Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning
This addresses fairness issues in residential solar energy distribution for grid operators and households, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of unfair solar PV curtailment due to overvoltage events by using reinforcement learning to optimize fair strategies, showing that all six fairness metrics allowed efficient learning compared to an optimal oracle.
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.