Geothermal Energy最新文献

Multi-objective optimization and CFD simulation are conducted to optimize the design of a multi-borehole ground heat exchanger (GHE) system and assess its long-time performance. The multi-objective optimization is performed to minimize the entropy generation number (EGN) and total cost rate by using various evolutionary algorithms, including NSGA-II, GDE-3, MOEA/D, PESA-II, SPEA-II, and SMPSO. NSGA-II and GDE-3 algorithms perform best in obtaining Pareto optimal solutions. Three prominent points on the NSGA-II Pareto frontier, representing the results of single-objective thermodynamic, single-objective economic, and multi-objective optimizations, are simulated in three dimensions over three months. The trends of EGN variations extracted from the transient CFD simulation agree well with those from the steady analytical model. The EGN obtained from multi-objective optimization is 58.8% lower than the EGN obtained using single-objective economic optimization and 1.9 times higher than that calculated from single-objective thermodynamic optimization. Likewise, the total cost rate obtained from multi-objective optimization is 64.4% lower than the value obtained from single-objective thermodynamic optimization and four times higher than that calculated using single-objective economic optimization. The proposed optimization approach can be reliably applied to improve the design of multi-borehole GHE systems.

This paper introduces three novel approaches to size geothermal energy piles in a MILP, offering fresh perspectives and potential solutions. The research overlooks MILP models that incorporate the sizing of a geothermal borefield. Therefore, this paper presents a new model utilizing a g-function model to regulate the power limits. Geothermal energy is an essential renewable source, particularly for heating and cooling. Complex energy systems, with their diverse sources of heating and cooling and intricate interactions, are crucial for a climate-neutral energy system. This work significantly contributes to the integration of geothermal energy as a vital energy source into the modelling of such complex systems. Borehole heat exchangers help generate heat in low-temperature energy systems. However, optimizing these exchangers using mixed-integer-linear programming (MILP), which only allows for linear equations, is complex. The current research only uses R-C, reservoir, or g-function models for pre-sized borefields. As a result, borehole heat exchangers are often represented by linear factors such as 50 W/m for extraction or injection limits. A breakthrough in the accuracy of borehole heat exchanger sizing has been achieved with the development of a new model, which has been rigorously compared to two simpler models. The geothermal system was configured for three energy systems with varying ground and bore field parameters. The results were then compared with existing geothermal system tools. The new model provides more accurate depth sizing with an error of less than 5 % compared to simpler models with an error higher than 50 %, although it requires more calculation time. The new model can lead to more accurate borefield sizing in MILP applications to optimize energy systems. This new model is especially beneficial for large-scale projects that are highly dependent on borefield size.

Renewable energies, such as solar and wind, traditionally suffer from temporal incongruity. Society’s energy demand peaks occur at different times of day than the electricity generation potential of a photovoltaic panel or, often, a wind turbine. Heat demand, in particular, is subject to a significant mismatch between the availability of heat (in the summer) and the need for heat (in the winter). Thus, a future energy system design should incorporate underground thermal energy storage (UTES) to avoid this temporal mismatch and emphasize thermal applications. Such a basis of design would introduce new methods of energy arbitrage, encourage the adoption of geothermal systems, and decrease the carbon intensity of society. UTES techniques are becoming increasingly sophisticated. These methods of storage can range from simple seasonal storage for residential structures in a grouted borehole array (BTES), to aquifer thermal energy storage (ATES), deep reservoir storage (RTES) in basins, among others. The method that each of these techniques shares is the use of the earth as a storage medium. UTES can also be characterized for electricity production, but this work largely explores applications in heating and cooling, further limited in scope to sensible heat storage (SHS). Heating and cooling processes—residential, commercial, and industrial—make up large fractions of energy demand in North America. This is also true of other locales. With the increasing concerns of climate change, exacerbated by anthropogenic greenhouse gas emissions, developers and municipal planners are strategizing to decarbonize building heating and cooling at district scales. This review covers the integration of UTES techniques with thermal energy network (TEN) technology across large districts. Though storage has long been in use for conventional district heating networks, designs are rapidly innovating, indicating broader applications of UTES integration with a TEN is advantageous from both an efficiency and economic perspective. This rapid innovation indicates the need for the integrated review offered in this paper.

Characterizing the evolution of mechanical properties of hot dry rock (HDR) after supercritical CO₂ (CO₂(sc)) injection is crucial for assessing the heat extraction rate and reservoir security of CO₂ based enhanced geothermal systems. This study designed the experiments of triaxial seepage and mechanical properties considering no CO₂(sc) injection, CO₂(sc) injection, and alternating injection of water-CO₂(sc) (AIWC) in granite at 150–300 ℃. The experiments can reveal the mechanical properties of HDR in single-phase CO₂ zone, CO₂-water two-phase zone and dissolved CO₂ liquid phase zone in HDR reservoir. The results indicate that the failure mode of the rock samples primarily exhibits sudden instability after no CO₂(sc) injection and AIWC, whereas it predominantly manifests progressive instability after CO₂(sc) injection. Compared with 25 ℃, the uniaxial compressive strength (UCS) after no CO₂(sc) injection at 150–300 ℃ decreased by 13.86%–32.92%. After CO₂(sc) injection, the UCS decreased by 40.79%–59.60%. After AIWC, the UCS decreased by 27.74–40.48%. This shows that the strength of rock mass in the single-phase CO₂ zone is lower than that in the other two zones, and this weakening phenomenon increases with the increase of temperature difference. At the same temperature, the elasticity modulus after AIWC was greater than that after no CO₂(sc) injection and CO₂(sc) injection. With no CO₂(sc) injection, when the temperature was increased to 200 ℃ and 300 ℃, intergranular cracks and transgranular appeared respectively. After AIWC, mineral crystals such as calcite were precipitated on the surfaces of the connected large cracks, accompanied by kaolinite clay minerals. This increases the frictional contact of the mineral particles and enhances the stability of the HDR reservoir.

The development of a novel method for modelling fluid flow and heat transfer in naturally fractured geothermal reservoirs represents a significant advancement in geothermal energy research. This Study presents a hybrid approach, which combines discrete fracture and single continuum techniques, to effectively capture the complex interactions between fluid flow and heat transfer in geothermal fractured reservoirs. In addition, the incorporation of the local thermal nonequilibrium method for simulating heat transmission accounts for the disparities in temperature between the rock matrix and the fluid, providing a more realistic representation of heat transfer processes. The study also presents a fully coupled thermo-poro-elastic framework that integrates fluid flow and heat transfer to comprehensively evaluate reservoir responses to injection/production scenarios. This coupled approach allows for the prediction of changes in reservoir properties, such as permeability and porosity, under varying fluid pressure and temperature conditions. The application of the proposed model to evaluate a geothermal reservoir’s long-term response to injection/production scenarios provides valuable insights into the reservoir’s behaviour and potential energy production capacity. The sensitivity analysis further enhances the model’s utility by identifying the key reservoir parameters that significantly influence the thermal depletion of the reservoir. Overall, this novel modelling approach holds promise for improving the understanding and management of naturally fractured geothermal reservoirs, contributing to the optimization of geothermal energy extraction strategies.

This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop a thermal Earth model for the conterminous United States. The model was trained to approximately satisfy Fourier’s Law of conductive heat transfer by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other spatial and physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, electrical conductivity, and proximity to faults and volcanoes. With a spatial resolution of (18 km^2) per grid cell, we predicted heat flow at surface as well as temperature and rock thermal conductivity across depths of (0-7 km) at an interval of (1 km). Our model showed temperature, surface heat flow and thermal conductivity mean absolute errors of (6.4^circ C), (6.9 mW/m^2) and (0.04 W/m-K), respectively. This thorough modeling of the Earth’s thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources. Our thermal Earth model is available as web application at https://stm.stanford.edu, feature layers on ArcGIS at https://arcg.is/nLzzT0, and tabulated data on the Geothermal Data Repository at https://gdr.openei.org/submissions/1592.

Model predictive control (MPC) is a promising approach for optimizing the performance of borehole heat exchangers (BHEs) in ground-source heat pump systems. The central element of MPC is the forward model that predicts the thermal dynamics in the ground. In this work, we validate the prediction accuracy of four BHE modeling approaches against real-world measurement data across various operational events and timescales. We simulate the fluid temperature leaving a BHE using a fully discretized 3-D numerical model, a resistance–capacitance model, a g-function model, and a hybrid model. The simulated temperatures are compared to measured temperatures using three validation metrics that quantify temperature offset, noise, and accuracy. The main reason for a mismatch between measured and modeled temperatures is a temperature offset of the simulated temperature. To remove this effect, the models were calibrated for their most sensitive parameter, the ground temperature, and their prediction accuracy over 4 years was evaluated. Thereby, model calibration seems to be a viable solution to account for an unknown load history. The results show that the resistance–capacitance model provides decent predictions in the short term and the g-function model in the long term. However, both models are strongly dependent on accurate calibration. The hybrid model provides the most accurate short and long-term predictions and is less dependent on calibration. Still, its integration into optimization syntax poses challenges compared to the other models. Although not yet applied in model predictive control, the hybrid model stands out as a promising choice for optimizing BHE field operations across various timescales.

Space heating applications account for a high share of global greenhouse gas emissions. To increase the renewable share of heat generation, seasonal thermal energy storage (STES) can be used to make thermal energy from fluctuating renewable sources available in times of high demand. A popular STES technology is pit thermal energy storage (PTES), where heat is stored underground, using water as a storage medium. To evaluate the use of PTES in an energy system, easily adaptable, publicly accessible and tool independent models are needed. In this paper, we improve an existing PTES model developed in the Modelica modeling language. The model is cross-compared with a more detailed and previously validated COMSOL model, considering different amounts of insulation, showing a deviation of 2–13% in the observed annual charged and discharged amount of heat. The results indicate that the presented model is well suited for early design stage and an exemplary case study is performed to demonstrate its applicability in a system context. Dimensions of system components are optimized for the levelized cost of heat (LCOH), both with and without subsidies, highlighting the importance of subsidies for the transition towards climate friendly heating solutions, as the gas boiler use is reduced from 47.6% to 2.7%.

Hot dry rock (HDR) resources are gaining increasing attention as a significant renewable resource due to their low carbon footprint and stable nature. When assessing the potential of a conventional geothermal resource, a temperature field distribution is a crucial factor. However, the available geostatistical and numerical simulations methods are often influenced by data coverage and human factors. In this study, the Convolution Block Attention Module (CBAM) and Bottleneck Architecture were integrated into UNet (CBAM-B-UNet) for simulating the geothermal temperature field. The proposed CBAM-B-UNet takes in a geological model containing parameters such as density, thermal conductivity, and specific heat capacity as input, and it simulates the temperature field by dynamically blending these multiple parameters through the neural network. The bottleneck architectures and CBAM can reduce the computational cost while ensuring accuracy in the simulation. The CBAM-B-UNet was trained using thousands of geological models with various real structures and their corresponding temperature fields. The method’s applicability was verified by employing a complex geological model of hot dry rock. In the final analysis, the simulated temperature field results are compared with the theoretical steady-state crustal ground temperature model of Gonghe Basin. The results indicated a small error between them, further validating the method's superiority. During the temperature field simulation, the thermal evolution law of a symmetrical cooling front formed by low thermal conductivity and high specific heat capacity in the center of the fault zone and on both sides of granite was revealed. The temperature gradually decreases from the center towards the edges.