Variation-cognizant Probabilistic Power Flow Analysis via Multi-task Learning

TL;DR

This paper introduces a multi-task learning approach using neural networks and regression algorithms to efficiently and accurately perform probabilistic power flow analysis in power grids with high solar PV penetration.

Contribution

It develops a variation-aware multi-task learning framework that improves estimation accuracy and computational speed for probabilistic power flow analysis.

Findings

Achieves better estimation accuracy on IEEE test systems.

Reduces computation time compared to traditional methods.

Effectively models voltage and branch flow fluctuations.

Abstract

With an increasing high penetration of solar photovoltaic generation in electric power grids, voltage phasors and branch power flows experience more severe fluctuations. In this context, probabilistic power flow (PPF) study aims at characterizing the statistical properties of the state of the system with respect to the random power injections. To avoid repeated power flow calculations involved in PPF study, the present paper leverages regression algorithms and neural networks to improve the estimation performance and speed up the computation. Specifically, based on the variation level of the voltage magnitude at each bus, we develop either a linear regression or a fully connected neural network to approximate the inverse AC power flow mappings. The proposed multi-task learning technique further improves the accuracy of branch flow estimation by incorporating the errors of voltage angle differences into the loss function design. Tested on IEEE-300 and IEEE-1354 bus systems with real data, the proposed methods achieve better performance in estimating voltage phasors and branch flows.