Sergio Asanza

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Abstract
This chapter presents the fundamentals and state-of-the-art hosting capacity (HC), including its concept, historical development, considerations, applications, impact factors, and technologies for increasing the HC. It discusses the basics and importance of grid HC, the basic flow chart for HC analysis, the use of HC as a component of integrated distribution planning, and the HC as a process encompassing the input data, analysis, and application of results for informing interconnection and planning. Moreover, this chapter depicts two types of HC analysis based on the number of distributed energy resources (DERs) and load conditions (i.e., operating scenarios): static and dynamic HC analysis. It presents feeder and node HC levels based on the number of considered DERs. The main impact factors that influence the HC results and hinder the connection of additional DERs to the grid are also described. These impact factors include grid characteristics, DER characteristics, and other considerations such as time, performance metrics, and HC methods. Several review articles on HC and studies that explore the use of battery energy storage systems, electric vehicles, and smart inverters as strategies to increase HC in power distribution networks are presented. Finally, this chapter outlines the conclusion and future search directions for HC analysis. © 2025 Elsevier Inc. All rights reserved.

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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.

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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.