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The rise in residential photovoltaics and other distributed energy sourcesposes unprecedented challenges for the operation of power distribution grids.When high amounts of active power are injected into the grid by such powersources, the overall power flow is often limited because of voltages reachingtheir upper acceptable limits. Volt/VAr control aims to raise this power flowlimit by controlling the voltage using reactive power. This way, more activepower can be transmitted safely without physically reinforcing the grid. Inthis paper, we use real consumption and generation data on a low-voltageCIGR\'E grid model and an experiment on a real distribution grid feeder toanalyze how different Volt/VAr methods can enhance grid capacity, i.e., by howmuch they can improve the grid's capability to transmit active power withoutbuilding new lines. We show that droop control enhances the grid but vastlyunderutilizes the reactive power resources. We discuss how the effectiveness ofdroop control can be partially improved by employing machine-learningtechniques to tune the droop coefficients, but we demonstrate that localcontrol laws are inherently unable to achieve optimal grid enhancement. Incontrast, methods that coordinate the use of reactive power resources acrossthe grid, such as Online Feedback Optimization (OFO), can enhance the grid toits full potential. A numerical study performed on data from an entire yearusing a realistic grid model suggests that OFO can enable another 9\% ofmaximum active power injections compared to droop control. To achieve that, OFOonly requires voltage magnitude measurements, minimal model knowledge, andcommunication with the reactive power sources. A real-life experiment providesa demonstration of the practical feasibility of the proposed approach andenhanced the grid by another 10.5\% compared to droop control.

Power Distribution Grid Enhancement via Online Feedback Optimization