  Increasing adoption of solar photovoltaic (PV) presents new challenges to
modern power grid due to its variable and intermittent nature. Fluctuating
outputs from PV generation can cause the grid violating voltage operation
limits. PV smart inverters (SIs) provide a fast-response method to regulate
voltage by modulating real and/or reactive power at the connection point. Yet
existing local autonomous control scheme of SIs is based on local information
without coordination, which can lead to suboptimal performance. In this paper,
a deep reinforcement learning (DRL) based algorithm is developed and
implemented for coordinating multiple SIs. The reward scheme of the DRL is
carefully designed to ensure voltage operation limits of the grid are met with
more effective utilization of SI reactive power. The proposed DRL agent for
voltage control can learn its policy through interaction with massive offline
simulations, and adapts to load and solar variations. The performance of the
DRL agent is compared against the local autonomous control on the IEEE 37 node
system with thousands of scenarios. The results show a properly trained DRL
agent can intelligently coordinate different SIs for maintaining grid voltage
within allowable ranges, achieving reduction of PV production curtailment, and
decreasing system losses.
