可再生能源系统优化：高级算法对比分析及光伏-电解槽性能研究以降低成本与提高效率
收稿日期: 2024-09-04
修回日期: 2025-01-30
录用日期: 2025-02-26
网络出版日期: 2025-02-26
Optimization of Renewable Energy Systems: Comparative Analysis of Advanced Algorithms and Photovoltaic-Electrolyzer Performance for Cost Reduction and Efficiency Enhancement
Received date: 2024-09-04
Revised date: 2025-01-30
Accepted date: 2025-02-26
Online published: 2025-02-26
随着人们对低成本且高效率的可再生能源解决方案需求的不断增长，混合能源系统面临重大优化挑战。为此，本文通过对比分析三种先进优化算法——莱维飞行优化、阿基米德优化和量子黑猩猩优化——来实现混合可再生能源系统的总净现值成本和平准化能源成本最小化。在高级优化框架中整合了如资本支出、运营支出、更换成本和残值等关键成本参数，评估了风力涡轮机/燃料电池组合、光伏系统/燃料电池组合，以及光伏/风力涡轮机/燃料电池组合系统三种系统配置，并在不同可用性水平（100%、96%和92%）下进行分析比较。结果表明，在96%可用性水平下，莱维飞行优化算法实现了风力涡轮机/燃料电池系统总净现值成本最小值（＄0.051），显著优于量子黑猩猩优化算法（＄0.719）。本研究结果强调了选择合适优化策略的重要性，以达到在成本、性能和系统可靠性之间取得平衡。本研究为设计高效且经济可行的可再生能源系统提供了宝贵的见解，特别适用于需要持续高能量输出的应用，例如基于单晶硅和多晶硅光伏系统的配置。
莫哈梅德-阿明·巴拜 , 穆斯塔法·阿达尔 , 艾哈迈德·切巴克 , 穆斯塔法·马布鲁基 . 可再生能源系统优化：高级算法对比分析及光伏-电解槽性能研究以降低成本与提高效率[J]. 电化学, 2026 , 32(5) : 2409041 . DOI: 10.61558/2993-074X.3531
The increasing demand for cost-effective and efficient renewable energy solutions presents significant optimization challenges in hybrid energy systems. This paper addresses these challenges by conducting a comparative analysis of three advanced optimization algorithms—Lévy Flight Optimization (LFO), Archimedean Optimization (AO), and Quantum Gorilla Optimization (QGO)—to minimize the Total Net Present Cost (TNPC) and Levelized Cost of Energy (LCOE) in hybrid renewable energy systems. The study integrates critical cost parameters such as Capital Expenditure (CAPEX), Operational Expenditure (OPEX), replacement costs, and salvage values into an advanced optimization framework. Three system configurations are evaluated: Wind Turbines and Fuel Cells (WT/FC), Photovoltaic Systems and Fuel Cells (PV/FC), and a combined system (PV/WT/FC), under varying availability levels (100%, 96%, and 92%). The results demonstrate that LFO consistently outperforms the other algorithms, achieving the lowest TNPC of ＄0.051 for the WT/FC system at 96% availability, compared to ＄0.719 using QGO. These findings underscore the importance of selecting tailored optimization strategies to balance cost, performance, and system reliability. This research provides valuable insights into designing efficient and economically viable renewable energy systems, particularly, for applications requiring consistent high energy output, such as monocrystalline and polycrystalline PV-based configurations.
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