基于高效制氢的混合可再生能源系统的经济评估:OOA-RBFNN 方法

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Suresh Muthusamy, R. Suresh Kumar, N. Karthikeyan, P. Rajesh
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引用次数: 0

摘要

氢(H2)能源是一种清洁高效的能源,在能源革命和全球气候变化迫在眉睫的情况下,可持续发展的社会被认为会得到极大的帮助。由于需要逐步转向可再生能源,确定和实施绿色氢气生产方法的难度大大增加。为解决这些问题,本研究为使用多种 H2 生产方法的混合可再生能源(RES)系统提出了一种新型能源管理方法。该方法结合了鱼鹰优化算法(OOA)和径向基函数神经网络(RBFNN),即 OOA-RBFNN 技术。拟议战略的主要目的是最大限度地降低系统净成本。具体来说,OOA 用于降低由可再生能源组成的混合微电网的运营成本。RBFNN 用于预测不确定的可再生能源发电量和需求量。这项工作旨在提出一种利用太阳能和风能生产氢气的策略,同时利用水电解槽降低系统成本。OOA-RBFNN 技术用于确定系统的最佳规模和运行能源管理。建议的技术在 MATLAB 平台上实现,并与 salp 蜂群算法、卷积神经网络和随机森林算法等各种现有技术进行了比较。所提方法的计算时间为 0.8 秒,低于现有方法;能源成本为 23.22 美元,低于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Economic assessment of efficient hydrogen production-based hybrid renewable energy system: OOA-RBFNN approach

Economic assessment of efficient hydrogen production-based hybrid renewable energy system: OOA-RBFNN approach

A sustainable society is thought to be greatly aided by hydrogen (H2) energy as it is a clean and efficient energy source in light of the impending energy revolution and global climate change. Identifying and implementing green H2 production methods is made considerably more difficult by the need for a gradual switch to renewable energy. To address these issues, this study proposes a novel energy management approach for hybrid renewable energy resources (RES) systems using multiple H2 production methods. The proposed approach combines the osprey optimization algorithm (OOA) with a radial basis function neural network (RBFNN), known as the OOA-RBFNN technique. The principal purpose of the proposed strategy is to minimize net system costs. Specifically, OOA is used to lessen the operational cost of a hybrid microgrid consisting of RES. RBFNN is used to predict uncertain renewable energy generation and demand. This work aims to present a strategy for producing hydrogen from solar and wind energy while reducing system costs by using water electrolyzer. The OOA-RBFNN technique is used to define the optimal size and operating energy management of the system. The proposed technique was implemented in the MATLAB platform and compared with various existing techniques like the salp swarm algorithm, convolutional neural network and random forest algorithm. The computation time of the proposed approach is 0.8 s which is lower, and the cost for energy is 23.22$ which is lower than the existing methods.

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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed. Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).
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