Electric mobility is a sustainable alternative for mitigating carbon emissions by replacing the conventional fleet. However, the low availability of data from charging stations makes planning energy systems for the integration of electric vehicles (EVs) difficult. Given this, this work focuses on developing an adaptive computational tool for charging simulation, considering many EVs and mobility patterns. Technical specifications data from many EVs are considered for charging simulation, such as battery capacity, driving range, charging time, charging standard for each EV, and mobility patterns. Different simulations of charging many EVs and analyses of weekly charging load profiles are carried out, portraying the characteristics of the different load profiles and the challenges that system planners expect. The research results denote the importance of considering different manufacturers and models of EVs in the composition of the aggregate charging load profile and mobility patterns of the region. The developed model can be adapted to any system, expanded with new EVs, and scaled to many EVs, supporting different research areas.

Synchronized phasor measurement systems are being widely used around the world and have become essential elements in the evolution of the operation of large electrical power systems (EPS). These systems, called Phasor Measurement Units (PMUs), are capable of recording and communicating dynamic data from the EPSs in a synchronized way by GPS and with a high sampling rate, generate a huge set of data that, among many applications, has the capacity to detect events. In this way, this work presents a data management system architecture applied to a real PMU system located in the state of Paraná, Brazil that detects and storages events using principal component analysis and Pearson correlation. This method can detect and store electrical events that occurred during the operation of the national interconnected system of Brazil with good results.

To ensure the quality of the energy in distribution networks, configurations of voltage regulators (VRs) and Shunt Capacitor Banks (BCshunts) are traditionally used along the feeders. Series Capacitors Bank (BCseries) along the feeders can also help to improve the power quality. In this paper, a strategy is proposed based on a mathematical formulation for an optimization problem where the aim is to allocate and size BCshunts and VRs (as traditionally implemented in the electricity sector) together with BCseries in distribution networks. These devices are allocated using Genetic Algorithm together with Multiperiod Optimal Power Flow. Using the proposed strategy, it is analyzed the network performance by studying: (i) the allocation of VRs and BCshunts simultaneously, and (ii) the allocation of VRs, BCshunts and BCseries simultaneously. The objective function is based on the minimization of active power loss costs, penalties for voltage violations, and equipment costs. Results are presented for a 90-bus radial distribution system which show that the use of BCseries is suitable for feeders with abrupt load variations, and that this approach competes positively with the simultaneous allocation of VR and BCshunt devices, with shorter investment amortization times and improved quality indices.

Modern power systems have become increasingly complex, making the detailed modeling and analysis of large-scale networks computationally demanding and often impractical. Therefore, network reduction techniques are essential for representing a smaller area of interest while preserving the electrical behavior of the complete system. For electromagnetic transient (EMT) studies, such as load rejection analysis, reduced networks are commonly derived using classical methods like Kron reduction under maximum power transfer conditions. However, this approach can lead to discrepancies in load flow and short-circuit levels between the reduced and complete systems. In addition, Kron reduction may introduce negative resistances in the reduced-order model, compromising system stability by producing non-passive equivalents and potentially causing unrealistic or numerically unstable EMT simulations. To address these limitations, this paper proposes an optimization-based approach, termed PSO-OPF-Kron, which integrates Optimal Power Flow (OPF) with the Particle Swarm Optimization (PSO) algorithm to refine the equivalent network parameters. The method optimally determines power injections, bus voltages, transformer tap settings, and impedances to align the reduced model with the full system’s operating point and short-circuit levels. Validation on the IEEE 39-bus system demonstrates that the proposed method significantly improves accuracy and numerical stability, ensuring reliable EMT simulations for load rejection studies.