An Innovative Approach to Radiality Representation in Electrical Distribution System Reconfiguration: Enhanced Efficiency and Computational Performance

• Published in: Energies (Basel) 2024-06, Vol.17 (11), p.2633
• Main Authors: Cortés Sanabria, Pablo José, Tabares Pozos, Alejandra, Álvarez-Martínez, David, Noriega Barbosa, Diego Alejandro
• Format: Article
• Language: English
• Subjects: Artificial intelligence; computational efficiency; Efficiency; Integer programming; Linear programming; linearization techniques; mathematical modeling; Mathematical programming; Optimization techniques; power distribution system reconfiguration; radial operation
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17112633

The reconfiguration problem (DPSR) in electrical distribution systems is a critical area of research, aimed at optimizing the operational efficiency of these networks. Historically, this problem has been approached through a variety of optimization methods. Regarding mathematical models, a key challenge identified in these models is the formulation of equations that ensure the radial operation of the system, along with the nonlinear equations representing Kirchhoff’s laws, the last often necessitating complex relaxations for practical application. This paper introduces an alternative representation of system radiality, which potentially surpasses or matches the existing methods in the literature. Our approach utilizes a more intuitive and compact set of equations, simplifying the representation process. Additionally, we propose a linearization of the current calculation in the power flow model typically used to solve DPSR. This linearization significantly accelerates the process of obtaining feasible solutions and optimal reconfiguration profiles. To validate our approach, we conducted rigorous computational comparisons with the results reported in the existing literature, using a variety of test cases to ensure robustness. Our computational results demonstrate a considerable improvement in computational time. The objective functions used are competitive and, in many instances, outperform the best reported results in the literature. In some cases, our method even identifies superior solutions.