A Possible Classification for Metaheuristic Optimization Algorithms in Engineering and Science

Objetivo: En este documento, se presenta una metodología de optimización híbrida para la ubicación y el tamaño óptimos de generadores distribuidos (DG) en redes de distribución eléctrica. Se propone un modelo de problema no lineal de enteros mixtos (MINLP) para la formulación matemática, cuya función objetivo es la minimización de las pérdidas de potencia debido al efecto Joule en los conductores. Las restricciones incluyen el equilibrio de potencia activa y reactiva, regulación del voltaje, el porcentaje de penetración de las DG en la red de distribución y las DG totales permitidas en dicha red. Metodología: Para resolver el modelo MINLP, se emplea una estrategia maestro-esclavo que utiliza el algoritmo genético Chu-Beasley (CBGA) y el modelo de flujo de potencia óptimo (OPF) como algoritmos maestro y esclavo, respectivamente. Esta técnica híbrida ayuda a reducir la complejidad del modelo MINLP al eliminar las variables binarias a través del algoritmo maestro y luego resolver el problema no lineal resultante (PNL), que corresponde al modelo OPF, utilizando un método clásico de punto interior disponible en la caja de herramientas fmincon de MATLAB Resultados: Se prueba la eficiencia y la solidez de la metodología propuesta en redes de distribución radial de 33 y 69 nodos. Los resultados muestran su alto rendimiento en términos de reducción de pérdida de potencia y dimensionamiento final de DG Conclusiones: Existe una relación directa y proporcional entre las pérdidas técnicas, el porcentaje de penetración de generación distribuida y el número de generadores disponible.

Nowadays, optimization methodologies based on combinatorial strategies (i.e., metaheuristic methods) and exact methods can be easily found through-out the scientific literature in all areas of engineering, including electrical, mechanical, chemical, computational, and food engineering, among others. The common denominator in these areas of research corresponds to the complexity of the optimization models, as well as to the large dimensions of the solution space where these models are defined [1]. In addition, most of these models combine binary (also integer) decision variables with continuous ones into nonlinear non-convex formulations, which complicates the application of exact solution methods.

The power flow solution is a classical problem in electrical engineering that has been studied for more than 60 years [1]. One of the most widely used methods corresponds to the Newton-Raphson approach, which is currently employed for analyzing power systems with meshed configurations and multiple generation sources, i.e., it is typically employed for power systems in high-voltage levels [2].

Solving the power flow problem for transmission grids is crucial for ensuring the reliable and efficient operation of electrical power systems. Power flow analysis allows engineers to determine the voltage, current, and power flow of a network, which is essential for maintaining system stability and avoiding overloads.

This number of the Ingeniería journal explores the participation of the researchers from Bogotá City in Colombia that selects our journal to present their contributions in different research areas of Engineering, which includes Industrial, Electrical, Electronic, Civil, Environmental, Agricultural, and Mechanical Engineering, among others.

Most of the concepts involved in Electrical Engineering have been developed from the 20th century to the present day. One of the most popular studies in this field is the power flow problem for electrical systems composed of multiple nonlinear loads (i.e., loads with constant power consumption) [1].