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Load models have a significant influence on power system simulation. However, current load modeling approaches can hardly satisfy the diversity and time- varying characteristics of loads [including electric vehicles (EVs) and battery storage] in terms of model accuracy and computing efficiency. An online modeling method for composite load models based on measurement information is proposed in this paper. Firstly, the dominant factors in load model output are analyzed based on the active subspace of parameter space. Then the clustering algorithm is applied to cluster the large number of underlying loads based on the characteristics of load daily output curves. Finally, the underlying loads are equivalently aggregated from the low voltage levels to the high voltage levels to construct the composite load model. Simulation results obtained based on PSCAD/EMTDC demonstrate that the load model constructed by the proposed approach can accurately reflect the actual load characteristics of a power system. [ABSTRACT FROM AUTHOR]

The proliferation of electronic loads has led to a substantial increase in harmonic emissions within low-voltage distribution networks. The accurate estimation of the expected levels of harmonics in a network is a daunting task for network operators. Stochastic-based harmonic estimation models can offer a comprehensive assessment of the expected levels of harmonics in the presence of existing and future loads, including electric vehicles and smart-grid-enabled devices. Such models offer a valuable tool for network operators to assess the potential impact of harmonics on future networks and to create sustainable design solutions to meet the increasing demand for electricity while achieving net zero targets. However, several variables associated with these estimations models involve a level of uncertainty due to their stochastic nature, leading to inaccuracies in the estimations. This paper aims to provide a more realistic estimate of these uncertainties in order to improve the outcomes of harmonic estimation models for the development of sustainable distribution networks. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets. [ABSTRACT FROM AUTHOR]

A study was conducted to develop a porous aerostatic rectangular thrust bearing model, with the aim of assessing how different operational conditions and geometric factors influence its static capabilities. Initially, the Reynolds equation was analytically solved. Subsequently, simulations were performed on the rectangular air bearing model. Analyzing the impact of throttle hole configurations, air film thickness, orifice size, and supply pressure revealed their significant effect on the bearing's load capacity, air consumption, peak airflow speed in the air film gap, and rigidity. Experimental validations were further conducted on manufactured bearings, corroborating the theoretical findings. It was observed that extending the length of the rectangular throttle hole array progressively increases gas consumption and diminishes stability, while the load capacity and stiffness initially surge then taper off. A thinner air film enhances load capacity and reduces gas flow, contributing to increased stability. Conversely, enlarging the orifice diameter boosts both load capacity and stability but escalates mass flow and diminishes stiffness. Elevating gas supply pressure enhances load capacity, flow rate, and stiffness, albeit at the cost of reduced stability. A comparative analysis among experimental data, finite element analysis, and analytical solutions showed strong congruence, affirming the precision of the latter two methods for predicting the bearing's performance. This investigation aids with refining bearing design for precision devices and offers insights to enhance bearing efficiency and lifespan and to reduce friction and wear. Given its lower computational demands, the analytical approach provides a rapid means to assess static characteristics, underscoring its utility alongside finite element techniques for optimizing aerostatic bearing parameters. [ABSTRACT FROM AUTHOR]

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model’s ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification.

Because of the ongoing discussion about global warming, many nations have developed several approaches to address this problem. Some strategies are: renewable energy integration, agricultural robotic solutions, and electric vehicle penetration. The last strategy, the electric vehicle (EV), has gained considerable attention due to the paradigm shift in the transport sector driven by internal combustion engines and EV penetration can also achieve efficient operation of power grids. However, there are numerous challenges associated with the penetration of these technologies within active distribution networks. It is necessary to analyze the increased amount of electricity consumption in these grids, the degradation in the voltage profile in these networks and the hosting capacity assessment of electric vehicle integration in these grids. This paper analyzes the influence of EV penetration on a 34-bus active distribution system through five EV stations and proposes two alternatives for improving the introduction of these technological elements. Specifically, it analyzes the voltage levels at the system nodes and proposes an intelligent management of resources through capacitor banks and transformer taps. From the results, the effects on the network were voltage drop on some bus bars and an increase or decrease of the loading on lines depending on the EV penetration. It is also evident that 9% of the bus bars were not working under acceptable voltage ranges in the worst-case scenario.

In the composite load model (CLM), which is commonly used in China, an equivalent motor and equivalent static load are used to represent all electrical equipment and networks connected to a load bus. Existing research has determined typical values of electrical and mechanical parameters for load models of different load types, which improves the basis for load modeling. However, the motor proportion parameter is not the same for different load buses or at different times; therefore, obtaining the actual motor proportion is key to establishing an accurate load model. In the existing load modeling method, motor proportion is commonly identified along with other parameters under rare large disturbances; therefore, the value of the motor proportion is fixed by the time when a large disturbance occurs. In this paper, formulae are derived to estimate motor proportion under small disturbances, and these formulae allow direct calculation of motor proportion without using any optimization algorithm. The proposed estimation formulae do not rely on any parameters of load model or power system but instead rely only on measurement of the voltage and active power at steady-state points before and after a small disturbance. Because of universality of small disturbances in power systems, estimating time-varying motor proportion under small disturbances will be helpful for solving the time-varying problem of load models. Finally, the proposed motor proportion estimation formulae are validated by simulations, physical experiments, and field experiments.

In the contemporary power system landscape, characterized by a significant integration of renewable resources and the proliferation of inverter-connected devices, system inertia is on a declining trajectory. In this evolving scenario, the influence of various load types on system frequency becomes significant. In this regard, it seems that a comprehensive study on the effect of load types on the system’s frequency is missing in the literature. Hence, this paper aims to fill this gap by aggregating various load models known to affect system frequency and subsequently assessing the influence of each load type on the power systems’ frequency response. To achieve this, the IEEE 30-bus test system is employed to investigate the impact of different load parameters and their penetration levels on the systems’ frequency response.

Load models have a significant influence on power system simulation. However, current load modeling approaches can hardly satisfy the diversity and time-varying characteristics of loads [including electric vehicles (EVs) and battery storage] in terms of model accuracy and computing efficiency. An online modeling method for composite load models based on measurement information is proposed in this paper. Firstly, the dominant factors in load model output are analyzed based on the active subspace of parameter space. Then the clustering algorithm is applied to cluster the large number of underlying loads based on the characteristics of load daily output curves. Finally, the underlying loads are equivalently aggregated from the low voltage levels to the high voltage levels to construct the composite load model. Simulation results obtained based on PSCAD/EMTDC demonstrate that the load model constructed by the proposed approach can accurately reflect the actual load characteristics of a power system.