Aggregate Power Demand Model and Parameter Identification for Voltage Stability Enhancement

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.

Summary form only given. Power system load modelling is a mature and generally well researched area which, as many other in electrical power engineering at the present time, is going through a period of renewed interest in both industry and academia. This interest is fuelled by the appearance of new non-conventional types of loads (power electronic-based, or interfaced through power electronics) and requirements to operate modern electric power systems with increased penetration of non-conventional and mostly intermittent types of generation in a safe and secure manner. As a response to this renewed interest, in February 2010 CIGRE established working group C4.605: Modelling and aggregation of loads in flexible power networks. One of the first tasks of the working group was to identify current international industry practice on load modelling for static and dynamic power system studies. For that purpose, a questionnaire was developed and distributed during the summer/autumn of 2010 to more than 160 utilities and system operators in over 50 countries on five continents. This paper summarizes some of the key findings from about 100 responses to the questionnaire received by September 2011 and identifies prevalent types of load models used as well as typical values of their parameters.

The power system loads are often modelled by ag-gregated load models, which describe the voltage characteristics of the bus loads. The bus loads may include distributed generation (DG) units, which affect the apparent load behaviour. In power systems with decreasing amount of dispatchable generation, the voltage characteristics of aggregated loads have raised interest as a source of load flexibility in the context of Conservation Voltage Reduction (CVR). The available CVR potential is often assessed based on the static load models of the bus loads. In this paper, equations for evaluating the impact of DG on the exponential model of an aggregated load are presented. It is also discussed based on numerical examples, how the two calculation methods behave at high penetration level of DG. The results indicate that the methods provide similar results at over 80% penetration level of DG (if the load has resistive characteristic). Above that level the numerical results disembark. In addition, it is shown how the operation point of calculation affects the calculation results of the methods.

This paper presents the methodology developed to derive the linear and nonlinear load composition of an aggregate harmonic load model based on harmonic measurements taken at the service entrance feeder. Based on harmonic current spectra of typical nonlinear loads and aggregate harmonic loads obtained through measurement at service entrance, a weighted coefficient representative of the percentage combination of each type of nonlinear load is determined by solving a set of simultaneous equations with complex variables. Subsequently, linear load fraction is determined through SUPERHARM simulation of the modeled aggregate load.

Modelling and Aggregation of Loads in Flexible Power Networks – Scope and Status of the Work of CIGRE WG C4.605

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.

This paper proposes a new method for voltage and reactive power control in power systems. The proposed method is based on tabu search that is effective for combinatorial problems. Tabu search makes use of the idea of the hill-climbing method, but some search directions are fixed to escape from local minima. To improve the solution accuracy, a parallel scheme for tabu search is developed so that the neighborhood is computed in a parallel way with different tabu lengths. In this paper, the proposed method is applied to voltage and reactive power control in power systems. The efficiency of the proposed method is demonstrated in a sample system.

The load model and its parameters have great effect on the accuracy of power system simulation. This paper proposed a method of real-timely collecting load data based on intelligent measuring equipment, charring out the bottom-up accurate modeling of power load under cyber physical system (CPS) environment. Using intelligent measuring equipment as online measurement and identification device, identified load model on different voltage levels based on online load data, and established the aggregated load model using the weighting-mean method based on the capacity. Finally, considering the distribution system parameter, established the integrated load model of 220/110kV bus by gradually converting. Proposed method can realize real-time dynamical update of power load model, and improve the accuracy of power system simulation. Simulation indicated that the load model obtained by proposed method can better reflect the load dynamic characteristics compared to the traditional overall identification method.

This paper presents a neural network approach intended to aid electricity consumers or power system planner in the task of classification of harmonic load types using the measurements (data samples) collected from one of the power station in Malaysia. In order to allow the classification of the type of harmonic loads, harmonic currents order produced by aggregate harmonic loads and the level of emission are modelled using the Multi-Layer Extreme Learning Machine with autoencoder (hereinafter denoted as ML-ELM-AE). The feasibility of ML-ELM-AE on the classification of the Harmonic empirical data set will be probed, for when the classification results of the Harmonic load types become available, it can come in handy to power system analyst or engineers for analysis in later stage. They can use it to determine the harmonic distortion patterns and to characterize the harmonic currents at network buses. Depending on whether the aggregate load is residential, commercial, or industrial, the load characteristics in terms of its harmonic contents are likely to be different. The achieved results demonstrate the effectiveness of the investigated technique in dealing with the real world power system application of harmonic load type classification, that can be useful in providing good indication to the power system or distribution network planner.

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.

<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>

This work confers the simultaneous analysis of voltage and frequency control of the 3-area interconnected hybrid power system (IHPS) consisting of parabolic-trough solar power system (PSP), wind power system (WPS) and dish-stirling solar power system (DSP) under the paradigm of microgrid. The speculated result of the IHPS is presented and analyzed considering real and reactive power as the function of both voltage and frequency. 9The proposed IHPS under investigation has been mathematically modeled for direct coupling like active power–frequency and reactive power–voltage relationships and cross coupling like active power–voltage and reactive power–frequency​ relationships. The system responses under different operating conditions have been investigated to see the cross-coupling behavior of the proposed IHPS in the presence of voltage compensating devices like dynamic voltage restorer (DVR) and Static Synchronous Compensator (STATCOM). Further, Demand Response Scheme (DRS) as a frequency control strategy has been considered to enhance the system stability. System responses have been critically analyzed under Mine Blast Algorithm (MBA) based proportional–integral–derivative (PID) controllers

In statistical load modeling methodologies, aggregate electric load behavior is derived by propagating the ensemble statistics of an individual load process which is representative of the loads in the aggregate. Such a modeling philosophy tends to yield models whereby if physical meaning is present at the elemental level, it is preserved at the aggregate level. This property is essential for applications involving direct control of power system loads. The potential applicability of statistical load models is a strong function of one's ability to limit the volume of unusual data required to build those. An identification algorithm for a previously proposed stochastic hybrid-state Markov model of individual heating-cooling loads is presented. It relies only on data routinely gathered in power systems (device energy consumption over constant time intervals). It exploits an alternating renewal viewpoint of the load dynamics. After deriving some general results on the occupation statistics of time homogeneous alternating renewal processes, the analysis is focused on the specific model. In the process, however, some intriguing features likely to be shared by a wide class of alternating renewal processes are revealed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

A Multi-Objective Model Predictive Controller for Power Oscillation Damping and Voltage Control in Power System