Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer
Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids...
- Autores:
-
Rajagopalan, Arul
Nagarajan, Karthik
Montoya, Oscar Danilo
Dhanasekaran, Seshathiri
Kareem, Inayathullah Abdul
Perumal, Angalaeswari Sendraya
Lakshmaiya, Natrayan
Paramasivam, Prabhu
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12396
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12396
- Palabra clave:
- Grid;
Power Sharing;
Inverters
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer |
| title |
Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer |
| spellingShingle |
Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer Grid; Power Sharing; Inverters LEMB |
| title_short |
Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer |
| title_full |
Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer |
| title_fullStr |
Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer |
| title_full_unstemmed |
Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer |
| title_sort |
Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer |
| dc.creator.fl_str_mv |
Rajagopalan, Arul Nagarajan, Karthik Montoya, Oscar Danilo Dhanasekaran, Seshathiri Kareem, Inayathullah Abdul Perumal, Angalaeswari Sendraya Lakshmaiya, Natrayan Paramasivam, Prabhu |
| dc.contributor.author.none.fl_str_mv |
Rajagopalan, Arul Nagarajan, Karthik Montoya, Oscar Danilo Dhanasekaran, Seshathiri Kareem, Inayathullah Abdul Perumal, Angalaeswari Sendraya Lakshmaiya, Natrayan Paramasivam, Prabhu |
| dc.subject.keywords.spa.fl_str_mv |
Grid; Power Sharing; Inverters |
| topic |
Grid; Power Sharing; Inverters LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model’s optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former’s efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction. © 2022 by the authors. |
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2022 |
| dc.date.issued.none.fl_str_mv |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2023-07-21T20:50:56Z |
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2023-07-21T20:50:56Z |
| dc.date.submitted.none.fl_str_mv |
2023 |
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info:eu-repo/semantics/article |
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draft |
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Rajagopalan, A., Nagarajan, K., Montoya, O. D., Dhanasekaran, S., Kareem, I. A., Perumal, A. S., ... & Paramasivam, P. (2022). Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer. Energies, 15(23), 9024. |
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https://hdl.handle.net/20.500.12585/12396 |
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10.3390/en15239024 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Rajagopalan, A., Nagarajan, K., Montoya, O. D., Dhanasekaran, S., Kareem, I. A., Perumal, A. S., ... & Paramasivam, P. (2022). Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer. Energies, 15(23), 9024. 10.3390/en15239024 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12396 |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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openAccess |
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24 páginas |
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Cartagena de Indias |
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Rajagopalan, Arul6d04d6b3-17a1-49be-a90b-6ea66be6d1c6Nagarajan, Karthik25d92d9a-eed0-4d52-b32e-7bc44bf5e0f8Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Dhanasekaran, Seshathirib58811a3-16d3-4287-9957-841ec20e841aKareem, Inayathullah Abdulbb05b1f4-f4ee-4034-a892-36f1067264baPerumal, Angalaeswari Sendrayaf1424917-7a53-42bd-907a-37aed57b6ddcLakshmaiya, Natrayan31b4ebcf-9f0e-4107-9ddc-e3d142bf3967Paramasivam, Prabhu77b4465a-a5c3-4f55-b649-2c54235ded2b2023-07-21T20:50:56Z2023-07-21T20:50:56Z20222023Rajagopalan, A., Nagarajan, K., Montoya, O. D., Dhanasekaran, S., Kareem, I. A., Perumal, A. S., ... & Paramasivam, P. (2022). Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer. Energies, 15(23), 9024.https://hdl.handle.net/20.500.12585/1239610.3390/en15239024Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarOptimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model’s optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former’s efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction. © 2022 by the authors.24 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2EnergiesMulti-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizerinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Grid;Power Sharing;InvertersLEMBCartagena de IndiasNagarajan, K., Rajagopalan, A., Angalaeswari, S., Natrayan, L., Mammo, W.D. Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm (Open Access) (2022) Computational Intelligence and Neuroscience, 2022, art. no. 6461690. Cited 35 times. http://www.hindawi.com/journals/cin doi: 10.1155/2022/6461690Karthik, N., Parvathy, A.K., Arul, R. A review of optimal operation of microgrids (2020) International Journal of Electrical and Computer Engineering, 10 (3), pp. 2842-2849. Cited 6 times. http://ijece.iaescore.com/index.php/IJECE/article/view/12673/pdf doi: 10.11591/ijece.v10i3.pp2842-2849Konstantinopoulos, S.A., Anastasiadis, A.G., Vokas, G.A., Kondylis, G.P., Polyzakis, A. Optimal management of hydrogen storage in stochastic smart microgrid operation (2018) International Journal of Hydrogen Energy, 43 (1), pp. 490-499. Cited 45 times. http://www.journals.elsevier.com/international-journal-of-hydrogen-energy/ doi: 10.1016/j.ijhydene.2017.06.116Aghajani, G., Yousefi, N. Multi-objective optimal operation in a micro-grid considering economic and environmental goals (2019) Evolving Systems, 10 (2), pp. 239-248. Cited 6 times. http://www.springer.com/engineering/journal/12530 doi: 10.1007/s12530-018-9219-yLv, T., Ai, Q., Zhao, Y. A bi-level multi-objective optimal operation of grid-connected microgrids (2016) Electric Power Systems Research, 131, pp. 60-70. Cited 83 times. doi: 10.1016/j.epsr.2015.09.018Kim, H.J., Kim, M.K., Lee, J.W. A two-stage stochastic p-robust optimal energy trading management in microgrid operation considering uncertainty with hybrid demand response (2021) International Journal of Electrical Power and Energy Systems, 124, art. no. 106422. Cited 42 times. https://www.journals.elsevier.com/international-journal-of-electrical-power-and-energy-systems doi: 10.1016/j.ijepes.2020.106422Gad, Y., Diab, H., Abdelsalam, M., Galal, Y. Smart energy management system of environmentally friendly microgrid based on grasshopper optimization technique (2020) Energies, 13 (18), art. no. 5000. Cited 13 times. https://www.mdpi.com/1996-1073/13/19/5000 doi: 10.3390/en13195000Jain, D.K., Tyagi, S.K.S., Neelakandan, S., Prakash, M., Natrayan, L. Metaheuristic Optimization-Based Resource Allocation Technique for Cybertwin-Driven 6G on IoE Environment (2022) IEEE Transactions on Industrial Informatics, 18 (7), pp. 4884-4892. Cited 55 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424 doi: 10.1109/TII.2021.3138915Ahmed, D., Ebeed, M., Ali, A., Alghamdi, A.S., Kamel, S. Multi-objective energy management of a micro-grid considering stochastic nature of load and renewable energy resources (2021) Electronics (Switzerland), 10 (4), art. no. 403, pp. 1-22. 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