International industry practice on power system load modelling

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

This paper focuses on the development of a fast aggregate electricity load model under steady-state and dynamic conditions. An important requirement of such a load model is that it should be robust, analytically sound, and conceptually clear. Besides, the model can be easily incorporated into existing programs of power system analysis. By using quasi-linear approximation made on the dynamic components of power demands, the general model is described in the paper by a combination of a linear system and a non-linear function. From the general model, the step response of the load is derived. The model is then reduced into a discrete one based on the step response. Successive applications of such a discrete model provide a good approximation of load dynamics. To evaluate parameters of the proposed discrete load model, an identification algorithm is developed to read data from a sliding sample window of bus voltages and load responses. By comparing the identification results with the detailed simulation of a test system, the proposed fast model is shown to provide good representation of the consumer load response under static and dynamic conditions. Furthermore, aggregate load characteristics have been taken into account in the voltage control of a study power system to demonstrate the importance of aggregate load modelling with respect to suppressing transients, reducing control errors and preventing reverse actions in voltage regulation.

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.

In this paper, a generic load model is presented, suitable for dynamic simulations over a wide range of loading conditions. The proposed model is based on the well-established exponential recovery load model. Contrary to the conventional first-order form of this model, the proposed formulation employs variable-order transfer functions, enabling the accurate simulation of high-order load dynamics. A complete methodology is proposed for the identification of the required load model parameters from real field measurements. For this purpose, the Vector Fitting technique is introduced for the first time as a method for measurement-based load modeling. Several simulation scenarios are performed using the NEPLAN software to validate the accuracy of the proposed model. Numerical studies indicate that the developed load model captures accurately the load dynamic behavior over a wide range of loading conditions.

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.

Load model parameters are affected due to uncertainties in power system parameters caused by higher renewable distributed generation (DG) and new types of loads. The inherent uncertainties associated with the renewable DGs and system loads are neglected in the deterministic approach of quantifying load model parameters, and hence the probabilistic approach is important to adopt with the load modeling in modern power systems. In this paper, a probabilistic approach has been presented to assess the impact of renewable DGs and system loads uncertainties on static load model parameter, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{p}$</tex> . In total, eight probability density functions (pdf) are adopted, and the most suitable probability distribution is determined with respect to various levels of solar PV and wind penetration, and multiple load compositions. The IEEE 33 bus network is simulated in DIgSILENT PowerFactory software. The results identified that the normal, lognormal and generalized extreme value (GEV) represent the most suitable pdf for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{p}$</tex> distribution with 20%, 40%, and 60% solar PV penetration, respectively, while GEV represents the most suitable pdf for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{p}$</tex> with 20%, 40%, and 60% for both wind penetration and dynamic load in the load composition.

Guest Editorial: Situational awareness of integrated energy systems

A Solid-State Transformer as Enabling Technology for Microgrids for Integration into the Power Distribution System: An Overview

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 paper presents how power electronics concepts are utilized to teach the topics in power system analysis course. Power system analysis is one of the multiple core courses on power engineering in almost all Electrical Engineering programs around the world. At Grand Valley State University, it is the only course in power engineering offered to Electrical Engineering students during a twelve-week summer semester in junior year. This course covers the fundamental concepts and their applications in all three sections of a complex power system namely generation, transmission, and distribution systems. In recent years, there has been a significant change in the role and application of power electronics in modern power systems. However, at GVSU power electronics is a graduate course and it is also taken by some senior students with deep interest in it as an elective. Hence, attempts have been made to get all Electrical Engineering students, not only the willing seniors, introduced to power electronics and its applications in all three sections of a power system. These include teaching some power system concepts with applications of power electronics and administering power converter based course projects. This paper presents the design of the course to implement power electronics concepts and the implementation of course projects in power system analysis course. The pedagogical challenge is to enhance students' learning of modern power system. This paper shows how it enhanced and changed students' perception about the scope of power engineering.

This paper describes a practical application of the Expectation Maximization algorithm for computing the static load model of large electricity consumers. Such a model is necessary for effective and reliable operational control of electric power systems. The possibility of estimating basic states of electrical load using arrays of measured voltage and power data is shown. The criteria for selecting the most appropriate clusters are formulated and a step by step linear load model parameter estimation method is proposed. A practical method application is illustrated by using measurements of voltage and real power obtained at a large industrial facility. As a result, a load model with two basic states and corresponding linear models is obtained.

Comparison of impedance based fault location methods for power distribution systems

Load modelling attracts renewed interests these days in maintaining peak load conditions, supplying new types of loads and accommodating more renewable generation into electricity networks. This work describes real measurement data acquisition and step-by-step signal processing for developing aggregate load models at 11kV and 6.6kV level. Challenges in analyzing real measurement data are highlighted and issues to improve measurement-based load modelling are discussed. Load models at 15 substations from a UK distribution network are presented with subsequent model parameters. These load models will provide an insight to the operational flexibility, network resilience and management requirements of the measurement sites and related up-and-downstream substations.

Identification of combined power quality disturbances in the modern power systems by considering the development of different types of loads and distribution generations has become increasingly important. The novelty of this paper comes from the accurate and fast identification of the combined power quality disturbances in the presence of different distributed generations and loads such as photovoltaic cell, wind turbine with doubly fed induction generators, diesel engines, electric arc furnace, DC machine, 6-pulse and 12-pulse rectifier loads. In this paper, the features are extracted using variational mode decomposition, just from voltage waveforms. To reduce the redundant data, dimension of features vector, and time, the Relief-F method and correlation feature selection method are applied on the extracted features and these two methods are compared together. In this paper, the K-nearest neighbors classifier is used to classify the multiple power quality disturbances. To verify the effectiveness of the proposed method, different scenarios such as misfiring, variation of sun radiation and wind speed, entrance and exit of loads, capacitors and distributed generators, different fault at the grid in half-load to full-load were simulated. This method can be used as an added algorithm for smart metering in modern and smart power systems.

Contingency analysis (CA) is a well-known function in power system planning and operation. In accordance with CA results, the system operator dispenses information regarding static security of the power system (overloads and/or voltage outside tolerable limits). However, classic CA with remedial action schemes cannot distinguish safe operating regimes from potentially dangerous ones in terms of voltage (in)stability. In fact, voltage instability is considered as one of the major threats leading to power system insecurity. Therefore, in this study an enhanced contingency analysis (ECA) is presented where the classical CA is extended with static voltage analysis based on the modal analysis. The article presents a dedicated methodology for the proposed ECA tool, with special emphasis on the analysis of corrective measures provided by the system operator, intended for enhancing power system security (regulation transformer action, distributed generation and energy storage). Also the influence of the load model was analyzed by simulation and the main conclusions are presented. The study demonstrated the advantages that distributed generation resources and energy storage can provide in the context of voltage stability. Also, the simulations acknowledged the importance of correct load modeling, since over or under estimation of a certain load-type component can result in too optimistic or too pessimistic power system operation limits.