A bottom-up modeling approach is presented that uses a Markov chain Monte Carlo (MCMC) method to develop demand profiles. The demand profiles are combined with the electrical characteristics of the appliance to create detailed time-varying models of residential loads suitable for the analysis of smart grid applications and low-voltage (LV) demand-side management. The results obtained demonstrate significant temporal variations in the electrical characteristics of LV customers that are not captured by existing load profile or load model development approaches. The software developed within this work is made freely available for use by the community.

This paper applies optimal power flow (OPF) to evaluate and maximize network benefits of demand-side management (DSM). The benefits are quantified in terms of the ability of demand-responsive loads to relieve upstream network constraints and provide ancillary services, such as operating reserve. The study incorporates detailed information on the load structure and composition, and allows the potential network benefits, which could be obtained through management of different load types, to be quantified and compared. It is demonstrated that the actual network location of demand-manageable load has an important influence on the effectiveness of the applied DSM scheme, since the characteristics of the loads and their interconnecting networks vary from one location to another. Consequently, some network locations are more favorable for implementation of DSM, and OPF can be applied to determine the optimal allocation of demand-side resources. The effectiveness of the presented approach is assessed using a time-sequential OPF applied to typical radial and meshed U.K. distribution networks. The results of the analysis suggest that network operators could not just participate in, but also encourage and add value to the implementation of specific DSM schemes at the optimum network locations in order to maximize the total benefit from DSM.

With the proliferation of harmonic sources in power systems, current transformers (CTs), which are the most widely used current transducers in power systems, will have to measure and monitor signals with increasing levels of distortion. The accuracy of the measurement can be affected by the nonlinear response of the CT, which is dependent on the distortion in the measured signal, but nonlinear mathematical models can be used to compensate the measured value. In this paper, the complex ratio of the CT is modeled using a frequency domain model based on tensor linearization, obtaining a real-valued compensation matrix. An accurate measurement setup has been built to characterize the CTs performance in distorted conditions. Experimental results using the proposed compensation technique are presented and discussed for two commercial CTs of accuracy class 0.5 and 1. It is demonstrated that the proposed technique can accurately reconstruct the harmonic components (up to several kilohertz) of the CT primary current from the measured CT distorted secondary current.

This paper presents results of experimental and analytical evaluation of power-dependent harmonic emission of three common types of modern low-voltage (LV) power-electronic (PE) devices. After a detailed analysis of comprehensive test results, based on both existing and new waveform distortion indices, the development of component-based models of PE devices is discussed. This paper demonstrates the importance of including PE devices' controls for accurate modelling of their characteristics over the entire range of operating powers. Most of the analyzed PE devices exhibit strong power-dependent changes of characteristics, additionally influenced by supply-voltage conditions, which are important for the analysis of both existing networks and future “smart grids”.

The number of fully electric cars is expected to increase significantly in the near future in many countries. They are commonly known as electric vehicles (EVs) and use power electronic-based rectifiers for charging their batteries. These circuits may significantly impact power quality in LV and MV distribution grids, in particular the distortion at frequencies below 2 kHz (harmonics) and above 2 kHz up to 150 kHz (supraharmonics). While harmonic emission of EVs strongly depends on the supply voltage distortion, emission of supraharmonics at switching frequency and its multiples is mostly determined by the design of the EV's filter circuits. This paper presents the results of testing and analyzing the harmonic and supraharmonic current distortion of 19 different on-board EV chargers, representing a significant portion of EVs currently available on the German and European markets. The analysis is based on extensive measurements performed at a test stand that is capable of reproducing almost any supply voltage waveform with a desired distortion. The paper finishes with selected results from a field test with almost 100% EV penetration connected to a feeder in an urban LV grid.

The secure operation of the transmission grid is of primary importance for any power system operator. However, the introduction of new technologies, market deregulation, and increasing levels of interconnectivity have made the analysis and assessment of power system security both more challenging and essential than ever. In this context, data-driven-based methodologies are being increasingly employed to classify and anticipate insecure future states, and make inferences on potential triggers of undesired operational conditions. This paper provides a comprehensive and systematic review of this fast-moving research area and covers data-driven-based methodologies deployed in both static and dynamic security assessment. Particular attention is paid to recent trends, such as the use of spatiotemporal feature selection algorithms and the increasing research activity in short-term voltage stability and frequency stability, which are not yet widely assessed as a collective in the existing literature.