利用强化学习缓解鸭子曲线问题的光伏逆变器控制算法

Yu-Quan Chen, I. Jiang, Katherine A. Kim
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引用次数: 0

摘要

随着越来越多的太阳能光伏(PV)系统在世界各地安装,电力消耗和太阳能发电曲线不同步的事实导致了一个被称为鸭子曲线的问题。随着光伏渗透率的增加,由于增加的斜坡率增加了电网的压力,问题变得更加严重。另一个挑战是,能量分布在不同的白天和季节有很大的不同。我们提出了一种使用强化学习的系统控制算法,用于实时、动态和自适应的电池集成光伏转换器系统。结果表明,四个目标之间具有良好的平衡性,台湾和德国的实际数据集验证了这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PV Inverter Control Algorithm Using Reinforcement Learning to Mitigate the Duck Curve Problem
As more solar photovoltaic (PV) systems are installed around the world, the fact that power consumption and solar generation profiles do not synchronize leads to a problem called the duck curve. As PV penetration increases, the problem is exacerbated due to an increasing ramp rate that adds strain to the electricity grid. Another challenge is that the power profiles vary considerably by day and by season. We propose a system control algorithm using reinforcement learning for a battery-integrated PV converter system that works in real-time, is dynamic, and is adaptive. Results show a good balance among four objectives, which are verified by real data sets from Taiwan and Germany.
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