This paper addresses the phase-balancing problem in three-phase power grids with the radial configuration from the perspective of master–slave optimization. The master stage corresponds to an improved version of the Chu and Beasley genetic algorithm, which is based on the multi-point mutation operator and the generation of solutions using a Gaussian normal distribution based on the exploration and exploitation schemes of the vortex search algorithm. The master stage is entrusted with determining the configuration of the phases by using an integer codification. In the slave stage, a power flow for imbalanced distribution grids based on the three-phase version of the successive approximation method was used to determine the costs of daily energy losses. The objective of the optimization model is to minimize the annual operative costs of the network by considering the daily active and reactive power curves. Numerical results from a modified version of the IEEE 37-node test feeder demonstrate that it is possible to reduce the annual operative costs of the network by approximately 20% by using optimal load balancing. In addition, numerical results demonstrated that the improved version of the CBGA is at least three times faster than the classical CBGA, this was obtained in the peak load case for a test feeder composed of 15 nodes; also, the improved version of the CBGA was nineteen times faster than the vortex search algorithm. Other comparisons with the sine–cosine algorithm and the black hole optimizer confirmed the efficiency of the proposed optimization method regarding running time and objective function values.

This paper presents an integrated methodology for planning and operation of distribution systems in an environment of smart grids. The decision variables considered are location and sizing of technologies such as: distributed generation (wind, solar and small scale hydroelectric), energy storage, protection elements for fault isolation and automatic reclosers for load transfer. The integration of these technologies enable a higher automation of the distribution system the which in turn brings advantages such as reduction in the energy and active power losses and improvement in the voltage profile and service quality. The methodology consists of three stages: i) a specialized genetic algorithm for location of devices, ii) a particles swarm optimization algorithm for dimensioning of elements and iii) a non-dominated sorting genetic algorithm to solve the multi-objective problem associated with the location of reclosers. The tests were performed on a system of 102 nodes to verify the performance of the proposed methodology.

This paper presents a stochastic mixed-integer linear mathematical model for finding the optimal placement and sizing of distributed generation in a DC distribution network, considering the uncertainty of electrical demand and distributed renewable sources. The proposed model accurately represents the original mixed-integer nonlinear model, obtaining a globally optimal solution in less computational time with low errors. The mathematical model allows for considering constraints related to the maximum limits for the penetration of distributed generation, such as those specified by Resolution CREG 174 of 2021. Furthermore, the uncertainties of the electrical demand, wind energy-based distributed generation (DG), and solar energy-based DG are considered in the mathematical models using a two-stage stochastic programming approach. The accuracy and efficiency of the proposed model were tested and validated on a 21-node DC test system from the specialized literature, and the effectiveness and robustness were assessed on a 69-node DC test system. The obtained results show that the proposed stochastic mixed-integer linear mathematical model performs well.

En este trabajo se presenta una metodología eficiente para la selección óptima de conductores en sistemas de distribución (SD) por medio de la implementación de la técnica de optimización metaheurística búsqueda tabú (BT). Para representar el problema es empleado un modelo matemático monoobjetivo del tipo no lineal entero mixto. Como función objetivo, es considerada la minimización de los costos de inversión en conductores en conjunto con las pérdidas técnicas de la red para un período de estudio de un año, considerando un equivalente de red monofásico. El conjunto de restricciones corresponde a las características técnicas y operativas del sistema, las que son evaluadas por medio de un flujo de potencia del tipo barrido iterativo. Para construir la configuración inicial para el BT, es empleado un algoritmo heurístico constructivo que garantiza factibilidad de la solución inicial y para la generación del vecindario, son utilizadas estrategias de búsqueda en árbol y teoría de grafos. Con el fin de verificar aplicabilidad y eficiencia, se emplearon dos sistemas de la literatura especializada, obteniendo resultados de mejor calidad que los reportados en la literatura especializada.

In this paper, we propose a master–slave methodology to address the problem of optimal integration (location and sizing) of Distributed Generators (DGs) in Direct Current (DC) networks. This proposed methodology employs a parallel version of the Population-Based Incremental Learning (PPBIL) optimization method in the master stage to solve the location problem and the Vortex Search Algorithm (VSA) in the slave stage to solve the sizing problem. In addition, it uses the reduction of power losses as the objective function, considering all the constraints associated with the technical conditions specific to DGs and DC networks. To validate its effectiveness and robustness, we use as comparison methods, different solution methodologies that have been reported in the specialized literature, as well as two test systems (the 21 and 69-bus test systems). All simulations were performed in MATLAB. According to the results, the proposed hybrid (PPBIL–VSA) methodology provides the best trade-off between quality of the solution and processing times and exhibits an adequate repeatability every time it is executed.