Araceli Hernandez

The accelerating transition toward sustainable transportation is expected to significantly increase the penetration of electric vehicles (EVs) into distribution networks. EVs, as nonlinear loads with a AC-to-DC convertors, introduce harmonic distortion that may exceed regulatory limits or impact power system components. Accurate prediction of resulting voltage harmonics requires probabilistic models capturing the non-linearity and inherent variability of EV harmonic emissions. In this work, an extensive field measurement campaign was conducted to record harmonic current phasors for odd harmonic orders up to the 50th, across more than 54 charging cycles from 14 EVs representing nine different models. The measurements show significant differences in harmonic injection profiles across EVs, attributed to variations in design, power electronic, and charging technologies. A Multivariate Gaussian Mixture Model (GMM) is applied to characterize EV harmonic current injections and to generate synthetic samples consistent with observed distributions. The proposed model accurately captures the underlying patterns and correlations between harmonic magnitudes and angles. Model validation has been performed by comparing the probability distributions obtained from measured and synthetically generated harmonic currents, as well as the harmonic voltage levels resulting in a distribution feeder. The field measurement data has been made publicly available to support further research.

The Transient Stability-Constrained Optimal Power Flow (TSC-OPF) incorporates dynamic stability constraints into the OPF formulation to ensure secure and economical operation under disturbances. While discretizing system dynamics enables the use of nonlinear programming techniques, it significantly increases computational burden. To enhance scalability, many studies simplify the network by representing loads as constant admittances, allowing the use of Kron reduction. However, computing the Kron reduction outside the optimization requires a voltage-based assumption to convert loads from constant power to constant admittance. This paper proposes a practical voltage-based load admittance approximation and evaluates the errors it may introduce in rotor angle and speed deviation trajectories. Case studies on the WECC 9-bus system show that the proposed approach reproduces rotor dynamics consistent with time-domain simulations during the first few seconds while considerably reducing implementation effort and mitigating convergence issues. The proposed framework thus offers a simple and effective strategy for scalable TSC-OPF implementations.

Voltage unbalance, caused by variations in voltage magnitude and phase angle, is a significant power quality issue in three-phase systems, leading to equipment inefficiencies and increased system losses. The integration of distributed energy resources (DER) into the grid adds complexity, as DER can either reduce or worsen voltage unbalance, depending on factors such as grid configuration and the distribution of loads and DER themselves. This study explores the effects of DER penetration on voltage unbalance levels and the accuracy of the different indices most commonly used to quantify this unbalance. The results highlight the varying impacts of DER on unbalance and index performance, emphasizing the need for effective strategies to assess voltage unbalance in modern distribution systems.

Voltage sags are power quality disturbances mainly caused by faults with a highly random nature. For this reason, the severity and frequency of occurrence of voltage sags is typically analyzed by means of probabilistic methods, such as Monte Carlo simulations, where different probability distributions are selected to model the uncertain inputs (e.g., fault impedance, fault location, fault rate, etc.). The selection and parameterization of the appropriate probability functions involves many difficulties to ensure that they represent the distribution of input variables in a realistic way and so, many different approaches can be found in literature. This paper studies the sensitivity of voltage sags severity to the uncertainty in the input models in order to focus modeling effort on the input variables that are more critical and effectively have an influence on voltage sags levels, whereas non-influential parameters can be neglected or modeled by means of simplified hypothesis. The method proposed is based on Design of Experiments and Analysis of Variance (ANOVA) and is able to discriminate with a statistic confidence level whether the uncertainty in the input significantly affects voltage sags severity indices or not. The effect of uncertainty on voltage sags indices SARFI90 and SARFI70 has been evaluated in realistic scenarios for IEEE test networks of 24 and 118 buses and in the power system of Ecuador with 357 nodes. General trends have been also established that help to understand the effect of the modeling of input parameters on the estimated number of sags.

In recent years, harmonic current levels have increased in residential distribution networks due to the growing penetration of nonlinear loads at customer premises. To properly model the harmonic current injection of residences, probabilistic models that account for the stochastic behavior of domestic loads are needed. As a part of the study reported in this paper, a field measurement campaign was performed in 24 dwellings during a one-week period with a one-minute resolution. Harmonic current magnitudes and phase angles of odd harmonic orders up to the 25th as well as the active and reactive power demanded were recorded. By applying two different unsupervised learning techniques (i.e., nonparametric density estimation and Gaussian mixture models) to these measurements, two probabilistic models of harmonic injection of individual residential sites have been derived. Statistical probability distributions have been determined for the magnitude and phase of each harmonic order segmented in different power demand intervals. The developed models are validated on a test feeder by comparing harmonic voltages caused by the injection of the synthetically generated currents with those obtained from measurements. Both field measurement results and the detailed data defining the probabilistic harmonic current models are made public in an Open Science database repository.