Energy Science & Engineering最新文献

Band pressure operation has become the main way of oil and gas well workover in the world, to solve the gel breaking problem in the gel plugging pressure technology, acrylamide and ester-based cross-linking agent UCL-1 were used to synthesize a self-degradable gel that can be used at 40°C–60°C by the one-pot method. The cross-linking reaction principle of the gel was analyzed by infrared spectroscopy; in addition, the degradation performance of the gel and the effects of acrylamide, UCL-1, initiator and metal ions on the degradation performance of the gel as well as the influence law were investigated; finally, sand-filled tubing and casing were used to simulate the stratigraphy and the wellbore, respectively, thus evaluating the sealing performance of the gel. The results showed that the cross-linking reaction of the gel was a double-bond copolymerization reaction; the viscosity of the gel after complete degradation in the range of 40°C–60°C was 51–450 mPa-s, and the degradation time was 115–220 h, and the degradation time of the gel could be adjusted by changing the formulation components and the mineralization degree; moreover, the pressure-bearing capacity of the gel in the formation at 40°C–60°C was 8.5–14.9 MPa, and the pressure-bearing capacity of gel in wellbore is 52–73 kPa, and the blocking time is 3–6 d, which meets the construction time requirement of pressurized operation. This study extends the breaking method of gel plugging pressure technology and further promotes the development and application of pressure work technology.

The agglomeration of the power industry can not only improve industrial production efficiency but also promote energy structure adjustment, which is of great significance for improving national energy security and environmental protection levels. This paper is based on panel data from 30 provinces in China from 2001 to 2021, using the improved location entropy method to measure the agglomeration level of the power industry. The spatial Durbin model (SDM) is used to empirically test the influencing factors and spatial effects of the agglomeration level of the power industry. Research has found that (1) there is a significant spatial correlation in the agglomeration level of China's power industry, and the agglomeration level of the power industry in a region is influenced by neighboring regions; (2) the industrial structure, economies of scale, and power consumption of this region have a significant positive spatial effect on the level of power industry agglomeration, while the population of this region and factors such as the industrial structure, economies of scale, and power consumption of adjacent regions have a significant negative spatial effect on power industry agglomeration. Based on empirical results, relevant suggestions have been proposed to improve the agglomeration level of China's power industry. Based on empirical results, relevant suggestions have been proposed to improve the agglomeration level of China's power industry.

In this study, hybrid LSTM-SVM and hybrid LSTM-KNN models were developed to predict hourly PV power one day ahead. The performances of these hybrid models were compared with K-nearest neighbors (KNN), long short-term memory (LSTM), and support vector machine (SVM) models. The input data of these models were pressure, cloudiness, humidity, temperature, and solar intensity, while the output data was the daily photovoltaic (PV) power one day ahead. The performances of the models were evaluated using mean square error (MSE), root mean square error (RMSE), normalized root mean square error (NRMSE), and peak signal-to-noise ratio (PSNR). The prediction accuracies of hybrid LSTM-KNN, LSTM, KNN, hybrid LSTM-SVM, and SVM were 98.72%, 95.8%, 90.25%, 76.3%, and 48.87%, respectively. Hybrid LSTM-KNN predicted the daily PV power of the day ahead with higher accuracy than LSTM, KNN, SVM, and hybrid LSTM-SVM. The effect of input variables on output variables was examined with sensitivity analysis. Sensitivity analyses showed that the most important meteorological data affecting the daily PV power one day ahead was solar intensity with a rate of 95%.

Currently, there are various methods for predicting natural fractures using logging data, however these methods are primarily for predicting the number and location of fractures. This is making it difficult to determine fracture types. This paper introduces the R/S-FD method, and combined with the natural fracture development pattern in the study area, secondary R/S analysis was introduced to construct the Secondary R/S-FD method. This method overcomes the limitations of traditional R/S-FD methods that can only predict the location of fractures and cannot predict the type of fractures. After eliminating systematic errors, the prediction accuracy of the Secondary R/S-FD method for bedding fractures and high-angle fractures reaches 73% and 74%, respectively. By analyzing the fracture development characteristics of 23 wells in the study area, the research provided insights into the development characteristics of bedding fractures and high-angle fractures in oil layers within the region. The secondary R/S-FD method is a precise, fast, and cost-effective approach for predicting the development characteristics of different types of natural fractures. The next step involves leveraging a large number of fracture prediction cases as the data foundation, based on big data analysis and machine learning techniques, to establish a correlation between the F value and fracture type and number to enabling more accurate predictions of the types and quantities of natural fractures.

The C-S reservoir in the YQ district of Ordos basin, China, is located at a relatively shallow depth (240–720 m), with an original pressure coefficient of approximately 0.85 for the oil layer. Calculations indicate that the initial pressure of the oil layer ranges from 4.1 to 6.0 MPa, averaging 4.75 MPa, with an average temperature of 30°C. The reservoir is classified as shallow, low-pressure, low-temperature sandstone. This research examines the C-S tight sandstone oil reservoir located in the Ordos Basin, providing an in-depth analysis of its mineral and rock composition along with its porosity and permeability characteristics. Through the analysis of the microscopic geological features of the reservoir, significant geological factors that may contribute to reservoir degradation are identified. Research shows that the C-S reservoir has an average porosity of 8.39%, average permeability of 0.54 × 10⁻³ μm², a micro-thin-necked pore type, and a median pore radius of 1.9060 μm. The reservoir exhibits strong heterogeneity, characterized by low porosity and permeability. Laboratory experiments revealed sensitivity characteristics including weak sensitivity to velocity and water, as well as moderate sensitivity to acid and salt. Water-phase seal test results show that the self-absorption rate decreases to less than 0.1 g/h within about 12 h, leading to significant water-phase seal formation damage due to high water saturation (above 45%) within a short time. The research suggests that limited fluid passageways in the reservoir result in insufficient in situ energy for fluid migration and increased viscosity, which complicates the process of returning fractured reservoirs to their original state after digitalization.

Recognizing the challenges faced by electric busses that must utilize a portion of their battery energy to heat the passenger compartment in colder locations, thus reducing their driving range, this work has devised an effective solution to this issue. A compact single-row thermal storage system was designed to fulfill the heating needs of electric busses. Thermal resistance investigation demonstrated that this device provides exceptional insulating efficacy and heat dissipation rate. This study utilizes an aluminum-silicon alloy as the phase transition material for heat storage, with 316 stainless steel as the encapsulating medium. Air serves as the heat exchange medium, and a numerical model has been established. A small-scale experimental apparatus has been established to verify the accuracy of the numerical model. The study offers a comprehensive examination of the flow dynamics of the heat exchange fluid in storage tanks of varying diameters, the solidification pattern of the aluminum-silicon alloy phase change material, and the attributes of temperature distribution. Under equal inlet temperature and flow rate conditions, increased tank diameters lead to prolonged solidification durations for the aluminum-silicon alloy, elevated output temperatures, and a more heterogeneous temperature distribution inside the thermal storage medium. Elevating the inlet temperature in tanks of identical diameter results in increased exit temperatures and extended solidification durations for the aluminum-silicon alloy. Conversely, maintaining a constant intake temperature while augmenting the inlet flow rate reduces the output temperature and decreases the solidification duration of the aluminum-silicon alloy.

Aquaponics, a symbiotic integration of aquaculture and hydroponics, has emerged as a promising solution for sustainable food production, offering efficient water and land utilisation. However, the high energy costs associated with maintaining optimal water conditions remain a critical factor in ensuring its long-term viability. While renewable energy sources like solar and wind power can offset the high energy costs, their intermittent nature limits their effectiveness. Batteries, often used as energy buffers during these intermittencies, but introduce additional costs and environmental concerns. This study presents a novel energy optimisation approach for aquaponic systems. We employed a dynamic control algorithm to intelligently adjust water temperature based on solar forecasts. By leveraging system water as a thermal energy buffer, the method reduces reliance on grid power during solar intermittencies, thereby enhancing renewable energy integration. Simulations reveal that this approach can achieve up to 26.9% annual reduction in energy consumption for aquaponic systems compared to conventional methods. This strategy not only decreases energy usage but also highlights the potential for aquaponics to evolve into a more sustainable and cost-effective solution for food production.

Electricity production from photovoltaic panels is a clean and promising technology. However, increased panel temperatures resulting from solar intensity notably reduce productivity. Cooling these panels through diverse technologies becomes essential to enhance power generation and extend cell lifetime. In this study, electricity generation for concentrated photovoltaic (CPV) panels was enhanced by cooling with Al₂O₃/water nanofluid. An experimental analysis of the thermal and electrical efficiency of cooled and uncooled CPV was employed. Various loadings (0.3–0.9 wt%) of Al₂O₃ were utilized to investigate the effect of Al₂O₃ on overall performance. Each run was carried out at a flow rate of 1.0 L/min. The results showed that Al₂O₃/water nanofluid at a loading of 0.9 wt% resulted in a significant decrease in photovoltaic surface temperature. The temperature at the surface of CPV was significantly decreased by 52%. The electrical yield reached its maximum at 45 and 46 W/h using CPV without and with cooling water, respectively. The electricity generation was remarkably enhanced up to 54 W/h at 0.9 wt% Al₂O₃/water nanofluid. Electrical and thermal efficiency improved by 21% and 65%, respectively using 0.9 wt% of Al₂O₃. The total daily savings in CO₂ reached 0.35 kg/kW for 0.9 wt%, Al₂O₃.

In the present study, an interconnected photovoltaic-thermal system and solar thermal collector with half-tubes are presented as a new generation of solar systems to produce maximum thermal and electrical power. Performance comparison of the photovoltaic module, photovoltaic-thermal system, solar thermal collector, and proposed system shows that the maximum power of 1336.27 W is generated by the proposed system. Also, the outlet fluid temperature increases by 28.03% and 20.88% compared to the photovoltaic-thermal systems and solar thermal collectors, respectively, which indicates higher quality of the generated thermal power. To improve the system performance, fins with different heights are used inside the half-tubes. The results indicated that the overall generated power increases using the fin by up to 2.93%. A parametric analysis using response surface method showed that among four parameters including flow rate, incident solar radiation, wind speed, and ambient temperature, the solar radiation and ambient temperature have the most and least impact on the system output, respectively. Also, using the response surface method, two models are provided to predict the electrical and thermal power generation of the system. Single-objective and multi-objective optimization of the system is also investigated using these models.

This study presents a new methodology for distributed multi-agent optimization utilizing a genetic algorithm to address Multi-Area Economic Dispatch Problem (MAEDP) in a power system. While numerous studies have been conducted on various optimization methods for distributed multi-agent systems, this paper proposes a model for solving the optimal economic dispatch equations in different areas of the power system in a distributed and coordinated manner. In this model, each area is represented by an agent responsible for coordinating data exchange with other areas and solving the generation dispatch equations within its own area. The coordination model between agents and areas is described in the form of an algorithm, whereby the exchanged data values converge after several iterations, and the final solution to the problem is obtained from the perspective of each agent. The objective of each agent in each area is to minimize generation costs and meet its own area's load demand while maintaining voltage profiles. Each agent sets the power generation values of resources in each area using the genetic algorithm rules and then solves the distributed power flow equations using the proposed method. Upon achieving convergence, each agent evaluates all operational constraints within its designated region, calculates the associated generation cost, and shares the cost value to other agents, thereby facilitating the computation of the total cost for each agent. This process continues until the best possible solution is found. The results of implementing the proposed model and algorithm on several different test networks of power systems demonstrate the capability and effectiveness of the method in decomposing the optimal economic dispatch problem into smaller sub-problems and then finding the final optimal solution through simultaneous solving with agent consensus in coordinated steps.