Chance-constrained Solar PV Hosting Capacity Assessment for Distribution Grids Using Gaussian Process and Logit Learning

TL;DR

This paper introduces a novel probabilistic framework using Gaussian Process and Logit learning to accurately and efficiently estimate the maximum solar PV capacity in distribution grids while managing over-voltage risks under different control strategies.

Contribution

It presents a new chance-constrained PV hosting capacity estimation method that accounts for uncertainty and risk, with high accuracy and low computational cost.

Findings

Achieves up to 93% accuracy in predicting over-voltage events.

Effective for different voltage control strategies.

Computationally efficient, taking only a few seconds.

Abstract

Growing penetration of distributed generation such as solar PV can increase the risk of over-voltage in distribution grids, affecting network security. Therefore, assessment of the so-called, PV hosting capacity (HC) - the maximum amount of PV that a given grid can accommodate becomes an important practical problem. In this paper, we propose a novel chance-constrained HC estimation framework using Gaussian Process and Logit learning that can account for uncertainty and risk management. Also, we consider the assessment of HC under different voltage control strategies. Our results have demonstrated that the proposed models can achieve high accuracy levels of up to 93% in predicting nodal over-voltage events on IEEE 33-bus and 123-bus test-cases. Thus, these models can be effectively employed to estimate the chance-constrained HC with various risk levels. Moreover, our proposed methods have simple forms and low computational costs of only a few seconds.