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Background Short tandem repeat (STR) markers are widely used in forensic DNA analysis due to their ability to provide automated and standardised typing. However, incorrect STR typing can have a significant impact on forensic outcomes.Aim In this study, we detected drop-out alleles at the SE33 locus in a putative father-son pair using the Microreader™ 28 A ID System. This result could lead to a false conclusion of non-paternity.Subjects and methods To investigate the cause of the drop-out alleles, we developed a nest and touch-down PCR program for Sanger sequencing of the SE33 locus. Subsequently, we investigated the mutation frequency in 300 unrelated individuals and reviewed the results of 429 paternity tests.Results The results showed that the frequency of the G > T mutation at this locus was less than 0.01, which is a novel and rare mutation. Our analysis revealed a novel G > T mutation in the primer-binding region of both samples, which was a rare single-nucleotide mutation site in the Chinese population. This variation was found to be responsible for the drop-out alleles observed in the samples.Conclusion Our findings have important implications for optimising primer design and constructing DNA databases for forensic analysis.

Nonlinear resonators of superconducting thin-film kinetic inductance have attracted considerable research interest in the fields of detectors, qubits, parametric amplifiers, and more. By tuning the deposition parameters, niobium titanium nitride (NbTiN) films (∼12 nm in thickness) present high resistivity(∼2000 μΩ cm) and large sheet kinetic inductance (∼0.7 nH/□). We designed resonators with NbTiN micronwire as the inductor and aluminum as the capacitor, which result in a high internal quality factor and a Kerr nonlinearity of a few hertz. The radio frequency response of the resonators demonstrates nonlinear behavior similar to that of the cubic Duffing oscillator, including frequency shift and hysteresis region. The kinetic inductance resonator is a promising candidate for high saturation power parametric amplifiers.

Abstract Transient stability is the key aspect of power system dynamic security assessment, and data-driven methods are becoming alternative measures of assessment. The current data-driven methods only construct correlations between variables while neglecting causal relationships. Therefore, they face problems such as poor robustness, which restrict their practical application. This paper introduces an improved constraint-inference approach for causality exploration of power system transient stability. Firstly, a causal structure discovery method of power system transient stability is proposed based on a PC-IGCI algorithm, which addresses the shortage caused by Markov equivalence and massive variables. Then, a relative average causal effect index is proposed to reveal the relationship between relative intervention strength and causal effects. The results of a case study verify that the proposed method can identify the causal structure between the transient stability variables entirely based on data. In addition, the causal effect sorting between “cause” and “outcome” of transient stability variables is revealed. This paper provides a new approach for data mining to uncover the causal mechanisms between variables in power systems and expand the capabilities of data-driven methods in power system application.

Abstract With the rapid development of wind power, the pressure on peak regulation of the power grid is increased. Electrochemical energy storage is used on a large scale because of its high efficiency and good peak shaving and valley filling ability. The economic benefit evaluation of participating in power system auxiliary services has become the focus of attention since the development of grid-connected hundred megawatt-scale electrochemical energy storage systems (ESS). Based on the relationship between power and capacity in the process of peak shaving and valley filling, a dynamic economic benefit evaluation model of peak shaving assisted by hundred megawatt-scale electrochemical ESS considering the equivalent life of the battery is proposed. The model considers the investment cost of energy storage, power efficiency, and operation and maintenance costs, and analyzes the dynamic economic benefits of different energy storage technologies participating in the whole life cycle of the power grid. Then, according to the current ESS market environment, the auxiliary service compensation price, peak-valley price difference and energy storage cost unit price required to make the energy storage technology achieve the balance of payments are calculated, and the economic balance points of different energy storage types are clarified. Finally, based on the measured data of different provincial power grids, the economies of six energy storage types applied to three provincial power grids are compared and analyzed, and the rationality and effectiveness of the relevant models proposed are verified. The work has theoretical guiding significance for the economic benefit evaluation of hundred megawatt-scale electrochemical energy storage.

ABSTRACTThe tensile and shear mechanical properties of laver were investigated in order to reduce the tensile and shear forces, tensile and shear energies consumption during the harvesting and processing. Loading rate, harvest time and origin were selected as the effect factors for laver tensile and shear tests, while mathematical models of the variation of tensile and shear mechanical properties with three factors were also developed. The following results were obtained: tensile force, shear force, tensile energy and shear energy of laver are affected by loading rate, harvest time and origin. Tensile force, shear force and shear energy were generally inversely proportional to the loading rate, while tensile energy was generally positively proportional to the loading rate. The minimum values are 0.0557 N, 0.1650 N, 0.7267 N.mm and 0.7393 N.mm, respectively. Tensile force, shear force and tensile energy all increased with increasing harvest time, while shear energy decreased with increasing harvest time. The maximum values are 0.1581 N, 0.4437 N, 1.5367 N.mm and 1.5177 N.mm, respectively. Both tensile and shear forces (0.0857 N, 0.2549 N) were greatest in Dalian for the first harvest of laver. However, the first harvest of Qingdao laver has the lowest tensile and shear energy. The microstructure after stretching and shearing changed significantly with normally shaped cells deformed and ruptured. The changes in microstructure were consistent with the macro-mechanical properties, verifying the reliability of the test data.

Abstract CaFeAsF is an iron-based superconductor parent compound whose Fermi surface is quasi-two dimensional, composed of Dirac-electron and Schrödinger-hole cylinders elongated along the c axis. We measured the longitudinal and Hall resistivities in CaFeAsF with the electrical current in the a b plane in magnetic fields up to 45 T applied along the c axis and obtained the corresponding conductivities via tensor inversion. We found that both the longitudinal and Hall conductivities approached zero above ~40 T as the temperature was lowered to 0.4 K. Our analysis indicates that the Landau-level filling factor is ν = 2 for both electrons and holes at these high field strengths, resulting in a total filling factor ν = ν hole − ν electron = 0. We therefore argue that the ν = 0 quantum Hall state emerges under these conditions.

With the expansion of power grid scale and the deepening of component coupling, the operation behavior of power system becomes more and more complex, and the traditional function decoupling dispatching architecture is not available anymore. Firstly, this paper studies the corresponding relationship between reinforcement learning method and power system dispatching decision problem, and constructs the artificial intelligent dispatching knowledge learning model of power system based on reinforcement learning (AIDLM). Then, a data-driven intelligent dispatching knowledge learning method is proposed, and interpretable dispatching decision knowledge is obtained. Finally, a knowledge efficiency evaluation indexes is proposed and used to guide the extraction of original acquired knowledge. The intelligent economic dispatching problem of a regional power grid is analyzed. The results show that the AIDLM method can intelligently give the dispatching strategy of power generation according to the time series changing load, which effectively reduces the cost of power generation in the grid. The method proposed in this paper can make up for the shortcomings of traditional dispatching methods and provide strong support for modern power system dispatching.

IntroductionTo enhance the application of mechanized harvesting and supplement research on harvesting theory in mudflat-buried shellfish harvesting in China, a brush-screen cooperative mudflat-buried shellfish vibration harvester was designed.MethodsThe harvester is primarily composed of a crank rocker double-layer vibrating screen, two stage rolling brush, and a conveyor chain. White clams (Mactra veneriformis) cultured in mudflats were used as the research objects in this paper, and the mechanics and motion states of the shellfish on the vibrating screen were analyzed. The shellfish harvesting simulation response surface experiments based on the discrete element method (DEM) were conducted to analyze the influence of the main operating parameters on the quantity of shellfish harvested.ResultsThe results revealed that the number of shellfish harvested was significantly influenced (p< 0.01) by vibrating screen amplitude, first-stage spiral rolling stainless steel brush rotation rate, and harvester travel speed. The optimal combination of key parameters was 1.4 mm, 40 rpm, and 10 m/min, respectively. With these values, the projected shellfish crushing rate was 2.82% and the shellfish harvesting efficiency was 125 pieces/m2. The equipment was then manufactured and the shellfish harvesting verification test was performed under the same operating parameters as the simulation. Test results indicated that the harvesting efficiency of the equipment was 114 pieces/m2 and the shellfish crushing rate was 6.97%.DiscussionThe shellfish harvesting work could be completed by the equipment effectively and with low loss. The results of this study provide a theoretical reference for a novel mechanized method of harvesting mudflat-buried shellfish.

Abstract Guo, Alexandradinata, et al. have recently proposed that quantum-oscillation frequencies from Dirac/Weyl fermions exhibit a negative shift proportional to T 2 because of the energy dependence of the effective mass peculiar to a linear band-dispersion. We have measured Shubnikov–de Haas oscillation in CaFeAsF up to T = 9 K. The frequency of the α Dirac electron exhibits a negative shift with increasing T, while that of the β Schrödinger hole does not. For T ⩾ 5 K where β is negligible, the α-frequency shift is proportional to T 2 and its rate agrees with the theoretical prediction within experimental accuracy. At lower temperatures, the shifts of α and β deviate from theoretical expectations, which we ascribe to the inaccuracy in the frequency determination due to unfavorable interference between frequencies. Our results confirm that the topological frequency shift can be utilized to identify Dirac/Weyl fermions when quantum-oscillation frequencies can be determined accurately.

This paper investigates the effect of origin, harvest times and loading rates on the biomechanical properties of laver, aiming to develop laver harvesting and postharvest transportation equipment. The values and changing regular of biomechanical properties were obtained via a combination of morphological and mechanical tests as well as numerical statistics. The correlation between biological and mechanical properties was detected simultaneously. The results show that the biological properties are affected dramatically by origin and harvest times. The values of length, width, thickness and mass of laver from Dalian exceeded those found in Qingdao and Lianyungang. The width, thickness and mass increased, whereas the length-to-width ratio decreased with the increasing harvest time. Meanwhile, the mechanical properties are also influenced significantly by loading rates, origin and harvest times. Tensile and shear strength displayed an overall decreasing trend, whereas adhesive force and adhesiveness in general increased with the increasing loading rate. The tensile and shear strengths were greatest for laver from Qingdao, while the adhesive force and adhesiveness were greatest for laver from Dalian. Tensile strength, adhesive force and adhesiveness increased, and shear strength decreased with the delay of harvest time. In addition, the tensile strength and thickness of the laver at different harvest times were positively correlated. The maximum tensile strength, shear strength, adhesive force and adhesiveness were 3.56 MPa, 4.79 MPa, 0.32 N and 1.01 N·mm, respectively. These results are believed to be able to provide a reference for the design and optimization of machineries such as harvest, postharvest transportation and laver processing.

This paper designed a centrifugal shellfish seeding device with a conical disc structure to improve the seeding efficiency and uniformity of the centrifugal shellfish seeding device. The dynamic model of the shellfish seedling moving on the conical disk is established, and the main parameters affecting the shellfish seedling’s movement characteristics are determined as the cone angle of the impeller, the speed of the impeller, and the offset angle of the blade. Using the EDEM software and the shellfish seedlings blanking rate as the influencing factors, a single factor simulation test was performed. The research found that the variation trend of shellfish seedlings distribution in a single statistical area gradually decreased along the horizontal direction of both sides, with the origin being the center of the seedling sowing device. Test orthogonal rotation simulations using the variation coefficient of seedlings distribution uniformity and the effective job width as evaluation indicators. Use Design Expert software to optimize the structure of the seedling sowing device based on the test results, and determine the optimal structure with the maximum width and the minimum variation coefficient as constraints: the impeller cone angle is 7.72°, and the blade offset angle is -0.9°. Use the above-mentioned seeding device for simulation and actual seeding testing. The simulation results show that when the impeller speed is 750r/min and the blanking rate is 0.65kg/s, the variation coefficient of seedlings distribution uniformity and the effective job width are 12.84% and 24.98m, respectively, while the actual test results are 13.77% and 23.25m, respectively. The object of the seeding performance test was commonly used size shellfish seedlings of the Philippines clam. The results showed that the impeller speed, blanking rate, quadratic term of shellfish seedling size, quadratic term of blanking rate, and interactive term of impeller speed and blanking rate had significant effects on the variation coefficient of the distribution uniformity of shellfish seedlings (P

The Manila Clam is an important economic shellfish in China’s seafood industry. In order to improve the design of juvenile Manila Clam seeding equipment, a juvenile clam discrete element method (DEM) particle shape was established, which is based on 3D scanning and EDEM software. The DEM contact parameters of clam-stainless steel, and clam-acrylic were calibrated by combining direct measurements and test simulations (slope sliding and dropping). Then, clam DEM simulation and realistic seeding tests were carried out on a seeding wheel at different rotational speeds. The accuracy of the calibrated clam DEM model was evaluated in a clam seeding verification test by comparing the average error of the variation coefficient between the realistic and simulated seeding tests. The results showed that: (a) the static friction coefficients of clam-acrylic and clam-stainless steel were 0.31 and 0.23, respectively; (b) the restitution coefficients of clam-clam, clam-acrylic, and clam-stainless steel were 0.32, 0.48, and 0.32, respectively. Furthermore, the results of the static repose angle from response surface tests showed that when the contact wall was acrylic, the coefficient rolling friction and static friction of clam-clam were 0.17 and 1.12, respectively, and the coefficient rolling friction of clam-acrylic was 0.20. When the contact wall was formed of stainless steel, the coefficient rolling friction and static friction of clam-clam were 0.33 and 1.25, respectively, and the coefficient rolling friction of clam-stainless steel was 0.20. The results of the verification test showed that the average error between the realistic and simulated value was

This systematic review was conducted to identify, evaluate and characterize the overall progress of health economics research conducted for Africa. Health economics studies carried out from 1991 to 2020 for Africa were retrieved from the EconLit database using relevant searching strategies. According to the methodology of Preferred Reporting Items for Systematic Review and Meta-Analysis, qualified journal papers were included. Using bibliometrics, we ran a series of analyses on authorship, studied countries, affiliations, and countries of origin, journals, and research topics. A total of 2935 studies were screened, and 178 were included in this review. We observed that the determinants of illness is the most researched topics. The United States, World Bank, University of California Berkeley, are respectively the most influential countries, world organizations, and academic institutions in the field of health economics of Africa. HIV/AIDs is still the leading health issue in highly cited health economics studies in Africa. Health Policy and Planning is the most productive and academically influential journal, and Kenya is the most studied country by health economists among all African countries. African health systems are vulnerable compared to developed countries, as many of them are underfunded. The academic strength in Africa is much weaker than that of leading health economics countries. Even within the continent, the academic development and the attention it receives are uneven. More influential health economics studies of Africa should be published in addition to the disease focus of HIV/AIDS.

Power flow calculation is the foundation of security analyses in a power system, and the phenomenon of convergence failure is becoming more prominent with the expansion of the power grid. The existing convergence failure diagnosis methods based on optimization modeling and local feature recognition are no longer viable for bulk power systems. This paper proposes a diagnosis method based on intermediate iteration data and the identification of the transmission power congested channel. Firstly, the transmission power congestion index is constructed, and then a method for identifying transmission congestion channels is proposed. The reasons for convergence failure of the power flow are diagnosed from two aspects: excessive power to be transmitted and insufficient transmission capacity. Finally, with the aim of alleviating transmission channel congestion, a correction strategy for power flow injection space data was constructed, which generates relaxation schemes for operational variables. The effectiveness of the proposed strategy was verified using the simulation results of an actual provincial power grid and a standard example power system with 13,659 buses. The method proposed in this paper is entirely based on intermediate power flow iteration data, which avoids the complex modeling of the power flow adjustment and provides methodological support for power flow diagnosis in bulk power systems.

Bus loads in electric grids have inherently a spatial-temporal behavior and also a certain degree of randomness. The spatial-temporal feature based bus load forecasting, which provides additional information on the spatial distribution and the uncertainty of future electric loads, is of importance to power systems dispatching and planning, in particular, with intermittent renewable power generation. In this paper, a method for extracting spatial-temporal features, including abnormal states of multiple bus loads in electric grids, is proposed. The abnormal spatial load states are firstly identified by using one-class support vector machine. Then, only the load fluctuations of normal states are mapped into a dynamic model space supported by polynomials in order to approximate the time series of bus loads. The parameters of polynomials are clustered by the Dirichlet process mixture model for deriving the patterns of load state evolution. As a result, the extracted spatial-temporal patterns are a set of different distributions of bus loads with static features and dynamic features displayed explicitly. The method is tested against the bus loads of an electric grid in a city in the Northeast China. The proposed methodology is validated with respect to the bus loads in time slots of the future 10 days.

Bad data in a power system fail to accurately reflect its state of operation. Cleaning bad data is thus necessary for the situational analysis of a data-based power system. Methods for the identification and modification of bad data based on state estimation may fail to converge in iterative calculations, and yield poor results when the errors are significant. State estimation also involves non-linear computation, which incurs a significant computational overhead and makes it difficult to efficiently handle large volumes of data. To solve these problems, this paper proposes a method for the detection and identification of bad data based on constraints on spatial–temporal correlation. In the spatial domain, we fix the equivalent power balance condition through the topology of the network. In sequential distribution, we establish data constraints based on the similarity of data among temporal sections. We then check the data in combination with the spatial–temporal correlation constraints. The proposed method was applied to an IEEE 14-bus system and a provincial grid, and the results show that it can reliably detect and modify bad data to significantly improve their quality. The proposed approach avoids non-linear calculations, is computationally efficient, and can quickly detect and modify bad data.

Transient stability assessment (TSA) based on security region is of great significance to the security of power systems. In this paper, we propose a novel methodology for the assessment of online transient stability margin. Combined with a geographic information system (GIS) and transformation rules, the topology information and pre-fault power flow characteristics can be extracted by 2D computer-vision-based power flow images (CVPFIs). Then, a convolutional neural network (CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency set. The network consists of two components: the classification network classifies the input samples into the credibly stable/unstable and uncertain categories, and the prediction network is utilized to further predict the generator stability indices of the categorized samples, which improves the network ability to distinguish between the samples with similar characteristics. The proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system, and the simulation results validate the effectiveness of the method.

To solve the problems of low net harvesting rate, high loss rate, and uneven stubble height during the harvest of laver, the laver (Porphyra yezoensis) was selected as the research object, the analysis of the cultivation mode, biomechanical characteristics, harvesting trajectory and force of laver were carried out. A sectional hob type harvesting device was designed. A rigid-flexible coupling model related to the interaction between the cutting mechanism and the laver was constructed based on ANSYS/LS-DYNA. The Box–Behnken design method was used to simulate the effects of different structural parameters and process parameters on the force of laver cutting, and the bench test of the laver harvesting device was carried out. The simulation results showed that the four factors that significantly affect the force exerted on the laver during cutting in proper order were cutter revolving speed, knife extension length, knife inclination angle and forward velocity. When the combination of the forward velocity, the cutter revolving speed, the knife extension length and inclination angle was 0.77 m/s, 900 r/min, 40 mm, and 110°, respectively, the cutting force on laver was the smallest, which was 4.21 N. The bench test of harvesting performance showed that the cutter revolving speed has a significant impact on the recovery rate, and the forward velocity has a significant impact on the loss rate. When the harvesting speed ratio was λ4 (the cutter revolving speed was 900 r/min and the forward velocity was 0.77 m/s), the net harvesting rate and the loss rate were 97.45% and 3.38%, respectively, and the cutting proportion of laver can reach 77.5%. The results of the study provide a theoretical basis for the development of harvesting for laver.

In order to solve a series of problems with kelp drying including long drying time, high energy consumption, low drying efficiency, and poor quality of dried kelp, this work proposes the design of a novel greenhouse double-evaporator solar-assisted heat pump drying system. Experiments on kelp solar-assisted heat pump drying (S−HP) and heat pump drying (HP) under the condition of irradiance of 100−700 W/m2 and a temperature of 30, 40, or 50 °C were conducted and their results were compared in terms of system performance, drying kinetics, and quality impact. The drying time was reduced with increasing irradiance or temperature. The coefficient of performance (COP) and specific moisture extraction rate (SMER) of S−HP were 3.590−6.810, and 1.660−3.725 kg/kW·h, respectively, roughly double those of HP when the temperatures are identical. The Deff of S-HP and HP were 5.431 × 10−11~11.316 × 10−11 m2/s, and 1.037 × 10−11~1.432 × 10−11 m2/s, respectively; additionally, solar radiation greatly improves Deff. The Page model almost perfectly described the changes in the moisture ratio of kelp by S−HP and HP with an inaccuracy of less than 5%. When the temperature was 40 °C and the irradiance was above 400 W/m2, the drying time of S−HP was only 3 h, and the dried kelp maintained the green color with a strong flavor and richness in mannitol. Meanwhile, the coefficient of performance was 6.810, the specific moisture extraction rate was 3.725 kg/kWh, and the energy consumption was 45.2%, lower than that of HP. It can be concluded that S−HP is highly efficient and energy-saving for macroalgae drying and can serve as an alternate technique for the drying of other aquatic products.

Being aware of the reliable margin of vital tie-lines, acting on the connection of power exporting area and power importing area, is significant to power systems. However, the high penetration of wind power causes fast variation of boundary limit parameters such as the available amount of power that can be transferred on the tie-lines, namely, total transfer capability (TTC), which may result in the inaccurate security assessment. Unfortunately, the traditional optimal power flow-based TTC model has computation burden for online applications. To address this problem, computational efficiency is improved via a data-driven TTC predictor based on an ensemble learning architecture in this paper. In the first stage, a daily profiles-based method including probabilistic sampling is proposed to simulate plenty of operation scenarios as data samples for ensemble training. Then, a hybrid feature selection approach, which is composed of the maximal information coefficient and nonparametric independence screening, is applied to determine the most correlative features to the objective variable. To enable the TTC predictor with high accuracy and generalization ability, a novel ensemble learning scheme for TTC predictor is constituted through clustering few adaptive hierarchical GA-based neural networks (AHGA-NNs predictor). At last, a modified New England test system is used to validate the proposed methodology. The results illustrate that combining with the appropriate feature selection, the presented ensemble learning has high performance on creating the accurate TTC predictor, which enables online secure margin monitoring for the vital tie-lines.

Intermetallic clathrates are materials characterized by a large cage structure where guest atoms can move anharmonically, providing these materials exotic thermoelectric properties. Unfortunately, the dynamical and atomic nature of the rattling phonons, and their interactions with the electronic structure, are not fully understood. Here, we report that a germanium isotope effect can trigger an inherent guest rattling and cage distortion in clathrate Ba8Ga16Ge30 (BGG). Raman-scattering spectroscopy and advanced electron microscopy demonstrate that the atomic germanium isotope effect induces an off-centre rattling at the 6d sites as well as a tetrakaidecahedron deformation which is anisotropic for n-type BGG but isotropic for p-type BGG. The present findings indicate that the large n-type germanium isotope effect arises from the strong electron-phonon coupling, which opens up a novel avenue for manipulating dynamical motions of phonons via atomic isotope engineering. Keywords: Thermoelectric, Clathrate, Isotope effect, Electron-phonon coupling

The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data-tolerant mechanism, and the accuracy of their performance in terms of identification is thus affected by the quality of data. Topology identification is related to the link prediction problem. The graph neural network can be used to predict the state of unlabeled nodes (lines) through training on features of labeled nodes (lines) with fault tolerance. Inspired by the characteristics of the graph neural network, we applied it to topology identification in this study. We propose a method to identify the topology of a power network based on a knowledge graph and the graph neural network. Traditional knowledge graphs can quickly mine possible connections between entities and generate graph structure data, but in the case of errors or informational conflicts in the data, they cannot accurately determine whether the relationships between the entities really exist. The graph neural network can use data mining to determine whether a connection obtained between entities is based on their eigenvalues, and has a fault tolerance mechanism to adapt to errors and informational conflicts in the graph data, but needs the graph data as database. The combination of the knowledge graph and the graph neural network can compensate for the deficiency of the single knowledge graph method. We tested the proposed method by using the IEEE 118-bus system and a provincial network system. The results showed that our approach is feasible and highly fault tolerant. It can accurately identify network topology even in the presence of conflicting and missing measurement-related information.

With more and more big wind farms integrating to grid, the faults of doubly fed induction generators (DFIGs) grouping tripped off (GTO) have occurred and bring serious power impacts on the grid. Usually, it was considered that the fault of DFIGs GTO is only related to the low/high voltage disturbances in the grid and manipulated as an electromagnetic transients. But, we discover that the fault of DFIGs GTO are also related to operating condition of DFIGs and schemes of crowbar protection equipped with DFIGs, and it may be extended to an electromechanical transient in some situations. Based on the investigation of the factors related to DFIGs GTO , such as degree of voltage disturbances, operating conditions of DFIGs and schemes of crowbar protection, three types of DFIGs GTOs are classified and corresponding mechanisms are revealed. The calculation methods to confirm the mechanism are established. The effectiveness of the proposed methods are verified by simulating some cases of DFISs GTO.

Abstract The cost of Energy Storage System (ESS) for frequency regulation is difficult to calculate due to battery’s degradation when an ESS is in grid-connected operation. To solve this problem, the influence mechanism of actual operating conditions on the life degradation of Li-ion battery energy storage is analyzed. A control strategy of Li-ion ESS participating in grid frequency regulation is constructed and a cost accounting model for frequency regulation considering the effect of battery life degradation is established. The estimated operating life and annual average cost of the Li-ion ESS under different dead bands and SOC set-points are calculated. The case studies show that the estimated operating life of the Li-ion ESS under the actual operating condition differs significantly from the nominal life provided by the manufacturer under the standard condition and the full discharge mode. This paper provides an accurate costing method for the ESS participating in grid frequency regulation to help the promotion of the ESS to participate in the ancillary service market.

We report the critical current density (Jc) and vortex pinning properties in single crystals of a novel iron-based superconductor (IBS) KCa2Fe4As4F2 with large Jc in the pristine state, before and after introduction of artificial defects by swift-particle irradiation. The effects of 2.6 GeV U and 3 MeV proton irradiations in KCa2Fe4As4F2 single crystals on transition temperature Tc and Jc, including its dose dependence, are systematically studied. Jc~8 MA/cm2 under a self-field at 2 K in the pristine crystal is strongly enhanced up to 19.4 and 17.5 MA/cm2 by irradiation of 2.6 GeV U-ions and 3 MeV protons, respectively. Suppression of Tc and dose dependence of Jc in KCa2Fe4As4F2 is different from that in a representative IBS of (Ba,K)Fe2As2, which can be explained by considering the presence of embedded defects in pristine KCa2Fe4As4F2. The vortex dynamics in the pristine and proton irradiated KCa2Fe4As4F2 single crystals are also investigated from the analyses of the field dependence of Jc and the normalized magnetic relaxation rate. In addition to the contribution of embedded defects, weak collective pinning is considered for comprehensive analyses. Vortex dynamics in KCa2Fe4As4F2 is similar to those in (Ba,K)Fe2As2 to some extent, and different from that in anisotropic Li0.8Fe0.2OHFeSe. Large anisotropy, due to the presence of insulating blocking layers in KCa2Fe4As4F2, which leads to much lower irreversibility field (Hirr) compared with 122-type IBSs, strongly affect the vortex dynamics.

Abstract The strongly correlated electron fluids in high temperature cuprate superconductors demonstrate an anomalous linear temperature (T) dependent resistivity behavior, which persists to a wide temperature range without exhibiting saturation. As cooling down, those electron fluids lose the resistivity and condense into the superfluid. However, the origin of the linear-T resistivity behavior and its relationship to the strongly correlated superconductivity remain a mystery. Here we report a universal relation $$d{\boldsymbol{\rho }}/{\boldsymbol{dT}}=({{\boldsymbol{\mu }}}_{{\bf{0}}}{{\boldsymbol{k}}}_{{\boldsymbol{B}}}/{\boldsymbol{\hslash }})\,{{\boldsymbol{\lambda }}}_{{\boldsymbol{L}}}^{{\bf{2}}}$$ d ρ / dT = ( μ 0 k B / ℏ ) λ L 2 , which bridges the slope of the linear-T-dependent resistivity (dρ/dT) to the London penetration depth λ L at zero temperature among cuprate superconductor Bi2Sr2CaCu2O8+δ and heavy fermion superconductors CeCoIn5, where μ 0 is vacuum permeability, k B is the Boltzmann constant and ħ is the reduced Planck constant. We extend this scaling relation to different systems and found that it holds for other cuprate, pnictide and heavy fermion superconductors as well, regardless of the significant differences in the strength of electronic correlations, transport directions, and doping levels. Our analysis suggests that the scaling relation in strongly correlated superconductors could be described as a hydrodynamic diffusive transport, with the diffusion coefficient (D) approaching the quantum limit D ~ ħ/m*, where m* is the quasi-particle effective mass.

Big wind farms must be integrated to power system. Wind power from big wind farms, with randomness, volatility and intermittent, will bring adverse impacts on the connected power system. High precision wind power forecasting is helpful to reduce above adverse impacts. There are two kinds of wind power forecasting. One is to forecast wind power only based on its time series data. The other is to forecast wind power based on wind speeds from weather forecast. For a big wind farm, due to its spatial scale and dynamics of wind, wind speeds at different wind turbines are obviously different, that is called wind speed spatial dispersion. Spatial dispersion of wind speeds and its influence on the wind power forecasting errors have been studied in this paper. An error evaluation framework has been established to account for the errors caused by wind speed spatial dispersion. A case study of several wind farms has demonstrated that even if the forecasting average wind speed is accurate, the error caused by wind speed spatial dispersion cannot be ignored for the wind power forecasting of a wind farm.

Thick graphene film can be widely used in surface protection, heat dissipation, heating devices and other fields. Here we present a study on the growth and characterization of large area continuous and patterned graphite-like films prepared on Ni foil through CVD. Effects of parameters on the material growth are studied experimentally and theoretically in detail. Thickness of the graphite-like film in this work can reach 500 nm and can be regulated by growth process. Characterization results show that the graphite-like film is formed by free stacking of large area graphene continuous films and possesses strong mechanical strength, chemical stability, good electrical conductivity and flexibility. The graphite-like film shows potential for application in metal surface protection, flexible conduction, heating and electromagnetic shielding.

Integrating a battery energy storage system (ESS) with a large wind farm can smooth the intermittent power obtained from the wind farm, but the smoothing function will not be achieved if multiple ESSs are used to smooth the fluctuations in individual wind power plants in a distributed pattern. Therefore, this study focuses on the development of a control strategy to optimize the use of multiple ESSs to accelerate the adoption of wind energy resources. This paper proposes a quasi-automated generation control (QAGC) strategy to coordinate multiple ESSs, which responds to the grid dispatch demand rather than smoothing out the intermittent power from individual wind farms. The aims of QAGC are to ensure that multiple ESSs provide a service that is as balanced as possible, so more wind power systems at various scales can be accepted by the grid, as well maximizing the low-carbon benefits of ESSs. The effectiveness of QAGC is demonstrated by using data from an actual gigawatt scale cluster of wind plants.

Large scale wind power integration has a negative influence on the frequency response. Assistant measurement improves the frequency stability of power systems under high wind penetration. The Proportional Curtailment Strategy (PCS) for wind turbines provides a primary frequency reserve for power systems. To solve the worthless curtailed wind power, the PCS is used to improve the utilization of wind power curtailment. Then the wind turbine and the lithium battery Energy Storage System (ESS) provide primary frequency reserves together. Different control strategies of ESS have been proposed based on the different methods for selecting valid reserves. The economic benefits of different control strategies have been compared based on the same frequency regulation reserve. The optimal control strategy is the maximum method. The economic benefit of the maximum value method is ¥4,445,300.

Despite the fact that 1111-type iron arsenides hold the record transition temperature of iron-based superconductors, their electronic structures have not been studied much because of the lack of high-quality single crystals. In this study, we comprehensively determine the Fermi surface in the antiferromagnetic state of CaFeAsF, a 1111 iron-arsenide parent compound, by performing quantum oscillation measurements and band-structure calculations. The determined Fermi surface consists of a symmetry-related pair of Dirac electron cylinders and a normal hole cylinder. From analyses of quantum-oscillation phases, we demonstrate that the electron cylinders carry a nontrivial Berry phase π. The carrier density is of the order of 10^{-3} per Fe. This unusual metallic state with the extremely small carrier density is a consequence of the previously discussed topological feature of the band structure which prevents the antiferromagnetic gap from being a full gap. We also report a nearly linear-in-B magnetoresistance and an anomalous resistivity increase above about 30 T for B∥c, the latter of which is likely related to the quantum limit of the electron orbit. Intriguingly, the electrical resistivity exhibits a nonmetallic temperature dependence in the paramagnetic tetragonal phase (T>118 K), which may suggest an incoherent state. Our study provides a detailed knowledge of the Fermi surface in the antiferromagnetic state of 1111 parent compounds and moreover opens up a new possibility to explore Dirac-fermion physics in those compounds.

Low-temperature specific heat (SH) is measured on the postannealed Ba(Fe1−xNix)2As2 single crystal with x = 0.058 under different magnetic fields. The sample locates on the overdoped sides and the critical transition temperature Tc is determined to be 14.8 K by both the magnetization and SH measurements. A simple and reliable analysis shows that, besides the phonon and normal electronic contributions, a clear T2 term emerges in the low temperature SH data. Our observation is similar to that observed in the Co-doped system in our previous work and is consistent with the theoretical prediction for a superconductor with line nodes in the energy gap.

Many combined heat and power (CHP) units have been installed in domestic buildings to increase energy efficiency and reduce energy costs. However, inappropriate sizing of a CHP may actually increase energy costs and reduce energy efficiency. Moreover, the high manufacturing cost of batteries makes batteries less affordable. Therefore, this paper will attempt to size the capacity of CHP and optimise daily energy costs for a domestic building with only CHP installed. In this paper, electricity and heat loads are firstly used as sizing criteria in finding the best capacities of different types of CHP with the help of the maximum rectangle (MR) method. Subsequently, the genetic algorithm (GA) will be used to optimise the daily energy costs of the different cases. Then, heat and electricity loads are jointly considered for sizing different types of CHP and for optimising the daily energy costs through the GA method. The optimisation results show that the GA sizing method gives a higher average daily energy cost saving, which is 13% reduction compared to a building without installing CHP. However, to achieve this, there will be about 3% energy efficiency reduction and 7% input power to rated power ratio reduction compared to using the MR method and heat demand in sizing CHP.

The transport properties, phase diagram, and dopant distribution are investigated in systematically Nd-doped CaFe _2 As _2 single crystals. Coexistence of two superconducting (SC) phases with different critical transition temperature ( T _c ) is observed. The low- T _c phase emerges as x ≥ 0.031, and the T _c value increases to its maximum value of about 20 K at x = 0.083, the maximum doping level in our study. As x ≥ 0.060, the high- T _c phase with a T _c value of about 40 K is observed. The structure transition (STr) from tetragonal to orthorhombic phase vanishes suddenly around x = 0.060, where a new STr from tetragonal to collapsed tetragonal phase begins to turn up. Compared to the low- T _c phase, the end point of SC transition of the high- T _c phase is more sensitive to the magnetic field, showing a characteristic of Josephson weak-link behavior. Possible scenarios about this system are discussed based on our observations. We also find that the non-uniform SC properties cannot be attributed to the heterogeneous Nd distribution on the micro scale, as revealed by the detailed energy dispersive x-ray spectroscopy measurements.