Hybrid Renewable Energy System Integration with Grid – A Review
Keywords: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.
Chauhan, A., & Saini, R. P. (2014). A review on Integrated Renewable Energy System based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control. Renewable and Sustainable Energy Reviews, 38, 99–120. https://doi.org/10.1016/j.rser.2014.05.079
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
Copyright (c) 2021 Aftab Alam, Md. Varsha Mehar
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.