Fair Reinforcement Learning Algorithm for PV Active Control in LV Distribution Networks

TL;DR

This paper proposes a reinforcement learning-based method to manage voltage issues in distribution networks with PV panels, ensuring fair active power curtailment among customers.

Contribution

It introduces a novel reinforcement learning approach that balances voltage control with fairness in active power curtailment in PV-integrated distribution networks.

Findings

Effective voltage control demonstrated in experiments

Fairness in active power curtailment achieved

Reinforcement learning outperforms traditional methods

Abstract

The increasing adoption of distributed energy resources, particularly photovoltaic (PV) panels, has presented new and complex challenges for power network control. With the significant energy production from PV panels, voltage issues in the network have become a problem. Currently, PV smart inverters (SIs) are used to mitigate the voltage problems by controlling their active power generation and reactive power injection or absorption. However, reducing the active power output of PV panels can be perceived as unfair to some customers, discouraging future installations. To solve this issue, in this paper, a reinforcement learning technique is proposed to address voltage issues in a distribution network, while considering fairness in active power curtailment among customers. The feasibility of the proposed approach is explored through experiments, demonstrating its ability to effectively control voltage in a fair and efficient manner.