Distribution-Level Phasor Measurement Units Application to Composite Load Model Validation

The time-varying parameter identification of load models has attracted broadly attention when large amounts of intermittent distributed generations (DGs) and stochastic loads are integrated in active distribution network (ADN). However, in traditional time-varying parameter identification approaches, the load model is usually developed with the composite load model (CLM) or synthesis load model (SLM), which are not suitable for representing the dynamics of DGs. Moreover, the plateau phenomenon and the continuous low-quality data further reduce the performance of traditional load models with time-varying parameter. Therefore, to address these issues, a novel time-varying parameter identification approach with extended Kalman filter (EKF) is designed for the load modeling. To represent the behavior of modern loads, an improved load model contains a parallel SLM and voltage source converter (VSC) is developed. Then, the target parameters, whose changes produce larger variations in model outputs, are selected with trajectory sensitivity to avoid plateau phenomenon. Also, the Chi-square test and the proposed weighted suppression strategy are utilized to suppress the continuous low-quality data. The simulation results on a system-level ADN model show that the proposed approach could accurately identify the parameters of time-varying load models.

The importance of having accurate load models in power system stability studies has been well established in the literature as being essential for precise power system transient event investigations. The power industry presently uses composite load models in typical stability programs (e.g. LOADSYN and PSS/E). However, the parameters of the composite load models need to be tuned for each type of disturbance based on an assumed load composition, and are often inadequate for matching the modeled dynamics of the power system disturbance event to actual measured results. A stochastic time series technique in the form of an ARMAX mathematical model is presented in this paper as a novel alternative for dynamic load modeling. The model parameters are estimated using on-line measurement data for a number of disturbance events, collected from five substations in the Victorian electricity network in Australia. The performance of the model is then evaluated for other transient events, and compared against the recorded response for these events. The results achieved show that this heuristic-based model is robust and effective in predicting the dynamic response of a power system load across a range of events spanning various seasons and locations.

A methodology for measurement-based load modeling based on integrating dynamic models is presented in this paper. A composite load model (CLM) and an exponential recovery load (ERL) model are combined and used as a model structure for load representation coupled with a heuristic criterion. An improved particle swarm optimization (IPSO) algorithm that considers a speed update modification to enhance its global search capabilities is used to identify the model's parameters. Several simulations are performed in two test systems, using load response measurements after small and severe disturbances, considering noise measurement to simulate monitoring devices. Results verify that the proposed load model has a better performance than the individual load models, and also IPSO algorithm is adequate for parameter identification.

Recently, many aspects of power system dynamic analysis, including oscillation identification, load model identification and system state estimation, are conducted based on the measurement data from phasor measurement unit (PMU). However, the impact of PMU measurement error, especially in power system dynamic process, has an assignable impact on these analysis results and thus cannot be neglected. In this paper, the requirements from IEEE standards and the analyze method for PMU measurement error are proposed first. Then, the ambient signal based identification method and the impact of measurement error for dynamic load model parameters is introduced. Afterwards, the stochastic distribution and frequency spectrum of PMU measurement error are measured through an experiment carried in a power system hardware simulation platform. Finally, the impact of PMU measurement error on the identification results of load model is simulated in WSCC 9 bus system.

The WECC Composite Load Model (CLM) is one of the most comprehensive load models used in transmission system dynamic studies. It is expressed by numerous parameters representing an aggregated load. The default parameter values of the CLM cannot always properly represent the distribution system. In this paper, a systematic method for determining the values for the parameters affected by the load composition and configuration of a distribution system is described with the aid of the Combined Transmission-Distribution System (CoTDS) modeling method. Since the CoTDS model considers the detailed behavior of the loads in the distribution system, it serves as a guiding tool to tune the CLM parameters. Simulation of several test cases are performed using a WECC 9-bus transmission system with a 160-node distribution system feeder. The results demonstrate the effectiveness of the proposed method to properly represent the distribution system in the CLM.

while it has been widely accepted that the constant P-Q leads to conservative load margin calculation of voltage stability, lately the studies show that induction motor loads can lead to an even more conservative load margin calculation. This paper analyzes the impacts of the composite load model (the composite of ZIP and induction motor load models) on the load margin of voltage stability. In order to take into consideration the composite load model, a new set of parameterized power flow equations is derived. A procedure of integrating the composite load model is integrated into CPFLOW to model the load increase. Numerical studies on test system are conducted and numerical results indicate that the load margin with the composite load model can be significantly less than that with the constant P-Q load model. These numerical results are contradicted to the belief that the constant P-Q leads to conservative load margin calculation of voltage stability.

Monitoring power quality events that occur in a power grid and determining their causes are important in terms of taking preventive actions. Power quality events are generally caused by structural changes in the network. Network loads are also affected by power quality events as a part of the network or they may cause power quality events themselves due to a sudden change in their structure. In this respect, when a power quality event occurs, the estimation of the behavior of the network loads contributes to the determination of the causes of the power quality events. In this study, a new method is developed to identify the parameters of a composite load model to estimate the response of network loads to certain power quality events. The method identifies model parameters using the voltage sag or swell data. By this model, it is possible to estimate the response of the loads and detect whether there is a change in the load. In this way, it becomes easier to understand whether the event is caused by the load or the network, which eventually helps us find the causes of power quality events. Simulation studies show that the load parameters identified by the method using a voltage sag are close to the actual load parameters, and the load behavior estimated by the load model in case of any power quality event is very close to the real load. Cite this article as: M. Karadeniz, M. T. Aydemir & S. Ayasun. Online parameter identification of a simplified composite load model by voltage sag events.Turk J Electr Power Energy Syst, 2022; 2(1): 21-30.

In this paper, a new generic composite load model structure for industrial facilities is proposed, which consists of four main loads, an induction motor, a synchronous motor, a motor drive system, and a static load. This structure can be applied to existing or future industry facilities. Major factors to be considered when developing this new composite load model are demonstrated. To achieve the final composite load model for an industrial facility of interest, a two-step procedure is recommended in this paper. Step 1 is to create a power system full model for existing or future industrial facilities. In this step, the full model for existing facilities can be created with the known system configurations and electrical single-line diagrams; while the full model for future industrial facilities with unknown system configurations can be created using the template-based load modeling technique. Step 2 is to perform effective load aggregation in order to obtain a simplified equivalent load model in the format of the proposed composite load model structure for industrial facilities.

This paper presents an update on the load model development undertaken by the utilities and grid operators in the Western Electricity Coordinating Council (WECC). The paper describes the driving forces for accurate load modeling in WECC. The structure of the proposed composite load model is presented. The paper also discusses the need for special modeling of compressor motors in single-phase air-conditioners and refrigerators. The paper describes the data requirements for the new composite load model and outlines the structure of the load model data tool. The issues related to load model validation are discussed. Finally, the paper provides expectations of the impact that the load model will have on the system performance studies

This paper discusses the development of a load component database for household appliances and office equipment. To develop more accurate load models at both the transmission and distribution levels, a better understanding of the behaviors of home appliances and office equipment associated with the variations of the power system voltage becomes more and more critical. The Bonneville Power Administration (BPA) has performed a series of voltage tests against home appliances and office equipment since 2005. Since 2006, researchers at Pacific Northwest National Laboratory have collaborated with BPA personnel and developed a load component database based on the appliance testing results to facilitate load modeling work for the Western Electricity Coordinating Council (WECC). In this paper, the testing procedure and results are first presented. Then, the load model parameters are derived and grouped. Recommendations are given for aggregating the individual appliance models to the feeder level, the models of which are used for distribution and transmission level studies.

Distributed resources (such as wind turbines) can provide power to local loads in the electrical distribution system as well as benefits such as improved reliability. As more wind energy is connected to utility systems, it becomes important to understand and manage the impact of wind generation on system operations. This paper studies the impact of wind generation on load modeling of distribution network based on MATLAB simulation tools and theory of measurement-based method. It is pointed out that the synthetic load of distribution network including wind generation can be described by the generalized composite load model of asynchronous machine in parallel with static load. In order to exactly reflect the influence of wind generation on composite load model, the value of proportion of dynamic load must be extended to any real number. The validity of this composite load model with any proportion of dynamic load is testified by modeling of three typical operation styles under different capacity proportions of wind generation and load level in this paper.

Load model has significant impact on power system simulation. Current load modeling approaches are inadequate on revealing the accuracy and time-variation of load compositions. The application of wireless sensors dispersed in power distribution networks provides further opportunities for load modeling. In this article, a data-driven online aggregated load modeling approach is proposed systematically. First, all the electricity consumers are clustered according to big data of power consumption behaviors. In each cluster, typical users are designated to stand for the characteristics of the cluster, and intrusive measurement is adapted to capture these typical users’ time-varying information by employing wireless intelligent terminals, which can identify the composition of static load and induction motor load online. Second, the load models of other users in each cluster are assumed identical to typical users, including static impedance–current–power models and induction motor models. Finally, the composite load model is achieved by hierarchical aggregation and bottom-to-up stepwise equivalence. Simulations demonstrate that the load model built by proposed approach reflects higher accuracy and adaptability in power system.

This paper is a test case study based paper. In this paper, line indices-based approach is utilized for testing the effect of various load models on estimation of maximum acceptable load at weak bus of a particular power system. It has lot of importance for the voltage stability analysis. Based on result analysis, most preferable load at a bus or substation is also identified. Different exponential-type load models and composite load models are considered in this work. The change of weak bus voltage and maximum load for various load models are analyzed. Each line loss and total loss in the system for different load models are also analyzed. Methodology of this work is implemented on standard IEEE 14 bus test case system. All the simulations of this work are implemented in MATLAB 2019a.

This paper presents an adaptive wide-area control strategy based on energy function. The proposed method enhances the post-fault oscillation damping capability of Static Var Compensators (SVC) by incorporating load dynamics in the wide-area controller design that adapts to load variations. The load dynamics are obtained using Phasor Measurement Units (PMU) through load power and voltage data measurements, which are used to estimate a Moving Average (MA) dynamic load model. The obtained load model is then integrated into the wide-area controller to obtain the required SVC supplementary control action which modulates the load power in response to a disturbance based on inter-area dynamics obtained by employing a nonlinear Kalman filter and PMU measurements. The performance of the proposed strategy is evaluated on the simplified 14 generator model of the southeast Australian power system by using composite load models comprised of induction motor loads of various sizes and combinations. The obtained results show significant improvements in the system stability when the proposed control strategy is applied.

In this thesis, a transfer function-based load model is determined for commercial and industrial load. This model is derived from the composite load model which consist of an induction motor and static load. This developed model is compared to composite load model by considering two cases: 1) a small motor composition load or commercial load and 2) higher motor composition load or industrial load. The research is conducted through MATLAB/Simulink simulation. In order to compare the dynamic response of developed model, a comparative study has been done between the two models. In addition, the influence of voltage and frequency dependency terms on the overall model accuracy for developed model has been evaluated through several case studies considering both voltage and frequency dependency disturbances. A short-term load forecast model is developed for an electrically heated house. This research work is based on experimental data collected by installing current sensors in a house in St. Johns, Newfoundland, Canada. The data was collected for three years and only one-year data is used for this model. The model is based on Recurrent Neural Network (RNN) with wavelet transform. The proposed model is verified by comparing other developed models in the literature through MATLAB deep learning toolbox and wavelet toolbox. The proposed model can more accurately forecast the load.