  Electronic power inverters are capable of quickly delivering reactive power
to maintain customer voltages within operating tolerances and to reduce system
losses in distribution grids. This paper proposes a systematic and data-driven
approach to determine reactive power inverter output as a function of local
measurements in a manner that obtains near optimal results. First, we use a
network model and historic load and generation data and do optimal power flow
to compute globally optimal reactive power injections for all controllable
inverters in the network. Subsequently, we use regression to find a function
for each inverter that maps its local historical data to an approximation of
its optimal reactive power injection. The resulting functions then serve as
decentralized controllers in the participating inverters to predict the optimal
injection based on a new local measurements. The method achieves near-optimal
results when performing voltage- and capacity-constrained loss minimization and
voltage flattening, and allows for an efficient volt-VAR optimization (VVO)
scheme in which legacy control equipment collaborates with existing inverters
to facilitate safe operation of distribution networks with higher levels of
distributed generation.
