Practical Data-Driven Methods to Improve the Accuracy and Detail of Hosting Capacity Analysis

Distributed energy resource (DER) hosting capacity of distribution feeders is commonly analyzed with scenario-based methods assuming specific load conditions. For feeders with existing DER, additional processing is necessary to capture conditions of these DERs, that can impact hosting capacity indicators. Only modeling min/max scenarios for the load and DER may not represent realistic worst-case scenarios and may lead to under- or over-estimating the hosting capacity. This paper proposes a method based on historical data that determines a set of scenarios which captures worst-case conditions and enables accurate determination of hosting capacity values. This paper also proposes a probabilistic hosting capacity analysis method that provides valuable information for robust system planner decisions.