Abstract
Renewable energy-based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long-term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen-based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting-edge methodologies, such as machine learning-based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy methodologies for hydrogen-based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

Abstract
Reliability analysis of large power networks requires accurate aggregate models of low voltage (LV) networks to allow for reasonable calculation complexity and to prevent long computational times. However, commonly used lumped load models neglect the differences in spatial distribution of demand, type of phase-connection of served customers and implemented protection system components (e.g., single-pole vs three-pole). This paper proposes a novel use of state enumeration (SE) and Monte Carlo simulation (MCS) techniques to formulate more accurate LV network reliability equivalents. The combined SE and MCS method is illustrated using a generic suburban LV test network, which is realistically represented by a reduced number of system states. This approach allows for a much faster and more accurate reliability assessments, where further reduction of system states results in a single-component equivalent reliability model with the same unavailability as the original LV network. Both mean values and probability distributions of standard reliability indices are calculated, where errors associated with the use of single-line models, as opposed to more detailed three-phase models, are quantified.

Abstract
As the integration of solar photovoltaic (PV) power plants into distribution networks grows, quantifying the amount of PV power that distribution networks can host without harmfully impacting power quality becomes critical. This work aims to determine the best number, location, and size of PV systems to be installed on a distribution feeder, as well as the best control set -points of the PV inverters, to maximize the PV hosting capacity (HC). Therefore, a simulation -optimization framework is proposed for siting and sizing ground -mounted PV power plants equipped with smart inverters (SIs). Single (decentralized) and multiple (distributed) allocations are analyzed by considering the connection of one, two, and three PV systems. Genetic algorithm (GA) and particle swarm optimization (PSO) metaheuristics are employed to solve the optimization problem. The simulation -optimization framework is tested on a real -world feeder model from an Ecuadorian utility. Installing two PV systems with their SIs operating with the Volt-VAr control function yields maximum PV HC, which is increased by 32.1 % compared to a single PV power plant operating at a unity power factor. Moreover, a comparative analysis of the two metaheuristic algorithms reveals that the PSO method provides better results than GA.

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 %.