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

被引:2
作者
Mathebula, Nelisiwe O. [1 ]
Thango, Bonginkosi A. [1 ]
Okojie, Daniel E. [2 ]
机构
[1] Univ Johannesburg, Dept Elect & Elect Engn Technol, ZA-2092 Johannesburg, South Africa
[2] Pan Atlantic Univ, Dept Elect & Elect Engn, Lagos 105101, Nigeria
关键词
energy management; oil refineries; particle swarm optimisation; renewable energy; sustainability; South Africa; SUSTAINABLE ENERGY; EMISSIONS;
D O I
10.3390/en17163929
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
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页数:22
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