Model Of Load Research Articles

A Bi-Level Optimal Scheduling Strategy for Microgrids for Temperature-Controlled Capacity and Time-Shifted Capacity, Considering Customer Satisfaction

Since microgrids can effectively integrate renewable energy, energy storage devices, and controllable loads, this advantage promotes the rapid development and application of microgrid technology. However, with the high proportion of renewable energy access, only considering how energy is optimally distributed in microgrids can no longer meet the actual demand. How to aggregate user-side controllable loads to form regulation resources has become a research hotspot, and the users, as a passive party in the load scheduling process, should also be an important consideration in their perception of the use of electricity. First, a control model for temperature-controlled loads and a time-shift model for time-shiftable loads are developed. Then, the comprehensive electricity satisfaction model of users is established, and the two-layer optimal scheduling model of microgrids considering users’ satisfaction is proposed, with users as the upper layer and microgrids as the lower layer, and the two-layer model is transformed into a single-layer model according to the KKT condition for solving. Finally, the effect of the weighting factor for satisfaction on the economy is discussed through the analysis of examples, which verifies the effectiveness of the two-layer model.

Research on the equivalent impact test method for different pulse widths based on micro-electro-mechanical system devices

The impact pulse width generated by kinetic weapons during launch differs significantly from that produced by conventional laboratory overload impact methods. Consequently, there is an urgent need to develop a method for evaluating the equivalent impact between overload environments characterized by different pulse widths. In the Hopkinson Bar overload test environment, this study presents a method for performing equivalent impact tests based on the Sum of Effective Impact Energy for micro-electromechanical system acceleration sensors, in accordance with the impact dynamics theory and the law of energy variation. It establishes an equivalent impact model for inertial loads with different pulse widths. The dynamic response of the accelerometer to various impact loads was evaluated through the Hopkinson Bar overload test and live firing test, with an error margin of less than 10 %. Timely guidance can be provided for selecting overload parameters in dynamic impact testing, thereby advancing the engineering process of simulating extreme overload environments.

Predictive models for short-term load forecasting in the UK's electrical grid.

There are global efforts to deploy Electric Vehicles (EVs) because of the role they promise to play in energy transition. These efforts underscore the e-mobility paradigm, representing an interplay between renewable energy resources, smart technologies, and networked transportation. However, there are concerns that these initiatives could burden the electricity grid due to increased demand. Hence, the need for accurate short-term load forecasting is pivotal for the efficient planning, operation, and control of the grid and associated power systems. This study presents robust models for forecasting half-hourly and hourly loads in the UK's power system. The work leverages machine learning techniques such as Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) to develop robust prediction models using the net imports dataset from 2010 to 2020. The models were evaluated based on metrics like Root Mean Square Error (RMSE), Mean Absolute Prediction Error (MAPE), Mean Absolute Deviation (MAD), and the Correlation of Determination (R2). For half-hourly forecasts, SVR performed best with an R-value of 99.85%, followed closely by GPR and ANN. But, for hourly forecasts, ANN led with an R-value of 99.71%. The findings affirm the reliability and precision of machine learning methods in short-term load forecasting, particularly highlighting the superior accuracy of the SVR model for half-hourly forecasts and the ANN model for hourly forecasts.

A multi-objective spatio-temporal pricing method for fast-charging stations oriented to transformer load balancing

To address the challenges posed by the fast-charging demand of electric vehicles, causing feeder load and voltage imbalances during operation, this paper introduces a spatio-temporal pricing strategy tailored to enhance feeder operation equilibrium. This approach facilitates the spatio-temporal guidance of fast-charging loads for electric vehicles in operation. This paper begins by formulating a spatio-temporal distribution model for electric vehicle fast-charging loads, considering owners’ preferences. It further develops a behavioral model for the travel choices of electric vehicles, illustrating the impact of spatio-temporal electricity pricing at fast-charging stations on load distribution. Next, it proposes a multi-objective spatio-temporal pricing model and its solution method specifically designed for feeder-balance-oriented fast-charging stations. This model targets the minimization of the spatio-temporal imbalance in feeder voltage and load. It takes a comprehensive approach, considering the constraints of the spatio-temporal load distribution model and optimal power flow model. The resulting spatio-temporal pricing model for fast-charging stations is effectively solved using the extended Pareto evolutionary algorithm. To validate the effectiveness of the proposed method in achieving feeder balancing, this paper analyzes two examples: a self-built 29-node road network and a 9-node distribution network, as well as a 66-node road network and a 33-node distribution network in the Xinjiang region. The results show that the proposed method can effectively guide the charging of electric vehicles and make the load distribution more balanced.

Flexible Regulation and Synergy Analysis of Multiple Loads of Buildings in a Hybrid Renewable Integrated Energy System

The insufficient flexibility of the hybrid renewable integrated energy system (HRIES) causes renewable power curtailment and weak operational performance. The regulation potential of flexible buildings is an effective method for handling this problem. This paper builds a regulation model of flexible heat load according to the dynamic heat characteristics and heat comfort elastic interval of the buildings, as well as a regulation model of the flexible electrical load based on its transferability, resectability, and rigidity. An operation optimization model, which incorporates flexible regulation of multiple loads and a variable load of devices, is then developed. A case study is presented to analyze the regulation and synergy mechanisms of different types of loads. Its results show a saturation effect between heat and electrical loads in increasing renewable energy consumption and a synergistic effect in decreasing the operating cost. This synergy can reduce the operating cost by 0.73%. Furthermore, the operating cost can be reduced by 15.13% and the curtailment rate of renewable energy can be decreased by 12.08% when the flexible electrical and heat loads are integrated into the operation optimization of HRIES.

Effects of prestress in the coating of an elastic disk

An elastic disk is coated with an elastic rod, uniformly prestressed with a tensile or compressive axial force. The prestress state is assumed to be induced by three different models of external radial load or by ‘shrink-fit’ forcing the coating onto the disk. The prestressed coating/disk system, when loaded with an additional and arbitrary incremental external load, experiences incremental displacement, strain, and stress, which are solved via complex potentials. The analysis incorporates models for both perfect and imperfect bonding at the coating/disk interface. The derived solution highlights the significant influence not only of the prestress but also of the method employed to generate it. These two factors lead, in different ways, to a loss or an increase in incremental stiffness for compressive or tensile prestress. The first bifurcation load of the structure (which differs for different prestress generations) is determined in a perturbative way. The results emphasize the importance of modelling the load and may find applications in flexible electronics and robot arms subject to pressure or uniformly-distributed radial forces.

Wear life prediction of piston ring and cylinder liner based on integration of experiment and bench test data

The wear data of piston ring-cylinder liner (PRCL) of high-power diesel engines faces the problems of small samples, randomness, and underutilization, which leads to the wear life prediction of PRCL has been a difficult task in the engineering field. A wear life prediction method of PRCL based on the integration of experimental data and bench test data is proposed. First, a large amount of wear data is rapidly obtained through accelerated wear tests on specimens, and the distribution type of wear depth of PRCL is determined. Secondly, based on Rhee's wear equation, the nonlinear relationship between time, load, temperature, and wear depth is considered, and the acceleration models of temperature and load were introduced as the Arrhenius model and the inverse power law model, respectively. The wear life prediction model (WLPM) for specimens and parts with stochasticity is developed. The eigenvalues of WLPM’s parameters of specimens and parts were utilized to construct the mapping relationship from the specimen to the part. Finally, the Bayesian statistical method is used to merge the wear data of specimens and the parts, and the WLPM of specimens and the mapping relationship are taken as the a priori information, the bench data are taken as the new sample information, and the MCMC-Gibbs sampling method is used to estimate the mapping-based WLPM of parts. The results show that through the validation test, the method predicts the wear depth of PRCL with 100 % falling into the 95 % confidence interval better than the WLPM prediction results of a single specimen or part, and the errors of predicting the mean wear life of PRCL are all within 10 %. The method can provide a reference for the quality evaluation and engineering application of PRCL.

Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone

The study focuses on developing an accurate prediction model for suspended sediment load (SSL) based on antecedent SSL and water discharge values. Two Artificial Intelligence (AI) models, Hybrid and Parallel, were employed to test on the Kelantan and Mississippi Rivers in different climate zones and river sizes. The parallel model showed better performance than the hybrid in most cases, with the best results based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) (432.06 and 782.15 respectively) for Kelantan and (31672.25 and 62356.60 respectively) for Mississippi. The multifunctional GA neural-network model results have proven its ability to predict SSL in tropical and semi-arid zones. In the Kelantan River, the 8-input combination set was the best prediction model, showing an improvement of more than 38% compared to traditional models. The proposed method has proven to be more accurate than traditional models, ensuring better water resource planning, agricultural management and reservoir operation.

Research on interval optimization of power system considering shared energy storage and demand response

Considering the low utilization rate of energy storage system under uncertainty of source-load and the coarse demand response mechanism, an interval optimization model of power systems based on shared energy storage and refined demand response is proposed. The dual-side uncertainty of source-load is expressed by interval numbers, and the refined demand response mechanism and shared energy storage optimization model for different building load are analyzed. Then, the source-grid-load-storage interval optimization model with shared energy storage is solved and analyzed. In order to improve the accuracy of the optimization results, sensitivity analysis is performed on the linearized segmentation number of the objective function under dual-side uncertainty of source-load. Finally, by comparing the simulation with the traditional centralized energy storage system, the results show that the dual-side uncertainty of source-load can be effectively handled using interval optimization. Considering shared energy storage and demand response, it can effectively improve the energy storage utilization rate and system operation economy, and realize the source-grid-load-storage synergistic interaction.

Revaluation of occupancy duration for live load using big data of enterprise credit information

The occupancy duration is an essential variable in establishing the time-varying model of the live load. The manual questioning and inquiries, which are the common survey methods for occupancy duration, suffer from being expensive and time-consuming. This has hindered the updating of the data basis and progress in live load research. This study proposes a novel big data survey method, which utilizes 210 million enterprise credit information to collect occupancy duration samples. Along with the big data survey method, an address-oriented mining method is presented to efficiently extract duration samples of the target type from big data. Based on the actual distribution of the extracted duration data, a more general time-varying model, called the compound renewal process, is suggested to describe the live load. The stochastic harmonic function representation method and probability density evolution method are then adopted to access the maximum load distribution of the live load process. The maximum load distributions of the sustained and combined loads are calculated and compared with previous studies to evaluate the impact of the new survey results. Comparison shows that the occupancy duration of office buildings is significantly shorter, resulting in an increase in the maximum load. The proposed data survey method enables continuous updating of duration samples, and the compound renewal process allows better compatibility for new survey data. The up-to-date duration data and a realistic time-varying model provide new ideas for determining the design live load.

Distribution network tariff design: Facilitate flexible resource under uncertain future energy scenarios

The uncertainty embedded in low-carbon transition pathways and future energy scenarios will reshape the way of distribution network planning and operation. Flexible solutions provided by flexible resources could help distribution network operators (DNOs) to reduce network costs and risks under uncertainties. However, the current tariff paradigm neglects such contributions, which cannot generate sufficient incentives to facilitate flexible resource development. To tackle this issue, this paper first introduces an improved price control method that caps DNOs' revenue while encouraging DNOs' sustainable investment under uncertainties. A bi-level planning model is then proposed to determine the optimal investment plan and evaluate the risk reduction effect by scheduling flexible loads. Finally, a novel pricing method is proposed to derive network tariffs for existing and potential flexible loads based on their contributions to risk reduction and demonstrated in a test system. The result shows a 24% network cost reduction given by 6% DNO risk compensation rate, and 0%–43% and 1%–22% network tariff reductions for existing and future flexible demands, respectively. This evidence proves the proposed method can incentivize DNOs to well manage risks, meanwhile promote the connection and engagement of flexible loads. Regulators and DNOs are recommended to review the existing pricing framework, enhance risk management in price regulation, and explore suitable tariff designs or business models for flexible loads to cope with future uncertainties through flexibility.

Neural-Network-Based Model for Trailing-Edge Flap Loads in Preliminary Aircraft Design

Accurately predicting the forces and moments acting on trailing-edge devices under different flight conditions is a critical aspect in the design of the kinematics and actuation for high-lift or variable-camber applications. However, accurate modeling without elaborated computational fluid dynamics (CFD) analyses in the subsonic and transonic regimes needs a sophisticated model. Thus, the objective of this paper is to create such a model that accurately predicts the forces and moments acting on flaps during different flight conditions while remaining applicable to the preliminary aircraft design. The target values in this model are the three-dimensional (3D) forces and moments on the flap, which were obtained through 3D CFD simulations. The chosen input values required for the model include two-dimensional airfoil data, and wing geometry data for three different aircraft types: short-, medium-, and long-range, including a high-aspect-ratio configuration. Among several potential approaches, a neural network was deemed to be the most promising for predicting the target values. The neural network was used as a regression tool to accelerate the model development process in the preliminary aircraft design. Consequently, multiple studies were conducted on how the setup of the neural network, including the number of neurons, activation functions, and initialization, influences the results. The results reveal that the developed neural network accurately predicts the flap forces and moments with a mean deviation of under 2% for the vertical force Fz and the lateral force Fx and under 4% for the moment My.

Multiparameter Bifurcation Analysis of Power Systems Integrating Large-Scale Solar Photovoltaic and Wind Farms Power Plants

In this paper, we propose a novel codimension-three-parameter bifurcation analysis of equilibria and limit cycles when integrating Renewable Energy Sources (RESs) power plants with an exponential static load model. The study investigates the effect of solar photovoltaic generation margin, wind power generation margin, and loading factor on the local bifurcation of the modified IEEE nine-bus system. The proposed technique considers the real case of the West System Coordination Council (WSCC), the western states of the USA, by using specific models of RES power plants and static loads. The proposed technique helps to create a set of linearly varying parameters for critical operating points of nonlinear systems. The study explores detailed voltage stability analysis through the examination of bifurcation diagrams. The Hopf, limit-induced, and saddle-node bifurcation branches are identified, defining the parameter space’s stable and unstable operational regions. Additionally, the stability regions surrounding the equilibrium points are diligently explored, clarifying the consequences that various bifurcations may exert on these regions. The study offered in this proposed work aids in determining the best ways to monitor and improve these margins while considering system variables and load design.

Construction of Auxiliary Service Transaction Model of Power Reserve for Interruptible Load

To ensure the safe and stable operation of the power system in the power market environment, a transaction model of power reserve ancillary services oriented to interruptible loads is constructed. By analyzing the benefits of interruptible users and power grid enterprises, a transaction model of interruptible load is established based on it. After obtaining the value of the minimum cost of power system to purchase interruptible load, the capacity cost, opportunity cost and standby use cost of power reserve are calculated respectively. The objective function of auxiliary power reserve service transaction model with interruptible load is established and the constraint conditions are set, and then the transaction result of auxiliary power reserve service is obtained by solving the objective function of the model. The experimental results show that the model can effectively calculate the power interruption compensation price of user interruptible load, and obtain the interruptible load transaction results according to the rapid capacity quotation, power quotation, etc. At the same time, the model has the lowest cost of reserve capacity and reserve use cost in a certain period of time, and the application effect is good.

Integrated Demand Response for Micro-Energy Grid Accounting for Dispatchable Loads

Micro-energy networks are the smallest element of integrated energy systems, and tapping into the integrated demand response potential of micro-energy networks is conducive to improving energy use efficiency and promoting the development of new energy sources on a large scale. This paper proposes a day-ahead integrated demand response strategy for micro-energy grid that takes into account the dispatchable loads. Considering the gradient use of thermal energy, a typical micro-energy grid structure including electricity, gas, medium-grade heat, low-grade heat, and cold energy is constructed, a comprehensive energy equipment model is established, and the refined scheduling models of the dispatchable loads are given. On this basis, with the operating economy of the micro-energy grid as the optimization objective, the integrated demand response strategies of tariff-type and incentive-type are proposed. Through case study analysis, it is verified that the proposed strategy can optimize the energy consumption structure of the micro-energy grid under the guidance of time-of-use tariffs, reducing the operating costs. The proposed strategy fully exploits the demand response potential of the micro-energy grid through the dispatchable loads and the multi-energy complementarity of electricity, heat, and cold, realizes the comprehensive coordination and optimization of source-network-load-storage, provides a larger peak-regulating capacity, and exhibits practical applicability in engineering.

Impact of the COVID-19 pandemic on the use of an inpatient and community palliative care service.

The COVID-19 pandemic disrupted the usual provision of healthcare, changing models of care, clinical loads, service provision and patient behaviour. This study assesses the impact of COVID-19 on community and inpatient palliative care service provision. A retrospective audit and comparison of service use conducted over two defined periods, before and during the COVID-19 pandemic, 2019-2020. The community palliative care service had a 9% increase in referrals, with a lower proportion of referrals (2.4%) from subacute/palliative care hospitals during the COVID-19 pandemic. Provision of care during the pandemic included less face-to-face contact with patients (24.1% versus 30.2% before), and markedly more contact with patients via videoconference (2.1% versus 0.1% before the pandemic). The community specialist palliative care service was busier during the pandemic period, and experienced a shift in mode of care delivery, while the inpatient unit experienced no difference in service use.

Deformation characteristics of a ring-stiffened cylindrical shell induced by shock waves and coalesced bubbles in double-charge underwater explosions

This study investigates the deformation characteristics of a ring-stiffened cylindrical shell induced by shock waves and coalesced bubbles in double-charge underwater explosions. A numerical model for coupling underwater multi-point explosion loads with the cylindrical shell is established by the Arbitrary Lagrange Euler method, and underwater explosion experiments with double charges are also conducted. The numerical model's effectiveness is validated by comparing shock wave superposition characteristics, bubble coalescence processes, and bubble pulsation periods with the experimental results. Based on the numerical model, the influences of detonation intervals and layout angles of charge on shock wave superposition effects, temporal and spatial distribution characteristics of shock waves, and the evolution process of coalesced bubbles in underwater explosions with double charges are explored. Additionally, the deformation characteristics of cylindrical shells induced by shock waves and coalesced bubbles for double charges with different detonation intervals and layout angles of charge are analyzed. The results indicate that double charges can induce more substantial deformation on the cylindrical shell at a specific detonation interval than a single charge with equal total mass. During the shock wave phase, the cylindrical shell's deformation decreases nonlinearly with an increased layout angle. Conversely, during the bubble load phase, the deformation of the cylindrical shell shows an approximately linear decrease with an increase in layout angle. A critical angle exists, below which the impact of double charges on the cylindrical shell is more substantial when detonated with an interval than simultaneous detonation.

Risk assessment of distribution network operation with scale distributed photovoltaics

In the context of large-scale photovoltaic integration into the distribution network, it is of great significance to comprehensively consider the uncertainty of photovoltaic output and load and scientifically and effectively evaluate the operational risks of the distribution network, in order to ensure the reliable and economic operation of the distribution network. At the same time, with the increase of distributed photovoltaic installed capacity, the small interference stability caused by it has an increasing impact on the operation of the distribution network. In this paper, we first establish a probabilistic model of photovoltaic output and load. Then, we propose a risk calculation method based on the non-sequential Monte Carlo method and probabilistic power flow calculation. Then, a risk analysis of large-scale photovoltaic access to the distribution network was carried out. A new small interference stability risk index was constructed to evaluate the risk of system oscillation caused by photovoltaic access. On this basis, the comprehensive risk value is calculated, and the risk levels are divided based on the voltage over-limit risk and power flow over-limit risk. Finally, the feasibility and effectiveness of the risk assessment model are verified through case analysis.

Rectifier load modeling and zero voltage switching design of inductive power transfer system with S‐type topology on the secondary side

SummaryRectifier load in inductive power transfer system has nonlinear characteristics. However, the traditional analysis method equates it to a pure resistor, which leads to a certain deviation between the theoretical analysis and the actual circuit. This will reduce the accuracy of the inductive power transfer (IPT) system model and affect the soft switching operation of the inverter. In this paper, the IPT system with S‐type topology on the secondary side is studied. Firstly, the resistance–inductance characteristics of the rectifier load are analyzed by simulation, and then a second‐order circuit response model is established to solve the time‐domain expression of voltage and current. Then, the fundamental component of voltage and current is extracted by Fourier transform to calculate the impedance value of the rectifier load, and the mathematical calculation model of the rectifier load is established. On this basis, the influence of the inductive component of the rectifier load on the input impedance of the IPT system is analyzed. Then, a parameter design method for zero voltage switching is proposed for S‐S and LCC‐S topologies. Finally, an experimental platform of S‐S and LCC‐S IPT system is built. The correctness and effectiveness of the theoretical analysis and the proposed parameter design method are verified by experiments.

Allostatic Load in Childhood, Adolescence, and Young Adulthood: Are Assumptions of Measurement Invariance Warranted?

Allostatic load represents the cumulative toll of chronic mobilization of the body's stress response systems, as indexed by biomarkers. Higher levels of stress and disadvantage predict higher levels of allostatic load, which, in turn, predict poorer physical and mental health outcomes. To maximize the efficacy of prevention efforts, screening for stress- and disadvantage-associated health conditions must occur before middle age-that is, during childhood, adolescence, and young adulthood. However, this requires that models of allostatic load display properties of measurement invariance across age groups. Because most research on allostatic load has featured older adults, it is unclear if these requirements can be met. To address this question, we fit a series of exploratory and confirmatory analytic models to data on eight biomarkers using a nationally representative sample of N = 4260 children, adolescents, and young adults drawn from the National Health and Nutrition Examination Survey dataset. Exploratory and confirmatory models indicated that, consistent with allostatic load theory, a unidimensional model was a good fit to the data. However, this model did not display properties of measurement invariance; post-hoc analyses suggested that the biomarkers included in the final confirmatory model were most strongly intercorrelated among young adults and most weakly intercorrelated among adolescents. These results underscore the importance of testing assumptions about measurement invariance in allostatic load before drawing substantive conclusions about stress, disadvantage, and health by directly comparing levels of allostatic load across different stages of development, while underscoring the need to expand investigations of measurement invariance to samples of longitudinal data.