# Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2212.06542 · HTML: https://mlsanity.com/paper/2212.06542/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Robust Online Voltage Control with an Unknown Grid Topology](https://mlsanity.com/paper/2206.14369/) (2022)
- [Distributed Optimal Power Flow Algorithm for Balanced Radial Distribution Networks](https://mlsanity.com/paper/1404.0700/) (2014)
- [Fair Coordination of Distributed Energy Resources with Volt-Var Control and PV Curtailment](https://mlsanity.com/paper/2203.04842/) (2022)

## Built on by (most cited first, 2 of 2)

- [Fair Reinforcement Learning Algorithm for PV Active Control in LV Distribution Networks](https://mlsanity.com/paper/2409.09074/) (2024)
- [Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity](https://mlsanity.com/paper/2504.18078/) (2025)

## Abstract (arXiv)

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.