@inproceedings{2f70542ccc24494f8aeef65e4771651c,
title = "Feasibility study of a solar PV system for a Foundry entity",
abstract = "Load shedding and electricity tariff increases have significant implications for industrial consumers. It disrupts their system operations and increases electricity costs. To address these challenges, companies are exploring demand-side solutions. These solutions aim to enhance energy security, reduce energy costs, and promote environmental sustainability. In this paper a case study of a metal casting entity in South Africa is considered. The entity is considering the installation of a solar PV system on its rooftop. Such a system generates electricity from sunlight and can offset a significant portion of a company's energy consumption. System Advisor Model (SAM) was used to design and model the solar PV systems. It helps assess the feasibility of the solar project, predict energy generation, and estimate cost savings. The results show that the optimal solution is a 547 kWdc solar PV system which is expected to have energy cost savings of approximately 46% for the company. The installation of the solar PV systems is seen as a viable solution for South African businesses looking to reduce energy costs and enhance energy security. These measures align with the broader global trend of transitioning to cleaner and more sustainable energy sources.",
keywords = "cost savings, electricity bill, energy savings, Solar PV system",
author = "Talent Duma and Oliver Dzobo and Bonani Seteni",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 18th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2024 ; Conference date: 24-06-2024 Through 26-06-2024",
year = "2024",
doi = "10.1109/PMAPS61648.2024.10667318",
language = "English",
series = "PMAPS 2024 - 18th International Conference on Probabilistic Methods Applied to Power Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "PMAPS 2024 - 18th International Conference on Probabilistic Methods Applied to Power Systems",
address = "United States",
}