The rapid growth of renewable generation, driven by increasing concerns about climate change, sustainable energy sources, and energy independence, has presented significant challenges for distribution system operators (DSOs). Integrating intermittent sources while ensuring grid stability and reliability demands a robust evaluation of hosting capacity (HC) in power distribution networks. However, prevailing HC analysis methodologies predominantly rely on conservative assumptions or worst-case scenarios, often leading to impractical and unreliable outcomes. Commercial tools, though offer readily available solutions for hosting capacity analysis, suffer from limitations as they typically assess HC of individual nodes independently or focus on only a few distributed scenarios.

More elaborate methods have been proposed, attempting to cope with the complexity and stochastic nature of the HC problem. However, no much consideration has been given to their scalability to large distribution systems.

In response to the complexity and stochastic nature of the HC problem, this paper introduces an efficient methodology based on stochastic power system simulations and statistical analysis. The proposed approach undergoes comprehensive assessments to substantiate its efficacy, with a key focus on validating its accuracy for reliable HC estimates. Utilizing the bootstrap method, a resampling technique, multiple samples are generated to mimic possible Distributed Energy Resources (DERs) deployments, enabling estimation of confidence intervals for HC metrics.

By applying statistical analysis to power system simulation results, insights into the expected variability and uncertainty of the problem are gained. These insights guide the selection of the minimum number of DER deployment scenarios necessary to ensure an efficient and well-informed HC assessment process.

The proposed methodology effectively addresses the challenges of HC analysis, offering scalability to large distribution networks. Its efficiency and enhanced accuracy make it a valuable tool for DSOs in facilitating the integration of renewable generation in a dependable and practical manner.