Distributed generation (DG) integration is a critical requirement in modern distribution networks to enhance power grid quality. An essential aspect of this endeavor involves strategically placing and sizing various D...
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Distributed generation (DG) integration is a critical requirement in modern distribution networks to enhance power grid quality. An essential aspect of this endeavor involves strategically placing and sizing various DG resources optimally. The primary objective of this study is to determine an efficient approach for allocating Photovoltaic (PV) and Wind Turbine resources to minimize overall power losses within the grid. To achieve this goal, we introduce a novel hybrid optimization technique that combines Particle Swarm Optimization (PSO) with geneticalgorithms (GAs). Furthermore, we explore the influence of factors such as variations in PV daily consumption, load profile fluctuations, penetration levels, and changing climatic conditions on the optimization process across two comprehensive case studies presented in this research. Our modeling results reveal that the hybrid PSO-GA approach outperforms traditional PSO in terms of integration speed, power loss reduction, and enhancements to grid quality, including voltage and frequency profiles. Additionally, this study highlights the dynamic impact of load curve fluctuations and climate changes on the optimal location and capacity of DG resources, leading to a substantial reduction in grid power losses.
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