Use of real-time load profile measurement to optimize photovoltaicsystems dimensioning in shea butter production

Abstract

Productive use of renewable energy, particularly solar power, is essential for sustainable energy provision, especially in resource-constrained regions like sub-Saharan Africa. Accurate data on energy consumption patterns is crucial for properly sizing photovoltaic systems. However, conventional sizing methods, particularly for commercial and industrial needs often overestimate requirements, leading to economically onerous systems. Intuitive methods rely on simplified computations based on worstcase scenarios, such as lowest monthly average irradiation and daily load demand. They fail to consider solar irradiation fluctuations. Numerical methods, involve simulations at regular intervals. However, their practical application relies on interviews or electrical bills, which lacks accuracy in evaluating dynamic electrical consumption. This study tackles this challenge by developing a remote measurement system to monitor power consumption in a shea butter production facility (SOTOKACC, Toussiana, Burkina Faso). Shea, a popular product in the cosmetic, pharmaceutic, and food industries globally, originates solely from sub-Saharan Africa, where it sustains livelihoods for over 16 million rural women. While traditional methods still dominate shea butter production, initiatives aimed at adopting mechanical presses for extraction are on the rise. The system developed comprises two Arduino devices: a weather station and a power sensor. The weather station, powered by solar energy, recorded solar irradiation, ambient temperature and relative humidity. The power sensor, equipped with current clamp and voltage sensors, monitors various electrical parameters across three phases. The data were transmitted to an online platform via a Wi-Fi network. Over a two-month period, constant measurements were conducted to delineate the facility’s load profile. Sizing was performed using the HOMER Pro software to determine the characteristics of the most cost-effective photovoltaic system for the facility. A comparison was made between the conventional sizing procedure based on monthly electrical consumption and that based on remote measurements. Results indicate that load profile evaluation yields more cost-effective solutions with reduced storage requirements compared to traditional methods. This research highlights the potential of affordable measurement tools in developing sustainable energy solutions for small and medium-sized enterprises (SMEs).