Siting and Sizing Method of GFM Converters Based on Genetic Algorithm

The rising integration of renewable energy sources has resulted in a diminished capacity for voltage support within the system, which is characterized by low inertia and a reduced short circuit ratio (SCR). In order to improve grid strength and enhance the capacity for renewable energy integration, an initial analysis was conducted on the grid support capabilities of grid-forming (GFM) stations, followed by an investigation into how grid strength influences the dominant operational modes of GFM converters. Subsequently, leveraging the definition of the multi-infeed short circuit ratio, a calculation method for the SCR, applicable to new energy base stations featuring GFM substations, is developed. Additionally, a strategic approach to optimal location selection and sizing of these substations aimed at enhancing the SCR within new energy grids is proposed, with the model being solved through genetic algorithms. Finally, the effectiveness of the proposed method is verified based on the IEEE39-node system and a real new energy station. The results show that the system strength is greatly improved after the optimized configuration of the GFM equipment, and the maximum tolerable space of 90% new energy stations reaches 95% of the theoretical maximum tolerable space of each new energy station.