Chance-Constrained AC Optimal Power Flow for Unbalanced Distribution Grids

TL;DR

This paper introduces a chance-constrained optimization method for unbalanced distribution grids that effectively manages uncertainty from renewable energy sources and load forecasting errors without approximations.

Contribution

It presents a novel reformulation of the chance-constrained optimal power flow problem for distribution grids and proposes two efficient iterative solution algorithms.

Findings

Both algorithms successfully enforce chance constraints in simulations.

The methods perform well with real PV and load data.

The approach improves power quality management under uncertainty.

Abstract

The growing penetration of distributed energy resources (DERs) is leading to continually changing operating conditions, which need to be managed efficiently by distribution grid operators. The intermittent nature of DERs such as solar photovoltaic (PV) systems as well as load forecasting errors not only increase uncertainty in the grid, but also pose significant power quality challenges such as voltage unbalance and voltage magnitude violations. This paper leverages a chance-constrained optimization approach to reduce the impact of uncertainty on distribution grid operation. We first present the chance-constrained optimal power flow (CC-OPF) problem for distribution grids and discuss a reformulation based on constraint tightening that does not require any approximations or relaxations of the three-phase AC power flow equations. We then propose two iterative solution algorithms capable of efficiently solving the reformulation. In the case studies, the performance of both algorithms is analyzed by running simulations on the IEEE 13-bus test feeder using real PV and load measurement data. The simulation results indicate that both methods are able to enforce the chance constraints in in- and out-of-sample evaluations.