Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation
This addresses voltage regulation and efficiency in distribution grids with distributed generation, offering a decentralized solution that is incremental in combining existing techniques.
The paper tackles the problem of decentralized optimal power flow and voltage regulation in distribution grids by proposing a data-driven regression-based inverter control method that maps local measurements to near-optimal reactive power injections, achieving near-optimal results for loss minimization and voltage flattening.
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.