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

IF 3 4区 工程技术 Q3 ENERGY & FUELS Energies Pub Date : 2024-08-08 DOI:10.3390/en17163929
Nelisiwe O. Mathebula, Bonginkosi A. Thango, Daniel E. Okojie
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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.
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基于粒子群优化算法的工业应用可再生能源管理:炼油厂案例研究
南非需要过渡到净零经济,受此激励,本研究调查了将可再生能源(RES)整合到炼油厂的情况,并考虑了其中的独特挑战和机遇。研究重点是使用粒子群优化(PSO)优化可再生能源的分配,这是一种数据驱动型方法,可适应实时运行条件。传统的能源管理系统往往难以应对可再生能源固有的可变性,导致能源分配不理想和排放增加。因此,本研究提出了一种基于 PSO 的可再生能源分配策略,专为炼油厂设计。它考虑了能源平准化成本、地理位置和可用技术等因素。该方法包括制定优化问题、开发 PSO 模型并在模拟炼油厂环境中实施。结果表明,PSO 算法的收敛性很强,从而得出了整合可再生能源的最佳配置,实现了降低成本和可持续发展的目标。通过迭代获得的优化结果为 4,457,527.00 南非兰特,远高于以线性规划为基准模型获得的 4,829,638.88 南非兰特。表明性能稳定的平均成本保持在原始值 4,457,527.00 南非里亚尔,突显了收敛性。主要发现包括:平均距离测量值从 4.2 降至 3.4,表明粒子趋同;粒子群直径从 4.7 降至 3.8,表明粒子群集中在有希望的解决方案上;平均速度从 7.8 降至 4.25,表明粒子运动精细;最佳成本函数为 4,457,527 南非兰特,标准偏差为零,表明稳定性和最佳解决方案识别。这项研究为寻求有效整合可再生能源的炼油厂提供了宝贵的解决方案,有助于南非向可持续能源的未来过渡。
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来源期刊
Energies
Energies ENERGY & FUELS-
CiteScore
6.20
自引率
21.90%
发文量
8045
审稿时长
1.9 months
期刊介绍: Energies (ISSN 1996-1073) is an open access journal of related scientific research, technology development and policy and management studies. It publishes reviews, regular research papers, and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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