Charge Pattern Research Articles

Classification of energy use patterns and multi-objective optimal scheduling of flexible loads in rural households

The significant stochastic volatility of renewable energy makes the regulation of flexible loads important in improving the matching degree of energy supply and demand. Under the background of urbanization, rural China is developing rapidly with an increasingly obvious trend of rural differentiation, resulting in different energy use patterns in different households. To accurately assess the flexibility potential, this study identified the use patterns of typical flexible load equipment based on the data from a field survey in the rural Guanzhong Plain, and classified rural households there accordingly. Giving full consideration to flexible loads, this study built a multi-objective optimal load scheduling model aimed at economic efficiency and user comfort. The non-dominated sorting genetic algorithm (NSGA-II) was applied to solve the proposed multi-objective optimization problem in MATLAB software. Then technique for order preference by similarity to an ideal solution (TOPSIS) method was to make the final decision from the Pareto frontier. According to the results of this study: 1) The usage patterns of washing machines were divided into morning usage pattern (Wm) and the late afternoon usage pattern (Wa). The charging patterns of scooters were classified into evening charging pattern (Ve) and night charging pattern (Vn). The usage patterns of air conditioners included midday usage pattern (ACm) and evening usage pattern (ACe). 2) The Wm-Vn-ACe households show greatest flexibility potential under the Scenario TOU (Time-of-use), the peak-valley difference of load curve is reduced by 22.9% compared with that before the optimal dispatching; The Wa-Vn-ACm households show greatest flexibility potential under the Scenario PVS (Photovoltaic system), the self-consumption increased by 10.7% compared with that before the optimal dispatching.3) Air conditioner usage patterns had a significant impact on the flexibility potential.

Multi-Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model

We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the drivers. Rather than one prediction (step, model, or variables) made by conventional LSTM models, three charging load (energy demand) predictions of EVs were made depending on different multi-feature inputs. Data fusion was used to combine and optimize the different charging load prediction results. The performance of the final implemented model was evaluated by the mean absolute prediction error of the forecast. The implemented model had a prediction error of 3.29%. This prediction error was lower than initial prediction results by the LSTM model. The numerical results indicate an improvement in the performance of the EV load forecast, indicating that the proposed model could be used to optimize and improve EV load forecasts for electric vehicle charging stations to meet the energy requirements of EVs.

Temperature Dependence of Spin and Charge Orders in the Doped Two-Dimensional Hubbard Model

Competing and intertwined orders including inhomogeneous patterns of spin and charge are observed in many correlated electron materials, such as high-temperature superconductors. Introducing a new development of the constrained-path auxiliary-field quantum Monte Carlo (AFQMC) method, we study the interplay between thermal and quantum fluctuations in the two-dimensional Hubbard model. We obtain an accurate and systematic characterization of the evolution of the spin and charge correlations as a function of temperature $T$ and how it connects to the ground state, at three representative doping levels $\delta = 1/5$, $1/8$, and $1/10$. We find increasing short-range commensurate antiferromagnetic correlations as $T$ is lowered. As the correlation length grows sufficiently large, a modulated spin-density-wave (SDW) appears. At $\delta = 1/5$, the SDW saturates and remains short-ranged as $T \rightarrow 0$. In contrast, at $\delta = 1/8$ and $1/10$ this evolves into a ground-state stripe phase. We study the relation between spin and charge orders and find that formation of charge order appears to be driven by that of the spin order. We identify a finite-temperature phase transition below which charge ordering sets in and discuss the implications of our results for the nature of this transition.

Improved predictions of phase behaviour of intrinsically disordered proteins by tuning the interaction range.

The formation and viscoelastic properties of condensates of intrinsically disordered proteins (IDPs) is dictated by amino acid sequence and solution conditions. Because of the involvement of biomolecular condensates in cell physiology and disease, advancing our understanding of the relationship between protein sequence and phase separation (PS) may have important implications in the formulation of new therapeutic hypotheses. Here, we present CALVADOS 2, a coarse-grained model of IDPs that accurately predicts conformational properties and propensities to undergo PS for diverse sequences and solution conditions. In particular, we systematically study the effect of varying the range of the nonionic interactions and use our findings to improve the temperature scale of the model. We further optimize the residue-specific model parameters against experimental data on the conformational properties of 55 proteins, while also leveraging 70 hydrophobicity scales from the literature to avoid overfitting the training data. Extensive testing shows that the model accurately predicts chain compaction and PS propensity for sequences of diverse length and charge patterning, as well as at different temperatures and salt concentrations.

Study of MFL signal identification in pipelines based on non-uniform magnetic charge distribution patterns

Magnetic flux leakage (MFL), a pipeline internal detection, is the mainstream technology for international oil and gas pipeline safety maintenance. To address the problem of large errors in the assessment of defects in pipeline inspection by existing theoretical analysis models, this paper introduces the charge density parameter to establish a mathematical model of non-uniform magnetic charge for pipeline leakage detection and calculates the non-uniform distribution pattern of magnetic charge on the defect side wall. The contour plot method is proposed to visually analyze the size of defects. To compare the error between uniform and non-uniform models, and verify the correctness of the theoretical model, experiments were systematically conducted on the X70 pipeline with internal MFL technology. The results show that: the magnetic charge distribution curve is symmetrically shaped at the center of the defect, with a valley at the center and two peaks at the defect edge. As the defect depth and width increase, the magnetic charge density increases sharply near the surface and edge. The peak-to-valley spacing and peak-to-valley values in the contour map of the MFL signal can directly reflect the location and size of defects. The signal characteristic values follow the trend of a first-order decreasing exponential function as the value of the lift-off increases. The first-order derivative of the signal characteristic value tends to decrease and then increase as the mesh size decreases, and the extreme point of the derivative curve is the best mesh size.

Nanoscale topotactic phase transformation modulated by triboelectrification for high memory storage

The applications of triboelectrification have attracted much attention due to the direct regulation of charges with external mechanical stimuli. Atomic force microscopy (AFM) is an effective method to achieve controllable nanoscale friction. Herein, we demonstrated a phase transformation from the brownmillerite SrCoO2.5 (BM-SCO) to perovskite SrCoO3-δ (PV-SCO) as modulated by nanoscale friction with the contact mode AFM. Besides, such phase transformation can be reversible by applying a negative voltage. More importantly, when defining the surface potential of different phases as binary bits of “1” and “0”, we could reach high-density data storage as the surface charge is constantly written and erased in nanoscale regions. This work opens a new way for the regulation of phase transformation by triboelectrification, and realizes the surface charge patterning based on different phases at the nanoscale by AFM tip friction, which is of great significance for the application of nano devices in high-density data storage.

A Robust Renewable Energy Source-oriented Strategy for Smart Charging of Plug-in Electric Vehicles Considering Diverse Uncertainty Resources

Nowadays, the notion of plug-in electric vehicle (PEV) as a valuable tool of energy management has been extensively employed in smart distribution grids. The main advantage of clean energy as well as elastic behaviour of operation in both electrical load/generation modes can sufficiently justify the utilization of such emerging technology. Moreover, the specific capability of renewable energy sources (RESs) in terms of contribution in PEV smart charging/discharging scheme would cause to remarkable techno-economic benefits in smart grids. However, the load demand, RES generation and also the electrical energy price encounter with uncertainty in practice required to be properly handled. Hence, a non-deterministic optimization model based on information gap decision theory (IGDT) is proposed in this paper to specify a robust PEV smart charging pattern. To solve the multi-objective proposed IGDT-based PEV smart charging (IGDT-PSC) model, the multi-objective version of particle swarm optimization (MOPSO) is utilized to define a set of Pareto optimal solutions. Furthermore, the final solution among the Pareto solutions is selected by means of a linear fuzzy satisfaction rule. The simulation results for a test smart microgrid comprising a PEV, a set of RES units and a load demand verify the effectiveness of the proposed IGDT-PSC model.

Functional partitioning of transcriptional regulators by patterned charge blocks

Components of transcriptional machinery are selectively partitioned into specific condensates, often mediated by protein disorder, yet we know little about how this specificity is achieved. Here, we show that condensates composed of the intrinsically disordered region (IDR) of MED1 selectively partition RNA polymerase II together with its positive allosteric regulators while excluding negative regulators. This selective compartmentalization is sufficient to activate transcription and is required for gene activation during a cell-state transition. The IDRs of partitioned proteins are necessary and sufficient for selective compartmentalization and require alternating blocks of charged amino acids. Disrupting this charge pattern prevents partitioning, whereas adding the pattern to proteins promotes partitioning with functional consequences for gene activation. IDRs with similar patterned charge blocks show similar partitioning and function. These findingsdemonstrate that disorder-mediated interactions can selectively compartmentalize specific functionally related proteins from a complex mixture of biomolecules, leading to regulation of a biochemical pathway.

Electric Vehicle Charging/Discharging Models for Estimation of Load Profile in Grid Environments

The increasing electric vehicles (EVs) and their integration with the electrical networks to achieve the charging demands has highly affected the operation of the power system. These effects are due to the increased and variable amount of electricity consumption caused by the irregular charging patterns of EVs. As the EV penetration level increases with such irregular charging patterns, the overall load profile in a network will increase resulting in load peaks. To overcome this, a well-planned charging-discharging algorithm is needed for the EV users and the power suppliers. In light of the above issues and requirements, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation-based EV charging (EVC) behavior models and charging-discharging power prediction models. The proposed methods analyze the traveling behavior of EVs, and their charging patterns to make EV a flexible load. This helps in understanding the variable electricity consumption and mitigating the peak loads and load variations. Further, to assess the operation of the proposed methods, the charging-discharging power prediction is carried out for a large EV fleet based on the historical data. The power predictions are carried out for 100 days and the convergence of the proposed algorithm is targeted to be kept below 0.5% to achieve optimal power prediction. The results showed the credibility of the proposed approach in identifying the daily average charging-discharging load curves.

Self-Assembly of Tunable Intrinsically Disordered Peptide Amphiphiles.

Intrinsically disordered peptide amphiphiles (IDPAs) present a novel class of synthetic conjugates that consist of short hydrophilic polypeptides anchored to hydrocarbon chains. These hybrid polymer-lipid block constructs spontaneously self-assemble into dispersed nanoscopic aggregates or ordered mesophases in aqueous solution due to hydrophobic interactions. Yet, the possible sequence variations and their influence on the self-assembly structures are vast and have hardly been explored. Here, we measure the nanoscopic self-assembled structures of four IDPA systems that differ by their amino acid sequence. We show that permutations in the charge pattern along the sequence remarkably alter the headgroup conformation and consequently alter the pH-triggered phase transitions between spherical, cylindrical micelles and hexagonal condensed phases. We demonstrate that even a single amino acid mutation is sufficient to tune structural transitions in the condensed IDPA mesophases, while peptide conformations remain unfolded and disordered. Furthermore, alteration of the peptide sequence can render IDPAs to become susceptible to enzymatic cleavage and induce enzymatically activated phase transitions. These results hold great potential for embedding multiple functionalities into lipid nanoparticle delivery systems by incorporating IDPAs with the desired properties.

Coordinated expansion planning of transmission and distribution systems integrated with smart grid technologies

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.

A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities

Electric vehicle (EV) charging patterns are highly uncertain in both location, time, and duration particularly in association with the predicted high demand for electric mobility in the future. An EV can be charged at home, at charging stations near highway ramps, or on parking lots next to office buildings, shops, airports, among other locations. Charging time and duration can be fixed and continuous or flexible and intermittent. EV user preferences of charging services depend on many factors (e.g., charging prices, choice of destinations), causing EV charging patterns to shift in real-time. Hence, there is a need for a highly flexible EV charging network to support the rapid adoption of the technology. This study presents a dynamic scheduling scheme for EV charging facilities considering uncertainties in charging demand, charger availability, and charging rate. The problem is formulated as a dynamic programming model that minimizes the travel and waiting costs and charging expenses while penalizing overcharging attempts. An integrated generalized Nash equilibrium technique is introduced to solve the problem that incorporates a Monte Carlo tree search algorithm to efficiently capture the uncertainties and approximate the value function of the dynamic program. Numerical experiments on hypothetical and real-world networks confirm the solution quality and computational efficiency of the proposed methodology. This study will promote EV adoption and support environmental sustainability by helping users lower the charging spot search burden via a real-time, user-adaptive optimizer. Stakeholders can retrieve charger utilization and pricing data and get feedback on their charging network policies.

전기차 운전자의 충전패턴 유형과 영향요인 분석: 잠재계층분석법의 응용

Reliable charging infrastructure is an essential element to transform the current fossil fuel-centered automobile market into electric vehicles. In Korea, the supply level of public charging infrastructure is better than that of other countries, but the residential charging infrastructure is hard to expand due to the domestic characteristics. Therefore, in order to meet the electric vehicle era in the future, charging infrastructure supply strategies suitable for the domestic situation should be prepared. This study analyzed the charging patterns of electric vehicle drivers as essential data necessary for future charging infrastructure plans and decision-making on the supply of charging facilities. This study utilized the data of one-week charging events survey of 297 electric car drivers conducted in 2021, and the Latent Class Analysis was applied to identify the charging pattern of individual driver. As a result, the charging patterns of electric car drivers were classified into four types: Mixed & Slow 69.3%, Home & Slow 16.5%, Public-centric 8.2%, and Work & Slow 6.1%. As a result of analyzing the predictive variables of the charging pattern through multi-logit analysis, accessibility by charging infrastructure type and preference by type of charging infrastructure were found to be statistically significant affecting factors for all charging patterns. For some classes of charging pattern, annual driving mileage and parking conditions at home were also found to have a significant effect.

A nanofiltration membrane with outstanding antifouling ability: Exploring the structure-property-performance relationship

Membrane fouling is inevitable during nanofiltration of industrial liquids. Understanding the influence of membrane physicochemical properties and structures on its antifouling performance can guide the membrane selection and preparation. In this study, the antifouling ability of three commercial nanofiltration membranes (NF270, NFA4 and DK) was assessed using organic foulants with different charge patterns. Besides, the pristine and fouled membranes were systematically characterized. The results showed that although the negatively charged polyamide membrane had a strong antifouling ability to negatively charged substances, the fouling extent was still affected by the membrane pore size distribution. For the small foulants with positive charge, driven by electrostatic adsorption effect, they were easier to be anchored on the membrane surface with higher roughness. Due to the synergistic effect of membrane charge and roughness, the flux decay ratio increased from 5.1% for the NFA4 to 63.1% for the DK with more negative charge and highest roughness. Moreover, according to FTIR, XPS deep profiling and SEM cross-section analysis, it was found that the structure of the separation layer (e.g., single/double layer or cross-linking density in horizontal/longitudinal) closely related to its antifouling performance. Therefore, a nanofiltration membrane with narrow pore size distribution, moderate charge, smooth and hydrophilic surface is more prone to resist organic fouling formation. The outcomes of the work also offer several strategies to prepare antifouling nanofiltration membranes.

Review of the energy forecasting and scheduling model for electric buses

Electric buses are getting more interest to be implemented in the public transport sector by different countries as a carbon mitigation strategy. However, there are a few issues with electric buses such as limited driving range and long recharging time compared to the conventional diesel bus. Due to its limited energy storage, estimation of the energy consumption for the electric buses becomes a crucial research area to prevent sufficient energy for electric buses during the service. The long recharging time of the electric buses requires a vehicle and a charging scheduling to ensure fulfilling the passenger demand without compromising the energy demand. The paper will review the state-of-the-art model for the energy forecasting model of the electric buses and electric bus charging scheduling models. The paper then compares the parameter in the energy forecasting model, the optimization objective, the charger technology, the charging pattern, and the constraint in the electric bus scheduling model. The purpose is to provide a well-structured comprehensive review on these energy forecasting and electric bus charging scheduling for engineers, researchers, investors, policy makers, bus depot companies, and other interested parties.

Charge order landscape and competition with superconductivity in kagome metals.

In the kagome metals AV3Sb5 (A = K, Rb, Cs), three-dimensional charge order is the primary instability that sets the stage for other collective orders to emerge, including unidirectional stripe order, orbital flux order, electronic nematicity and superconductivity. Here, we use high-resolution angle-resolved photoemission spectroscopy to determine the microscopic structure of three-dimensional charge order in AV3Sb5 and its interplay with superconductivity. Our approach is based on identifying an unusual splitting of kagome bands induced by three-dimensional charge order, which provides a sensitive way to refine the spatial charge patterns in neighbouring kagome planes. We found a marked dependence of the three-dimensional charge order structure on composition and doping. The observed difference between CsV3Sb5 and the other compounds potentially underpins the double-dome superconductivity in CsV3(Sb,Sn)5 and the suppression of Tc in KV3Sb5 and RbV3Sb5. Our results provide fresh insights into the rich phase diagram of AV3Sb5.

Emergency Department Utilization for Substance Use DisorderDuring Pregnancy and Postpartum in the United States (2006-2016).

We aimed to better understand emergency department (ED) use, admission patterns, and demographics for substance use disorder in pregnancy and postpartum (SUDPP). In this longitudinal study, the United States Nationwide Emergency Department Sample was queried for all ED visits by 15- to 50-year-old women with a primary diagnosis defined by International Classification of Diseases, 9th or 10th edition Clinical Modification, codes of SUDPP between 2006 and 2016. Patterns of ED visit counts, rates, admissions, and ED charges were analyzed. Annual national estimated ED visits for SUDPP increased from 2,919 to 9,497 between 2006 and 2016 (a 12.4% annual average percentage change), whereas admission rates decreased (from 41.9% to 32.0%). ED visits were more frequent among women who were 20-29years old, using Medicaid insurance, in the lowest income quartile, living in the South, and in metropolitan areas. Compared with the proportion of ED visits, 15- to 19-year-olds had significantly lower admission rates, whereas women with Medicaid and in the lowest income quartile had higher admission rates (p<.001). Opioid use, tobacco use, and mental health disorders were most commonly associated with SUDPP. The ED average inflation-adjusted charges for SUDPP increased from $1,486 to $3,085 between 2006 and 2016 (7.1% annual average percentage change; p<.001), yielding total annual charges of $4.02 million and $28.53 million. Despite the decrease in admissions, the number and charges for ED visits for SUDPP increased substantially between 2006 and 2016. These increasing numbers suggest a continuous need to implement preventive public health measures and provide adequate outpatient care for this condition in this population specifically.

Surface characterisation reveals substrate suitability for cyanobacterial phototaxis

Cyanobacteria respond to light stimulation, activating localised assembly of type IV pili for motility. The resulting phototactic response is highly dependent on the nature of the incoming light stimulus, and the final motility parameters depend on the surface properties. Conventionally, phototaxis studies are carried out on hydrogel surfaces, such as agarose, with surface properties that vary in time due to experimental conditions. This study considers five substrates, widely utilized in microfluidic technology, to identify the most suitable alternative for performing reliable and repeatable phototaxis assays. The surfaces are characterised via a contact angle goniometer to determine the surface energy, white light interferometry for roughness, zeta-potentials and AFM force distance curves for charge patterns, and XPS for surface composition. Cell motility assays showed 1.25 times increment on surfaces with a water contact angle of 80° compared to a reference glass surface. To prove that motility can be enhanced, polydimethylsiloxane (PDMS) surfaces were plasma treated to alter their surface wettability. The motility on the plasma-treated PDMS showed similar performance as for glass surfaces. In contrast, untreated PDMS surfaces displayed close to zero motility. We also describe the force interactions of cells with the test surfaces using DLVO (Derjaguin-Landau-Verwey-Overbeek) and XDLVO (extended DLVO) theories. The computed DLVO/XDLVO force-distance curves are compared with those obtained using atomic force microscopy. Our findings show that twitching motility on tested surfaces can be described mainly from adhesive forces and hydrophobicity/hydrophilicity surface properties. Statement of significanceThe current article focuses on unravelling the potential Micro-Electro-Mechanical System (MEMS) compatible surfaces for studying phototactic twitching motility of cyanobacteria. This is the first exhaustive surface characterization study coupled with phototaxis experiments, to understand the forces contributing to twitching motility. The methods shown in this paper can be further extended to study other surfaces and also to other bacteria exhibiting twitching motility.

Effect of Charge Distribution on the Dynamics of Polyampholytic Disordered Proteins.

The stability and physiological function of many biomolecular coacervates depend on the structure and dynamics of intrinsically disordered proteins (IDPs) that typically contain a significant fraction of charged residues. Although the effect of relative arrangement of charged residues on IDP conformation is a well-studied problem, the associated changes in dynamics are far less understood. In this work, we systematically interrogate the effects of charge distribution on the chain-level and segmental dynamics of polyampholytic IDPs in dilute solutions. We study a coarse-grained model polyampholyte consisting of an equal fraction of two oppositely charged residues (glutamic acid and lysine) that undergoes a transition from an ideal chain-like conformation for uniformly charge-patterned sequences to a semi-compact conformation for highly charge-segregated sequences. Changes in the chain-level dynamics with increasing charge segregation correlate with changes in conformation. The chain-level and segmental dynamics conform to simple homopolymer models for uniformly charge-patterned sequences but deviate with increasing charge segregation, both in the presence and absence of hydrodynamic interactions. We discuss the significance of these findings, obtained for a model polyampholyte, in the context of a charge-rich intrinsically disordered region of the naturally occurring protein LAF-1. Our findings have important implications for understanding the effects of charge patterning on the dynamics of polyampholytic IDPs in dilute conditions using polymer scaling theories.

Effects of Charged Polyelectrolytes on Amyloid Fibril Formation of a Tau Fragment.

The microtubule-associated protein tau is involved in more than 20 different neurological disorders characterized by aberrant intracellular aggregation of tau in the brain. Here, we investigated the aggregation of a novel 20-residue model peptide, tau298-317, which is derived from the key microtubule binding domain of the full sequence tau. Our results show that tau298-317 highly mimics the physical and aggregation properties of tau. Under normal physiological conditions, the peptide maintains a disordered random coil without aggregation. The presence of polyanionic heparin (Hep) significantly promotes the aggregation of this peptide to form amyloid fibrils. The Hep-induced aggregation is sensitive to the ionic strength of the solution and the introduction of the negatively charged phosphate group on a serine (Ser305) residue in the sequence, suggesting an important role of electrostatic interactions in the mechanism of Hep-mediated aggregation. In addition, two positively charged polysaccharides, chitosan (CHT) and its quaternary derivative N-trimethyl chitosan (TMC), were found to effectively inhibit Hep-induced aggregation of tau298-317 in a concentration-dependent manner. Attractive electrostatic interactions between the positively charged moieties in CHT/TMC and the negatively charged residues of Hep play a critical role in inhibiting Hep-peptide interactions and suppressing peptide aggregation. Our results suggest that positively charged polyelectrolytes with optimized charged groups and charge distribution patterns can serve as effective molecular candidates to block tau-Hep interactions and prevent aggregation of tau induced by Hep and other polyanions.