Traditional Load Research Articles

Short-term microgrid load probability density forecasting method based on k-means-deep learning quantile regression

Traditional short-term load forecasting (STLF) methods for large utility grid systems usually provide the forecasted load with deterministic points. However, deterministic load forecasting cannot reveal the load pattern and uncertainty of controllable load in a microgrid, where the prediction errors may exceed the expected range due to the high volatility and strong randomness. In order to deal with this matter, a probability density forecasting method is proposed to predict the microgrid load with uncertainty for robust power scheduling in this paper. The proposed probability forecasting method effectively combines several data-driven and statistical algorithms, including the k-means algorithm, quantile regression long short-term memory neural network (QRLSTM), and kernel density estimation (KDE). Firstly, similar days related to the prediction day are selected through the k-means algorithm, and the historical load data of these selected days are divided into two subsets including the training dataset and the testing dataset. Secondly, a QRLSTM-based model is established and used to predict the microgrid load for different quantiles. Finally, the probability density function of the predicted points is obtained by KDE on the target day. The prediction accuracy is evaluated roundly and the results demonstrate that the proposed method can effectively reproduce the probability density distribution of the load and provide noticeably better performance than some benchmark methods.

Crystal plasticity including a phase-field deformation twinning model for the high-rate deformation of cyclotetramethylene tetranitramine

A new finite strain thermomechanical model for the high-rate deformation of the β-polymorph of cyclotetramethylene tetranitramine (β-HMX) has been developed and applied to simulations of plate impact experiments. The crystal plasticity model is based on a model developed previously for RDX (Luscher et al. 2017), which is extended to incorporate deformation twinning. Twinning during normal plate impacts is simulated with a phase-field twin model. First, material parameters governing the kinetics of dislocation slip are calibrated on the subset of simulations which had negative Schmid factors for the twin system. Second, a parametric study of the twin material parameters was performed to find suitable values. The results of the simulations with the phase-field twinning model are reported for impacts on several crystal orientations. We find that the twin growth decreases with increasing distance from the impact surface because of dissipation of the shock front via dislocation-mediated plasticity, and that the simulated interface velocity with and without phase-field twinning do not show appreciable differences. These modeling results suggest that the significance of twinning in β-HMX cannot be determined with traditional loading configurations and diagnostics.

Ultrathin, elastic, and self-adhesive nanofiber bio-tape: An intraoperative drug-loading module for ureteral stents with localized and controlled drug delivery properties for customized therapy

During the postoperative management of urinary diseases, oral or intravenous administration of drugs and implanting ureteral stents are usually required, making localized drug delivery by ureteral stent a precise and effective medication strategy. In the traditional drug loading method, the drug was premixed in the implants in production lines and the versatility of drugs was restricted. However, the complex situation in the urinary system fails the possibility of finding a “one fits all” medication plan, and the intraoperative drug-loading of implants is highly desired to support customized therapy. Here, we designed an ultrathin (8 μm), elastic, and self-adhesive nanofiber bio-tape (NFBT) that can easily encapsulate drugs on the stent surface for controllable localized drug delivery. The NFBT exhibited high binding strength to a ureteral stent, a sustained release over 7 d in PBS for hydrophilic drug, and a zero-order release curve over 28 days for the hydrophobic drug nitrofurantoin (NFT). Further in vivo experiments using a porcine ureteral tract infection model demonstrated that NFBT loaded with NFT could significantly reduce the bacterial concentration in urine. The total amount of NFT delivered by the NFBT was about 2.68 wt% of the recommended dose for the systemic administration.

Short-Term Power Load Forecasting of Integrated Energy System Based on Attention-CNN-DBILSTM

In view of the fact that the potential high-dimensional features in the historical sequence are difficult to be effectively extracted by traditional power load forecasting methods and the coupling factors of electricity, heat, and gas have not been considered, the correlation of electric heating and gas load is considered in this paper, and a short-term power load forecasting method for integrated energy systems based on Attention-CNN- (Convolutional Neural Network-) DBILSTM (Deep Bidirectional Long-Short-Term Memory) is proposed. First, the correlation between the multiple load influencing factors is considered, and the Pearson coefficient is used to quantitatively calculate the correlation between the multiple loads. Second, a CNN network consisting of a one-dimensional convolutional layer and a pooling layer is established. High-dimensional features reflecting the dynamic changes of the load are extracted, and the proposed feature vector is constructed in the form of time series as the input of the DBILSTM network; the dynamic change law of time series data is modeled and learned. Then, the Attention mechanism is introduced to assign different weights to the hidden state of DBILSTM through the mapping weight and the learning parameter matrix, to reduce the loss of historical information and strengthen the impact of key information, and the Dense layer is used to output the load prediction results. Finally, the influence of the correlation of multiple loads and its influencing factors on the power load forecasting results is analyzed, based on the historical load data of the integrated energy system in a certain area of Northeast China. The simulation results of the calculation example show that the prediction accuracy of the method reaches 97.99%, and the integrated energy system electric, heat, and gas load correlation coefficients as the input parameters of the Attention-CNN-DBILSTM network can reduce the average prediction error by 0.37%∼1.93%. The proposed method has been verified to effectively improve the prediction accuracy by comparison with the prediction model results of CNN-LSTM network, CNN-BILSTM network, and CNN-DBILSTM network.

The development of the refrigerated-container loading problem model for perishable fishery products considering internal temperature distribution

The paper aims to develop a refrigerated-container loading problem (R-CLP) model for perishable fishery products in a cold chain. The novelty of this study is to enhance the models and algorithms of CLP to deal with two different temperatures in reefers: those of cargoes and those of containers. This research considers synchronising the required optimum temperature of products and the internal temperature distribution of reefers to minimise the quality deterioration of the products. The objective of the R-CLP is to minimise total loading costs while fulfiling quality requirement of the products. The quality requirement is based on the temperature distribution inside the reefer, which was obtained by computational fluid dynamics (CFD) simulation. The model includes traditional loading constraints of CLP. Moreover, this research has developed a heuristics algorithm that deals with various types of products, cargo box dimensions and temperature distribution data. A prototype application has been developed to simulate the loading plan visually using an application prototype named REFLOAD. The numerical experiment shows the ability of the model to minimise the quality loss while maintaining the utilisation of the reefer. The R-CLP algorithm can reduce the total loading costs to about 16% compared with those of CLP algorithm.

Adaptive load control of electrolytic aluminum for power system frequency regulation based on the aluminum production operation state

The national ”double carbon” plan promotes the reform of the power system, and large-scale distributed energy is connected to the power system. It is difficult to control the fluctuation of new energy only by the reserve of thermal power units. Therefore, it is urgent to excavate new auxiliary service subjects and increase the flexibility of power grid frequency control. Taking electrolytic aluminum as an example, the traditional high load capacity load has large capacity continuous regulation ability, but it is seldom used in frequency stability control of power grid. Thus this paper proposes the precise coordinated frequency modulation control strategy of electrolytic aluminum load in the case of large N-1 failure. By means of updating the running status of electrolytic aluminum load regularly and calculating the load damping coefficient adaptively, this strategy can realize the rapid response to the system frequency change and ensure that the system frequency fluctuation does not exceed the limit. Finally, we built the regional power grid of Wenshan in Yunnan province and the results verify the feasibility of the effectiveness of the proposed strategy in dealing with DC locking.

A novel short‐term load forecasting approach based on kernel extreme learning machine: A provincial case in China

With the rapid development of re-electrification, traditional load forecasting faces a significant increase of influencing factors. Existing literature focuses on examining the influencing factors related to load profiles in order to improve the prediction accuracy. However, a large number of redundant features may lead to the overfitting of the forecasting engine. To enhance the performance of extreme learning machine (ELM) under massive data scale, this paper presents a kernel extreme learning machine (KELM) based method which can be used for short-term load prediction. First, a feature dimensionality reduction is performed using a kernelized principal component analysis, which aims to eliminate redundant input vectors. Then, the hyperparameters of KELM are optimized to improve the prediction accuracy and generalization. Case studies based on a province-level power system in China demonstrate that the presented method can significantly improve the accuracy of load forecasting by 3.14% in contrast to traditional ELM.

Anti-Rollover Control of a Counterbalance Forklift Based on Roll Energy Classification

This study proposes an energy stability index suitable for a counterbalance forklift, aiming to solve the problem that the traditional vehicle stability index load transfer rate is difficult to accurately judge the stability state of such a forklift. The analysis of the structure of the counterbalance forklift and contact relationship between the body and rear axle are used as bases to establish a two-stage dynamic model and derive real-time instability energy of a forklift truck in the rollover process under different conditions. Additionally, this research proposes an anti-rollover control strategy of the counterbalance forklift based on roll energy classification. The corresponding calculation formula of the forklift energy stability index is selected according to different stability conditions to perform roll energy classification. An anti-rollover oil cylinder and active rear wheel steering are used as an anti-rollover control actuator, and an anti-rollover control is implemented based on model predictive control. Last, a simulation based on European standard operating conditions and real vehicle test verification is conducted. Results show that the anti-rollover control strategy based on roll energy classification can effectively reduce the risk of forklift rollover and improve the stability and active safety of a counterbalance forklift.

A modified rule-based energy management scheme for optimal operation of a hybrid PV-wind-Stirling engine integrated multi-carrier energy system

In this study, rule-based energy management strategies (EMS) based on the modifications of the traditional load following (LF) and circuit charging (CC) have been proposed and developed to effectively coordinate the operation of an integrated multi-carrier hybrid energy system. The proposed EMS aim to overcome some of the challenges of the traditional rule-based EMS and broaden their application to the management of complex energy systems. The study deploys a bi-level optimisation scheme to obtain the optimal number of system components that simultaneously minimises the cost, reliability and emissions, in the outer-loop and implements the rule-based EMS in the inner-loop. Also, the results of the optimal system have been simulated for a 48 h timespan, to investigate the effects of the proposed EMS on the Stirling back-up start-ups, battery storage limits, and generation of other energy vectors. The results indicate the deployment of split back-up and batteries minimise the commitment of the back-up, dumped power and emissions. However, the number of start-ups of the back-up increases appreciably by 15.34% and 36%, with the deployment of 2-split and 4-split Stirling, respectively in CC with battery storage. Correspondingly, the operational cost of the system rises as the number of splits increases, but only a slight change in the energy cost is observed, because of the significant reductions in the capacity of the green generators. Interestingly, the batteries record many duty cycles, store less energy and attain lower discharge limits as many small capacity ST back-ups are deployed. Other results demonstrate the additional capabilities of the proposed EMS in handling complex energy systems by the substantial increase in the generation of heating and cooling with increasing splits of the back-up and inclusion of batteries in the optimal system.

Adaptive modeling for Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) is an energy-saving technology that has been widely concerned. It can estimate detailed energy consumption information from aggregated power data, eliminating the need to install sub-meters on individual appliances. In addition to the power consumption information from each appliance, traditional load monitoring algorithms often require additional information to help train the model, which incurs extra user interaction during the modeling process, limiting the wide application of NILM. In this paper, we presented a novel model named Adaptive Factorial Hidden Markov Model (Adaptive-FHMM) to characterize the transition between working states of each appliance. In the proposed model an adaptive clustering process is introduced, which could automatically determine the number of hidden states according to the power variation at different working stages. Then the working states information of each appliance is utilized to build a combined model to predict the appliance’s power consumption. Experiments are conducted on two publicly available datasets and one dataset collected from the laboratory, and the results show that the proposed model outperforms five state-of-the-art models in the metrics of energy-based F1, Mean Absolute Error(MAE), and Match Rate. Specifically, the proposed Adaptive FHMM model achieves 26.8% and 52% reduction in MAE compared to the second-best model on the real-world dataset of REDD and AMPds, respectively.

Estimating the Ice Loads on Concrete Dams Based on Their Structural Response

In the assessment of concrete dams in cold climate, it is common that the theoretical stability becomes insufficient for load cases that include ice loads. However, the magnitude and return period of these ice loads have a high degree of uncertainty. This study estimates the magnitude of ice loads on eight concrete dam monoliths using measurements of their displacement from 29 winters. In the displacement signals, events are identified and assumed to be caused solely by ice loads. The observed displacement during an event is interpreted as an ice load using a load–displacement relationship derived from FE simulations of each dam. These simulations show that ice loads of the magnitudes given in design guidelines and recorded in previous measurements would significantly affect the structural response of the studied dams. However, only small traces of ice loads can be found in the observed responses of the studied dams. The estimated ice loads are significantly lower than the ice loads recorded in traditional ice load measurements. These results indicate that the average magnitude of ice load on an entire monolith is significantly lower than the measured local pressures. This would imply that ice loads may be a smaller concern regarding dam safety than previously believed.

A direct current control strategy based on vector resonance controller for shunt active power filter

From the perspective of complex vector, this paper derives the mathematical model of the three-phase three-wire shunt active power filter (SAPF) current control object, and based on it, derives the corresponding vector resonance control expression form coordinate transformation, and the steady-state accuracy, anti-interference ability, and stability of the system are analyzed. The deduction demonstrates the equivalence of the traditional detection load harmonic current control and the direct grid current control in this paper, and the consistency of the current regulator. Then, a direct vector resonance control structure of grid current based on the dq coordinate without detecting harmonic currents is designed, and the digital realization of the vector resonance controller, the adaptive ability to frequency, and the phase lag at the high-order control frequency are designed. And finally experimental verification under different working conditions is presented, the experimental results prove the practicability and effectiveness of the direct current control strategy based on the vector resonance controller proposed in this paper.

Designing Porous Ion Emitters for Thermal Ionization Mass Spectrometry: Evaluating Metal-Organic Frameworks.

This work describes the first exploration of metal-organic frameworks (MOFs) as "next-generation" ion emitters for thermal ionization mass spectrometry (TIMS). MOFs were identified as promising candidates for this application given the synthetic control over their desired structural properties. This tunability results in well-ordered, high-surface-area, high-porosity frameworks with targeted sorption affinities. Here, we explored an aluminum-based, bipyridine-containing MOF (MOF-253) with and without incorporating a high work function metal, rhenium (Re). After analysis of an Nd-bearing MOF, we hypothesized that the well-dispersed, sponge-like interconnected network of the degraded structure would enhance Nd ionization more than traditional TIMS loading techniques (i.e., phosphoric acid). Compared to filaments loaded with phosphoric acid that require an additional benzene carburization step, the Nd ionization efficiencies (atoms detected relative to atoms loaded) for heated filaments loaded with MOF-253 were similar (∼1%). Electron microscopy after TIMS analysis demonstrated that the MOF was retained on the filament. While these results are preliminary, they demonstrate that MOFs have potential to enhance ionization and exceed the performance of traditional loading techniques by forming nanoporous ion emitters. Thus, further experimentation is likely to exceed this performance through more specific selection of the base MOF structure and modifications to porosity and composition. This work represents a novel application of MOFs and a next step in the pursuit of advanced thermal ionization with potential to expand across the periodic table.

On the Feasibility of Integrating the Concept of Contemporary Green Environment Development in Creating Screen Prints by Digital Means.

In the 21st century with the popularization of digital technology, as a printmaking born out of industrial printing, it has undergone a revolutionary evolution. Silkscreen printmaking is a type of print with high technology content, strong dependence on media, and no traditional load. Thirty years ago, as soon as silkscreen prints settled in China, they began artistic exploration and experimentation with a brand-new attitude. Several generations of screen print workers have achieved it, expanded it, and made it independent in the fusion of modern language methods and oriental cultural spirit, and in the exploration of screen vocabulary, characteristics and character, it has an independent status and ranks among the international elite qualifications. This paper analyzes the root problems of high-tech printmaking tools, materials, technical principles, and causes of aesthetics. Starting from the background of the concept of green development, it expounds its related concepts, analyzes the theoretical origin of green development, and clearly recognizes the high-tech. The limitation of printmaking in the development space—that is, the expression language of digital plate making, is relatively correct in theory, so as to carry out step-by-step exploration and practice.

Do Educators Have an Alternative to Tutoring to Increase Their Income?

The article presents the results of a study of the workload and income of teachers that are using online educational platforms and marketplaces to find main and additional job. The approach based on the concept of shadow education. The authors find out to what extent the income of teachers depends on the workload and the combination of types of pedagogical activity in educational organization and outside them. The research question is: whether there are statistically significant differences in the cost of an hour of tutoring, platform and traditional pedagogical load, at which conditions the income of teachers equal to comparable occupations and in line with decent work indicators. An analysis of the data of an online sociological survey using linear regression modeling showed that a teacher working only at school, even with excessive workload, will not achieve earnings comparable to the income of the other types of pedagogical activity. The article for the first time reasonably reveals the reasons for the formation — while maintaining a pool of teachers who work only at school — a large group of teachers employed exclusively in the tutoring sector, both independently and on online platforms.

An Advanced Control Strategy for Dual-Actuator Driving System in Full-Scale Fatigue Test of Wind Turbine Blades

A new dual-actuator fatigue loading system of wind turbine blades was designed. Compared with the traditional pendulum loading mode, the masses in this system only moved linearly along the loading direction to increase the exciting force. However, the two actuators and the blade constituted a complicated non-linear energy transferring system, which led to the non-synchronization of actuators. On-site test results showed that the virtual spindle synchronous strategy commonly used in synchronous control was undesirable and caused the instability of the blade’s amplitude eventually. A cross-coupled control strategy based on the active disturbance rejection algorithm was proposed. Firstly, a control system model was built according to the synchronization error and tracking error. Furthermore, based on arranging the transition process, estimating the system state and error feedback, and compensating disturbance, an active disturbance rejection controller was designed by adopting the optimal control function. Finally, on-site test results showed that the cross-coupled control strategy based on the active disturbance rejection algorithm could ensure the synchronization of two actuators. The maximum speed synchronization error of the two motors was less than 16 RPM, the displacement synchronization error of the two actuators was less than 0.25 mm and approaching zero after 4 seconds, and the peak value of vibration of the blade was less than 5 mm, which satisfied the fatigue test requirement.

Preparation and Biocompatibility Evaluation of Nanoscale Isoniazide-Loaded Mineralized Collagen Implants for Tuberculous Bone and Joint Repair.

Bone and joint tuberculosis is an extremely severe infectious disease that commonly occurs due to the primary infection of a type of mycobacteria, called Mycobacterium tuberculosis. Under the current scenario, there are very limited supplies of bone grafts available for the treatment of deceased bone, including autogenous bone and synthetic biomaterials. The present study aimed to construct a nanoscale isoniazid-loaded mineralized collagen implant, and then to explore its physicochemical properties and to investigate its biocompatibility suitable for bone and joint repair. Using type I collagen as raw material and the principle of biomimetic mineralization for self-assembly of bone tissue, a new drug-loaded mineralized collagen implant was constructed by molecular coprecipitation with isoniazid. Its surface morphology, elemental composition, and porosity were characterized by field emission scanning electron microscope (SEM), X-ray diffraction (XRD), and pycnometer. The performance of the implant was gauged by sustained release and degradation, which were studied using an ultraviolet spectrophotometer and a simulated in vivo environment. The drug loading and encapsulation rates of the implants were (6.25 ± 0.48)% and (54 ± 2.34)%, respectively. The in vitro release time of the scaffold was more than 12 weeks and the degradation performance was excellent. The scaffold was then implanted into mice, and the inflammatory reaction of local tissue was observed by Haemotoxylin and Eosin (H&E) and Masson. The in vivo evaluation in mice showed that the scaffold was biocompatible. Overall, compared with traditional drug loading systems, the isoniazid biomimetic mineralized collagen implant constructed here has better drug release performance, biodegradability, and biocompatibility. This kind of collagen implant may find potential applications in tuberculous bone and joint repair.

Non-Intrusive Adaptive Load Identification Based on Siamese Network

The traditional non-intrusive load monitoring (NILM) algorithms are mostly based on classification models, which have several deficiencies. Firstly, a large amount of labeled data is required to train the classification model. Secondly, these algorithms cannot identify unknown devices that frequently encountered in practical application. Finally, these models have poor performance in versatility, which means they only adapt to the trained data. These shortcomings greatly influence the practicality of these NILM algorithms. To tackle these problems, this paper has proposed a non-intrusive adaptive load identification model based on the Siamese network, which uses both the V-I trajectory and active power as the load signatures. The Siamese network is utilized to calculate the similarity of the V-I trajectory, and the load identification is realized by matching the signature with the feature library. Through adding new features to the feature library dynamically, the identification of unknown load can be realized. In addition, the Siamese network is a typical network for few-shot learning, thus the proposed model can be trained with a small number of samples to achieve ideal recognition effect. At last, the validity and versatility of the model are verified in PLAID dataset and COOLL dataset.

Empowerment of cluster and grid load balancing algorithms to support distributed exascale computing systems with high compatibility

The nature of the scientific applications that need to be distributed exascale systems is in a way that both traditional and distributed exascale systems programs are required. As a result, the load balancer not only supports traditional mechanisms of the load balancer, but also these mechanisms should be empowered to support the states caused by the occurrence of events with a dynamic and interactive nature. In this paper, a mathematical model examines the traditional load balancer that is used in cluster and grid computing systems that should support which characteristics to manage the dynamic and interactive nature in processes and execution in distributed exascale computing systems. In cluster and grid systems that can support the specified characteristics in this article, in 60% of the cases, the load balancer can manage events with a dynamic and interactive nature and use the mathematical model as the load balancing mechanism.

Simultaneous Estimation of Behind-the-Meter Solar and Wind Power at the Bulk Supply Point

The presence of distributed energy resources in private premises has observed a large deployment worldwide driven by favorable governmental policies and technology development. In Northern Ireland, the small-scale generation capacity including solar and wind power has fast-grown accounting for over 17% of peak load in 2021, which makes net demands more volatile and diminishes the accuracy of electric demand forecasting activities. Different from traditional load forecasting methods based on the relationship between heterogeneous variables and net demand fluctuation, a novel approach is proposed to separately forecast the invisible components (e.g. wind & solar power) within the net demand. The critical challenge in the separate forecasting is how to extract behind-the-meter components from the measured net demand data, namely net demand disaggregation, for which continuous wavelet transform is applied to bring out the detailed fluctuation of net demand and an iterative search algorithm is proposed to separate the components in the net demand. The results reported show that the proposed demand disaggregation method contributes to reduce the forecasting error by more than 14%. Methods like the proposed net demand disaggregation can benefit utilities and system operators in performing their daily activities regarding load forecasting in serving areas and power systems they operate.