Distribution System Voltage Control under Uncertainties using Tractable Chance Constraints

TL;DR

This paper presents a robust and computationally efficient chance constraint approach for voltage control in distribution networks with high renewable energy penetration, accounting for uncertainties and correlations.

Contribution

It introduces a convex, sample-based chance constraint method for reactive power control that improves robustness and tractability over traditional approaches.

Findings

Method effectively handles correlated uncertainties in renewable resources.

Convex formulation applies to various probabilistic distributions.

Demonstrated improved robustness on IEEE test feeders.

Abstract

Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this paper, we focus on reactive power compensation to control voltage in the presence of uncertainties. We adopt a chance constraint approach that accounts for arbitrary correlations between renewable resources at each of the buses. We show how the problem can be solved efficiently using historical samples via a stochastic quasi gradient method. We also show that this optimization problem is convex for a wide variety of probabilistic distributions. Compared to conventional per-bus chance constraints, our formulation is more robust to uncertainty and more computationally tractable. We illustrate the results using standard IEEE distribution test feeders.