  The rapid adoption of residential solar photovoltaics (PV) has resulted in
regular overvoltage events, due to correlated reverse power flows. Currently,
PV inverters prevent damage to electronics by curtailing energy production in
response to overvoltage. However, this disproportionately affects households at
the far end of the feeder, leading to an unfair allocation of the potential
value of energy produced. Globally optimizing for fair curtailment requires
accurate feeder parameters, which are often unknown. This paper investigates
reinforcement learning, which gradually optimizes a fair PV curtailment
strategy by interacting with the system. We evaluate six fairness metrics on
how well they can be learned compared to an optimal solution oracle. We show
that all definitions permit efficient learning, suggesting that reinforcement
learning is a promising approach to achieving both safe and fair PV
coordination.
