Planeacion eficiente de sistemas de distribucion desbalanceados: Un enfoque metaheuristico para la implementacion de generacion fotovoltaica y D-STATCOMs

Abstract

Currently, the planning of electrical systems faces significant challenges arising from accelerated growth, technological advances, and the availability of natural resources required for electric power generation. For these reasons, efficiency in planning is essential to ensure electrical systems that are economically viable, technically efficient, and that produce an increasingly lower environmental impact. The specialized literature has addressed various perspectives to improve the efficiency of electrical systems. Multiple techniques have been proposed to reduce power losses, minimize environmental impact, lower the system’s economic costs, and improve its reliability, among other aspects. In this context, the integration of distributed generation has emerged as an opportunity to improve both technical and economic aspects, while contributing to the mitigation of the environmental impact associated with conventional power generation. Among specific research efforts addressing these challenges, the optimization problem in unbalanced distribution systems stands out, particularly in terms of the sizing and location of photovoltaic generation and static reactive power compensators in distribution (D-STATCOM). In this context, the present work proposes a comprehensive methodology for the planning of unbalanced distribution electrical systems, aiming to improve both the technical performance and the economic efficiency of the network. The proposed methodology includes the evaluation of the impact of integrating distributed generation and D-STATCOM, using algorithms to solve the power flow and determine the location and size of the elements within the network. To assess the effectiveness of the proposed methodology, it will be applied to systems reported in specialized literature, which will be optimized through a single-objective approach targeting: (i) total operating costs and (ii) total energy losses. To solve the proposed model, four master–slave methodologies based on metaheuristic algorithms were used. In the master stage, four different algorithms were implemented: Vortex Search Algorithm, Generalized Normal Distribution Optimization, Particle Swarm Optimization, and Chu & Beasley Genetic Algorithm . In the slave stage, a matrix-based version of the successive approximations power flow method was employed to determine the value of the objective functions and to evaluate the technical and operational constraints defined in the mathematical model.