profiles constrained by maximum temperature limits in passive cooling configurations or those characterised by significant cooling demand peaks exhibit substantial potential benefits from a combined active and passive cooling approach. Real-world case studies further validate our approach, demonstrating achievable total cost of ownership (TCO) savings of 35% and 33% when optimising the integration of active cooling, compared to traditional passive cooling methods. To facilitate further research and industry applications, the algorithm has been integrated into the open-source software GHEtool.

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