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

Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation