Chance Constraint Tuning for Optimal Power Flow

TL;DR

This paper introduces a novel chance constraint tuning method for optimal power flow that efficiently manages uncertainties from renewables without relying on distributional assumptions, demonstrated on the IEEE 24-bus system.

Contribution

We develop a distribution-agnostic tuning approach for chance-constrained optimal power flow, improving constraint enforcement without over-conservatism.

Findings

Method is computationally efficient.

Successfully enforces chance constraints.

Applicable to both single and joint constraints.

Abstract

In this paper, we consider a chance-constrained formulation of the optimal power flow problem to handle uncertainties resulting from renewable generation and load variability. We propose a tuning method that iterates between solving an approximated reformulation of the optimization problem and using a posteriori sample-based evaluations to refine the reformulation. Our method is applicable to both single and joint chance constraints and does not rely on any distributional assumptions on the uncertainty. In a case study for the IEEE 24-bus system, we demonstrate that our method is computationally efficient and enforces chance constraints without over-conservatism.