Abstract

Abstract
Este artículo presenta un algoritmo para localización de fallas basado en un método de estimaciones de estado, válido para sistemas de distribución activos de media tensión. El algoritmo utiliza las mediciones registradas por unos pocos dispositivos de medición sincrofasoriales uPMUs, junto con pseudomediciones, para localizar con éxito la línea con falla. Primero se presenta la formulación de general de las estimaciones de estado bajo la suposición de que todas las barras son monitoreadas. Posteriormente, se define el método a seguir para conseguir detectar una falla con mínimo dos uPMUs. Finalmente, se desarrolla un algoritmo de localización óptima, cuyas restricciones se basan en mejorar los índices de confiabilidad del sistema. El método propuesto es validado en un sistema de distribución trifásico de 39 barras, donde el índice de confiabilidad de duración de interrupciones es reducido en un 22.01% con el despliegue de tan sólo dos uPMUs.

Abstract
Este artículo presenta un modelo de programación lineal entera mixta para resolver el problema de selección óptima del tamaño del conductor en sistemas de distribución radial considerando la integración de comunidades energéticas locales. La operación en estado estable del sistema de distribución basado en inyecciones de corriente se modela utilizando técnicas de linealización. La formulación propuesta considera la presencia de comunidades energéticas locales y restricciones operativas tales como límites de magnitud de voltaje y corriente. La formulación presentada se probó en un sistema de distribución utilizado en la literatura especializada. Los resultados muestran la eficiencia del método y demuestran que el modelo puede ser utilizado como solución del problema de selección óptima del tamaño del conductor.

Abstract
Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (uPMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of uPMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the uPMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two uPMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %.

Abstract
The number of electric vehicles (EVs) continues to increase in the automobile market, driven by public policies since they contribute to the global decarbonization of the transportation sector. Still, the main challenge to increasing EV adoption is charging infrastructure. Therefore, the site selection of public EV charging stations should be made very carefully to maximize EV usage and address the population’s range anxiety. Since electricity demand for charging EVs introduces new load shapes, the interrelationship between the location of charging stations and long-term electrical grid planning must be addressed. The selection of the most suitable site involves conflicting criteria, requiring the application of multi-criteria analysis. Thus, a geographic information system-based Multicriteria Decision Analysis (MCDA) approach is applied in this work to address the charging station site selection, where the demographic criteria and energy density are taken into account to formulate an EV increase model. Several methods, including Fuzzy TOPSIS, are applied to validate the selection of suitable sites. In this evaluation, the impact of the EV charging station on the substation capacity is assessed through a high EV penetration scenario. The proposed method is applied in Cuenca, Ecuador. Results show the effectiveness of MCDA in assessing the impact of charging stations on power distribution systems ensuring suitable system operation under substation capacity reserves.

Abstract
The massive interconnection of rooftop solar photovoltaic (PV) generation to the distribution network constitutes a challenge for the operation and planning of distribution network. In this environment, the performance of the distribution network may be affected and a comprehensive analysis of the hosting capacity in the medium and low voltage network is necessary. A quasi-static time-series and short-circuit analysis considering scenarios of PV systems penetration is proposed to accurately capture the effects of resource intermittency and load dynamics. Six metrics are used: overvoltage, voltage unbalance, thermal loading, fault currents, reverse power flow, and network losses. To evaluate the performance of the method, a real distribution feeder is used, as well as characteristic data of the classes of consumers. Since the relevance of a metric in the impact of the operation depends on the particular characteristics of each case, it is convenient to quantify these different metrics.