Research on a Method for Assessing Renewable Generation Hosting Capacity in Distribution Networks Based on Python and PSD-BPA Co-Simulation

Abstract

Efficient assessment of renewable generation hosting capacity (RGHC) is essential for distribution network planning with high penetration of distributed energy resources. Existing engineering practices often suffer from limited automation, conservative multi-node capacity allocation, and difficulty in jointly verifying power-flow and short-circuit constraints. To address these issues, this paper proposes an automated RGHC assessment method based on Python and PSD-BPA co-simulation. The proposed framework automates the complete simulation process, including model modification, BPA calculation invocation, result parsing, and constraint evaluation. A five-dimensional constraint system is developed, covering voltage deviation, reverse loading ratio, short-circuit current, short-circuit capacity, and short-circuit ratio. Moreover, a two-stage capacity search algorithm is proposed, where an adaptive incremental search first identifies a feasible initial solution, followed by coordinate search and inter-node capacity exchange to further improve the total admissible capacity. The method is validated on a practical 78-node multi-voltage-level distribution network. Results show that the maximum discrepancy between the automated Python-BPA workflow and manual BPA calculations is less than 0.4%. Compared with fixed-step sequential search, the proposed algorithm increases the maximum admissible renewable generation capacity by 5.52% and reduces computational time by approximately 75.0%. These results demonstrate the accuracy, search stability, and engineering efficiency of the proposed method for automated RGHC assessment.