Optimal Sizing and Economic Assessment of a Hydrogen-Based Hybrid Renewable Energy System

Öz

The growing emphasis on energy independence and sustainability has led to increased interest in hybrid renewable energy systems (HRESs) supported by long-term energy storage solutions. In this study, the optimal sizing of a standalone hybrid renewable energy system consisting of photovoltaic modules, wind turbines, an electrolyzer, hydrogen storage tanks and a fuel cell is examined. A comprehensive techno-economic optimization model is developed with the objective of reducing the Net Present Cost (NPC) and the Cost of Energy (COE) while ensuring an acceptable level of system reliability. To solve the optimization problem, three widely used metaheuristic techniques (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA)) are implemented and comparatively evaluated based on their convergence characteristics and economic outcomes. The comparative analysis reveals that although all algorithms produce technically feasible solutions, PSO yields the most economical configuration with the lowest NPC and COE values. The findings demonstrate that the proposed optimization framework provides an effective and practical design approach for hydrogen-supported HRESs, supporting the development of reliable and sustainable off-grid power applications.

Anahtar Kelimeler

• Hybrid renewable energy system
• Hydrogen-based energy storage
• Optimal sizing

Hidrojen Tabanlı Hibrit Yenilenebilir Enerji Sisteminin Optimum Boyutlandırılması ve Ekonomik Değerlendirilmesi

Öz

Enerji bağımsızlığı ve sürdürülebilirliğe verilen önemin artması, uzun süreli enerji depolama çözümleriyle desteklenen hibrit yenilenebilir enerji sistemlerine (HRES) olan ilgiyi artırmıştır. Bu çalışmada, fotovoltaik modüller, rüzgâr türbinleri, bir elektrolizör, hidrojen depolama tankları ve bir yakıt hücresinden oluşan bağımsız bir hibrit yenilenebilir enerji sisteminin optimal boyutlandırılması incelenmektedir. Kabul edilebilir bir sistem güvenilirliği düzeyini sağlarken Net Bugünkü Maliyet (NPC) ve Enerji Maliyeti (COE) değerlerini azaltmayı amaçlayan kapsamlı bir tekno-ekonomik optimizasyon modeli geliştirilmiştir. Optimizasyon problemini çözmek için yaygın olarak kullanılan üç meta-sezgisel yöntem (Genetik Algoritma (GA), Parçacık Sürü Optimizasyonu (PSO) ve Tavlama Benzetimi (SA)) uygulanmış ve yakınsama özellikleri ile ekonomik sonuçları açısından karşılaştırmalı olarak değerlendirilmiştir. Karşılaştırmalı analiz, tüm algoritmaların teknik olarak uygulanabilir çözümler üretmesine rağmen, en düşük NPC ve COE değerleriyle en ekonomik konfigürasyonu PSO yönteminin sağladığını ortaya koymaktadır. Elde edilen bulgular, önerilen optimizasyon çerçevesinin hidrojen destekli HRES’lerin tasarımı için etkili ve pratik bir yaklaşım sunduğunu ve güvenilir ile sürdürülebilir şebekeden bağımsız enerji uygulamalarının geliştirilmesini desteklediğini göstermektedir.

Anahtar Kelimeler

• Hibrit yenilenebilir enerji sistemi
• Hidrojen tabanlı enerji depolama
• Optimum boyutlandırma

Kaynakça

1. [1] Ngouleu C.A.W., Koholé Y.W., Fohagui F.C.V., and Tchuen G., Techno-economic analysis and optimal sizing of a battery-based and hydrogen-based standalone photovoltaic/wind hybrid system for rural electrification in Cameroon based on meta-heuristic techniques, Energy Conversion and Management, 2023, 280, pp. 116794, DOI: 10.1016/j.enconman.2023.116794.
2. [2] Bacha B., Ghodbane H., Dahmani H., Betka A., Toumi A., and Chouder A., Optimal sizing of a hybrid microgrid system using solar, wind, diesel, and battery energy storage to alleviate energy poverty in a rural area of Biskra, Algeria, Journal of Energy Storage, 2024, 84, pp. 110651, DOI: 10.1016/j.est.2024.110651.
3. [3] Chen Q., Kuang Z., Liu X., and Zhang T., Optimal sizing and techno-economic analysis of the hybrid PV-battery-cooling storage system for commercial buildings in China, Applied Energy, 2024, 355, pp. 122231, DOI: 10.1016/j.apenergy.2023.122231.
4. [4] Abdoulaye M.A., Waita S., Wekesa C.W., and Mwabora J.M., Optimal sizing of an off-grid and grid-connected hybrid photovoltaic–wind system with battery and fuel cell storage system: A techno-economic, environmental, and social assessment, Applied Energy, 2024, 365, pp. 123201, DOI: 10.1016/j.apenergy.2023.123201.
5. [5] Fabianek P. and Madlener R., Techno-economic analysis and optimal sizing of hybrid PV–wind systems for hydrogen production by PEM electrolysis in California and Northern Germany, International Journal of Hydrogen Energy, 2024, 67, pp. 1157–1172, DOI: 10.1016/j.ijhydene.2023.10.072.
6. [6] Araoye T.O., Ashigwuike E.C., Mbunwe M.J., Bakinson O.I., and Ozue T.I., Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm, Renewable Energy, 2024, 229, pp. 120712, DOI: 10.1016/j.renene.2024.120712.
7. [7] Al-Shalabi M., Shehab M., Alshammari M.T., Alazmi M., Alrawashdeh R.O., Abualigah L., and Mahdi M.A., Optimal sizing of smart hybrid renewable energy system using Lotus Effect Optimization Algorithm, Energy Reports, 2025, 14, pp. 1936–1948, DOI: 10.1016/j.egyr.2024.12.086.
8. [8] Pamuk N., Techno-economic feasibility analysis of grid configuration sizing for hybrid renewable energy system in Turkey using different optimization techniques, Ain Shams Engineering Journal, 2024, 15, pp. 102474, DOI: 10.1016/j.asej.2023.102474.

9. [9] Nassar Y.F., Abdunnabi M.J., Sbeta M.N., Hafez A.A., Amer K.A., Ahmed A.Y., and Belgasim B., Dynamic analysis and sizing optimization of a pumped hydroelectric storage-integrated hybrid PV/Wind system: A case study, Energy Conversion and Management, 2021, 229, pp. 113744, DOI: 10.1016/j.enconman.2020.113744.
10. [10] Ekren O., Canbaz C.H., and Güvel Ç.B., Sizing of a solar–wind hybrid electric vehicle charging station by using HOMER software, Journal of Cleaner Production, 2021, 279, pp. 123615, DOI: 10.1016/j.jclepro.2020.123615.
11. [11] Oyewole O.L., Nwulu N.I., and Okampo E.J., Optimal design of hydrogen-based storage with a hybrid renewable energy system considering economic and environmental uncertainties, Energy Conversion and Management, 2024, 300, pp. 117991, DOI: 10.1016/j.enconman.2024.117991.
12. [12] Winsly B.W., Ramalingam V.K., Santhappan J.S., Maria A.J., Chokkalingam M.P., and Rajendran V., Optimal sizing and techno-economicenvironmental evaluation of biomass–solar–wind–grid hybrid energy system: A case study of an institute in South India, Energy Conversion and Management, 2025, 325, pp. 119352, DOI: 10.1016/j.enconman.2025.119352.
13. [13] Talla Konchou F.A., Djeudjo Temene H., Tchinda R., and Njomo D., Techno-economic and environmental design of an optimal hybrid energy system for a community multimedia centre in Cameroon, SN Applied Sciences, 2021, 3(1), pp. 127, DOI: 10.1007/s42452-021-04151-0.
14. [14] Kaabeche A. and Diaf S., Optimisation du dimensionnement d’un système hybride photovoltaïque/éolien par des méthodes méta heuristiques, Journal of Renewable Energies, 2017, 20(2), pp. 267–284, DOI: 10.54966/jreen.v20i2.626.
15. [15] Maleki A. and Askarzadeh A., Comparative study of artificial intelligence techniques for sizing of a hydrogen-based stand-alone photovoltaic/wind hybrid system, International Journal of Hydrogen Energy, 2014, 39(19), pp. 9973–9984, DOI: 10.1016/j.ijhydene.2014.04.147.
16. [16] Nasser M., Megahed T.F., Ookawara S., and Hassan H., Performance evaluation of PV panels/wind turbines hybrid system for green hydrogen generation and storage: Energy, exergy, economic, and enviroeconomic, Energy Conversion and Management, 2022, 267, pp. 115870, DOI: 10.1016/j.enconman.2022.115870.
17. [17] Holland J.H., Adaptation in Natural and Artificial Systems, 1975, University of Michigan Press.
18. [18] Kennedy J. and Eberhart R., Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, 1995, 4, pp. 1942–1948.
19. [19] Kirkpatrick S., Gelatt C.D., and Vecchi M.P., Optimization by simulated annealing, Science, 1983, 220(4598), pp. 671–680.
20. [20] IED, Plan Directeur d’Electrification Rurale du Cameroun, 2016, Available from: https://rise.esmap.org/data/files/library/cameroon/Documents/Energy%20Access/Cameroon_PDER.pdf