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Particle Swarm Optimisation Algorithm-Based Renewable Energy Source Management for Industrial Applications: An Oil Refinery Case Study

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  • Nelisiwe O. Mathebula

    (Department of Electrical & Electronic Engineering Technology, University of Johannesburg, Johannesburg 2092, South Africa)

  • Bonginkosi A. Thango

    (Department of Electrical & Electronic Engineering Technology, University of Johannesburg, Johannesburg 2092, South Africa)

  • Daniel E. Okojie

    (Department of Electrical and Electronics Engineering, Pan-Atlantic University, Lagos 105101, Nigeria)

Abstract

Motivated by South Africa’s need for the transition to a net-zero economy, this study investigates the integration of renewable energy sources (RESs) into oil refineries, considering the unique challenges and opportunities therein. The research focuses on optimising RES allocation using particle swarm optimisation (PSO), a data-driven approach that adapts to real-time operational conditions. Traditional energy management systems often struggle with the inherent variability of RESs, leading to suboptimal energy distribution and increased emissions. Therefore, this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil refineries. It considers factors like the levelised cost of energy, geographical location, and available technology. The methodology involves formulating the optimisation problem, developing a PSO model, and implementing it in a simulated oil refinery environment. The results demonstrate significant convergence of the PSO algorithm, leading to an optimal configuration for integrating RESs and achieving cost reductions and sustainability goals. The optimisation result of ZAR 4,457,527.00 achieved through iterations is much better than the result of ZAR 4,829,638.88 acquired using linear programming as the baseline model. The mean cost, indicating consistent performance, has remained at its original value of ZAR 4,457,527.00, highlighting the convergence. The key findings include the average distance measurement decreasing from 4.2 to 3.4, indicating particle convergence; the swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; the average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the optimum cost function achieved at ZAR 4,457,527 with zero standard deviation, highlighting stability and optimal solution identification. This research offers a valuable solution for oil refineries seeking to integrate RESs effectively, contributing to South Africa’s transition to a sustainable energy future.

Suggested Citation

  • Nelisiwe O. Mathebula & Bonginkosi A. Thango & Daniel E. Okojie, 2024. "Particle Swarm Optimisation Algorithm-Based Renewable Energy Source Management for Industrial Applications: An Oil Refinery Case Study," Energies, MDPI, vol. 17(16), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3929-:d:1452460
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    References listed on IDEAS

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    1. Yang, Yun & Zhang, Shijie & Xiao, Yunhan, 2015. "An MILP (mixed integer linear programming) model for optimal design of district-scale distributed energy resource systems," Energy, Elsevier, vol. 90(P2), pages 1901-1915.
    2. Maurice Clerc, 2010. "Beyond Standard Particle Swarm Optimisation," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 1(4), pages 46-61, October.
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    1. Seyed Mohammad Sharifhosseini & Taher Niknam & Mohammad Hossein Taabodi & Habib Asadi Aghajari & Ehsan Sheybani & Giti Javidi & Motahareh Pourbehzadi, 2024. "Investigating Intelligent Forecasting and Optimization in Electrical Power Systems: A Comprehensive Review of Techniques and Applications," Energies, MDPI, vol. 17(21), pages 1-35, October.

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