Dynamic load models for industrial facilities

Not only static load model but dynamic load model is necessary for improving the accuracy of time-domain simulation results because only dynamic load model can express the dynamic response of the load. The measurement based auto-derivation method for estimating load model parameters from PQVF monitoring devices has been developed. The PQVF monitoring device which has already been installed into power system captures disturbance data triggered by voltage deviation and frequency deviation. The transfer-function based load model which consists of voltage characteristics index, frequency characteristics coefficient, transient gain, and time constant was used as the dynamic load model. The simple transfer function-based dynamic load model can express 80% of 182 load responses precisely. Therefore, the transfer function-based dynamic model can have applicability to represent the dynamic behavior of the load response concisely and accurately on condition that the quantity of voltage drop during the disturbance is less than 30%.

Modelling of voltage dependent load is always a current topic due to existence of an adequate load model is a prerequisite for calculation of steady states and the analysis of transient regimes of power systems. Given that it is important to know the load model parameters, we have performed the measurements in electric power system of Bosnia and Herzegovina (EPS BIH). The main idea is to identify the values of voltage parameters of dynamic load model. Therefore, this paper describes the dynamic load models and the methodology for exponential dynamic load model parameter identification. It also presents the voltage parameters of dynamic load model for a node in EPS BIH and compares them to similar published studies, concluding that the difference in given parameter values is the consequence of local load characteristics.

Microgrids suitable operation depends on the system's proper design, based on accurate component models. A complete model considering entire system dynamics is a vital necessity for a successful design. Using dynamic load in microgrid small‐signal model results in a model that shows transient and steady‐state dynamics, since designing a low‐inertia system like microgrid need extra accuracy. In this paper, an inverter‐based microgrid's small‐signal model in islanded operation mode containing a dynamic load model has been achieved. The Exponential Recovery Load (ERL) model is presented here to study the dynamic behavior of load. Microgrid stability analysis has been carried out for both static and dynamic load models. To attain this goal, the entire component equations are obtained and linearized around an operating point, then the state‐space model is formed and eigenvalues are derived. System stability condition has been investigated by plotting eigenvalues and root locus method. In the final step, a simulation on Matlab/Simulink is performed to show the microgrids operation in the presence of a static and dynamic load.

As an extension of previous studies on modeling and identification of nonlinear dynamic load models, this letter describes the influence of the normalization in dynamic reactive load models. Using measured data from normal operation, it is shown that the reactive power level, which has previously used as normalization factor is inappropriate. If instead apparent power level is used, the variability in the parameters that describe the reactive load response is drastically reduced.

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 a large-scale transmission network, such as the European power system, the regional characteristics of electrical demand may have an impact on local voltage stability. The dynamic load behaviour depends on the sectoral composition of demand, which is subject to regional and seasonal variation. An aggregate load model is presented, which accounts for the sectoral characteristics of 210 European regions for all hours a year from the perspective of the high, medium, and low voltage levels. The proposed load model is used to better understand the influence of regional characteristics on voltage recovery during a short-circuit event, as well as the static point of voltage collapse. Several countries are considered in terms of their sectoral composition, to represent intermediate regions or such dominated by the industry and households. Moreover, the dynamic load model is benchmarked against two static load models, assuming a constant power (PQ) load or a constant impedance (Z) load. The conducted study demonstrates that regional differences in the composition of demand highly impact local voltage stability.

The behavior of the load model in power system stability studies has been shown by various investigators to be a very important factor in determining the stability of power system elements. Load models which are presently available in typical stability programs make it difficult if not impossible to model the actual dynamics of power system load. Present load models allow easy static representations, but require detailed individual dynamic models such as induction or synchronous motor models. Thus a simple load model that can represent power system composite load dynamics will be a valuable tool in the stability study Presented herein is a second order state space load model which can represent power system composite load dynamics. In addition, the parameters of this state space load model can be identified from actual system load measurements by a weighted least squares parameter identification process. Several authors recognized the significance of load behavior to changes in voltage as long as 25 years ago. However, most of the attention in stability studies has been directed toward improved generator and control system models. Until recently, little modeling has been considered necessary for power system loads since the effects of load models in stability studies were considered only secondary. At present the load representation in typical stability programs is limited to a static load model where the load is represented as constant MVA, constant current, constant impedance, or some combination of the three.

This study proposes a novel approach for load modelling fulfilled in Guangdong Power Grid, Southern China. Dynamic load modelling lies on real‐time data provided by load disturbances. These data can be acquired by power quality equipment fixed on each substation. This study introduces the approach to data processing and load modelling during asymmetric disturbance to solve the problems of insufficient inartificial disturbance data. In order to improve the global optimisation capability and identification efficiency in multi‐dimensional function, an improved clone selection algorithm is proposed, which includes adaptive‐adjust Gaussian mutation operators and the directional evolution mechanism, and also using the character of parameters independence between induction motor model and ZIP model referring to the third‐order induction‐motor paralleling ZIP model in BPA. The results of practical load modelling prove that the proposed algorithm has great effects on improving model precision and adaptability. Finally, the influences on power angle, frequency, voltage and power between dynamic load model and ZIP are discussed.

This paper evaluates the impact of dynamic loads (induction motors) on the damping of Subsynchronous Resonance (SSR) based on load location and aggregate load inertia for the IEEE 68 bus network simulated in DIgSILENT. The severity of SSR oscillations is shown to increase in the presence of Subsynchronous Resonance Load Interactions (SSRLI), whereby dynamic loads may contribute towards negative damping under certain network topologies. This change in severity is investigated for each load location by replacing the static (constant impedance) load model, traditionally used in SSR studies, with a 50% static and 50% dynamic load model. The paper shows that the torsional damping and thus the severity of SSR is sensitive to the location and aggregate inertia of the dynamic load. These results establish the sensitivity of torsional damping to dynamic load location, size and aggregate inertia. In the absence of accurate knowledge of load composition, 50% static and 50% dynamic load composition can be used as a recommendation for the modelling of load during EMT studies of SSR, to avoid conservative results and over/under design of protection or mitigation strategies.

Power load is an important element in power system. While, the traditional load model is fixed. So, when the load components change, the load model will be inappropriate. Based on the Multiple Model Theory, this paper proposes the Adaptive Synthesis Dynamic Load Model (ASDLM). In load modeling, the load model set is established, and every model in the model set has a weight. The time-varying classical load model (TVCLM) is proposed as the basic structure of the load models in load model set. The parameters and weights of the power load model can be adjusted when there is a maneuvering in the actual load system. Then, the models in the model set are integrated to ASDLM. The load data fitting results of ASDLM verify the adaptability and practicability of the proposed model structure and modeling ideology.

Online measurements can be used for aggregate load modeling, taking advantage of the advent of smart grids. In this paper initial load modeling results are presented using measurements recorded in a laboratory scale microgrid. The examined microgrid configuration includes a load bank and an adjustable speed drive. A methodology for the identification of the model parameters from measurements is presented and the performance of a static and a dynamic load model is investigated in grid-connected and islanded operation modes. Numerical studies indicate that the dynamic model behaves satisfactorily enough in both modes of operation, while the static model can only be used in the islanded case. Generic load models suitable to simulate the real and reactive power for a wide range of load disturbances are proposed and their accuracy is evaluated using the cross-validation technique.

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.

This paper introduces the application and investigation of technical measures improving power transmission capability in China and the first TCSC project with home-made devices.

Accurate dynamic load models allow more precise calculations of power system controls and stability limits. System identification methods can be applied to estimate load models based on measurements. Parametric and nonparametric (functional) are the two main classes in system identification methods. The parametric approach has been the only one used for load modeling so far. In this paper, the performance of a functional load model based on a polynomial artificial neural network is compared with a linear model and with the popular "ZIP" model. The impact of clustering different load compositions is also investigated. Substation buses (138 kV) from the Brazilian system feeding important industrial consumers have been modelled.

<p>Load modeling is an important task in power system stability analysis and control. Taking this into account, the development of dynamic load models using a measurement-based load modeling strategy and an improved particle swarm optimization algorithm is presented in this paper. To accomplish this objective, a measurement-based parameter estimation method is used for identification of an exponential recovery load model. Measurements are obtained performing dynamic simulation of an IEEE 30-bus test system under several disturbances, and additionally, cross validation technique is applied for an analysis of load model generalization capability. An adequate load modeling improves the comprehension of load behavior and the capability of reproduce transient events on power systems.</p>