用强化学习公平调控光伏出力，解决低压配网电压问题
Fair Reinforcement Learning Algorithm for PV Active Control in LV Distribution Networks
- 基于强化学习优化光伏逆变器的有功无功调节策略
- 在保障电压稳定的前提下，实现各用户间有功削减的公平性
- 适合关注分布式能源公平调度的电网研究人员与工程师
分布式能源（尤其是光伏面板）的广泛部署给电力网络控制带来了新挑战。由于光伏大规模发电，配电网中电压问题日益突出。当前通过光伏智能逆变器（SIs）调节有功功率输出和无功功率注入/吸收来缓解电压问题，但降低光伏有功出力可能被视为对部分用户的不公平，影响未来安装意愿。本文提出一种强化学习方法，在解决配电网电压问题的同时，兼顾用户间有功削减的公平性。实验验证了该方法在公平、高效控制电压方面的可行性。
原文摘要 · Abstract (English)
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.
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