The ambitious global targets on decarbonization present the need for massive integration of renewable generation in power systems, resulting in a significant decrease in the system inertia. In addition to the reduction in system inertia, the transmission system in Great Britain (GB) faces some unique challenges owing to its relatively small capacity, while being decoupled from other transmission systems and with the renewable resources largely non-uniformly distributed across the system. This paper presents opinions and insights on the challenges associated with frequency control in a low-inertia system and the potential solutions from a GB perspective. In this paper, we focus on three main techniques that act over different time scales: synchronous condensers, inertia emulation, and fast frequency response. We evaluate their relative advantages and limitations with learnings from recent research and development projects in GB, along with the opinions on their roles in addressing the frequency control challenges in future low-inertia systems.
Multi-terminal hybrid high-voltage direct current (HVDC) systems have been developed quickly in recent years in power transmission area. However, for voltage-source converter (VSC) stations in hybrid HVDC systems, no direct current (DC) filters are required. In addition, the DC reactor is also not installed at the line end because the DC fault can be limited by the converter itself. This means that the boundary element at the line end is absent, and the single-ended protections used in line commutated converter based HVDC (LCC-HVDC) systems or VSC-HVDC systems cannot distinguish the fault line in multi-terminal hybrid HVDC systems. This paper proposes a novel single-ended DC protection strategy suitable for the multi-terminal hybrid HVDC system, which mainly applies the transient information and active injection concept to detect and distinguish the fault line. Compared with the single-ended protections used in LCC-HVDC and VSC-HVDC systems, the proposed protection strategy is not dependent on the line boundary element and is thus suitable for the multi-terminal hybrid HVDC system. The corresponding simulation cases based on power systems computer aided design (PSCAD)/electromagnetic transients including DC (EMTDC) are carried out to verify the superiority of the proposed protection.
Combined heat and electricity operation with variable mass flow rates promotes flexibility, economy, and sustainability through synergies between electric power systems (EPSs) and district heating systems (DHSs). Such combined operation presents a highly nonlinear and nonconvex optimization problem, mainly due to the bilinear terms in the heat flow model—that is, the product of the mass flow rate and the nodal temperature. Existing methods, such as nonlinear optimization, generalized Benders decomposition, and convex relaxation, still present challenges in achieving a satisfactory performance in terms of solution quality and computational efficiency. To resolve this problem, we herein first reformulate the district heating network model through an equivalent transformation and variable substitution. The reformulated model has only one set of nonconvex constraints with reduced bilinear terms, and the remaining constraints are linear. Such a reformulation not only ensures optimality, but also accelerates the solving process. To relax the remaining bilinear constraints, we then apply McCormick envelopes and obtain an objective lower bound of the reformulated model. To improve the quality of the McCormick relaxation, we employ a piecewise McCormick technique that partitions the domain of one of the variables of the bilinear terms into several disjoint regions in order to derive strengthened lower and upper bounds of the partitioned variables. We propose a heuristic tightening method to further constrict the strengthened bounds derived from the piecewise McCormick technique and recover a nearby feasible solution. Case studies show that, compared with the interior point method and the method implemented in a global bilinear solver, the proposed tightening McCormick method quickly solves the heat-electricity operation problem with an acceptable feasibility check and optimality.
Communication-dependent and software-based distributed energy resources (DERs) are extensively integrated into modern microgrids, providing extensive benefits such as increased distributed controllability, scalability, and observability. However, malicious cyber-attackers can exploit various potential vulnerabilities. In this study, a programmable adaptive security scanning (PASS) approach is presented to protect DER inverters against various power-bot attacks. Specifically, three different types of attacks, namely controller manipulation, replay, and injection attacks, are considered. This approach employs both software-defined networking technique and a novel coordinated detection method capable of enabling programmable and scalable networked microgrids (NMs) in an ultra-resilient, time-saving, and autonomous manner. The coordinated detection method efficiently identifies the location and type of power-bot attacks without disrupting normal NM operations. Extensive simulation results validate the efficacy and practicality of the PASS for securing NMs.
With the growth of intermittent renewable energy generation in power grids, there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability. The flexibility of demand response (DR) resources has become a valuable solution to this problem. However, existing research indicates that problems on flexibility prediction of DR resources have not been investigated. This study applied the temporal convolution network (TCN)-combined transformer, a deep learning technique to predict the aggregated flexibility of two types of DR resources, that is, electric vehicles (EVs) and domestic hot water system (DHWS). The prediction uses historical power consumption data of these DR resources and DR signals (DS) to facilitate prediction. The prediction can generate the size and maintenance time of the aggregated flexibility. The accuracy of the flexibility prediction results was verified through simulations of case studies. The simulation results show that under different maintenance times, the size of the flexibility changed. The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.
Given the current global energy and environmental issues resulting from the fast pace of industrialization, the discovery of new functional materials has become increasingly imperative in order to advance science and technology and address the associated challenges. The boom in metal–organic frameworks (MOFs) and MOF-derived materials in recent years has stimulated profound interest in exploring their structures and applications. The preparation, characterization, and processing of MOF materials are the basis of their full engagement in industrial implementation. With intensive research in these topics, it is time to promote the practical utilization of MOFs on an industrial scale, such as for green chemical engineering, by taking advantage of their superior functions. Many famous MOFs have already demonstrated superiority over traditional materials in solving real-world problems. This review starts with the basic concept of MOF chemistry and ends with a discussion of the industrial production and exploitation of MOFs in several fields. Its goal is to provide a general scope of application to inspire MOF researchers to convert their focus on academic research to one on practical applications. After the obstacles of cost, scale-up preparation, processability, and stability have been overcome, MOFs and MOF-based devices will gradually enter the factory, become a part of our daily lives, and help to create a future based on green production and green living.
Designing and preparing high-performance lignin-based dispersants are crucial steps in realizing the value-added utilization of lignin on an industrial scale. Such process depends heavily on an understanding of the dispersion mechanism of lignin-based dispersants. Here, atomic force microscopy (AFM) is employed to quantitatively investigate the dispersion mechanism of a lignosulfonate/silica (LS/SiO2) system under different pH conditions. The results show that the repulsive force between SiO2 particles in LS solution is stronger than it is in water, resulting in better dispersion stability. The Derjaguin–Landau–Verwey–Overbeek (DLVO) formula as well as the DLVO formula combined with steric repulsion is utilized for the fitting of the AFM force/distance (F/D) curves between the SiO2 probe and substrate in water and in LS solution. Based on these fitting results, electrostatic and steric repulsive forces are respectively calculated, yielding further evidence that LS provides strong steric repulsion between SiO2 particles. Further studies indicate that the adsorbance of LS on SiO2 (Q), the normalized interaction constant (A), and the characteristic length (L) are the three critical factors affecting steric repulsion in the LS/SiO2 system. Based on the above conclusions, a novel quaternized grafted-sulfonation lignin (QAGSL) dispersant is designed and prepared. The QAGSL dispersant exhibits good dispersing performance for SiO2 and real cement particles. This work provides a fundamental and quantitative understanding of the dispersion mechanism in the LS/inorganic particle system and provides important guidance for the development of high-performance lignin-based dispersants.
Staphylococcus (S.) aureus is a leading human pathogen capable of producing severe invasive infections such as bacteremia, sepsis, and endocarditis with high morbidity and mortality, exacerbated by the increasingly widespread antibiotic resistance exemplified by methicillin-resistant strains (MRSA). S. aureus pathogenesis is fueled by the secretion of toxins—such as the membrane-damaging pore-forming atoxin,
which have diverse cellular targets including the epithelium, endothelium, leukocytes, and platelets. Here, we examine the use of human platelet membrane-coated nanoparticles (PNPs) as a biomimetic decoy strategy to neutralize S. aureus toxins and preserve host cell defense functions. The PNPs blocked platelet damage induced by S. aureus secreted toxins, thereby supporting platelet activation and bactericidal
activity. Likewise, the PNPs blocked macrophage damage induced by S. aureus secreted toxins, thus supporting macrophage oxidative burst, nitric oxide production, and bactericidal activity, and diminishing MRSA-induced neutrophil extracellular trap release. In a mouse model of MRSA systemic infection, PNP administration reduced bacterial counts in the blood and protected against mortality. Taken together, the results from the present work provide a proof of principle of the therapeutic benefit of PNPs in toxin neutralization, cytoprotection, and increased host resistance to invasive S. aureus infection.
Recent reports on the selective laser melting (SLM) process under a vacuum or low ambient pressure have shown fewer defects and better surface quality of the as-printed products. Although the physical process of SLM in a vacuum has been investigated by high-speed imaging, the underlying mechanisms governing the heat transfer and molten flow are still not well understood. Herein, we first developed a mesoscopic model of SLM under variable ambient pressure based on our recent laser-welding studies. We simulated the transport phenomena of SLM 316L stainless steel powders under atmospheric and 100 Pa ambient pressure. For typical process parameters (laser power: 200 W; scanning speed: 2 m∙s-1; powder diameter: 27 μm), the average surface temperature of the cavity approached 2800 K under atmospheric pressure, while it came close to 2300 K under 100 Pa pressure. More vigorous fluid flow (average speed: 4 m∙s-1) was observed under 100 Pa ambient pressure, because the pressure difference between the evaporation-induced surface pressure and the ambient pressure was relatively larger and drives the flow under lower pressure. It was also shown that there are periodical ripple flows (period: 14 μs) affecting the surface roughness of the as-printed track. Moreover, the molten flow was shown to be laminar because the Reynolds number is less than 400 and is far below the critical value of turbulence; thus, the viscous dissipation is significant. It was demonstrated that under a vacuum or lower ambient pressure, the ripple flow can be dissipated more easily by the viscous effect because the trajectory length of the ripple is longer; thus, the surface quality of the tracks is improved. To summarize, our model elucidates the physical mechanisms of the interesting transport phenomena that have been observed in independent experimental studies of the SLM process under variable ambient pressure, which could be a powerful tool for optimizing the SLM process in the future.
Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles. However, lithium-ion cells generate immense heat at high-current charging rates. In order to address this problem, an efficient fast charging–cooling scheduling method is urgently needed. In this study, a liquid cooling-based thermal management system equipped with mini-channels was designed for the fast-charging process of a lithium-ion battery module. A neural network-based regression model was proposed based on 81 sets of experimental data, which consisted of three sub-models and considered three outputs: maximum temperature, temperature standard deviation, and energy consumption. Each sub-model had a desirable testing accuracy (99.353%, 97.332%, and 98.381%) after training. The regression model was employed to predict all three outputs among a full dataset, which combined different charging current rates (0.5C, 1C, 1.5C, 2C, and 2.5C (1C = 5 A)) at three different charging stages, and a range of coolant rates (0.0006, 0.0012, and 0.0018 kg•s−1). An optimal charging–cooling schedule was selected from the predicted dataset and was validated by the experiments. The results indicated that the battery module's state of charge value increased by 0.5 after 15 min, with an energy consumption lower than 0.02 J. The maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8 °C, respectively. The approach described herein can be used by the electric vehicles industry in real fast-charging conditions. Moreover, optimal fast charging–cooling schedule can be predicted based on the experimental data obtained, that in turn, can significantly improve the efficiency of the charging process design as well as control energy consumption during cooling.
Fresh fruits are highly valued by consumers worldwide, owing to their delicious flavors, abundant nutrients, and health-promoting characteristics, and as such, fruits make up an important component of a healthy diet. The postharvest quality and safety of fresh fruit involve complex interactions among the fruit, environmental factors, and postharvest pathogens. Efficient regulation of fruit senescence and pathogen resistance, as well as disease-causing abilities of postharvest pathogens, is critical to understanding the fundamental mechanisms that underlie fruit quality and safety. This paper provides a comprehensive review of recent advances and currently available strategies for maintaining fruit quality and controlling major postharvest pathogens, mainly Botrytis cinerea and Penicillium expansum, which may promote sustainable and environmental-friendly development of the fruit industry.
This work analyzes and discusses the influence of human activities on the meteorological conditions related to winter haze events in Beijing, Tianjin, and Hebei (i.e., the Jing–Jin–Ji region) during 1961–2016, using the results of two numerical simulation experiments based on the Community Atmosphere Model version 5.1-1degree† used in the international CLIVAR C20C+ Detection and Attribution Project (C20C+ D&A). The results show that, under the influence of human activities, the changes in dynamical and thermal meteorological conditions related to winter haze events in the Jing–Jin–Ji region are conducive to the formation and accumulation of haze, and prevent the diffusion of pollutants. The dynamical conditions mainly include the obvious weakening of the East Asian winter monsoon (EAWM) and the enhancement of the near-surface anomalous southerly wind. The thermal conditions include the obvious increase in surface temperature, and the enhancement of water vapor transport and near-surface inversion. The relative contribution of dynamical and thermal conditions to the variation of haze days in the Jing–Jin–Ji region is analyzed using statistical methods. The results show that the contribution of human activities to the increase of haze days in the Jing–Jin–Ji region is greater than that of natural forcing for the study period. To be specific, the dynamical meteorological factors contribute more to the haze days than the thermal meteorological factors. The contribution of thermal meteorological factors is basically the same in both scenarios.