论文标题
使用加固学习的无模型方法来实现公平的太阳能光伏削减
Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning
论文作者
论文摘要
由于相关的反向功率流，导致住宅太阳能光伏（PV）的迅速采用导致了定期的过电压事件。目前，PV逆变器通过响应过电压来减少能源产生，以防止电子产品损坏。但是，这不成比例地影响喂食器远端的家庭，导致对产生的能源潜在价值的不公平分配。全球优化以进行公平限制需要准确的馈线参数，这通常是未知的。本文调查了增强学习，该学习通过与系统互动逐渐优化了公平的光伏减少策略。我们评估了六个公平度量标准，即与最佳解决方案Oracle相比，它们的学习程度如何。我们表明，所有定义都允许有效学习，这表明增强学习是实现安全和公平的PV协调的一种有前途的方法。
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.
