• Energy Research

Abstract Shallow geothermal energy is of great interest for HVAC (heating, ventilation and air conditioning) designers. While increasingly popular in the residential and commercial sectors, shallow applications are still little used in the industrial sector and unfamiliar to policy makers, designers and stakeholders [1]. Despite this, geothermal applications are feasible for industrial plant for several reasons: operating at high load factors and supplying energy to a single location, geothermal systems would cut energy costs, a large slice of overall industrial production costs. This paper presents the results of a feasibility study carried out for an industrial shallow geothermal project, where the required preheating to the innovative Expanding-Gas-Power-Transformation (EGPT) process was supplied through a Hybrid Geothermal-Air Transcritical Heat Pump. Focus was given to modeling the geothermal component to comply with the heat pump working temperature requirements, integrating this with geostatistics and numerical simulation of heat/water flows.

This paper introduces a semi-analytical model based on the spectral analysis method for the simulation of transient conductive-convective heat flow in an axisymmetric shallow geothermal system consisting of a double U-tube borehole heat exchanger embedded in a soil mass. The proposed model combines the exactness of the analytical methods with an important extent of generality in describing the geometry and boundary conditions of the numerical methods. It calculates the temperature distribution in all involved borehole heat exchanger components and the surrounding soil mass using the fast Fourier transform, for the time domain; and the complex Fourier and Fourier-Bessel series, for the spatial domain. Numerical examples illustrating the model capability to reconstruct thermal response test data together with parametric analysis are given. The CPU time for calculating temperature distributions in all involved components, pipe-in, pipe-out, grout, and soil, using 16,384 FFT samples, for the time domain, and 100 Fourier-Bessel series samples, for the spatial domain, was in the order of 3s in a normal PC. The model can be utilized for forward calculations of heat flow in a double U-tube geothermal heat pump system, and can be included in inverse calculations for parameter identification of shallow geothermal systems.

Abstract Background Thermal Response Test (TRT) is an onsite test used to characterize the thermal properties of shallow underground, when used as heat storage volume for shallow geothermal application. It is applied by injecting/extracting heat into geothermal closed-loop circuits inserted into the ground. The most common types of closed loop are the borehole heat exchangers (BHE), horizontal ground collectors (HGC), and energy piles (EP). The interpretation method of TRT data is generally based on a regression technique and on the calculation of thermal properties through different models, specific for each closed loop and test conditions. Methods A typical TRT record is a graph joining a series of experimental temperatures of the thermal carrier fluid. The proposed geostatistical approach considers the temperature as a random function non-stationary in time, with a given trend, therefore the record is considered as a ‘realization’, one of the possible results; the random nature of the test results is transferred to the fluctuations and a variogram modeling can be applied, which may give many information on the TRT behavior. Results In this paper, a nested probabilistic approach for TRT output interpretation is proposed, which can be applied for interpreting TRT data, independently of the different methodologies and technologies adopted. In the paper, for the sake of simplicity, the probabilistic approach is applied to the ‘infinite line source’ (ILS) methodology, which is the most commonly used for BHE. Conclusions The probabilistic approach, based on variogram modeling of temperature residuals, is useful for identifying with robust accuracy the time boundaries (initial time t 0 and the final time t f) inside which makes temperature regression analysis possible. Moreover, variograms are used into the analysis itself to increase estimation precision of thermal parameter calculation (ground conductivity λ g, ground capacity c g, borehole resistance R b). Finally, the probabilistic approach helps keep under control the effect of any cause of result variability. Typical behaviors of power, flows, and temperatures and of their interaction with the specific closed-loop circuit and geo-hydrological system are deepened by variogram analysis of fluctuations.

In this paper we present a new method (DCE - Drift and Conditional Estimation), coupling Infinite Line Source (ILS) theory with geostatistics, to interpret thermal response test (TRT) data and the relative implementing user-friendly software (GA-TRT). Many methods (analytical and numerical) currently exist to analyze TRT data. The innovation derives from the fact that we use a probabilistic approach, able to overcome, without excessively complicated calculations, many interpretation problems (choice of the guess value of ground volumetric heat capacity, identification of the fluctuations of recorded data, inability to provide a measure of the precision of the estimates obtained) that cannot be solved otherwise. The new procedure is based on a geostatistical drift analysis of temperature records which leads to a precise equivalent ground thermal conductivity (@l"g) estimation, confirmed by the calculation of its estimation variance. Afterwards, based on @l"g, a monovariate regression on the original data allows for the identification of the theoretical relationship between ground volumetric heat capacity (c"g) and borehole thermal resistance (R"b). By assuming the monovariate Probability Distribution Function (PDF) for each variable, the joint conditional PDF to the c"g-R"b relationship is found; finally, the conditional expectation allows for the identification of the correct and optimal couple of the c"g-R"b estimated values.