Reinforcement learning-based dynamic cleaning scheduling framework for solar energy system

TL;DR

This paper introduces a reinforcement learning framework using PPO and SAC algorithms to optimize cleaning schedules for solar panels in arid regions, significantly reducing operational costs and improving energy efficiency.

Contribution

It presents a novel RL-based approach for autonomous, dynamic cleaning scheduling in solar PV systems, outperforming traditional methods in cost savings and adaptability.

Findings

PPO achieved up to 13% cost savings over traditional methods.

RL-based scheduling adapts effectively to environmental uncertainties.

PPO outperformed SAC in the case study.

Abstract

Advancing autonomous green technologies in solar photovoltaic (PV) systems is key to improving sustainability and efficiency in renewable energy production. This study presents a reinforcement learning (RL)-based framework to autonomously optimize the cleaning schedules of PV panels in arid regions, where soiling from dust and other airborne particles significantly reduces energy output. By employing advanced RL algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), the framework dynamically adjusts cleaning intervals based on uncertain environmental conditions. The proposed approach was applied to a case study in Abu Dhabi, UAE, demonstrating that PPO outperformed SAC and traditional simulation optimization (Sim-Opt) methods, achieving up to 13% cost savings by dynamically responding to weather uncertainties. The results highlight the superiority of flexible, autonomous scheduling over fixed-interval methods, particularly in adapting to stochastic environmental dynamics. This aligns with the goals of autonomous green energy production by reducing operational costs and improving the efficiency of solar power generation systems. This work underscores the potential of RL-driven autonomous decision-making to optimize maintenance operations in renewable energy systems. In future research, it is important to enhance the generalization ability of the proposed RL model, while also considering additional factors and constraints to apply it to different regions.