Optimal Planning of Wind-Based DG: A Two-Stage Approach Linking Penetration Factor, Turbine Type Selection, Capacity, and Power Factors to Minimize Line Losses

The growing penetration of wind energy into power distribution networks necessitates advanced planning models to ensure efficient integration and improved network operation. This paper proposes an optimization planning model for the optimal sizing and placement of wind distributed generators (DGs) aiming to minimize investment costs while reducing line losses. The proposed model also determines the optimal operating power factor of the DG to minimize line losses and enhance voltage stability. Unlike conventional approaches, the proposed model uniquely captures the complex interdependencies among turbine type selection, capacity factor, and DG sizing, ensuring optimal DG integration. By aligning turbine type selection with the available wind resources at the site, the model maximizes energy production and operational efficiency. Additionally, the model incorporates the relationships between DG penetration factor, optimal corrected power factor, and line loss reduction ratio. By integrating these elements into a unified framework, this holistic approach optimizes the deployment of wind DGs, leading to significant reductions in line losses, improved voltage stability, and minimized investment costs. Traditional techniques often rely on line resistance-based formulas to calculate line losses, which fail to capture this correlation, resulting in a less holistic approach for DG integration. Simulation results and comparisons confirm that our proposed model surpasses comparable methods by optimally balancing the trade-off between DG size (investment cost) and loss reduction, preventing oversizing while enhancing voltage stability. This is achieved through a novel correlation between DG size, loss reduction, and power factor improvement, ensuring a cost-effective and efficient solution using a single-objective function instead of more complex multi-objective approaches. The uncertainty associated with wind DGs, and system demand are addressed in the model using a probabilistic technique. Validation through simulations on IEEE 30-Bus mesh test system and IEEE 33-bus radial test system demonstrates the effectiveness of the proposed approach.