Enhanced genetic algorithm-based framework for integration scope identification and theoretical line loss analysis in source-grid-load-storage systems

Abstract

The Source-Grid-Load-Storage (SGLS) system incorporates energy sources, grids, loads, and storage for effective power distribution. Optimization of addition scope and line losses is critical for refining system performance. Current approaches are inefficient, emphasizing the need for a better solution. The research addresses the optimization of SGLS systems by classifying incorporation scopes and minimizing theoretical line losses. A novel framework called, Enhanced Genetic Algorithm (EGA) is implemented for enhanced scope identification and reduced line losses, improving overall competence and sustainability. The SGLS system model includes energy sources, loads, and storage devices, interrelated in a grid. The optimization emphasizes on determining the finest configuration to integrate these components, guaranteeing minimal line losses and effectual energy flow for maintainable power distribution. EGA, includes advanced mutation, adaptive crossover, and local search methods. The proposed technique attained significant performance improvements, with reduced computation time and reduced iterations compared to standard Genetic Algorithms in optimizing SGLS configurations. Using IEEE 14-bus and custom-designed systems, the proposed method established improved optimization accuracy and reduced line losses. Results displayed 15.01 s computational time, improved competence in integrating sources, loads, and storage, outperforming traditional methods. The EGA efficiently optimizes incorporation and minimizes line losses in SGLS systems, offering significant developments over traditional methods. It offers a scalable and effective solution for modern grid systems, guaranteeing better energy management and sustainability.