Systematic Literature Review: Trends in the Development of Hybrid-Based Renewable Energy Systems in Mechanical Engineering

This study addresses the growing need for efficient and sustainable energy systems through the development of Hybrid Renewable Energy Systems (HRES), which integrate multiple renewable sources with advanced storage technologies to overcome intermittency and reliability issues. The research employs a quantitative modeling and simulation approach, utilizing secondary data on renewable resources, load demand, and component specifications, combined with simulation tools such as HOMER Pro and MATLAB. A multi-objective optimization framework based on metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), is applied to determine optimal system configurations. The results indicate that hydrogen-based configurations provide the highest reliability and lowest emissions, while biomass-based systems offer lower costs but higher environmental impact. Sensitivity analysis reveals that fuel price, load demand, and renewable resource availability significantly influence system performance. The discussion highlights the importance of integrating diverse storage technologies and adopting holistic optimization approaches that consider techno-economic, environmental, and resilience factors. In conclusion, the proposed framework effectively enhances HRES design by producing optimal and realistic solutions, thereby contributing to the advancement of sustainable and resilient energy systems.

1. Agajie, T., Ali, A., Fopah-Lele, A., Amoussou, I., Khan, B., Velasco, C., & Tanyi, E. (2023). A comprehensive review on techno-economic analysis and optimal sizing of hybrid renewable energy sources with energy storage systems. Energies. https://doi.org/10.3390/en16020642
2. Atawi, I., Abuelrub, A., Al‐Shetwi, A., & Albalawi, O. (2024). Design of a wind-PV system integrated with a hybrid energy storage system considering economic and reliability assessment. Journal of Energy Storage. https://doi.org/10.1016/j.est.2023.110405
3. Bamisile, O., Cai, D., Adun, H., Dagbasi, M., Ukwuoma, C., Huang, Q., Johnson, N., & Bamisile, O. (2024). Towards renewables development: Review of optimization techniques for energy storage and hybrid renewable energy systems. Heliyon, 10. https://doi.org/10.1016/j.heliyon.2024.e37482
4. Basnet, S., Deschinkel, K., Moyne, L., & Péra, M. (2023). A review on recent standalone and grid integrated hybrid renewable energy systems: System optimization and energy management strategies. Renewable Energy Focus. https://doi.org/10.1016/j.ref.2023.06.001
5. Egeland-Eriksen, T., Hajizadeh, A., & Sartori, S. (2021). Hydrogen-based systems for integration of renewable energy in power systems: Achievements and perspectives. International Journal of Hydrogen Energy. https://doi.org/10.1016/j.ijhydene.2021.06.218
6. Gómez, J., De León Aldaco, S., & Alquicira, J. (2023). A review of hybrid renewable energy systems: Architectures, battery systems, and optimization techniques. Eng. https://doi.org/10.3390/eng4020084
7. Gusain, C., Tripathi, M., & Nangia, U. (2023). Study of meta-heuristic optimization methodologies for design of hybrid renewable energy systems. Thermal Science and Engineering Progress. https://doi.org/10.1016/j.tsep.2023.101711
8. Hassan, R., Das, B., & Hasan, M. (2022). Integrated off-grid hybrid renewable energy system optimization based on economic, environmental, and social factors for sustainable development. Energy. https://doi.org/10.1016/j.energy.2022.123823
9. Jia, K., Liu, C., Li, S., & Jiang, D. (2023). Modeling and optimization of a hybrid renewable energy system integrated with gas turbine and energy storage. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2023.116763
10. Kallio, S., & Siroux, M. (2021). Hybrid renewable energy systems based on micro-cogeneration. Energy Reports. https://doi.org/10.1016/j.egyr.2021.11.158
11. Khaled, K. (2025). A review on designing hybrid energy systems for renewable integration. Metaheuristic Optimization Review. https://doi.org/10.54216/mor.030203
12. Khan, A., Minai, A., Pachauri, R., & Malik, H. (2022). Optimal sizing, control, and management strategies for hybrid renewable energy systems: A comprehensive review. Energies. https://doi.org/10.3390/en15176249
13. Medghalchi, Z., & Taylan, O. (2023). A novel hybrid optimization framework for sizing renewable energy systems integrated with energy storage systems with solar photovoltaics, wind, battery and electrolyzer-fuel cell. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2023.117594
14. Ramesh, T., & Balachander, K. (2024). Optimizing hybrid renewable energy systems with integrated electric vehicle using a hybrid approach. Journal of Energy Storage. https://doi.org/10.1016/j.est.2024.111655
15. Salman, M., Kashif, S., Fakhar, M., Rasool, A., & Hussen, A. (2025). Optimizing power generation in a hybrid solar wind energy system using a DFIG-based control approach. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-95248-8
16. Shi, Q., Sun, Z., Zhang, Z., & Lee, C. (2021). Triboelectric nanogenerators and hybridized systems for enabling next-generation IoT applications. Research, 2021. https://doi.org/10.34133/2021/6849171
17. Talebi, S., & Aly, H. (2025). Optimized renewable energy integration: Advanced modeling, control, and design of a standalone microgrid using hybrid FA-PSO. IEEE Access, 13, 63486–63503. https://doi.org/10.1109/access.2025.3559725
18. Uc, D., De León Aldaco, S., & Alquicira, J. (2024). Trends in hybrid renewable energy system (HRES) applications: A review. Energies. https://doi.org/10.3390/en17112578
19. Yahya, W., Saied, K., Nassar, A., Qader, M., Al-Nehari, M., Zarabia, J., & Jian, Z. (2024). Optimization of a hybrid renewable energy system consisting of PV/wind turbine/battery/fuel cell integration and component design. International Journal of Hydrogen Energy. https://doi.org/10.1016/j.ijhydene.2024.11.187
20. Yan, R., Wang, J., Huo, S., Qin, Y., Zhang, J., Tang, S., Wang, Y., Liu, Y., & Zhou, L. (2022). Flexibility improvement and stochastic multi-scenario hybrid optimization for an integrated energy system with high-proportion renewable energy. Energy. https://doi.org/10.1016/j.energy.2022.125779