As blasting technology starts to be used in a wide range of areas, blast loading has led to an increasing number of geological disasters such as slope deformation, collapses, and soil slippage. Slopes with weak interlayers are more likely to be deformed and damaged under the influence of blast loading. It is of great importance to study the evolution for the deformation of slopes with weak interlayers during blasting excavation. This study constructed a slope model with a weak interlayer to investigate the influence of different factors of blasting, including explosive charge, blast radius, blast origin, and multi-hole blasting, on the internal dynamic response. The deformation mechanism of slopes with weak interlayers under the influence of blast loading was analyzed. Test results show that each layer of the model had a different displacement response (uncoordinated dynamic response) to blasting with various factors. Explosive energy and the pattern of dynamic response of each layer varied depending on different settings of blasting factors such as explosive charge, blast radius, blast origin, and detonation initiation method. When the explosive energy produced under the influence of various factors was small, the change in the uncoordinated dynamic response between layers was significant, and the change gradually became less significant as the explosive energy increased. Therefore, this study has proposed the concept of critical explosive energy, and it is speculated that when the explosive energy produced with various factors is less than critical explosive energy, the dynamic response is mainly affected by the internal structure of the slope (property difference induced geologic layers). In other words, the uncoordinated motion of material’s particles in each layer is caused by different limitations and the degree of movement of the particles, which leads to the uncoordinated dynamic response and uncoordinated deformation of each layer. If the explosive energy is greater than the critical value, the dynamic response of each layer is mainly affected by the explosive energy. The differences in the internal structure of the slope are negligible, and the incoordination of dynamic responses between layers gradually weakens and tends to synchronize.
Phase-locked loop (PLL) is a fundamental and crucial component of a photovoltaic (PV) connected inverter, which plays a significant role in high-quality grid connection by fast and precise phase detection and lock. Several novel critical structure improvements and proportional-integral (PI) parameter optimization techniques of PLL were proposed to reduce shock current and promote the quality of grid connection at present. However, the present techniques ignored the differential element of PLL and did not acquire ideal results. Thus, this paper adopts Aquila optimizer algorithm to regulate the proportional-integral-differential (PID) parameters of PLL for smoothing power fluctuation and improving grid connection quality. Three regulation strategies (i.e., PLL regulation, global regulation, and step regulation) are carefully designed to systematically and comprehensively evaluate the performance of the proposed method based on a simulation model in MATLAB/Simulink, namely, “250-kW Grid-Connected PV Array”. Simulation results indicate that PLL regulation strategy can effectively decrease power fluctuation and overshoot with a short response time, low complexity, and time cost. Particularly, the Error(P) and the maximum deviation of output power under optimal parameters obtained by PLL strategy are decreased by 418 W and 12.5 kW compared with those under initial parameters, respectively.
With the increase of various loads connected to the low-voltage distribution system, the difficulty of identifying low-voltage series fault arcs has greatly increased, which seriously threatens the electricity safety. Aiming at such problems, a neural network algorithm based on multi-feature fusion is proposed. The fault current has the characteristics of randomness, high frequency noise, and singularity. A GA-BP neural network model is built, and the wavelet analysis method (based on singularity), Fourier transform method (based on high frequency noise), current cycle difference method (based on randomness), and current cycle similarity derivation method (based on randomness) are used for feature extraction and can more comprehensively reflect the characteristics of arc faults. Simulation results show that the multi-feature fusion algorithm has a higher recognition rate than other algorithms. Moreover, compared with the support vector machine model, logistic regression model, and AlexNet model, the GA-BP neural network model has a higher recognition accuracy than the other three models, which can reach 99%.
The utilization of fossil fuel has increased atmospheric carbon dioxide (CO2) concentrations drastically over the last few decades. This leads to global warming and climate change, increasing the occurrence of more severe weather around the world. One promising solution to reduce anthropogenic CO2 emissions is methanation. Many researchers and industries are interested in CO2 methanation as a power-to-gas technology and carbon capture and storage (CCS) system. Producing an energy carrier, methane (CH4), via CO2 methanation and water electrolysis is an exceptionally effective method of capturing energy generated by renewables. To enhance methanation efficiency, numerous researches have been conducted to develop catalysts with high activity, CH4 selectivity, and stability against the reaction heat. Therefore, in this mini-review, the characteristics and recent advances of metal-based catalysts in methanation of CO2 is discussed.
In order to study the effect of biomass on the pyrolysis characteristics of urea-formaldehyde resin, the thermogravimetric experiments were carried out respectively using urea-formaldehyde resin (UF), rice straw (RS), and their mixed pellets with different proportions. The pyrolysis kinetics analysis was conducted. The results showed that the pyrolysis process of UF resin and mixed pellets could be divided into three stages: the drying and dehydration of the material, the rapid decomposition of volatile matter, and residue decomposition. The reaction order of UF resin and mixed pellets was discussed using the Coats–Redfern method, the activation energy of UF resin was 54.27 kJ/mol, and this value decreased with the addition of rice straw. As the mass ratio of UF resin to rice straw was 3:1, the activation energy achieved the lowest value, which means that the addition of rice straw was beneficial to the pyrolysis process of UF. In the process of pellet preparation, the falling strength and compressive strength of UF resin pellets can be improved by adding an appropriate proportion of rice straw. In this test, the yield of pyrolytic carbon reached the highest value of 23.93%, as the mass ratio of UF resin to rice straw was 3:2. When the mass ratio was 4:1, the highest liquid product yield of 43.21% was achieved.
The sooner the system instability is predicted and the unstable branches are screened, the timelier emergency control can be implemented for a wind power system. In this paper, aiming at the problem that the existing unstable branch screening methods are lack prejudgment, an unstable branch screening method for power system with high-proportion wind power is proposed. Firstly, the equivalent external characteristics model of the wind farm was deduced. And based on this, the out-of-step oscillation characteristics of the power system with high proportion wind power was analyzed. Secondly, based on the oscillation characteristics, line weak-connection index (LWcI) was proposed to quantify the stability margin of a branch. Then an instability prediction method and an unstable branch screening method were proposed based on LWcI and voltage phase angle difference. Finally, the rapidity and effectiveness of the proposed method are verified through the simulation analysis of IEEE-118 system.
In light of China’s Carbon Neutrality Target and facing the fluctuating pressure of power supply brought on by new energy intermittent power generation, it is urgent to mobilize a large number of residential flexible loads that can respond instantaneously to mitigate peak–valley difference. Under a framework of demand-side management (DSM) and utility analysis, we empirically investigate customers’ costs from interrupting typical electrical terminals at the household level. Specifically, by using the contingent valuation method (CVM), we explore the factors that affect households’ Willingness to Accept (WTA) of voluntarily participating in the interruption management during the summer electricity peak and estimate the distribution of households’ WTA values. We find that given the value of WTA, households’ participation rate in the interruption management significantly decreases with the increase in interruption duration and varies with the type of terminal appliance that is on direct interruption management. Moreover, the majority of households are willing to participate in the interruption management even if the compensation amount is low. The factors that determine households’ WTA and the size of their influences vary with the type of electrical terminal. The results imply that differentiating the terminal electricity market and accurately locking on the target terminals by considering the household heterogeneity can reduce the household welfare losses arising from DSM.
This study evaluates the role of information in the environmental performance index (EPI) in different energy-consuming sectors in Pakistan through a novel slack-based data envelopment analysis (DEA). The index combines energy consumption as the primary input and gross domestic product (GDP) as the desirable output and CO2 emissions as the undesirable output. Yale’s EPI measures the efficiency of the sectoral level environmental performance of primary energy consumption in the country. Performance analysis was conducted from 2009 to 2018. The sectors were assigned scores between one and zero, with zero indicating maximum decision-making unit (DMU) inefficiency and one indicating maximum DMU efficiency. Despite being in the top-performing sector, agriculture scored only 0.51 in 2018, and the electricity sector obtained 0.412. Results also show that even the best-performing sector operates below the efficiency level. The mining and quarrying sector ranked second by obtaining 0.623 EPI and 0.035 SBEPI. Results also show that much of the energy supply of Pakistan (60.17%) is focused on fossil fuels, supplemented by hydropower (33%), while nuclear, wind, biogas, and solar power account for 5.15%, 0.47%, 0.32%, and 0.03%, respectively. Nonetheless, the overall results for both measures remained reasonably consistent. According to the literature and the energy crisis and climate instability dilemma, the authors conclude that changes to a diverse green power network are a possibility and an imminent need. Similarly, the government should penalize companies with poor performance. Furthermore, to ensure the capacity development and stability of environmental management and associated actions in the country, providing access to knowledge and training to groom human resources and achieve the highest performance is crucial.
The high proportion of renewable energy sources (RESs) in the system reduces the frequency support capacity and aggravates the generation of unbalanced power, while the dynamic frequency dispersion makes it difficult for a centralized energy storage system (ESS) to take into account the frequency requirements of different regions. In this context, the research takes the region with high penetration of RESs and frequent power fluctuations as the grid node of the ESS. By configuring the parameters of the ESS under the control strategy of virtual synchronous generators, the inertia and the primary frequency reserve of the system are supplemented, and the regulation characteristics of the ESS are depicted. Taking the steady-state recovery time and the amplitude coefficient as the evaluation indexes, the effects of the virtual inertia constant, the virtual damping coefficient, and the virtual frequency regulation coefficient on the behavior of the ESS are deeply analyzed. Finally, the quantitative configuration of the ESS is realized by considering the frequency response and the dynamic frequency dispersion.
China’s power industry is in a critical transformation period. The new round of power system reform in 2015 will have a profound impact on China’s power industry. Therefore, it’s necessary to analyze the influencing factors of thermal power generation efficiency. Based on the thermal power generation industry related data in China’s 30 provinces from 2005 to 2017, this paper studies the impacts of market segmentation on thermal power generation efficiency in China. And the empirical result shows that the market segmentation exhibit significant negative effects on the thermal power generation efficiency, that is, the thermal power generation efficiency significantly decrease 1.6799 for each unit increase of market segmentation index of thermal power industry. Besides, by decomposing the dynamic thermal power efficiency index, we find that the “innovation effect” is the primary channel for the market segmentation to make effects on the thermal power generation efficiency. Furthermore, our findings are still robust after considering endogenous problems and eliminating the relevant data. Finally, research conclusions of our study paper provide empirical supports for the efficient development of China’s power market.