Abstract
Motivated by South Africa's need in the transition to a net-zero economy; this
dissertation investigates the integration of renewable energy sources (RES) into oil
refineries, considering therein unique challenges and opportunities. The research
focuses on optimising RES allocation using Particle Swarm Optimisation (PSO), a datadriven
approach that adapts to real-time operational conditions. Traditional energy
management systems often struggle with the inherent variability of RES, leading to
suboptimal energy distribution and increased emissions. Therefore, this dissertation
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 RES and achieving cost reductions
and sustainability goals. The optimisation result of 4,457,527.00 Rand achieved by
iterations is much better than the result of 4,829,638.88 Rand acquired using the Linear
Programming as the baseline model. The mean cost, indicating consistent performance,
has remained at its original value of 4,457,527.00 Rand, highlighting the convergence.
Key findings include the average distance measure decreasing from 4.2 to 3.4,
indicating particle convergence; swarm diameter decreasing from 4.7 to 3.8, showing
swarm concentration on promising solutions; average velocity decreasing from 7.8 to
4.25, demonstrating refined particle movement; and the optimum cost function achieved
at 4,457,527 Rand with zero standard deviation, highlighting stability and optimal
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solution identification. This research offers a valuable solution for oil refineries seeking
to integrate RES effectively, contributing to South Africa's transition to a sustainable
energy future.
Keywords: Energy Management, Oil Refineries, Particle Swarm Optimisation,
Renewable Energy, Sustainability, South Africa.