  The rise in residential photovoltaics and other distributed energy sources
poses unprecedented challenges for the operation of power distribution grids.
When high amounts of active power are injected into the grid by such power
sources, the overall power flow is often limited because of voltages reaching
their upper acceptable limits. Volt/VAr control aims to raise this power flow
limit by controlling the voltage using reactive power. This way, more active
power can be transmitted safely without physically reinforcing the grid. In
this paper, we use real consumption and generation data on a low-voltage
CIGR\'E grid model and an experiment on a real distribution grid feeder to
analyze how different Volt/VAr methods can enhance grid capacity, i.e., by how
much they can improve the grid's capability to transmit active power without
building new lines. We show that droop control enhances the grid but vastly
underutilizes the reactive power resources. We discuss how the effectiveness of
droop control can be partially improved by employing machine-learning
techniques to tune the droop coefficients, but we demonstrate that local
control laws are inherently unable to achieve optimal grid enhancement. In
contrast, methods that coordinate the use of reactive power resources across
the grid, such as Online Feedback Optimization (OFO), can enhance the grid to
its full potential. A numerical study performed on data from an entire year
using a realistic grid model suggests that OFO can enable another 9\% of
maximum active power injections compared to droop control. To achieve that, OFO
only requires voltage magnitude measurements, minimal model knowledge, and
communication with the reactive power sources. A real-life experiment provides
a demonstration of the practical feasibility of the proposed approach and
enhanced the grid by another 10.5\% compared to droop control.
