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Abstract
The residential load of existing consumers can be increased significantly due to large-scale purchase of household appliances with high-energy consumption; consequently, changing the expansion plans of electrical distribution networks. In this paper, a spatial-Temporal model is proposed to estimate the load growth of distribution transformers owed for this kind of electrical appliances. In order to determine the location of inhabitants interested in buying these appliances, the proposed approach includes the socioeconomic characteristics of the consumers in a spatial form. After that, the number of appliances added each year is computed using a logistic regression. The results are the residential load curves of distribution transformers, including the additional yearly demand of the new appliances. These curves provide valuable information regarding the distribution network expansion planning.

Abstract
Smart inverter functionalities can enable higher penetration levels of inverter-based distributed energy resources. The smart inverter voltage-reactive power (Volt-VAr) control function adjusts the injection and/or absorption of reactive power as a function of the voltage at the connection point through the control curve set-points. In this work, an optimization problem is formulated to increase the photovoltaic capacity in distribution systems by determining the best Volt-VAr control curve set-points of the photovoltaic inverter.A simulation-based optimization framework is proposed, which uses the Particle Swarm Optimization algorithm in the optimization stage, while power flows are executed in the simulation stage through OpenDSS. The performance assessment is performed under a real-world distribution feeder of an Ecuadorian electric utility. The proposed method found both the maximum installed capacity of a photovoltaic power plant and the best Volt-VAr control settings for a set of candidate locations. Results showed the most suitable feeder location for the installation of a single photovoltaic power plant. Besides, the Volt-VAr control curve settings determined by the optimization method increased the maximum installed capacity by 45.21% compared to the case when the photovoltaic inverter operates with a unity power factor.

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
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.