Load Modeling in WECC

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.

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.

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.

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.

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.

The increased integration of distributed energy resources (DERs) has significantly changed the load composition. As compared to traditional static or induction motor loads, these inverter-based resources (IBRs) exhibit different behavior. Then, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. Western Electricity Coordinating Council (WECC) has developed the composite load model with distributed generation (CMPLDWG) to accurately represent static load, induction motors, and IBRs. In this paper, we propose a novel deep learning-based method using a conditional variational autoencoder (CVAE) to estimate the CMPLDWG model's posterior distributions. The effectiveness of the proposed method is validated using an IEEE 39-bus test system. The results show that the proposed approach can accurately and efficiently estimate the parameters' posterior distributions.

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.

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.

This paper presents the results of composite load model validation based on the distribution-level micro- phasor measurement unit (PMU) measurements. The actual micro-PMU measurements from the distribution feeders are collected by Lawrence Berkley National Laboratory (LBNL). The composite load model parameters are initially generated using the load model data tool developed by Pacific Northwest National Laboratory (PNNL). The generated load models are calibrated to match the performances with the micro-PMU measurements using disturbance records “play-in” capabilities available in GE PSLF simulation package. A trial-and-error approach based on engineering judgement is proposed in this paper to benchmark the generated load model parameters and is implemented in the prototype of load model validation tool, which would be a supplement to the load model data tool.

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.

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.

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.

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.

Growing model complexities in load modeling have created high dimensionality in parameter estimations, and thereby substantially increasing associated computational costs. In this paper, a tensor-based method is proposed for identifying composite load modeling (CLM) parameters and for conducting a global sensitivity analysis. Tensor format and Fokker-Planck equations are used to estimate the power output response of CLM in the context of simultaneously varying parameters under their full parameter distribution ranges. The proposed tensor structured is shown as effective for tackling high-dimensional parameter estimation and for improving computational performances in load modeling through global sensitivity analysis.

The integration of uncertain power resources is causing more challenges for traditional load modeling research. Parameter identification of load modeling is impacted by a variety of load components with time-varying characteristics. This paper develops a deep learning-based time-varying parameter identification model for composite load modeling (CLM) with ZIP load and induction motor. A multi-modal long short-term memory (M-LSTM) deep learning method is used to estimate all the time-varying parameters of CLM considering system-wide measurements. It contains a multi-modal structure that makes use of different modalities of the input data to accurately estimate time-varying load parameters. An LSTM network with a flexible number of temporal states is defined to capture powerful temporal patterns from the load parameters and measurements time series. The extracted features are further fed to a shared representation layer to capture the joint representation of input time series data. This temporal representation is used in a linear regression model to estimate time-varying load parameters at the current time. Numerical simulations on the 23- and 68-bus systems verify the effectiveness and robustness of the proposed M-LSTM method. Also, the optimal lag values of parameters and measurements as input variables are solved.