Abstract

High-resolution meteorological data is crucial for accurately assessing photovoltaic potential at the microclimatic level. However, due to the scarcity of highresolution data, modeling microclimates often becomes severely inaccurate, highlighting the need for technologies that improve the resolution of solar data. Additionally, current super-resolution models often fall short in accurately processing multimodal solar energy data, which is essential for improving the precision of PV potential estimation. This paper proposes a novel downscaling framework for meteorological data that leverages deep generative models. By integrating multimodal meteorological data from the National Solar Radiation Database (NSRDB), including Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI) and other relevant variables, with a diffusion model, the framework produces meteorological data at a high spatial resolution. Experimental results demonstrate that our superresolution approach not only outperforms baseline methods but also significantly enhances the accuracy of PV potential estimation when compared to using coarse resolution data. Consequently, the diffusion-based super-resolution framework shows great promise for widespread adoption in the field of photovoltaic energy.

Abstract

Urban decarbonization is an important step in achieving carbon neutrality as urban population is expected to increase substantially for the next decades globally. This research evaluates the performance of a combined system of rooftop photovoltaics (PV) integrated with Electrical Vehicles (EV) as batteries (PV+EV) and Green Hydrogen (GH2) as energy storage for the Santiago Metropolitan Region of Chile using a techno-economic analysis. Several scenarios were analyzed including different years (2019 and 2030) reflecting declining costs of technologies, different technology combinations, and net billing system. In the simulations, the surplus renewable electricity from PV+EV is used to produce GH2 through Electrolysis Cells (PEMEC) and stored in tanks. When the city’s demand cannot be met by the PV+EV system, GH2 is used to generate electricity through Fuel Cells (PEMFC) for the demand. Results show that for the PV + EV + NB scenario with the estimated costs of technologies in 2030, it is possible to supply 96% of the total electricity demand of the city with 97% CO2 emission reduction and 37% cost savings. The surplus electricity from the system is used to generate green hydrogen and met the demand of the city, when PV generation is low. Therefore, we conclude that the proposed rooftop PV+EV+GH2 system is a viable and effective option to deeply decarbonize the urban power system with local PV generation.

Abstract

This study was conducted to investigate the impact of Reynolds number on combustion stability and flame macrostructure in a dual annulus stratified swirl burner. Stratified burners are known for their lower NOx and increased flame stability, but their stability also depends on combustor geometry, stratification ratio, equivalence ratio, and flow dynamics. The study revealed that flame static stability decreases with increasing Reynolds number. The flame blowout equivalence ratio increases by 11.6% as Reynolds number increases from 1000 to 3500 which was due to low vortical temperature with less residence time in the recirculation zone. Flame macrostructure also revealed that at lower Reynolds numbers, the flame stabilizes at inner and central shear layers, while at higher Reynolds numbers, it stabilizes at only central shear layer. Thermoacoustic instability is not observed at lower Reynolds numbers up to 2000, but thermoacoustic instability is observed at higher Reynolds numbers of 2500 and above. An interesting phenomenon of beating oscillation was observed at 2500 Reynolds number at stoichiometric conditions.

Abstract

The proliferation of DC charging stations is increasing steadily, and the integration of multi-terminal DC systems is a crucial prerequisite for facilitating the charging of electric vehicles with direct current. The stochastic charging state of electric vehicles present a formidable challenge to the stability of DC systems. This research focuses on a multi-terminal DC system with N electric vehicles as its primary subject. To assess the system’s stability in the face of the stochastic charging state of N vehicles, a small signal stability analysis method that relies on reduced order matrixes is employed. Moreover, a novel numerical index is introduced to quantitatively evaluate the risk of instability, offering valuable insights for formulating effective charging control strategies. To validate the effectiveness of the proposed method, a nonlinear time domain model is constructed in Simulink and subjected to rigorous testing.

Abstract

During the implementation of CCUS-EOR projects, clearly defining the distribution of key attributes between injection and production wells relies on reservoir numerical simulation. However, the complex solution process of compositional models results in high computational costs for traditional numerical simulators. Deep learning surrogate models can serve as a reliable alternative to reservoir numerical simulators, significantly improving computational efficiency. This study establishes a surrogate model based on the Fourier Neural Operator (FNO) for 3D heterogeneous CO2 displacement numerical simulation. The model predicts the distribution of pressure, CO2 molar fraction, and oil saturation at each time step using heterogeneous porosity, permeability fields, and injection-production parameters. The research results demonstrate that the developed surrogate model can quickly and accurately predict the distribution of various attributes in the heterogeneous 3D reservoir. It can accurately capture the different displacement characteristics in the miscible and near-miscible regions during the CO2 flooding process. Additionally, the model is able to learn from the data the differences in gas influx across perforated layers in the vertically positive rhythm and reverse rhythmic heterogeneous reservoirs, as well as the impact of gravity override on CO2 displacement characteristics. After training, the surrogate model can achieve a 360-fold improvement in computational efficiency compared to the numerical simulator. The work in this paper has certain application prospects for engineering tasks such as rapid site selection of CO2 injection pilot areas in heterogeneous 3D reservoirs, optimization of injection and production parameters, and determination of the migration direction of the CO2 gas injection front.

Abstract

There has been an increase in the adoption of electric vehicles (EVs) due to growing environmental concerns, technological advancements, and supportive government policies. This rapid increase in EVs necessitates energy providers to procure sufficient power to meet the charging demands. However, uncertainties in EV usage due to variable driving patterns and charging preferences make it challenging for energy providers to predict the charging demand. To address these uncertainties, energy providers can use stochastic models and trade in multiple short-term electricity markets. Moreover, when smart charging, energy providers can use the EV flexibility to charge the vehicles during lower market price periods, reducing procurement costs. Despite these strategies, there is a time lag between trading and delivery during which users could change their EV usage patterns, leading to new user requirements during delivery. This update in the user requirements creates discrepancies between procured and updated power needs, causing imbalances. Our study analyzes whether EVs possess enough flexibility to overcome their uncertainties, satisfy user energy requirements, and reduce imbalance costs. We develop a two-step approach: 1) procuring energy in the day-ahead market and 2) rescheduling across each EV to meet updated requirements. We test three rescheduling strategies across 51 scenarios, reflecting the updated user requirements. Our findings reveal that, despite uncertainties, EVs have enough flexibility to meet user needs and reduce imbalance costs, with the potential for additional revenues.

Abstract

Macroscopic sand production laws often fail to reveal the microscopic essence of sand production. To investigate the microscopic sand production process and oil production capacity of unconsolidated sandstone reservoirs, a visual plate model was designed to explore the influence of displacement rate and oil viscosity. Results show that the evolution of the sanding channel undergoes three stages: “migration initiation-channel formation-channel expansion”. The corresponding fractal dimension increases initially, then decreases, and finally stabilizes. The sand production strength increases with the displacement rate and oil viscosity, but there is a critical value beyond which the strength increases sharply. These preliminary microscopic sand production morphologies and mechanisms provide important guidance for simulating microscopic sand production processes and quantitatively predicting sand production laws.

Abstract

This work investigates the winter heating performance of air source heat pumps with distributed defrost devices. A dynamic simulation model of the air source heat pump considering the frosting dimensionless correlation equation and the minimum defrost supplemental heat is established based on the finite-time thermodynamic method. The results show that the air source heat pump with different defrost devices has a certain performance improvement compared with the air source heat pump with the electric auxiliary heat system. The rear-mounted type system has the best operational performance during frosting. When the relative humidity is fixed at 98%, the COP of the rear-mounted type system is highest at 0°C. When the ambient temperature is fixed at -2°C, the COP of the rear-mounted type system is highest at the relative humidity of 70%.

Abstract

The characteristics of hydrate transformation of deep-sea seepage methane in sediments affect the fate of methane and have a great impact on the global climate. However, there are many kinds of deep-sea sediments with complex compositions, as one of the main seafloor sediments, the mechanism of chlorite’s influence on hydrate formation is not clear. This study aims to investigate the effects of chlorite and quartz sand on the kinetics of methane hydrate formation. The experimental results show that when the sediment particle size is the same, the change of initial water saturation has a certain effect on the formation rate of hydrate and the final water conversion rate, however, when the initial water saturation of sediments is the same, the change of particle size has little effect on the final water conversion. Although the sediments with a high specific surface area can provide more nucleation sites for hydrate to promote the formation of hydrate, they can also absorb more water, leading to a decrease in water activity and slowing down the formation of hydrate. In addition, the results of this study provide a further understanding of the conversion characteristics of methane hydrate in sediments.

Abstract

To address the issue of steam override and low oil-steam ratios in the middle to late stages of steam flooding development in heavy oil reservoirs, experiments and numerical simulation studies on flue gas-assisted steam flooding were conducted following steam flooding. This study investigates a heavy oil reservoir block in the Xinjiang Oilfield as its research subject and conducts one-dimensional core displacement experiments to comparatively analyze the impact of various injection media on oil displacement efficiency. The experimental results demonstrate that flue gas-assisted steam flooding can enhanced oil recovery by up to 5.84% in comparison to steam flooding. A mechanism model for flue gas-assisted steam flooding in heavy oil reservoirs with a five-spot well pattern was established based on a core numerical model calibrated to experimental data and supplemented by geological reservoir characteristics data. This model is used to systematically study the mechanism and effect of the above methods in heavy oil reservoir development. The results indicated that flue gas-assisted steam flooding is an effective technique to enhance the oil recovery. Injected flue gas not only increases the contact time between steam and crude oil but also mitigates steam override and expand steam sweep range. Oil recovery improvement is significant when the steam to flue gas molar fraction ratio is 8:2, and oil recovery is 63.36%. The results of the injection mode study show that slug injection facilitates superior steam penetration into the lower reservoir compared to continuous injection. At this moment, the periodic pressure difference formed in the reservoir can improve the efficiency of the oil displacement. The study findings could be valuable in designing flue gas-assisted steam flooding for heavy oil reservoirs.