  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.
