To accurately assess the overall performance of integrated energy systems, with a focus on their key characteristics of low carbon emissions and high efficiency, and to facilitate the safe integration of renewable energy, this study proposes a weighted energy utilization efficiency index. The integrated energy system in industrial parks is identified as a typical scenario for the application of comprehensive energy. Considering the relatively low energy conversion efficiency of renewable sources in these systems, the study evaluates the performance of the renewable and fossil fuel energy systems using energy efficiency ratios and primary energy utilization rates. Moreover, the proportion of supplied energy is utilized as a weighting factor to indicate the system’s relative significance within the total energy framework, leading to the calculation of the weighted energy utilization efficiency for the park’s integrated energy system. Through comparative analysis with conventional metrics such as primary energy utilization rate and exergy efficiency, the results indicate that the proposed index can effectively reflect the level of renewable energy integration, showcasing the system’s core features of low carbon and high efficiency. This is crucial for directing strategies towards energy saving and consumption reduction.

Geothermal power generation, as one of the main ways to develop and utilize geothermal resources, is of great significance to promote the low-carbon and clean energy structure and the realization of the “dual carbon”. Firstly, the development history of geothermal resources in the world is analyzed. Then, main geothermal power generation technologies such as dry steam power generation, flash steam power generation, binary cycle power generation and wellhead power generation technology are overviewed. On this basis, the hot dry rock power generation, thermovoltaic power generation, supercritical CO₂ cycle power generation, combined power generation technology and multi-energy eomplementary power generation technologies such as geothermal-solar, geothermal-wind, geothermal-biomass, and geothermal-ocean energy, are elaborated in detail. Finally, combining with the current situation and existing problems of geothermal power generation in China, some suggestions for the development of geothermal power generation are put forward, to provide reference for the future development of geothermal power generation.

At present, domestic research on gas control valves or other equipment in gas turbines is still lacking, while most of these researches’ models remain in a single hydraulic valve or a hydraulic cylinder. So this paper uses Simulink/Simscape software to model the entire gas turbine’s gas control valve including the PI controller to the gas pipeline, and conduct simulation analysis for faults such as fixed orifice blockage, wear of the spool valve core and hydraulic oil contamination to discuss their forms and causes. The result shows that, the blockage of the one-sided fixed orifice of the nozzle damper valve, the wear of the slide valve core and the impurities in the hydraulic oil will all cause the valve to respond slowly, or even become clogged and stuck to varying degrees. Finally, for the monitoring of valve data in current power plants some suggestions are thrown out: according to the actual operating conditions, monitoring of the parameters such as the servo valve spool displacement signal and hydraulic cylinder piston displacement can be introduced by using electronic feedback servo valve, to better judge the health status of the valve.

To solve the problems of fouling, slagging, and high-temperature corrosion in an ultra supercritical 1 000 MW unit boiler fueled by Zhundong coal, engineering verification test of nano-high-entropy ceramic coating in separated over fire air (SOFA) area of the boiler rear water wall was carried out, based on the coal characteristics, slagging condition and corrosion type of the boiler. Several methods such as macrographic check, scanning electron microscope (SEM), X-ray diffraction (XRD), Raman spectrum, friction coefficient and surface energy test were applied to observe the change of nano-high-entropy ceramic coating before and after experiments, thus to reveal the possible slag resistance and corrosion resistance mechanisms of nano-high-entropy ceramic coating. The results show that, the coating remained intact after 11 months’ boiler operation, with no obvious slagging and corrosion pits on the surface and no significantly thinning of the pipe wall. Nano-high-entropy ceramic coating can better solve the problems of fouling, slagging and high-temperature corrosion on the boiler water wall, which provides a guarantee for safe operation of the boiler fueled by Zhundong coal.

To unveil the ultra-low load operating characteristics and performance optimization method of large-scale units burning high moisture lignite, the influences of burners operating scheme under 33%BMCR condition on the coal combustion, heat transfer and NO_x transformation characteristics of a 660 MW unit utility boiler were investigated, based on an established and validated simulation model of coal-fired boiler. The results show that, well-organized flow and combustion field can still be formed inside the furnace under ultra-low load condition, but the overall boiler performance deteriorates evidently, such as obvious decreases in combustion temperature and heat transfer intensity, and increase in NO_x emission at outlet of the furnace. When 4 layers of burners are in-service, continuous lower-middle groups or middle-upper groups of burners should be put into operation, to prevent the significant deteriorations of combustion and heat transfer processes and the significant increase in NO_x emissions. The number of in-service burners layers significantly affects the overall boiler performance. When there are only two layers of burners in-service, the intense coal combustion area is too concentrated, which is not conducive to maintaining a high combustion temperature and heat transfer intensity, and NO_x emissions at the furnace outlet increase at the same time. These findings reveal the influences of burner operating scheme under ultra-low load condition on the overall performance of a 660 MW lignite boiler, which can provide guidance for deep peak shaving operation adjustment and optimization of coal-fired units in the context of large-scale renewable energy power grid connection in the future.

In order to explore the film cooling potential of crater holes, numerical simulations are performed to investigate the film cooling characteristics of equal-section crater hole, concentric elliptical crater hole, and two types of rounded corner crater holes proposed on the basis of these two types of crater holes. Cooling efficiency curves are analyzed for four types of crater holes at blowing ratios of 0.5, 1.0 and 1.5. The results show that, the crater spreading width of crater holes and crater film holes with rounded corners increases, which is beneficial to spreading coverage of the cooling film. After the crater holes are rounded at three blowing ratios, the Coanda effect strengthenes the ability of the cooling jet to adhere to the wall, and the film cooling efficiency in the near-hole region improves significantly. As the blowing ratio increases, the area-averaged film cooling efficiency after the rounded corner treatment increases by 76%, 139% and 155%, respectively, for the equal-section crater hole. The area-averaged film cooling efficiency improves by 18%, 27%, and 29%, respectively, for the concentric-elliptical crater hole with rounded corner compared with that of the concentric-elliptical crater hole.

In order to solve the problem of low accuracy of wind power prediction caused by wind speed uncertainty and volatility, this paper proposes a VMD-ISSA-GRU combination model based on variational mode decomposition (VMD), improved sparrow search algorithm (ISSA) and gated recurrent neural network (GRU). Firstly, the center frequency method is used to determine the number of modal components after VMD decomposition, which can effectively avoid over-decomposition or insufficient decomposition. Then, chaotic mapping, nonlinear decreasing weights and a mutation strategy are introduced to improve the sparrow search algorithm to optimize the gated recurrent neural network, and then an ISSA-GRU prediction model is established for each decomposed subsequence. Finally, the predicted value of each subseries is superimposed and the final predicted value is obtained. The experimental results show that, the mean absolute error, mean absolute percentage error and root mean square error of the VMD-ISSA-GRU model are 1.211 8, 1.890 0 and 1.591 6 MW, respectively. Compared with the conventional GRU, long short-term memory (LSTM) neural network, Bi-directional LSTM (BiLSTM) neural network model and other combination models, the prediction accuracy has been significantly improved, which can solve the problem of low prediction accuracy of wind power.

In the context of current energy structure transformation, conventional thermal power would gradually transform into grid source support and system regulation. Coupling with distributed photovoltaic power is an effective attempt for thermal power enterprises to achieve cost reduction and efficiency improvement. However, due to the lack of relevant guidance for the connection of non centralized power sources for the factory use, there is a lack of evaluation strategies and empirical references for system security and stability. For this purpose, taking the thermal photovoltaic complementary energy supply system of a thermal power plant in northwest China as an example, its operating characteristics are analyzed and a technical framework for stability evaluation is provided. In examples of different power units, grid connection levels, and minimum photovoltaic unit layout, application evaluation issues such as static power flow, transient stability, and power quality are discussed. It is calculated that the power consumption reduction efficiency of thermal power units in this case achieves an improvement of 18%~42%. This conclusion has typical reference significance for the application of the power generation technology model of “distributed photovoltaic access to plant use systems” in thermal power plants.

Accurately predicting solar irradiation (SI) is crucial for power scheduling and photovoltaic site selection. With the development of high-performance computing and large-capacity storage devices, data-driven deep learning models have gained widespread attentions in the SI prediction domain. However, the lack of physical interpretability due to the “black-box” nature of deep learning models restricts their credibility in specific scenarios. To enhance the interpretability of the model on the premise of maintaining prediction accuracy and keeping the model structure unchanged, and without increasing computational complexity, a model based on long short-term memory (LSTM) neural network is constructed, demonstrating an 8.07% performance improvement over the conventional neural networks and showing superior outlier handling capabilities. By employing layer-wise relevance propagation (LRP) algorithm, factors influencing the model output are scored from both temporal and spatial dimensions, enhancing the model’s interpretability. The research results indicate that the model possesses good interpretability under the premise of ensuring performance, with historical solar irradiation, time-related features (such as hour, day, week, month), solar altitude information (such as sunrise and sunset times), cloud cover, radiation time, temperature, and dew point temperature being the main factors influencing SI prediction.

Transformer insulating oil will gradually deteriorate during the operation of power equipment, resulting in a significant reduction in the electrical, physical and chemical properties of transformer oil. In this paper, the adsorption phase reaction technology is used to solidify hydrophilic SiO₂ nanoparticles on microcrystalline cellulose (MCC), to prepare modified cellulose dust collector materials with high adsorption performance, to purify and treat dirty transformer oil by combining with electrostatic adsorption technology. For the SiO₂ modified cellulose dust collector material, it can be concluded through the oil purification effect test that, the best preparation condition is dissolution and drying for 12 h, and adding 6 g ethyl orthosilicate (TEOS) as a silicon source. Then, the modified cellulose dust collector material prepared above conditions is placed in the electrostatic oil purification reactor. After synergistically purified by the two methods, the transformer oil’s main operational indicators such as moisture reduces from 32.0 mg/L (the initial value) to 23.5 mg/L or less, and other key indexes including medium loss factor, acid value and volume resistivity have reached the national standard of operational oil. It shows that the electrostatic technology combined with modified cellulose adsorbent material can effectively adsorb the impurity particles in the oil.