En este artículo se propone la metodología para realizar la partición eléctrica de una red de distribución utilizando algoritmos de agrupamiento de datos como K-means y DBSCAN. Los datos se obtienen generando variaciones en los parámetros de la red y simulando el perfil de voltaje con el software OpenDSS. La metodología propuesta se implementa en redes de distribución estándar de prueba IEEE de 34 y 123 barras. Los resultados obtenidos son comparados con métodos obtenidos de la literatura.

This paper presents the advantages of optimization algorithms and logical analysis algorithms for voltage and reactive power control on distribution networks with the presence of Distributed Generations (DGs). The problem is formulated as a multi-objective model that minimizes the variations of the voltage on the pilot bus using Pareto optimization, Pareto and Fuzzy, and Pareto and Fuzzy-PI. The proposed technique is applied to the IEEE 13-node test feeder with variable load and one DG. The results demonstrate the efficiency on the voltage variation when the techniques are combined.

This paper proposes an efficient methodology to optimally determine the best location and optimum size for Distributed Generators (DG), in a distribution network, while minimizing energy losses and improving voltage profile. The proposed methodology has been designed to consider variable demand and variable DG production scenarios, offering a set of optimum values for DG active power output and power factor. The optimization here presented uses the Genetic Algorithm (GA), and the OpenDSS power flow solver feature. The method has been tested on the IEEE 37 node test system comparing voltage profile and energy losses in three scenarios. Additional tests on the IEEE 123 node, 34 node and 13 node has been performed to compare the method proposed with results found in the literature.

Over the last decade, the electric grid decarbonization have been impulsed for the inclusion of renewable energy resources, one of the most promising and mature technologies is the solar photovoltaic, nevertheless, many challenges should to be addressed in order to increase the penetration level specially on distribution networks, a key challenge is to estimate the solar photovoltaic potential in heterogeneous surfaces as rooftops. In this study, we analyse a method that use not only the solar irradiation it also includes intrinsic characteristics for the effective area, converters, conductors and power losses to obtain a most precise estimation.

Adaptive traffic signal control can reduce congestion, but network-wide averages may conceal access-level penalties and spillback. This study evaluates an interpretable Coordinated Adaptive Control Model (MCAC) for two closely spaced signalized junctions in Loja, Ecuador. MCAC uses eight lane-area detectors to trigger temporary coordinated decongestion phases, inserting additional signal-control time before resuming an observed 137 s fixed-time base plan. A matched stochastic SUMO 1.27.0 experiment compared fixed-time, MCAC, and SUMO native actuated control at 80%, 100%, 120%, and 140% of nominal demand, with 10 common-random-number replications per condition (120 runs). The study is a controlled comparative simulation rather than a field-calibrated prediction of absolute corridor performance. Relative to fixed-time control, MCAC reduced mean waiting time by 51.1%, 38.7%, 36.2%, and 37.0%, respectively, with Holm-adjusted Wilcoxon $p=0.0234$ at each demand level. MCAC also reduced extreme waiting, departure delay, and a supplementary combined measure of pre-entry and in-network stopped waiting. Benefits were unevenly distributed: Orillas del Zamora improved strongly, both Guayaquil directions incurred systematic waiting penalties, and Nueva Loja shifted from a low-demand penalty to a high-demand benefit. The generic independently actuated benchmark exhibited prolonged blocking and reached 164 teleports at 140% demand; the data do not isolate the specific downstream-blocking mechanism or establish a consistent teleport advantage over fixed-time control. These comparative simulation findings support joint reporting of aggregate efficiency, access-level redistribution, and operational robustness, without implying field-validated effectiveness.