Online Gradient Descent for Flexible Power Point Tracking Under a Highly Fluctuating Weather and Load

TL;DR

This paper proposes an online gradient descent algorithm for flexible power point tracking in photovoltaic systems, effectively handling highly fluctuating weather conditions to maximize power output.

Contribution

It introduces an online optimization approach for MPPT and FPPT that adapts quickly to rapid weather changes, improving power extraction efficiency.

Findings

The proposed algorithm outperforms traditional methods under dynamic weather conditions.

Numerical analysis confirms the algorithm's effectiveness in real-time power tracking.

Code availability facilitates practical implementation and further research.

Abstract

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