Treffer: Optimal allocation and sizing of distributed generation for improvement of distribution feeder loss and voltage profile in the distribution network using genetic algorithm.

The increasing demand for electric power, coupled with rapid urbanization, necessitates a reliable and high-quality electricity supply to meet consumer expectations. However, existing passive distribution systems are inadequate to address the escalating power requirements, resulting in challenges such as increased power losses and suboptimal voltage profiles. In the base case scenario, the total active and reactive power losses were substantial, and many buses exhibited voltage magnitudes that fell outside acceptable limits. This study investigates the optimal placement and sizing of distributed generation (DG) resources to improve the performance of distribution feeders. A multi-objective optimization framework, utilizing a Genetic Algorithm (GA), was developed to minimize power losses and enhance voltage profiles. Load flow analysis was conducted using the Backward/Forward Sweep (BFS) method, allowing for precise evaluation of the distribution feeder under various DG configurations. Consequently, the study successfully enhanced the system through optimal DG allocation. Additionally, a comparative analysis was conducted to assess the performance of the proposed GA algorithm against other optimization techniques. The results indicate that, in nearly all cases, the GA method outperforms PSO by reducing system power losses and improving the voltage profile more effectively. [ABSTRACT FROM AUTHOR]