Risk-Based PV-Rich Distribution System Planning Using Generative AI

TL;DR

This paper presents a risk-based framework for hosting capacity assessment in distribution systems, utilizing generative AI to generate realistic load scenarios and better quantify voltage violation risks under uncertainty.

Contribution

It introduces a novel risk-based approach that explicitly considers the frequency, intensity, and duration of voltage violations using generative AI for scenario generation.

Findings

Extreme-percentile HC estimates underestimate PV hosting capacity.

Allowing a 5% risk level increases HC by about 18%.

Generative AI effectively models realistic load scenarios for risk assessment.

Abstract

Hosting capacity (HC) assessment plays a critical role in distribution system planning under increasing penetration of distributed energy resources (DERs) and associated uncertainties in load and generation. However, conventional approaches often rely on deterministic worst-case evaluation, leading to overly conservative HC estimates. This paper introduces a risk-based framework for HC assessment that explicitly accounts for the frequency, intensity, and duration of voltage violations under uncertain operating conditions. A generative AI-based approach is employed to generate realistic, time-correlated load demand scenarios conditioned on projected energy consumption growth levels. These scenarios are then used to assess voltage violations and quantify their risk using probabilistic intensity, duration, and frequency (IDF) metrics. The results show that extreme-percentile (zero-risk) approaches significantly underestimate PV-HC by treating all violations equally, regardless of their likelihood or persistence. For instance, allowing a 5% risk level increases HC by approximately 18% for a 15 min violation duration. The proposed approach provides a practical tool for risk-informed distribution system planning under uncertainty.