Optimization Techniques for Hybrid Renewable Energy Systems
A Review of Current Developments and Future Research Directions
Main Article Content
Abstract
This literature review provides a comprehensive analysis of optimization techniques applied to hybrid renewable energy systems (HRES) across diverse geographical locations and applications. The review examines multiple optimization methodologies including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Mixed Integer Linear Programming (MILP), hybrid algorithms, and novel bio-inspired metaheuristic approaches. Performance metrics analyzed include Cost of Energy (COE), Net Present Cost (NPC), Life Cycle Cost (LCC), Loss of Power Supply Probability (LPSP), Renewable Fraction (RF), and CO₂ emissions reduction. The review examined relevant studies published between 2014 and 2025, identified through searches of major academic databases using predefined keywords related to HRES optimization, system sizing, energy management, metaheuristic optimization, and technical, economic, reliability, and environmental performance. The reviewed studies encompass various system configurations incorporating photovoltaic panels, wind turbines, battery storage, diesel generators, small hydro power, fuel cells, tidal energy, and hydrogen storage systems. Findings indicate that while traditional GA and PSO methods remain widely used, MILP and hybrid algorithms demonstrate superior performance in achieving optimal solutions with reduced computational time. Simulation-based tools such as HOMER software continue to be valuable for system design and optimization. However, significant research gaps persist, including limited evaluation of system reliability, incomplete consideration of all renewable sources, and insufficient comparative analysis between different optimization techniques. The integration of artificial intelligence and novel bio-inspired algorithms presents promising opportunities for advancing HRES optimization, particularly for microgrid applications and systems operating under dynamic weather conditions.
Article Details
Copyright (c) 2026 Gbopa A. R, Adeyemo I. A, Adepoju G. A, Ogundeji O. A (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abd El-Sattar, H., Kamel, S., Hashim, F. A., & Sabbeh, S. F. (2024). Optihybrid: A modified firebug swarm optimization algorithm for optimal sizing of hybrid renewable power system. Neural Computing and Applications, 36, 21517–21543. https://doi.org/10.1007/s00521-024-10196-0 DOI: https://doi.org/10.1007/s00521-024-10196-0
Adebanji, B., Adepoju, G. A., Oni, J. O., & Olulope, P. K. (2017). Optimal sizing of an off-grid small hydro-photovoltaic-diesel generator hybrid power system for a distant village. International Journal of Scientific and Technology Research, 6(8), 208–213.
Alberizzi, J. C., Buratti, C., & Moretti, E. (2020). Optimal sizing of an off-grid hybrid renewable energy system for a remote mountain hut using mixed integer linear programming. Energy, 196, 117082. https://doi.org/10.1016/j.energy.2020.117082 DOI: https://doi.org/10.1016/j.egyr.2019.08.012
Al-Shamma'a, A. A., & Addoweesh, K. E. (2014). Techno-economic optimization of hybrid power system using genetic algorithm. International Journal of Energy Research, 38, 1608–1623. https://doi.org/10.1002/er.3191 DOI: https://doi.org/10.1002/er.3191
Anoune, K., Bouya, M., Astito, A., & Ben Abdellah, A. (2018). Sizing methods and optimization techniques for PV-wind based hybrid renewable energy system: A review. Renewable and Sustainable Energy Reviews, 93, 652–673. https://doi.org/10.1016/j.rser.2018.05.032 DOI: https://doi.org/10.1016/j.rser.2018.05.032
Ansari, M. S., Jalil, M. F., & Bansal, R. C. (2023). A review of optimization techniques for hybrid renewable energy systems. International Journal of Modelling and Simulation, 43(5), 722–735. https://doi.org/10.1080/02286203.2022.2119524 DOI: https://doi.org/10.1080/02286203.2022.2119524
Bansal, A. K. (2022). Sizing and forecasting techniques in photovoltaic-wind based hybrid renewable energy system: A review. Journal of Cleaner Production, 369, 133376. https://doi.org/10.1016/j.jclepro.2022.133376 DOI: https://doi.org/10.1016/j.jclepro.2022.133376
Bouaouda, A., & Sayouti, Y. (2022). Hybrid meta-heuristic algorithms for optimal sizing of hybrid renewable energy system: A review of the state-of-the-art. Archives of Computational Methods in Engineering, 29(6), 4049–4083, https://doi.org/10.1007/s11831-022-09730-x. DOI: https://doi.org/10.1007/s11831-022-09730-x
Dianabasi, E., Udofia, K., & Okpura, N. (2020). Modelling and optimization of cost function for hybrid power generation system using genetic algorithm. International Journal of Power Systems, 5, 40–48.
Eiben, A. E., & Smith, J. E. (2015). Introduction to evolutionary computing (2nd ed.). Springer. https://doi.org/10.1007/978-3-662-44874-8 DOI: https://doi.org/10.1007/978-3-662-44874-8
Emad, D., El-Hameed, M. A., Yousef, M. T., & El-Fergany, A. A. (2020). Computational methods for optimal planning of hybrid renewable micro grids: A comprehensive review and challenges. Archives of Computational Methods in Engineering, 27(4), 1297–1319. https://doi.org/10.1007/s11831-019-09353-9 DOI: https://doi.org/10.1007/s11831-019-09353-9
Gokul, B., Suresh, M., & Meenakumari, R. (2017). Optimal sizing of hybrid system for stand-alone application using genetic algorithm. World Journal of Engineering Research and Technology, 3(2), 45–56.
Gusain, C., Tripathi, M. M., & Nangia, U. (2023). Study of meta-heuristic optimization methodologies for design of hybrid renewable energy systems. Thermal Science and Engineering Progress, 39, 101711. https://doi.org/10.1016/j.tsep.2023.101711 DOI: https://doi.org/10.1016/j.tsep.2023.101711
Haddad, M., Nicod, J., Varnier, C., & Péra, M. (2019). Mixed integer linear programming approach to optimize the hybrid renewable energy system management for supplying a stand-alone data center. Tenth International Green and Sustainable Computing Conference, Alexandria, United States. Hal-02867786 DOI: https://doi.org/10.1109/IGSC48788.2019.8957199
Khan, A. A., Minai, A. F., Pachauri, R. K., & Malik, H. (2022). Optimal sizing, control, and management strategies for hybrid renewable energy systems: A comprehensive review. Energies, 15(17), 6249. DOI: https://doi.org/10.3390/en15176249
Khatri, K. C. A., Shah, K. B., Logeshwaran, J., & Shrestha, A. (2023). Genetic algorithm-based techno-economic optimization of an isolated hybrid energy system. ICTACT Journal on Microelectronics, 8(4), 1447–1450. https://doi.org/10.21917/ijme.2023.0249 DOI: https://doi.org/10.21917/ijme.2023.0249
Kirkpatrick, S., Gelatt, C. D., Jr., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. https://doi.org/10.1126/science.220.4598.671 DOI: https://doi.org/10.1126/science.220.4598.671
Maca-Chagüendo, M., and Cobos-Lozada, C.-A. (2024). A Systematic Review of Machine Learning-Enhanced Metaheuristics for Solving Capacitated Vehicle Routing Problems. Revista Facultad de Ingeniería, 33(68): 18379. DOI: https://doi.org/10.19053/uptc.01211129.v33.n68.2024.18379
Mellouk, L., Ghazi, M., Aaroud, A., Boulmalf, M., Benhaddou, D., & Zine-Dine, K. (2019). Design and energy management optimization for hybrid renewable energy system—Case study: Laayoune region. Renewable Energy, 139, 621–634. https://doi.org/10.1016/j.renene.2019.02.066 DOI: https://doi.org/10.1016/j.renene.2019.02.066
Memon, S. A., & Patel, R. N. (2021). An overview of optimization techniques used for sizing of hybrid renewable energy systems. Renewable Energy Focus, 39, 1–26. https://doi.org/10.1016/j.ref.2021.07.007 DOI: https://doi.org/10.1016/j.ref.2021.07.007
Mquqwana, M. A., & Krishnamurthy, S. (2024). Particle swarm optimization for an optimal hybrid renewable energy microgrid system under uncertainty. Energies, 17(2), 422. https://doi.org/10.3390/en17020422 DOI: https://doi.org/10.3390/en17020422
Naderi, E., Dejamkhoo, A., Seyedshenava, S. J., & Shayeghi, H. (2022). MILP based optimal design of hybrid microgrid by considering statistical wind estimation and demand response. Journal of Operation and Automation in Power Engineering, 10(1), 54–65.
Ogunjuyigbe, A. S. O., Ayodele, T. R., & Akinola, O. A. (2016). Optimal allocation and sizing of PV/wind/split-diesel/battery energy system for minimizing life cycle cost, carbon emissions and dump energy of remote residential building. Applied Energy, 171, 153–171. DOI: https://doi.org/10.1016/j.apenergy.2016.03.051
Omar, J., Aglzim, S., Chikh, A., & Ahmed, A. B. (2018). Economical evaluation and optimal energy management of a stand-alone hybrid energy system wind/PV/tidal/storage battery based on genetic algorithm. Electronics, 7(10), Article 233 https://doi.org/10.3390/electronics7100233 DOI: https://doi.org/10.3390/electronics7100233
Oyewole, O. L., Nwulu, N. I., & Okampo, E. J. (2024). Optimal design of hydrogen-based storage with a hybrid renewable energy system considering economic and environmental uncertainties. Energy Conversion and Management, 300, 117991. https://doi.org/10.1016/j.enconman.2023.117991 DOI: https://doi.org/10.1016/j.enconman.2023.117991
Pookpunt, S. (2019). Optimal design for hybrid renewable energy system using particle swarm optimization. International Journal of Renewable Energy Research, 9(4), 1616–1625.
Ramadan, R. (2025). A Comprehensive Review of Metaheuristic Algorithms: Classification, Applications, and Future Directions. International Journal of Computers and Informatics, 6: 64–69
Rathod, A. A., & Balaji, S. (2024). Modified Harris Hawks optimization for the 3E feasibility assessment of a hybrid renewable energy system. Scientific Reports, 14, 20127. https://doi.org/10.1038/s41598-024-70663-5 DOI: https://doi.org/10.1038/s41598-024-70663-5
Saber Rashid, N., and Zebari, I. M. I. (2025). A Comprehensive Review of Metaheuristic Algorithms for Combinatorial Optimization Problems. International Journal of Scientific World, 11(1): 83–92. https://doi.org/10.14419/d5pxkg39 DOI: https://doi.org/10.14419/d5pxkg39
Shezan, S. K. A., & Ping, H. W. (2017). Techno-economic and feasibility analysis of a hybrid PV-wind-biomass-diesel energy system for sustainable development at offshore areas in Bangladesh. Current Alternative Energy, 1(1), 20–32. https://doi.org/10.2174/2405463101666160531145048 DOI: https://doi.org/10.2174/2405463101666160531145048
Singh, A., Sharma, A., Rajput, S., Bose, A., & Hu, X. (2022). An investigation on hybrid particle swarm optimization algorithms for parameter optimization of PV cells. Electronics, 11(6), Article 909. https://doi.org/10.3390/electronics11060909 DOI: https://doi.org/10.3390/electronics11060909
Singh, S., Singh, M., & Kaushik, S. C. (2016). A review on optimization techniques for sizing of solar-wind hybrid energy systems. International Journal of Green Energy, 13(15), 1564–1578. https://doi.org/10.1080/15435075.2016.1207079 DOI: https://doi.org/10.1080/15435075.2016.1207079
Sobti, R., & Anjaneyulu, M. (2024). Hybrid renewable energy microgrids: A genetic algorithm approach to system design. MATEC Web of Conferences, 392, 01182. https://doi.org/10.1051/matecconf/202439201182 DOI: https://doi.org/10.1051/matecconf/202439201182
Taghizad-Tavana, K., Esmaeel Nezhad, A., Hagh, M. T., Canani, A., & Safari, A. (2025). Hybrid renewable energy systems for off-grid electrification: A comprehensive review of storage technologies, metaheuristic optimization approaches and key challenges. Eng, 6(11), 309. https://doi.org/10.3390/eng6110309 DOI: https://doi.org/10.3390/eng6110309
Talbi, E.-G. (2009). Metaheuristics: From design to implementation. Wiley. DOI: https://doi.org/10.1002/9780470496916
Thirunavukkarasu, M., Sawle, Y., & Lala, H. (2023). A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques. Renewable and Sustainable Energy Reviews, 176, 113192. https://doi.org/10.1016/j.rser.2023.113192 DOI: https://doi.org/10.1016/j.rser.2023.113192
Toughzaoui, Y., Elkhatib, R., Kaoutari, T., Louahlia, H., Chaoui, H., & Gualous, H. (2024). Advances in hospital energy systems: Genetic algorithm optimization of a hybrid solar and hydrogen fuel cell combined heat and power. International Journal of Hydrogen Energy, 83, 1319–1325. DOI: https://doi.org/10.1016/j.ijhydene.2024.09.001
Twahaa, S., and Ramlib, M. A. M. (2018). Metaheuristic Optimization Techniques for Renewable Energy Systems: A Review. Renewable and Sustainable Energy Reviews, 93: 274–289. https://doi.org/10.1016/j.rser.2018.05.020 DOI: https://doi.org/10.1016/j.rser.2018.05.020
Ukoima, K. N., Okoro, O. I., Obi, P. I., Akuru, U. B., & Davidson, I. E. (2024). Optimal sizing, energy balance, load management and performance analysis of a hybrid renewable energy system. Energies, 17(21), 5275. https://doi.org/10.3390/en17215275 DOI: https://doi.org/10.3390/en17215275
Yahya, W., Saied, K. M., Nassar, A., Qader, M. R., 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, 94, 1406–1418. https://doi.org/10.1016/j.ijhydene.2024.11.187 DOI: https://doi.org/10.1016/j.ijhydene.2024.11.187
Article Sidebar
