Load Model Research Articles

Equivalent load model for the numerical simulation of integrated induction heating and mechanical rolling forming of curved hull plates

Equivalent load model for the numerical simulation of integrated induction heating and mechanical rolling forming of curved hull plates

Assessing some statistical and physical modelling uncertainties of extreme responses for monopile-based offshore wind turbines, using metocean contours

This study examines the influence of probabilistic models for wave parameters in the joint environmental model and hydrodynamic/soil models on extreme mudline bending moments for monopile-based wind turbines at representative wind speeds, using the environmental contour method. For significant wave height, the 3-parameter Weibull model using the method of moments (MoM) provides the best fit to hindcast data across different wind classes, for the statistical models and data considered in the study. The hybrid Log-normal-Weibull (LonoWe) model also provides a reasonable fit but is sensitive to the transition point between distributions. Both models yield the largest extreme responses, with differences of approximately 0.5–3.5%. The 3-parameter Weibull model with maximum likelihood estimation (MLE) and the 2-parameter Weibull model result in less conservative contours, leading to up to 13% lower extreme responses, compared to LonoWe and Weibull (MoM). Regarding peak period, both the Log-normal and 3-parameter Weibull models provide reasonable fits, with the latter being more accurate near the steepness (breaking) limit. The stochastic variation among maxima due to seed variability and the uncertainty in quantile estimates as a function of number of samples was found to be crucial, particularly for severe sea states at the cut-out speed. Soil modelling is particularly important when the turbine is parked and encounters peak wave periods close to the turbine’s natural periods, while the effect of soil modelling on the extremes during turbine operation is negligible. Additionally, the impact of diffraction becomes relatively important for short wave periods. However, it is worth noting that the choice of load models has less impact on extreme responses compared to variations in the contours caused by different statistical models or seed variability.

Three-dimensional modelling of maximum fluid load on horizontally rotating cylinder

It is a well-known fact that when a rod coated with a viscous liquid such as honey is rotated at a sufficiently high speed, the liquid will stop draining. However, when the liquid is left on the rod for a sufficiently long time, fluid will accumulate in bands around the cylinder. When these bands reach a sufficiently large amplitude, the fluid will drain from the rod. While previous literature has explored the formation and growth of these bands, none has explored the conditions under which the fluid in these bands will drain. In this paper, we make use of linear stability analysis to find the growth rate of the band and hence, the time taken for the band to reach the critical amplitude before draining occurs. We verify this approximation with full 3D simulations. Afterwards, this is used to plot a phase plot to identify if the fluid is draining at a specific point in time. We come to the compelling conclusion that the inevitable formation of bands on the rod will invariably lead to fluid drainage. This has important implications for rolling-coating operations where a uniform fluid coating is required as the coating will need to be complete before the fluid accumulates in bands.

Optimal design of a clamp band system based on genetic algorithm and experimental verification

As one of the most common separation systems, the clamp band system (CBS) is widely used in the process of connection and release of spacecraft with the launch vehicle. An optimal design method and a separation shock response estimation method were proposed for a large-diameter CBS in this paper. First, a new application based on the parameter modeling method of MSC.Patran and genetic algorithm (GA) was proposed to design the CBS. Finite element techniques for 2D axisymmetric analysis of the CBS were developed, including the modeling of V-clamp, strap pre-tension, and loads, then the optimizing constraints and objectives were also defined. Secondly, a CBS with a diameter of 3 m was designed via the proposed method, which is verified by the 3D finite element analysis under MSC.Marc and a stiffness experiment. Thirdly, the separation process of the CBS and its high-frequency and high-amplitude shock response were calculated by an explicit dynamic solver, and the differences between the model and experimental results were discussed. Finally, a single degree of freedom (SDOF) spring-mass system was constructed to predict the weakening effect of the delay time on the shock response. The slow-release device was designed and the experiment was completed to verify the effectiveness of the method.

Copula-based estimation of directional extreme wind speeds: Application for wind-resistant structural design

Accurate probabilistic modelling of wind loads has important implications to the wind-resistant design of structures. Whereas, most previous studies merely concerned the statistical analysis of wind speed, while the effect of wind direction was usually neglected in the wind load modelling. This paper thereby proposes a novel copula-based approach to construct the joint distribution of wind speed and direction for determining the directional extreme wind speed. Based on the wind records at six typical sites in Hong Kong, a series of parametric and nonparametric copula models are applied to model the joint probability density function (JPDF) of wind speed and direction. The goodness-of-fit test indicates that the Bernstein copula is the best model to describe the correlation between variables. Furthermore, by applying the superior copula model, directional extreme wind speeds with a 10-year return period at the six selected sites are determined on basis of the conditional probability theory. The results show that the estimated extreme wind speeds vary obviously among different directions. And, the estimated directional extreme wind speed may be unreliable in some directions when ignoring the joint effect of wind speed and direction in wind load modelling. Notably, the presented method is an effective and practical tool to estimate the directional extreme wind speed with a certain return period. This paper aims to provide useful reference for the wind-resistant design in tropical cyclone-prone regions.

Stochastic Modelling and Integration of Electric Vehicles in the Distribution System Using the Jaya Algorithm

Electric vehicles (EVs) have enormous promise for the development of future transportation systems. The widespread use of EVs could negatively impact how power systems operate, particularly at the distribution level. Therefore, smart charging techniques are essential to increasing EV adoption in general. The connection between the electricity grid and the transportation network is made at electric vehicle charging stations (EVCS), and both networks will be simultaneously impacted by the operational behaviour of EVs. Therefore, EVCS must be placed in a distribution network in the best possible way. In this paper, an efficient and smart charging approach is formulated to schedule the charging of electric vehicles so that adverse effects like an increase in peak demand for the system and the cost of charging electric vehicles are minimized. The EV load model is formulated by considering factors such as the state of charge, trip distance travelled, and the user’s charging behaviour. The proposed electric charging schedule reduces the peak load and optimizes the cost of charging. Different types of EVs are considered based on their usage patterns for making realistic problem formulations. The smart charging technique presented in this work reduces the peak demand by 30% and the cost of charging by 50%. In addition, EVCS placement is implemented alongside distributed generation on IEEE 33 and 69 bus systems to reduce power losses and improve the voltage profile by using a metaheuristic algorithm known as Jaya Algorithm. The effectiveness of the proposed algorithm is established by comparing the results with published work.

Applying Bayesian optimization to predict parameters in a time-domain model for cross-flow vortex-induced vibrations

As the demand for harvesting offshore energy increases worldwide, the need for slender structures, such as marine risers and power cables, will increase. The dominating loads for deep water applications will primarily be caused by current-structure interactions, where vortex-induced vibrations (VIV) are known to be a challenging response type to handle correctly during design. Semi-empirical time-domain models are promising for VIV prediction, but uncertainties related to simplifications in the hydrodynamic load model and empirical parameters lead to over-conservative designs, where the parameters are estimated from model tests within a relatively low Reynolds number range. The aim of this paper is to present an efficient tool to estimate empirical hydrodynamic parameters directly from test data. To illustrate the method, the hydrodynamic parameters were estimated from pure cross-flow VIV model tests in a steady current with Reynolds numbers in the range of 40,000-120,000. The parameters were updated sequentially by using a Bayesian optimization framework, with the aim of minimizing an objective function that incorporates the response errors between time-domain VIV simulations and model test data. It is shown that three empirical parameters can be obtained simultaneously, resulting in small response errors but the optimal parameters were overly sensitive to variations in the flow velocity. The optimal parameters were used to reconstruct the drag amplification related to cross-flow VIV and in forced motion simulations to illustrate how the parameters in the load model would map into the traditional formulation in terms of the added mass and excitation coefficient.

Impacts of multiple demand-side management strategies on microgrids planning: a literature survey

Abstract Demand-side management (DSM) schemes play a crucial role in managing renewable energy generation and load fluctuations by utilizing demand–response programmes (DRPs). This paper aims to provide a detailed overview of DRPs that help microgrid operators to keep costs and reliability within acceptable ranges. Additionally, this review paper provides a detailed economic load model for DRPs based on initial load, demand–response (DR) incentive, DR penalty and elasticity coefficients. This article also aims to guide researchers in identifying research gaps in DSM applications in microgrids by comparing various DSM schemes from different countries and regions in terms of DSM strategies, objective functions and optimization techniques. Furthermore, this study analyses the impact of DRPs on microgrid configuration from the perspective of utilities and customers, considering technical and economic performance metrics. As a result, it can be concluded that none of the studied cases provides models or guidelines for choosing appropriate DSM schemes that consider different consumer interests or load-type features. Furthermore, a few researchers have addressed the features of a modern price-based DR strategy, renewable generation-based dynamic pricing DR, which offers higher customer satisfaction than traditional DRPs.

Constructing Attention‐LSTM‐VAE Power Load Model Based on Multiple Features

With the complexity of modern power system and the susceptibility to external weather influences, it brings challenges to build an accurate load model. This paper proposes a variational autoencoder (VAE) long short‐term memory (LSTM) load model based on the attention mechanism (Attention). First, the Prophet data decomposition method is used to decompose long sequences of load data at multiple time scales. Second, the correlation‐based feature selection with maximum information coefficient (CFS‐MIC) method is employed to select weather features based on their relevance, a subset of features with high correlation and low redundancy is chosen as model inputs. Finally, the Attention‐LSTM‐VAE model is constructed to capture the temporal variations laws of load. The dataset includes 2 years of load values and weather data collected in Caojiaping, Hunan Province, China. The experimental results show that the Attention‐LSTM‐VAE model has the lowest mean absolute error of 0.0374 and the highest R‐squared value of 0.9714, verifying the accuracy of the model. Therefore, the performance of the Attention‐LSTM‐VAE model is better than the general deep learning load models, which has important reference for the research of power load models. Comparisons with other deep learning methods, the experimental results show that the Attention‐LSTM‐VAE model has the lowest mean absolute error of 0.0374 and the highest R‐squared value of 0.9714. The Attention‐LSTM‐VAE has better robustness, stability, and accuracy in load modeling, which has an important reference for the research of power load models.

Synthesis Load Model With Renewable Energy Sources for Transient Stability Studies

Increased penetration of renewable energy sources (RES) has brought challenges to load modelling techniques. This paper proposes a synthesis load model with RES (SLMR), and elaborates related industrial practice conducted by China Electric Power Research Institute. Several practical issues are addressed in designing and implementing SLMR, such as load survey and representation of different types of RESs. Besides, three new modelling concerns brought by RESs are novelly addressed in the paper, i.e., aggregation of various RES parameters, consideration of various RES terminal voltage, and additional line loss due to RES response. Parameterization of the SLMR calibrates widely varied node voltage in distribution grid, and aggregates them to one equivalent node. The proposed SLMR is practically applied in industrial works, which is demonstrated in the paper.

Structural demand assessment of exceptional road transport actions on existing Italian simply-supported girder bridge decks

In the present work an in-depth analysis of the structural demand induced by several configurations of exceptional road transports has been carried out with the aim to quantify bending moment and shear demands over different geometrical layouts of simply-supported girder bridge decks and compare them with those deriving by the application of the traffic load models in force at the time of the design. On such bases, a structural demand coefficient has been computed in analogy to the Level 3 assessment suggested by the new Italian Guidelines for assessing the Risk of existing bridges. In this way, this structural demand index can be easily used by infrastructure owners for a fast assessment of the effects of exceptional road transport configurations potentially travelling over a bridge on its structural elements, if compared to the load models originally considered for its design, thus making possible a preliminary screening on the transitability of the infrastructure and the identification of potential road network bottlenecks.

Моделирование «ковидного провала» плановой госпитализации в период разгара пандемии COVID-19: анализ и оценка потерь использованной коечной мощности специализированного оториноларингологического стационара

Introduction. In the midst of the COVID-19 pandemic, healthcare organizations around the world were forced to change their operating hours, and some, especially those providing specialized medical care, were forced to stop operating completely. The purpose of the study is to develop a year-by-year model of the movement of patients before the pandemic and at its height, based on long-term data on the movement of inpatients. With its help, analyze possible losses in bed capacity (expressed in the number of idle bed days, or unserved patients) from the pandemic during 2020 — the year of the height of the pandemic. Materials and methods. The study was carried out according to the design of an observational retrospective analytical study. As research materials, we used anonymized data on the number of patients in the hospital at the Federal State Budgetary Institution «St. Petersburg Research Institute of ENT» of the Ministry of Health of Russia, obtained from the medical registration form N 007/u-02 „Sheet of daily records of the movement of patients and the bed capacity of a round-the-clock hospital, day hospital at a hospital facility“). The R and RStudio software environment was used as the main software product for solving statistical problems. Results. A load model has been created (the number of daily inpatients undergoing treatment) during the year. An analytical model of the “Covid failure” of bed capacity utilization during the height of the pandemic has been created. The coordinates of the dynamic points of bed release and return to typical bed occupancy values for this period of time were calculated (using linear regression forecasting). The share of hospital losses during the pandemic was calculated and assessed. Discussion. The use of “Covid failure” data superimposed on “pre-Covid” indicators allows not only to estimate annual losses, but also to predictively minimize losses during planned closures of hospital beds, for example, for repairs, given that the resumption of work of a medical institution’s beds will often be slower than the speed at which work stops. Analysis of linear regression coefficients of bed release and occupancy rates can give an idea of the speed of reaching standard values for the volume of medical care. As studies have shown during the COVID-19 pandemic, the minimum value of the quarantine period after the complete vacancy of beds is 14 days, and for high-risk scenarios 21 days. Based on this, for the conditions of organizing medical otorhinolaryngological care, the minimum predicted value of the time from the closure of hospitalization to its opening may be about 37 days after the start of patient discharge. The approach and mathematical functionality presented in the article can be used on the initial data of other institutions, as well as in preparation for the “Disease X” pandemic.

Probability distributions for dynamic and extreme responses of linear elastic structures under quasi-stationary harmonizable loads

The non-stationary load models based on the evolutionary power spectral density (EPSD) may lead to ambiguous structural responses. Quasi-stationary harmonizable processes with non-negative Wigner-Ville spectra are suitable for modeling non-stationary loads and analyzing their induced structural responses. In this study, random environmental loads are modeled as quasi-stationary harmonizable processes. The Loève spectrum of a harmonizable load process contains several random physical parameters. An explicit approach to calculate the probability distributions for the dynamic and extreme responses of a linear elastic structure subjected to a quasi-stationary harmonizable load is proposed. Conditioned on the specific values of the load spectral parameters, the harmonizable load process is assumed to be Gaussian. The conditional joint probability density function (PDF) of structural dynamic responses at any finite time instants and the conditional cumulative distribution function (CDF) of the structural extreme response are provided. By multiplying these two conditional probability distributions with the joint PDF of the load spectral parameters, and then integrating these two products over the parameter sample space, the joint PDF of structural dynamic responses at any finite time instants and the CDF of the structural extreme response can be calculated. The efficacy of the proposed approach is numerically validated using two linear elastic systems, which are subjected to non-stationary and non-Gaussian wind and seismic loads, respectively. The merit of the harmonizable load process model is highlighted through a comparative analysis with the EPSD load model.

Modelling of the nutrient load in the Sula River basin using the MONERIS

Modelling of the nutrient load in the Sula River basin using the MONERIS

Bench Testing for Power Flow Management in AC/DC Hybrid Microgrids Using Per-Unit System: Study and Design

This article presents an in-depth study and the design of a test bench dedicated to assessing the performance of power flow management within an AC/DC hybrid microgrid. With a focus on utilizing the per-unit system, our approach aims to accurately emulate the operational conditions of a real microgrid. We delve into the theoretical aspects of power flow management, implementing key concepts such as per-unit (PU) power. The design of the test bench includes the faithful modeling of renewable sources, loads, energy storage, and other constituents of the microgrid. Detailed simulation results and performance analyses are presented, demonstrating the Effectiveness and robustness of our testing approach. This test bench provides a versatile platform for evaluating and enhancing power flow management strategies in hybrid microgrids, thereby contributing to the ongoing development of decentralized and sustainable energy systems.

Integrated design of lateral-directional control law and vertical tail load for civil aircraft

According to new requirements of airworthiness regulation CS25.353, the advanced lateral-directional P-Beta control law design is studied in this paper. The LQR method based on the tracking command model is used to design the lateral-directional control law parameters. At the same time, the vertical tail load model of aircraft for yaw maneuver is introduced into the lateral-directional control law design model. The aim is to build the integrated design of control law and flight load. Finally, the simulation results show that the P-Beta control law can not only improve Dutch roll damping and coordination turn characteristics, but it can also reduce the yaw maneuver load by 18.5%. Thus, the structure weight of the aircraft’s vertical tail can be reduced.

Failure probability estimation of dynamic systems employing relaxed power spectral density functions with dependent frequency modeling and sampling

This work addresses the critical task of accurately estimating failure probabilities in dynamic systems by utilizing a probabilistic load model based on a set of data with similar characteristics, namely the relaxed power spectral density (PSD) function. A major drawback of the relaxed PSD function is the lack of dependency between frequencies, which leads to unrealistic PSD functions being sampled, resulting in an unfavorable effect on the failure probability estimation. In this work, this limitation is addressed by various methods of modeling the dependency, including the incorporation of statistical quantities such as the correlation present in the data set. Specifically, a novel technique is proposed, incorporating probabilistic dependencies between different frequencies for sampling representative PSD functions, thereby enhancing the realism of load representation. By accounting for the dependencies between frequencies, the relaxed PSD function enhances the precision of failure probability estimates, opening the opportunity for a more robust and accurate reliability assessment under uncertainty. The effectiveness and accuracy of the proposed approach is demonstrated through numerical examples, showcasing its ability to provide reliable failure probability estimates in dynamic systems.

Cost-Centric Energy Management of a Test Grid System Considering Load Flexibility for DSM

Demand-Side Management (DSM) adjusts load demand by rebuilding the load demand model of a distribution system and thus reduces operating costs. One potential approach to achieving cost savings is the redistribution of flexible loads to time periods characterized by lower utility expenses per unit, or alternatively, the elimination of nonessential loads. The primary objective of this research is to examine the use of DSM approaches in the scheduling of domestic appliances. The aim is to effectively minimize operating expenditures, the peak-to-average demand ratio (PADR), and any associated problems. Domestic appliances may be categorized into three distinct types based on their ability to adjust to variations in time, temperature, and light: time-flexible, temperature-flexible, and light-flexible. The second step of the study focuses on modelling the power consumption scheduling issue for demand-side clients. This is done by including operating cost priorities and modes of operation while taking into account both demand and supply factors. This research optimizes an IEEE 33 bus test grid system using load scheduling, fossil fuel generators, and renewable energy. The primary objective of this optimization is to minimize operating costs. In order to optimize the load model, it was necessary to restructure it according to the DSM participation level. After considering the restructured load demand models, distributed load scheduling ideas are disseminated to reduce test grid system power distribution costs.

A Review of Tools and Methods for Detection, Analysis, and Prediction of Allostatic Load Due to Workplace Stress

Chronic stress risks an individual's overall well-being. Chronic stress is associated with allostatic load, the body's wear-and-tear due to prolonged heightened physiological and psychological states. Increased allostatic load among workers increases their risk of injuries and the likelihood of diseases and illnesses. An allostatic load model could explain the basis of a stress response. Stress research in affective computing uses wearable devices, data processing algorithms, and machine learning methods to create models that could benefit from an allostatic load model of stress. We emphasize the need for the allostatic load model in affective computing to create disease and illness prediction models. Predictive models could enhance safeguards in the workplace by helping to create proactive mitigation strategies against chronic stress. First, we briefly introduce allostasis' physiological and psychological basis. Next, we reviewed stress studies within affective computing that may benefit from an allostatic load model of stress. We focused our review on studies conducted in dynamic settings, such as the workplace, and those incorporating typical stress study elements in affective computing. We conclude our review by identifying gaps between affective computing and neuroscientific stress studies and provide recommendations for adopting the allostatic load model of stress.

Tractable Data Enriched Distributionally Robust Chance-Constrained Conservation Voltage Reduction

This paper proposes a tractable distributionally robust chance-constrained conservation voltage reduction (DRCC-CVR) method with enriched data-based ambiguity set in unbalanced three-phase distribution systems. The increasing penetration of distributed renewable generation not only brings clean power but also challenges the voltage regulation and energy-saving performance of CVR by introducing high uncertainties to distribution systems. In most cases, the conventional robust optimization methods for CVR only provide conservative solutions. To better consider the impacts of load and PV generation uncertainties on CVR implementation in distribution systems and provide less conservative solutions, this paper develops a data-based DRCC-CVR model with tractable reformulation and data enrichment method. Even though the uncertainties of load and photovoltaic (PV) can be captured by data, the availability of smart meters (SMs) and micro-phasor measurement units (PMUs) is restricted by cost budget. The limited data access may hinder the performance of the proposed DRCC-CVR. Thus, we further present a data enrichment method to statistically recover the high-resolution load and PV generation data from low-resolution data with Gaussian Process Regression (GPR) and Markov Chain (MC) models, which can be used to construct a data-based moment ambiguity set of uncertainty distributions for the proposed DRCC-CVR. Finally, the nonlinear power flow and voltage dependant load models and DRCC with moment-based ambiguity set are reformulated to be computationally tractable and tested on a real distribution feeder in Midwest U. S. to validate the effectiveness and robustness of the proposed method.