Work

Smart control of standalone solar-powered oil press: Applying Reinforcement Learning for productivity and energy utilization improvement

Year: 2025

Type: article

Abstract: • First deep reinforcement learning-based control for off-grid mechanical oil-press. • Smart agent embedded on low-cost microcontroller for rural oil-press control. • Smart controller outperforms fixed schedule in PV use and energy savings. • Prototype is ready for deployment in industries. In resources constrained rural areas, solar-powered oil extraction can be enhanced through recent advances in artificial intelligence for energy optimization. This study introduces SolPrInt , a deep reinforcement-learning (DRL) based controller for a standalone, photovoltaic-battery powered mechanical oil press. A proximal policy optimization (PPO) agent was trained in MATLAB/Simulink using 15 years of PVGIS-SARAH2 radiation data and peanut-oil extraction benchmarks. A primary training phase followed by an adversarial phase on the 5% least-sunny days reinforced robustness under low-irradiance conditions. The developed agent adapts press rotational speed to real-time PV availability, battery state of charge, and system behavior to ensure energy-efficient use of solar resources. In-silico validation achieved stable rewards and simulated throughput of 96 ± 13.5 kg/d under sunny days and 90 ± 20.5 kg/d under cloudy days. Compared with conventional fixed-schedule operation (08:00–18:00) under sunny and cloudy conditions, SolPrInt extends operating time, and reduces power outages, while improves oil yield by 0.7 percentage points. Experimental validation on a PV-simulator bench confirmed real-time deployment feasibility on a low-cost ESP32 microcontroller interfaced with a Kern Kraft KK20 press. These findings demonstrate the potential of PV-sensitive DRL control to improve the performance of standalone renewable energy systems, supporting reliable decentralized energy use and contributing to sustainable energy access sustainable energy access. Supplementary materials supporting this work, are available at https://bonjoe.github.io/solprint.demo/

Source: Renewable energy focus

DOI: 10.1016/j.ref.2025.100772

Authors: Wiomou Joévin Bonzi, Zhangkai Wu, Sebastian Romuli, Klaus Meissner, Joachim Müller

Institution: University of Hohenheim

Language: English

Topic: Photovoltaic System Optimization Techniques

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $1,790