The use of power-electronics-based devices in distribution generation seeks to improve energy quality and reduce costs. The inverter-based distributed generator, that works in different operation modes, has emerged as a promising technology. In a high distributed generation penetration scenario it is important to know the voltage profile and fault information due to the uncertainty in the generator operation and the impact that have on the network. This study aims to use two proposed methods of analysis: for power flow, based on backward/forward sweep method, and short-circuit, based on hybrid impedance matrix, that considers the inverter operation modes and represents each generator as a voltage-controlled current source. The chosen network is the IEEE 34-Node Test Feeder with a generator on each load per phase. The voltage profiles obtained will be validated with a Simulink/Matlab phasorial model. The results show an average error of 2.39% and a gain in voltage profile processing time of 2185.24%, making its use consistent for larger systems.

The increasing integration of electric vehicles (EVs) into distribution systems is a global trend due to their advantages, such as reduced costs and decreased greenhouse gas emissions. To meet the increasing demand for EV charging, charging stations must be strategically incorporated into the power grid. This integration requires careful planning to minimize impacts on the electrical network while balancing costs for both system operators and EV users. This paper presents a systematic review of the state-of-the-art in the optimized planning of EV charging stations, aiming to guide future research and address emerging challenges associated with the rising energy demand from EVs. The review explores key methodologies for optimizing EV charging infrastructure, including the use of fast and slow charging options and their effects on the power system; the integration of distributed energy resources; the spatio-temporal distribution of EVs; the objective functions considered; and the optimization approaches adopted. It also examines the implementation of demand response strategies, such as time-of-use tariffs and smart charging. Finally, the review highlights the main research gaps, including the need to address environmental and economic factors, account for dynamic and uncertain variables, incorporate EV user behavior data, and integrate multiple objectives in practical applications.