Use Limited Renewable Energy Data to Estimate Node Distributed Generation Hosting Capacity

Distributed generation hosting capacity (DGHC) assessment is a critical step in the deployment of renewable energy-based distributed generation. In many cases, however, such assessments are limited by the availability of historical data on renewable energy resources. This is especially true for rural networks where high-resolution renewable energy data may be scarce or non-existent. Therefore, this study proposed a novel analytical DGHC estimation method that only requires block maximum of renewable energy resources, the average load demand of each bus, and network information. Specifically, the generalized extreme value distribution is used to model the tail distribution of renewable energy resources, while a linearized power flow model is used to derive the tail distributions of the bus voltage and branch flow. Finally, the DGHC at each bus is computed based on predefined acceptable overvoltage and overload risks. To demonstrate the effectiveness of the proposed method, it was tested in a 59-bus rural distribution network in Jiangsu, China. The results show that the proposed method can provide an approximate DGHC result with limited renewable energy data. The simplicity and low-data requirements of the proposed method make it a promising approach for DGHC assessment in rural distribution networks where data may be limited.