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

doi:10.61558/2993-074X.3531

摘要/Abstract

摘要：

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。

关键词: 能源优化, 莱维飞行优化, 量子黑猩猩优化, 光伏-电解槽系统, 成本参数

Abstract:

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.

Key words: Energy optimization, Levy flight optimization, Quantum gorilla optimization, Photovoltaic-electrolyzer system, Cost parameter

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（中英文）, 2026, 32(5): 2409041.

Mohamed-Amine Babay, Mustapha Adar, Ahmed Chebak, Mustapha Mabrouki. Optimization of Renewable Energy Systems: Comparative Analysis of Advanced Algorithms and Photovoltaic-Electrolyzer Performance for Cost Reduction and Efficiency Enhancement[J]. Journal of Electrochemistry, 2026, 32(5): 2409041.

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链接本文: https://electrochem.xmu.edu.cn/CN/10.61558/2993-074X.3531

https://electrochem.xmu.edu.cn/CN/Y2026/V32/I5/2409041

图/表 14

Ref.	System	Algorithm	New version	Main strategy used	Merit	Limitation
[36]	PV/WT/FC	Sine-Cosine Algorithm (SCA)	Improved sine-cosine algorithm (ISCA)	Non-linearly decreasing inertia weight strategy	Efficiently exploit and avoid local optima	The validation of the proposed algorithm under different benchmark functions needs to be considered
[37]	PV/WT/FC	Artificial Ecosystem Optimization (AEO)	Improved Artificial Ecosystem Optimization (IAEO)	Integrate Sine-Cosine Algorithm (SCA)	Improve the exploration phase and enhance the development search capability	Optimization method not compared with SCA, which used to improve Artificial Ecosystem Optimization
[38]	PV/WT/GD/BAT	Harmony Search algorithm (HS)	Fuzzy Harmony Search algorithm (FHS)	Fuzzy logic controller technique	Achieve a suitable equilibrium between exploration	Not used other optimization algorithms for comparison
[27]	PV/WT/FC	Grey Wolf Optimiser (GWO)	Hybrid grey wolf optimiser-sine-cosine algorithm (HGWOSCA)	Combining GWO with Sine-Cosine Algorithm (SCA) based on exponential decreasing function (EDF) and the inertia weight strategy.	Increase global exploration ability	Lack of more detailed information about the hybrid implementation of the proposed method
[39]	PV/WT/BG/BAT	Artificial Hummingbird Algorithm (AHA)	Gradient Artificial Hummingbird Algorithm (GAHA)	Local escape strategy of Gradient-based optimizer (GBO)	Avoid local optima	Sensitivity analysis is required
[32]	PV/WT/DG/BAT	Marine Predator Algorithm (MPA)	Improved Marine Predator Algorithm (Deep-MPA)	Reinforcement learning (RL)	Increase the population diversity	The proposed method has a significant computational time because of the use of RL strategy
[28]	PV/WT/DG/BAT	Arithmetic Optimization Algorithm (AOA)	Improved Arithmetic Optimization Algorithm (IAOA)	Leading operators of the Aquila Optimizer (AO)	Good balance between exploration and exploitation	The effects of microgrid systems under different techno-economic conditions are not investigated
[29]	HPV/WT/BAT	Grasshopper Optimization Algorithm (GOA)	Improved Grasshopper Optimization Algorithm (IGOA)	Non-linearly decreasing inertia weight strategy	Increase the population diversity	Computational complexity of the proposed algorithm is missing
[33]	PV/WT/DG/BAT	Gravitational Search Algorithm (GSA)	Fuzzy Gravitational Search Algorithm (FGSA)	Fuzzy logic controller technique	Achieve a suitable equilibrium between exploration	Not used other optimization algorithms for comparison
[40]	PV/WT/FC	Beluga Whale Optimization BWO	Quantum Beluga Whale Optimization (QBWO)	Quantum theory	Accelerate the convergence rate and enhance the capability to avoid local optima	----

参考文献 46

[1]	Adar M, Babay M A, Touairi S, Najih Y, Mabrouki M. Experimental validation of different PV power prediction models under Beni Mellal climate, implications for the energy nexus[J]. Energy Nexus, 2022, 5: 100050. https://doi.org/10.1016/j.nexus.2022.100050. doi: 10.1016/j.nexus.2022.100050 URL
[2]	Adar M, Babay M A, Boussif M, Khelil A, Mami A. Optimization of photovoltaic system modelling: A comparative study and experimental validation using bond graph methodology and a genetic algorithm[J]. Adv. Transdiscipl. Eng., 2024, 61: 723-730. https://doi.org/10.3233/ATDE240825.
[3]	Adar M, Babay M A, Taouiri S, Alioui A, Najih Y, Khaouch Z, Mabrouki M. Experimental validation of different PV power prediction models under Beni Mellal climate[M]//Saidi R, El Bhiri B, Maleh Y, Mosallam A, Essaaidi M. (Eds) Lecture Notes on Data Engineering and Communications Technologies, Springer, Cham., 2022, vol 110: 286-299. https://doi.org/10.1007/978-3-030-94188-8_27.
[4]	Adar M, Bazine M A, Mabrouki M. Performance assessment of three photovoltaic systems[M]//Motahhir S, El Haj Assad M. Performance Enhancement and Control of Photovoltaic Systems. Amsterdam: Elsevier, 2024: 97-113. https://doi.org/10.1016/B978-0-443-13392-3.00005-0.
[5]	Babay M A, Adar M, Mabrouki M. Modeling and simulation of a PEMFC using three-dimensional multi-phase computational fluid dynamics model[C]// 2021 9th International Renewable and Sustainable Energy Conference (IRSEC), Morocco, 2021, pp. 1-6, doi: 10.1109/IRSEC53969.2021.9741144.
[6]	Valverde-Isorna L, Ali D, Hogg D, Abdel-Wahab M. Modelling the performance of wind-hydrogen energy systems: Case study the Hydrogen Office in Scotland/UK[J]. Renew. Sustain. Energy Rev., 2016, 53: 1313-1332. https://doi.org/10.1016/j.rser.2015.08.044.
[7]	Götz M, Lefebvre J, Mors F, Koch A M, Graf F, Bajohr S, Reimert R, Kolb T. Renewable Power-to-Gas: A technological and economic review[J]. Renew. Energy, 2016, 85: 1371-1390. https://doi.org/10.1016/j.renene.2015.07.066. doi: 10.1016/j.renene.2015.07.066 URL
[8]	Abdin Z, Webb C J, Gray E M A. Solar hydrogen hybrid energy systems for off-grid electricity supply: A critical review[J]. Renew. Sustain. Energy Rev., 2015, 52: 1791-1808. https://doi.org/10.1016/j.rser.2015.08.011. doi: 10.1016/j.rser.2015.08.011 URL
[9]	Babay M A, Adar M, Nouri R, Chebak A, Mabrouki M. Integrated thermodynamic analysis and channel variation effects on solid oxide electrolysis for efficient hydrogen generation[J]. Procedia Comput. Sci., 2024, 236: 152-159. https://doi.org/10.1016/j.procs.2024.05.016. doi: 10.1016/j.procs.2024.05.016 URL
[10]	Bharti A, Natarajan R. Proton exchange membrane testing and diagnostics[M]//PEM Fuel Cells: Fundamentals, Advanced Technologies, and Practical Applications. Elsevier, 2022: 137-171. https://doi.org/10.1016/B978-0-12-823708-3.00009-2.
[11]	Babay M A, Adar M, Chebak A, Mabrouki M. Comparative sustainability analysis of serpentine flow-field and straight channel PEM fuel cell designs[J]. Int. J. Syst. Assur. Eng. Manag., 2024, 15(8): 3954-3970. https://doi.org/10.1007/s13198-024-02395-8. doi: 10.1007/s13198-024-02395-8 URL
[12]	Zhou T, Francois B. Modeling and control design of hydrogen production process for an active hydrogen/wind hybrid power system[J]. Int. J. Hydrogen Energy, 2009, 34(1): 21-30. https://doi.org/10.1016/j.ijhydene.2008.10.030. doi: 10.1016/j.ijhydene.2008.10.030 URL
[13]	Babay M A, Adar M, Chebak A, Mabrouki M. Dynamics of gas generation in porous electrode alkaline electrolysis cells: An investigation and optimization using machine learning[J]. Energies, 2023, 16(14): 5365. https://doi.org/10.3390/en16145365. doi: 10.3390/en16145365 URL
[14]	Atlam O, Barbir F, Bezmalinovic D. A method for optimal sizing of an electrolyzer directly connected to a PV module[J]. Int. J. Hydrogen Energy, 2011, 36(12): 7012-7018. https://doi.org/10.1016/j.ijhydene.2011.03.062. doi: 10.1016/j.ijhydene.2011.03.073 URL
[15]	Babay M A, Adar M, Touairi S, Chebak A, Mabrouki M. Numerical simulation and thermal analysis of pressurized hydrogen vehicle cylinders: Impact of geometry and phase change materials[J]. J. Adv. Res. Fluid Mech. Therm. Sci., 2024, 117(2): 71-90. https://doi.org/10.37934/arfmts.117.2.7190.
[16]	Shiva Kumar S, Himabindu V. Hydrogen production by PEM water electrolysis - A review[J]. Mater. Sci. Energy Technol., 2019, 2(3): 442-454. https://doi.org/10.1016/j.mset.2019.03.002.
[17]	Clarke R E, Giddey S, Ciacchi F T, Badwal S P S, Paul B, Andrews J. Direct coupling of an electrolyser to a solar PV system for generating hydrogen[J]. Int. J. Hydrogen Energy, 2009, 34(6): 2531-2542. https://doi.org/10.1016/j.ijhydene.2009.01.053. doi: 10.1016/j.ijhydene.2009.01.053 URL
[18]	Khouya A. Levelized costs of energy and hydrogen of wind farms and concentrated photovoltaic thermal systems. A case study in Morocco[J]. Int. J. Hydrogen Energy, 2020, 45(53): 31632-31650. https://doi.org/10.1016/j.ijhydene.2020.09.223. doi: 10.1016/j.ijhydene.2020.08.240 URL
[19]	Khalghani M R, Shamsi-nejad M A, Khooban M H. Dynamic voltage restorer control using bi-objective optimisation to improve power quality’s indices[J]. IET Sci. Meas. Technol., 2014, 8(3): 203-213. https://doi.org/10.1049/iet-smt.2013.0084. doi: 10.1049/smt2.v8.4 URL
[20]	Abaspour A, Tadrisi Parsa N, Sadeghi M. A new feedback linearization-NSGA-II based control design for PEM fuel cell[J]. Int. J. Comput. Appl., 2014, 97(10): 25-32. https://doi.org/10.5120/17044-7354.
[21]	Babay M A, Adar M, Chebak A, Mabrouki M. Exploring the sustainability of serpentine flow-field fuel cell, straight channel PEM fuel cells hight temperature through numerical analysis[J]. Energy Nexus, 2024, 14: 100283. https://doi.org/10.1016/j.nexus.2024.100283. doi: 10.1016/j.nexus.2024.100283 URL
[22]	Babay M A, Adar M, Chebak A, Mabrouki M. Forecasting green hydrogen production: An assessment of renewable energy systems using deep learning and statistical methods[J]. Fuel, 2024, 381: 133496. https://doi.org/10.1016/j.fuel.2025.133496. doi: 10.1016/j.fuel.2024.133496 URL
[23]	Ural Z, Gencoglu M T. Design and simulation of a solar-hydrogen system for different situations[J]. Int. J. Hydrogen Energy, 2014, 39(24): 8833-8840. https://doi.org/10.1016/j.ijhydene.2013.12.084. doi: 10.1016/j.ijhydene.2013.12.025 URL
[24]	Jahangir M H, Cheraghi R. Economic and environmental assessment of solar-wind-biomass hybrid renewable energy system supplying rural settlement load[J]. Sustain. Energy Technol. Assessments, 2020, 42: 100895. https://doi.org/10.1016/j.seta.2020.100895.
[25]	Shezan S A, Hasan K N, Rahman A, Datta M, Datta U. Selection of appropriate dispatch strategies for effective planning and operation of a microgrid[J]. Energies, 2021, 14(21): 7217. https://doi.org/10.3390/en14217217. doi: 10.3390/en14217217 URL
[26]	Gökçek M, Kale C. Techno-economical evaluation of a hydrogen refuelling station powered by Wind-PV hybrid power system: A case study for İzmir-Çeşme[J]. Int. J. Hydrogen Energy, 2018, 43(22): 10615-10625. https://doi.org/10.1016/j.ijhydene.2018.04.104. doi: 10.1016/j.ijhydene.2018.01.082 URL
[27]	Jahannoosh M, Nowdeh S A, Naderipour A. New hybrid meta-heuristic algorithm for reliable and cost-effective designing of photovoltaic/wind/fuel cell energy system considering load interruption probability[J]. J. Clean. Prod., 2021, 278: 123406. https://doi.org/10.1016/j.jclepro.2020.123406. doi: 10.1016/j.jclepro.2020.123406 URL
[28]	Kharrich M, Abualigah L, Kamel S, AbdEl-Sattar H, Tostado-Véliz M. An improved arithmetic optimization algorithm for design of a microgrid with energy storage system: Case study of El Kharga Oasis, Egypt[J]. J. Energy Storage, 2022, 51: 104343. https://doi.org/10.1016/j.est.2022.104343. doi: 10.1016/j.est.2022.104343 URL
[29]	Naderipour A, Ramtin A R, Abdullah A, Marzbali M H, Nowdeh S A, Kamyab H. Hybrid energy system optimization with battery storage for remote area application considering loss of energy probability and economic analysis[J]. Energy, 2022, 239: 122303. https://doi.org/10.1016/j.energy.2021.122303. doi: 10.1016/j.energy.2021.122303 URL
[30]	Abo-Elyousr F K, Elnozahy A. Bi-objective economic feasibility of hybrid micro-grid systems with multiple fuel options for islanded areas in Egypt[J]. Renew. Energy, 2018, 128: 37-56. https://doi.org/10.1016/j.renene.2018.05.066. doi: 10.1016/j.renene.2018.05.066 URL
[31]	Cai W, Li X, Maleki A, Heydari F, Moghadam A J. Optimal sizing and location based on economic parameters for an off-grid application of a hybrid system with photovoltaic, battery and diesel technology[J]. Energy, 2020, 201: 117480. https://doi.org/10.1016/j.energy.2020.117480. doi: 10.1016/j.energy.2020.117480 URL
[32]	Houssein E H, Ibrahim I E, Kharrich M, Kamel S. An improved marine predators algorithm for the optimal design of hybrid renewable energy systems[J]. Eng. Appl. Artif. Intell., 2022, 110: 104722. https://doi.org/10.1016/j.engappai.2022.104722. doi: 10.1016/j.engappai.2022.104722 URL
[33]	Mahmoudi S M, Maleki A, Rezaei Ochbelagh D. A novel method based on fuzzy logic to evaluate the storage and backup systems in determining the optimal size of a hybrid renewable energy system[J]. J. Energy Storage, 2022, 49: 104015. https://doi.org/10.1016/j.est.2022.104015. doi: 10.1016/j.est.2022.104015 URL
[34]	Davoudkhani I F, Dejamkhooy A, Nowdeh S A. A novel cloud-based framework for optimal design of stand-alone hybrid renewable energy system considering uncertainty and battery aging[J]. Appl. Energy, 2023, 344: 121257. https://doi.org/10.1016/j.apenergy.2023.121257. doi: 10.1016/j.apenergy.2023.121257 URL
[35]	Fatih Güven A, Mahmoud Samy M. Performance analysis of autonomous green energy system based on multi and hybrid metaheuristic optimization approaches. Energy Convers. Manag, 2022, 269: 116058. https://doi.org/10.1016/j.enconman.2022.116058. doi: 10.1016/j.enconman.2022.116058 URL
[36]	Jahannoush M, Arabi Nowdeh S. Optimal designing and management of a stand-alone hybrid energy system using meta-heuristic improved sine-cosine algorithm for Recreational Center, case study for Iran country[J]. Appl. Soft Comput., 2020, 96: 106611. https://doi.org/10.1016/j.asoc.2020.106611. doi: 10.1016/j.asoc.2020.106611 URL
[37]	Sultan H M, Menesy A S, Kamel S, Alghamdi A S, Abdel-Mawgoud A. An improved artificial ecosystem optimization algorithm for optimal configuration of a hybrid PV/WT/FC energy system[J]. Alexandria Eng. J., 2021, 60(1): 1001-1025. https://doi.org/10.1016/j.aej.2020.10.021. doi: 10.1016/j.aej.2020.10.027 URL
[38]	Mahmoudi S M, Maleki A, Rezaei Ochbelagh D. Optimization of a hybrid energy system with/without considering back-up system by a new technique based on a fuzzy logic controller[J]. Energy Convers. Manag., 2021, 229: 113723. https://doi.org/10.1016/j.enconman.2020.113723. doi: 10.1016/j.enconman.2020.113723 URL
[39]	El-Sattar H A, Kamel S, Hassan M H, Jurado F. An effective optimization strategy for design of standalone hybrid renewable energy systems[J]. Energy, 2022, 260: 124901. https://doi.org/10.1016/j.energy.2022.124901. doi: 10.1016/j.energy.2022.124901 URL
[40]	Bouaouda A, Sayouti Y. An optimal sizing framework of a microgrid system with hydrogen storage considering component availability and system scalability by a novel approach based on quantum theory[J]. J. Energy Storage, 2024, 92: 111894. https://doi.org/10.1016/j.est.2024.111894. doi: 10.1016/j.est.2024.111894 URL
[41]	Sun H Y, Ebadi A G, Toughani M, et al. Designing framework of hybrid photovoltaic-biowaste energy system with hydrogen Storage Considering Economic and Technical Indices Using Whale Optimization Algorithm[J]. Energy, 2022, 238: 121555. https://doi.org/10.1016/j.energy.2021.121555. doi: 10.1016/j.energy.2021.121555 URL
[42]	Pavlyukevich I. Lévy flights, non-local search and simulated annealing[J]. J. Comput. Phys., 2007, 226(2): 1830-1844. https://doi.org/10.1016/j.jcp.2007.06.011. doi: 10.1016/j.jcp.2007.06.008 URL
[43]	Yang X S, Deb S. Cuckoo search via Lévy flights[C]// 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). Piscataway, NJ: IEEE Press, 2009: 210-214. https://doi.org/10.1109/NABIC.2009.5393690.
[44]	Goldstein A A. Cauchy’s method of minimization[J]. Numer. Math., 1962, 4: 146-150. https://doi.org/10.1007/BF01386351. doi: 10.1007/BF01386306 URL
[45]	Juszczuk P, Kruś L. Soft multicriteria computing supporting decisions on the Forex market[J]. Appl. Soft Comput., 2020, 96: 106654. https://doi.org/10.1016/j.asoc.2020.106654. doi: 10.1016/j.asoc.2020.106654 URL
[46]	Adar M, Najih Y, Gouskir M, et al. Three PV plants performance analysis using the principal component analysis method[J]. Energy, 2020, 207: 118315. https://doi.org/10.1016/j.energy.2020.118315. doi: 10.1016/j.energy.2020.118315 URL

Config Name	Ns	Np	Vmp;Imp;Pmp (200 W·m^-2)	Vmp;Imp;Pmp (400 W·m^-2)	Vmp;Imp;Pmp (600 W·m^-2)	Vmp;Imp;Pmp (800 W·m^-2)
Electrolyzer A	5	1	12.16 V; 0.64 A; 7.82 W	13.53; 1.31; 17.68	14.33; 1.98; 28.33	14.91; 2.65; 39.51
Electrolyzer B	6	1	12.14 V; 0.65 A; 7.91 W	13.49; 1.32; 17.82	14.30; 1.99; 28.47	14.87; 2.66; 39.58
Electrolyzer C	7	1	7.91 W; 0.64 A; 7.82 W	13.53; 1.31; 17.68	14.33; 1.98; 28.33	14.91; 2.65; 39.51
Electrolyzer D	8	1	12.18 V; 0.63 A; 7.66 W	13.55; 1.30; 17.65	14.35; 1.97; 28.30	14.90; 2.63; 39.50
Electrolyzer E	9	1	12.20 V; 0.62 A; 7.58 W	13.57; 1.29; 17.55	14.37; 1.96; 28.20	14.92; 2.62; 39.20
Best Configuration	7	1	12.16 V; 0.64 A; 7.82 W	13.53; 1.31; 17.68	14.33; 1.98; 28.33	14.91; 2.65; 39.51