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As photovoltaic (PV) penetration continues to rise and smart inverterfunctionality continues to expand, smart inverters and other distributed energyresources (DERs) will play increasingly important roles in distribution systempower management and security. In this paper, it is demonstrated that aconstellation of smart inverters in a simulated distribution circuit can enableprecise voltage predictions using an asynchronous and decentralized predictionalgorithm. Using simulated data and a constellation of 15 inverters in a ringcommunication topology, the COLA algorithm is shown to accomplish the learningtask required for voltage magnitude prediction with far less communicationoverhead than fully connected P2P learning protocols. Additionally, a dynamicstopping criterion is proposed that does not require a regularizer like theoriginal COLA stopping criterion.

Distribution System Voltage Prediction from Smart Inverters using Decentralized Regression