Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm
In this study, we present a master–slave methodology to solve the problem of optimal power dispatch in a direct current (DC) microgrid. In the master stage, the Antlion Optimization (ALO) method solves the problem of power dispatch by the Distributed Generators (DGs); in the slave stage, a numerical...
- Autores:
-
Ocampo-Toro, J.A.
Garzon-Rivera, O.D.
Grisales-Noreña, L.F.
Montoya-Giraldo, O.D.
Gil-González, W.
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12416
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12416
- Palabra clave:
- Optimal Power Flow;
Reactive Power;
Particle Swarm Optimization
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm |
| title |
Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm |
| spellingShingle |
Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm Optimal Power Flow; Reactive Power; Particle Swarm Optimization LEMB |
| title_short |
Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm |
| title_full |
Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm |
| title_fullStr |
Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm |
| title_full_unstemmed |
Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm |
| title_sort |
Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm |
| dc.creator.fl_str_mv |
Ocampo-Toro, J.A. Garzon-Rivera, O.D. Grisales-Noreña, L.F. Montoya-Giraldo, O.D. Gil-González, W. |
| dc.contributor.author.none.fl_str_mv |
Ocampo-Toro, J.A. Garzon-Rivera, O.D. Grisales-Noreña, L.F. Montoya-Giraldo, O.D. Gil-González, W. |
| dc.subject.keywords.spa.fl_str_mv |
Optimal Power Flow; Reactive Power; Particle Swarm Optimization |
| topic |
Optimal Power Flow; Reactive Power; Particle Swarm Optimization LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
In this study, we present a master–slave methodology to solve the problem of optimal power dispatch in a direct current (DC) microgrid. In the master stage, the Antlion Optimization (ALO) method solves the problem of power dispatch by the Distributed Generators (DGs); in the slave stage, a numerical method based on successive approximations (SA) evaluates the load flows required by the potential solutions proposed by the ALO technique. The objective functions in this paper are the minimization of energy production costs and the reduction of CO 2 emissions produced by the diesel generators in the microgrid. To favor energy efficiency and have a lower negative impact on the environment, the DC microgrids under study here include three DGs (one diesel generator and two generators based on renewable energy sources, i.e., solar energy and wind power) and a slack bus connected to a public electrical grid. The effectiveness of the proposed ALO–SA methodology was tested in the 21- and 69-bus test systems. We used three other optimization techniques to compare methods in the master stage: particle swarm optimization, continuous genetic algorithm, and black hole optimization. Additionally, we combined SA with every method to solve the load flow problem in the slave stage. The results show that, among the methods analyzed in this study, the proposed ALO–AS methodology achieves the best performance in terms of lower energy production costs, less CO 2 emissions, and shorter computational processing times. All the simulations were performed in MATLAB. © 2021, King Fahd University of Petroleum & Minerals. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2023-07-24T20:48:33Z |
| dc.date.available.none.fl_str_mv |
2023-07-24T20:48:33Z |
| dc.date.submitted.none.fl_str_mv |
2023 |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/draft |
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http://purl.org/coar/resource_type/c_6501 |
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draft |
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https://hdl.handle.net/20.500.12585/12416 |
| dc.identifier.doi.none.fl_str_mv |
10.1007/s13369-021-05831-0 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12416 |
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10.1007/s13369-021-05831-0 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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12 páginas |
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application/pdf |
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Cartagena de Indias |
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Arabian Journal for Science and Engineering |
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Ocampo-Toro, J.A.07676a58-3824-402b-ac0d-7abce3741f7aGarzon-Rivera, O.D.7f839c44-6d86-4cd0-a102-b788eda85288Grisales-Noreña, L.F.98ba5e2d-fa38-40c5-a05c-d73772e8ab17Montoya-Giraldo, O.D.e1bb7b6c-6bce-4b30-8499-2430f2e519e4Gil-González, W.59bfddb4-d5c7-4bd3-8cbe-49b131a07e1c2023-07-24T20:48:33Z2023-07-24T20:48:33Z20212023https://hdl.handle.net/20.500.12585/1241610.1007/s13369-021-05831-0Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarIn this study, we present a master–slave methodology to solve the problem of optimal power dispatch in a direct current (DC) microgrid. In the master stage, the Antlion Optimization (ALO) method solves the problem of power dispatch by the Distributed Generators (DGs); in the slave stage, a numerical method based on successive approximations (SA) evaluates the load flows required by the potential solutions proposed by the ALO technique. The objective functions in this paper are the minimization of energy production costs and the reduction of CO 2 emissions produced by the diesel generators in the microgrid. To favor energy efficiency and have a lower negative impact on the environment, the DC microgrids under study here include three DGs (one diesel generator and two generators based on renewable energy sources, i.e., solar energy and wind power) and a slack bus connected to a public electrical grid. The effectiveness of the proposed ALO–SA methodology was tested in the 21- and 69-bus test systems. We used three other optimization techniques to compare methods in the master stage: particle swarm optimization, continuous genetic algorithm, and black hole optimization. Additionally, we combined SA with every method to solve the load flow problem in the slave stage. The results show that, among the methods analyzed in this study, the proposed ALO–AS methodology achieves the best performance in terms of lower energy production costs, less CO 2 emissions, and shorter computational processing times. All the simulations were performed in MATLAB. © 2021, King Fahd University of Petroleum & Minerals.Ocampo-Toro, J. A., Garzon-Rivera, O. D., Grisales-Noreña, L. F., Montoya-Giraldo, O. D., & Gil-González, W. (2021). Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm. Arabian Journal for Science and Engineering, 46(10), 9995-10006.12 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_abf2Arabian Journal for Science and EngineeringOptimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithminfo: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_2df8fbb1Optimal Power Flow;Reactive Power;Particle Swarm OptimizationLEMBCartagena de IndiasSchuldt, J.P., Konrath, S.H., Schwarz, N. "Global warming" or "climate change"? (2011) Public Opinion Quarterly, 75 (1), pp. 115-124. Cited 238 times. doi: 10.1093/poq/nfq073Montoya, O.D., Grajales, A., Garces, A., Castro, C.A. Distribution Systems Operation Considering Energy Storage Devices and Distributed Generation (2017) IEEE Latin America Transactions, 15 (5), art. no. 7910203. Cited 40 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9907 doi: 10.1109/TLA.2017.7910203Grisales-Noreña, L.F., Montoya, O.D., Gil-González, W. Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms (2019) Journal of Energy Storage, 25, art. no. 100891. Cited 42 times. http://www.journals.elsevier.com/journal-of-energy-storage/ doi: 10.1016/j.est.2019.100891Montoya, O.D., Gil-Gonzalez, W., Garces, A. A Sequential Quadratic Programming Model for the Economic-Environmental Dispatch in MT-HVDC (2019) 2019 IEEE Workshop on Power Electronics and Power Quality Applications, PEPQA 2019 - Proceedings, art. no. 8851570. Cited 2 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8844570 ISBN: 978-172811626-6 doi: 10.1109/PEPQA.2019.8851570Justo, J.J., Mwasilu, F., Lee, J., Jung, J.-W. AC-microgrids versus DC-microgrids with distributed energy resources: A review (2013) Renewable and Sustainable Energy Reviews, 24, pp. 387-405. Cited 914 times. doi: 10.1016/j.rser.2013.03.067Gjorgiev, B., Čepin, M. A multi-objective optimization based solution for the combined economic-environmental power dispatch problem (2013) Engineering Applications of Artificial Intelligence, 26 (1), pp. 417-429. Cited 65 times. doi: 10.1016/j.engappai.2012.03.002Rozlan, M.B.M., Zobaa, A.F., Abdel Aleem, S.H.E. The optimisation of stand-alone hybrid renewable energy systems using HOMER (Open Access) (2011) International Review of Electrical Engineering, 6 (4), pp. 1802-1810. Cited 33 times. www.praiseworthyprize.com/IREE.htmMostafa, M.H., Aleem, S.H.E.A., Ali, S.G., Abdelaziz, A.Y., Ribeiro, P.F., Ali, Z.M. Robust energy management and economic analysis of microgrids considering different battery characteristics (Open Access) (2020) IEEE Access, 8, art. no. 9040525, pp. 54751-54775. Cited 57 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 doi: 10.1109/ACCESS.2020.2981697Mandal, K.K., Chakraborty, N. Short-term combined economic emission scheduling of hydrothermal systems with cascaded reservoirs using particle swarm optimization technique (Open Access) (2011) Applied Soft Computing Journal, 11 (1), pp. 1295-1302. Cited 76 times. doi: 10.1016/j.asoc.2010.03.006Zaman, F., Sarker, R.A., Ray, T. Solving an economic and environmental dispatch problem using evolutionary algorithm (2014) IEEE International Conference on Industrial Engineering and Engineering Management, 2015-January, art. no. 7058862, pp. 1367-1371. Cited 8 times. http://ieeexplore.ieee.org/xpl/conferences.jsp ISBN: 978-147996410-9 doi: 10.1109/IEEM.2014.7058862Zhang, G.-L., Li, G.-Y., Xie, H., Ma, J.-W. Environmental/economic load dispatch based on weighted ideal point and hybrid evolutionary algorithm (2005) 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005, pp. 2466-2471. Cited 5 times. ISBN: 078039092X; 978-078039092-8Montoya, O.D., Gil-Gonzalez, W., Grisales-Norena, L.F. Vortex Search Algorithm for Optimal Power Flow Analysis in DC Resistive Networks with CPLs (Open Access) (2020) IEEE Transactions on Circuits and Systems II: Express Briefs, 67 (8), art. no. 8821394, pp. 1439-1443. Cited 15 times. http://www.ieee-cas.org doi: 10.1109/TCSII.2019.2938530Arteaga, J.A., Montoya, O.D., Grisales-Noreña, L.F. Solution of the optimal power flow problem in direct current grids applying the hurricane optimization algorithm (2020) Journal of Physics: Conference Series, 1448 (1), art. no. 012015. Cited 5 times. http://iopscience.iop.org/journal/1742-6596 doi: 10.1088/1742-6596/1448/1/012015Omar, A.I., Ali, Z.M., Al-Gabalawy, M., Abdel Aleem, S.H.E., Al-Dhaifallah, M. Multi-objective environmental economic dispatch of an electricity system considering integrated natural gas units and variable renewable energy sources (Open Access) (2020) Mathematics, 8 (7), art. no. 1100. Cited 23 times. https://res.mdpi.com/d_attachment/mathematics/mathematics-08-01100/article_deploy/mathematics-08-01100.pdf doi: 10.3390/math8071100Mirjalili, S. The ant lion optimizer (Open Access) (2015) Advances in Engineering Software, 83, pp. 80-98. Cited 2223 times. http://www.journals.elsevier.com/advances-in-engineering-software/ doi: 10.1016/j.advengsoft.2015.01.010Ali, E.S., Abd Elazim, S.M., Abdelaziz, A.Y. Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations (Open Access) (2017) Renewable Energy, 101, pp. 1311-1324. Cited 250 times. http://www.journals.elsevier.com/renewable-and-sustainable-energy-reviews/ doi: 10.1016/j.renene.2016.09.023Montoya, O.D., Garrido, V.M., Gil-Gonzalez, W., Grisales-Norena, L.F. Power Flow Analysis in DC Grids: Two Alternative Numerical Methods (Open Access) (2019) IEEE Transactions on Circuits and Systems II: Express Briefs, 66 (11), art. no. 8606244, pp. 1865-1869. Cited 60 times. http://www.ieee-cas.org doi: 10.1109/TCSII.2019.2891640Grisales-Noreña, L.F., Montoya, D.G., Ramos-Paja, C.A. Optimal sizing and location of distributed generators based on PBIL and PSO techniques (2018) Energies, 11 (4), art. no. en11041018. Cited 98 times. http://www.mdpi.com/journal/energies/ doi: 10.3390/en11041018Grisales-Noreña, L.F., Montoya, O.D., Ramos-Paja, C.A., Hernandez-Escobedo, Q., Perea-Moreno, A.-J. 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