Hybrid Renewable Energy System Integration with Grid – A Review


  • Aftab Alam
  • Md. Varsha Mehar




Hybrid Energy Systems, Distributed Generators, Microgrids, Differential Evolutionary Algorithm


In contrast to a framework rely on a specific source, hybrid power system systems (HESs) integrate various initiating, stockpiling, and consuming methodologies into a single system, enhancing overall benefits. Originally intended encompass long - established, depletable generation (e.g., diesel generators) and battery storage (BESSs), their definition was been expanded encompass mechanisms that are completely fueled by renewable energy [e.g., solar photovoltaics (PV) and wind], as well as constructions that combine different energy storage systems. This paper provides the systematic literature review relying on the usage of differential evolutionary algorithm to maintain the load demand in hybrid energy systems.


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Author Biographies

Aftab Alam

 M. Tech Scholar

Bhabha College of Engineering RKDF University
Bhopal, Madhya Pradesh, India

Md. Varsha Mehar

Assistant Professor

Bhabha College of Engineering RKDF University
Bhopal, Madhya Pradesh, India


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Fadaee, M., & Radzi, M. A. M. (2012). Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review. Renewable and Sustainable Energy Reviews, 16(5), 3364–3369. https://doi.org/10.1016/j.rser.2012.02.071

Fathima, A. H., & Palanisamy, K. (2015). Optimization in microgrids with hybrid energy systems - A review. Renewable and Sustainable Energy Reviews, 45, 431–446. https://doi.org/10.1016/j.rser.2015.01.059

Ramli, M. A. M., Bouchekara, H. R. E. H., & Alghamdi, A. S. (2018). Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm. Renewable Energy, 121, 400–411. https://doi.org/10.1016/j.renene.2018.01.058

Bilal, Pant, M., Zaheer, H., Garcia-Hernandez, L., & Abraham, A. (2020). Differential Evolution: A review of more than two decades of research. Engineering Applications of Artificial Intelligence, 90(January), 103479. https://doi.org/10.1016/j.engappai.2020.103479

Zahraee, S. M., Khalaji Assadi, M., & Saidur, R. (2016). Application of Artificial Intelligence Methods for Hybrid Energy System Optimization. Renewable and Sustainable Energy Reviews, 66, 617–630. https://doi.org/10.1016/j.rser.2016.08.028

Upadhyay, S., & Sharma, M. P. (2014). A review on configurations, control and sizing methodologies of hybrid energy systems. Renewable and Sustainable Energy Reviews, 38, 47–63. https://doi.org/10.1016/j.rser.2014.05.057

Bhandari, B., Poudel, S. R., Lee, K. T., & Ahn, S. H. (2014). Mathematical modeling of hybrid renewable energy system: A review on small hydro-solar-wind power generation. International Journal of Precision Engineering and Manufacturing - Green Technology, 1(2), 157–173. https://doi.org/10.1007/s40684-014-0021-4




How to Cite

Alam, A. ., & Mehar, M. V. . (2021). Hybrid Renewable Energy System Integration with Grid – A Review. SMART MOVES JOURNAL IJOSCIENCE, 7(11), 78–82. https://doi.org/10.24113/ijoscience.v7i11.459