论文标题
在高度波动的天气和负载下，在线梯度下降用于灵活的动力点跟踪
Online Gradient Descent for Flexible Power Point Tracking Under a Highly Fluctuating Weather and Load
论文作者
论文摘要
电力需求不断增长，并且需要以经济高效的方式满足这一需求，并需要对研究人员和开发人员构成新的挑战，以最大程度地提高这些可再生资源的产出。但是，可再生能源对网格的渗透量增加，对电网操作员构成了新的挑战。所有这些挑战和问题都引起了最大功率点跟踪器（MPPT）和灵活的功率点跟踪器（FPPT）的需求，以最大程度地提高从光伏（PV）系统中提取的功率并满足网格操作约束。这些算法的现有解决方案没有考虑到影响可以从PV模块中提取的输出功率的天气条件的非常高的动力学性质，而实际上，天气动态变化的速度比收敛所需的算法时间更快。本文档中的工作试图通过为此目的利用在线优化算法来解决此缺点的地址缺点。文档中介绍了数值分析和验证。可以在此链接上找到算法的代码
The increasing electricity demand and the need for clean and renewable energy resources to satisfy this demand in a cost-effective manner, imposes new challenges on researchers and developers to maximize the output of these renewable resources at all times. However, the increasing penetration of renewable energy into the grid imposes new challenges on the grid operators. All of these challenges and issues gave rise to the need of Maximum Power Point Tracker (MPPT) and Flexible Power Point Trackers (FPPT) in order to maximize the power extracted from Photovoltaic (PV) systems and meet the grid operation constraints. Existing solutions for these algorithms do not take into consideration the very high dynamical nature of weather conditions that affects the output power that can be extracted from the PV modules, whereas in practice, the weather changes dynamically faster than what the algorithms time needed to converge. The work in this document is an attempt to address this shortcoming address shortcoming by utilizing online optimization algorithms for this purpose. Numerical analysis and verification are presented in the document. Code for the algorithms can be found at this link
