Julija Matevosiyan

Bio: Julija Matevosiyan is an academic researcher. The author has contributed to research in topics: Power (physics) & Electric power system. The author has an hindex of 2, co-authored 2 publications receiving 184 citations.

Load Modeling—A Review

Abstract: Load modeling has significant impact on power system studies. This paper presents a review on load modeling and identification techniques. Load models can be classified into two broad categories: 1) static and 2) dynamic models, while there are two types of approaches to identify model parameters: 1) measurement-based and 2) component-based. Load modeling has received more attention in recent years because of the renewable integration, demand-side management, and smart metering devices. However, the commonly used load models are outdated, and cannot represent emerging loads. There is a need to systematically review existing load modeling techniques and suggest future research directions to meet the increasing interests from industry and academia. In this paper, we provide a thorough survey on the academic research progress and industry practices, and highlight existing issues and new trends in load modeling.

International Industry Practice on Power System Load Modeling

Abstract: Power system load modeling 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 fueled 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 modeling 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.

Power Quality Concerns in Implementing Smart Distribution-Grid Applications

Abstract: This paper maps the expected and possible adverse consequences for power quality of introducing several smart distribution-grid technologies and applications. The material presented in this paper is the result of discussions in an international CIGRE–CIRED joint working group. The following technologies and applications are discussed: 1) microgrids; 2) advanced voltage control; 3) feeder reconfiguration; and 4) demand-side management. Recommendations are given based on the mapping.

Artificial-Intelligence-Based Methodology for Load Disaggregation at Bulk Supply Point

Abstract: Real-time load composition knowledge will dramatically benefit demand-side management (DSM). Previous works disaggregate the load via either intrusive or nonintrusive load monitoring. However, due to the difficulty in accessing all houses via smart meters at all times and the unavailability of frequently measured high-resolution load signatures at bulk supply points, neither is suitable for frequent or widespread application. This paper employs the artificial intelligence (AI) tool to develop a load disaggregation approach for bulk supply points based on the substation rms measurement without relying on smart meter data, customer surveys, or high-resolution load signatures. Monte Carlo simulation is used to generate the training and validation data. Load compositions obtained by the AI tool are compared with the validation data and used for load characteristics estimation and validation. Probabilistic distributions and confidence levels of different confidence intervals for errors of load compositions and load characteristics are also derived.

Measurement-Based Dynamic Load Modeling Using the Vector Fitting Technique

Abstract: Accurate load modeling is essential for power system stability analysis and control. This topic has regained interest, due to the high penetration of new types of loads and the increased availability of measurements in extended power grids. In this paper, an aggregated load model based on measurement data is formulated for dynamic simulations of large power systems. The proposed model employs variable-order transfer functions, enabling the accurate simulation of complex load dynamics. A complete methodology for the automatic derivation of the minimum-required model order is proposed with the model parameters calculated via a robust multisignal identification procedure. For this purpose, the vector fitting method is introduced as a technique for measurement-based load modeling. Several simulations are performed using the NEPLAN software to investigate the accuracy and the generalization capabilities of the proposed model. The model performance is thoroughly compared with other conventional load models, also using measurements recorded on a laboratory-scale microgrid.