A modified and extended genetic algorithm for optimal distributed generation grid-integration solutions in direct current power grids

C.D. Zuluaga-Ríos

e-Prime - Advances in Electrical Engineering, Electronics and Energy Vol. 10, pp. 100857-1 - 100857-6

Summary:

The integration of distributed generation into direct current power grids presents a critical challenge in modern energy systems, as it directly impacts grid reliability, efficiency, and the successful transition to renewable energy. This study addresses the problem of optimizing distributed generation placement and sizing in direct current grids, a key issue for reducing power losses and improving energy distribution. To tackle this, a modified and extended genetic algorithm was developed, capable of handling both continuous and discrete variables simultaneously. The algorithm was tested on two standard direct current grid systems, a 21-bus microgrid and a 69-bus network. The results demonstrated significant improvements over existing methods, reducing power losses by 84.5% in the 21-bus microgrid and by 95% in the 69-bus direct current network, with notably reduced computation times. These findings indicate that the proposed algorithm not only optimizes distributed generation integration effectively but also offers superior performance compared to traditional approaches, without the need for additional methods or software. The novelty of this work lies in its ability to handle complex, nonlinear optimization problems within direct current grids using a single, efficient approach, advancing beyond previous efforts by achieving better results with fewer computational resources.

Spanish layman's summary:

Este estudio aborda la optimización de la ubicación y el dimensionamiento de la generación distribuida (GD) en redes de CC para aumentar la eficiencia y la fiabilidad. Utilizando un novedoso algoritmo genético, se redujeron las pérdidas de potencia en un 84,5% en una microrred de 21 buses y en un 95% en una red de 69 buses con tiempos de cálculo más bajos, superando a los métodos tradicionales.

English layman's summary:

This study tackles optimizing distributed generation (DG) placement and sizing in DC grids to boost efficiency and reliability. Using a novel genetic algorithm, it reduced power losses by 84.5% in a 21-bus microgrid and 95% in a 69-bus network with lower computation times, outperforming traditional methods.

Keywords: Direct current grids; Genetic algorithms; Meta-heuristic optimization methods; Mixed-integer nonlinear programming

DOI reference: https://doi.org/10.1016/j.prime.2024.100857

Published on paper: December 2024.

Published on-line: November 2024.

Citation:
C.D. Zuluaga-Ríos, A modified and extended genetic algorithm for optimal distributed generation grid-integration solutions in direct current power grids. e-Prime - Advances in Electrical Engineering, Electronics and Energy. Vol. 10, pp. 100857-1 - 100857-6, December 2024. [Online: November 2024]