Modeling, Prediction and Risk Management of Distribution System Voltages with Non-Gaussian Probability Distributions

TL;DR

This paper introduces a novel Gaussian mixture model-based approach for predicting non-Gaussian voltage variations in power distribution systems, enabling more accurate risk assessment and management amidst high renewable energy penetration.

Contribution

It proposes a new probabilistic UVC prediction method using Gaussian mixture models and formulates voltage risk management as linear programming problems.

Findings

Outperforms existing methods in voltage risk management.

Achieves only 15% deviation in risk estimates compared to traditional probabilistic methods.

Demonstrates computational efficiency on real distribution system data.

Abstract

High renewable energy penetration into power distribution systems causes a substantial risk of exceeding voltage security limits, which needs to be accurately assessed and properly managed. However, the existing methods usually rely on the joint probability models of power generation and loads provided by probabilistic prediction to quantify the voltage risks, where inaccurate prediction results could lead to over or under estimated risks. This paper proposes an uncertain voltage component (UVC) prediction method for assessing and managing voltage risks. First, we define the UVC to evaluate voltage variations caused by the uncertainties associated with power generation and loads. Second, we propose a Gaussian mixture model-based probabilistic UVC prediction method to depict the non-Gaussian distribution of voltage variations. Then, we derive the voltage risk indices, including value-at-risk (VaR) and conditional value-at-risk (CVaR), based on the probabilistic UVC prediction model. Third, we investigate the mechanism of UVC-based voltage risk management and establish the voltage risk management problems, which are reformulated into linear programming or mixed-integer linear programming for convenient solutions. The proposed method is tested on power distribution systems with actual photovoltaic power and load data and compared with those considering probabilistic prediction of nodal power injections. Numerical results show that the proposed method is computationally efficient in assessing voltage risks and outperforms existing methods in managing voltage risks. The deviation of voltage risks obtained by the proposed method is only 15% of that by the methods based on probabilistic prediction of nodal power injections.