Distribution System Voltage Prediction from Smart Inverters using Decentralized Regression

TL;DR

This paper demonstrates that a decentralized, asynchronous algorithm using smart inverter data can accurately predict distribution system voltages with reduced communication overhead, enhancing power management and security.

Contribution

It introduces a novel decentralized prediction algorithm (COLA) for voltage forecasting using smart inverters, reducing communication needs compared to traditional methods.

Findings

COLA algorithm achieves accurate voltage prediction with less communication.

Decentralized approach enables asynchronous learning in distribution systems.

Proposed dynamic stopping criterion improves efficiency without regularizers.

Abstract

As photovoltaic (PV) penetration continues to rise and smart inverter functionality continues to expand, smart inverters and other distributed energy resources (DERs) will play increasingly important roles in distribution system power management and security. In this paper, it is demonstrated that a constellation of smart inverters in a simulated distribution circuit can enable precise voltage predictions using an asynchronous and decentralized prediction algorithm. Using simulated data and a constellation of 15 inverters in a ring communication topology, the COLA algorithm is shown to accomplish the learning task required for voltage magnitude prediction with far less communication overhead than fully connected P2P learning protocols. Additionally, a dynamic stopping criterion is proposed that does not require a regularizer like the original COLA stopping criterion.