Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks
In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based o...
- Autores:
-
Rosales Muñoz, Andrés Alfonso
Grisales-Noreña, Luis Fernando
Montano, Jhon
Montoya, Oscar Danilo
Perea-Moreno, Alberto-Jesus
- 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:20.500.12585/12277
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12277
https://doi.org/10.3390/electronics11081287
- Palabra clave:
- Optimal Power Flow;
Reactive Power;
Particle Swarm Optimization
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| id |
UTB2_ab4af1d9ef20b3fdbac31faf27c7bc02 |
|---|---|
| oai_identifier_str |
oai:repositorio.utb.edu.co:20.500.12585/12277 |
| network_acronym_str |
UTB2 |
| network_name_str |
Repositorio Institucional UTB |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks |
| title |
Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks |
| spellingShingle |
Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks Optimal Power Flow; Reactive Power; Particle Swarm Optimization LEMB |
| title_short |
Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks |
| title_full |
Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks |
| title_fullStr |
Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks |
| title_full_unstemmed |
Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks |
| title_sort |
Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks |
| dc.creator.fl_str_mv |
Rosales Muñoz, Andrés Alfonso Grisales-Noreña, Luis Fernando Montano, Jhon Montoya, Oscar Danilo Perea-Moreno, Alberto-Jesus |
| dc.contributor.author.none.fl_str_mv |
Rosales Muñoz, Andrés Alfonso Grisales-Noreña, Luis Fernando Montano, Jhon Montoya, Oscar Danilo Perea-Moreno, Alberto-Jesus |
| 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 paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master–slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2023-07-21T15:43:24Z |
| dc.date.available.none.fl_str_mv |
2023-07-21T15:43:24Z |
| dc.date.submitted.none.fl_str_mv |
2023 |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/draft |
| dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
draft |
| dc.identifier.citation.spa.fl_str_mv |
Rosales Muñoz, A.A.; Grisales-Noreña, L.F.; Montano, J.; Montoya, O.D.; Perea-Moreno, A.-J. Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks. Electronics 2022, 11, 1287. https://doi.org/10.3390/electronics11081287 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12277 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.3390/electronics11081287 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Rosales Muñoz, A.A.; Grisales-Noreña, L.F.; Montano, J.; Montoya, O.D.; Perea-Moreno, A.-J. Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks. Electronics 2022, 11, 1287. https://doi.org/10.3390/electronics11081287 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12277 https://doi.org/10.3390/electronics11081287 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.cc.*.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.none.fl_str_mv |
33 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
| dc.source.spa.fl_str_mv |
Electronics Volume 11, Issue 8 |
| institution |
Universidad Tecnológica de Bolívar |
| bitstream.url.fl_str_mv |
https://repositorio.utb.edu.co/bitstream/20.500.12585/12277/1/electronics-11-01287-v2.pdf https://repositorio.utb.edu.co/bitstream/20.500.12585/12277/2/license_rdf https://repositorio.utb.edu.co/bitstream/20.500.12585/12277/3/license.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/12277/4/electronics-11-01287-v2.pdf.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/12277/5/electronics-11-01287-v2.pdf.jpg |
| bitstream.checksum.fl_str_mv |
260b0788a99caca1e92a21a339701408 4460e5956bc1d1639be9ae6146a50347 e20ad307a1c5f3f25af9304a7a7c86b6 9fdf3cbcaf46302d847f1c5899a06fab a6ef9166b97c8dc8d56869b45b8d1340 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional UTB |
| repository.mail.fl_str_mv |
repositorioutb@utb.edu.co |
| _version_ |
1814021679146336256 |
| spelling |
Rosales Muñoz, Andrés Alfonso1cadd052-2b2e-4872-b1d3-7679f6be5f2aGrisales-Noreña, Luis Fernando7c27cda4-5fe4-4686-8f72-b0442c58a5d1Montano, Jhon5edc0c05-f7f1-4a81-8b30-3981975c221dMontoya, Oscar Danilo9fa8a75a-58fa-436d-a6e2-d80f718a4ea8Perea-Moreno, Alberto-Jesuse78da438-8ed5-40ab-a12c-74e84e6d691b2023-07-21T15:43:24Z2023-07-21T15:43:24Z20222023Rosales Muñoz, A.A.; Grisales-Noreña, L.F.; Montano, J.; Montoya, O.D.; Perea-Moreno, A.-J. Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks. Electronics 2022, 11, 1287. https://doi.org/10.3390/electronics11081287https://hdl.handle.net/20.500.12585/12277https://doi.org/10.3390/electronics11081287Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarIn this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master–slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.33 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_abf2Electronics Volume 11, Issue 8Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networksinfo: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 IndiasGurven, M., Walker, R. Energetic demand of multiple dependents and the evolution of slow human growth (2006) Proceedings of the Royal Society B: Biological Sciences, 273 (1588), pp. 835-841. Cited 119 times. http://rspb.royalsocietypublishing.org/ doi: 10.1098/rspb.2005.3380Murillo, J., Trejos, A., OLAYA, P.C. Estudio del pronóstico de la demanda de energía eléctrica, utilizando modelos de series de tiempo (2003) Sci. Tech, 3, pp. 1-6. Cited 7 times. https://doi.org/10.22517/23447214.7379Reiss, P.C., White, M.W. Household electricity demand, revisited (2005) Review of Economic Studies, 72 (3), pp. 853-883. Cited 336 times. doi: 10.1111/0034-6527.00354Halvorsen, R. Demand for electric energy in the United States (1976) South. Econ. J, 42, pp. 610-625. Cited 31 times.Bull, S.R. Renewable energy today and tomorrow (2001) Proceedings of the IEEE, 89 (8), pp. 1216-1226. Cited 469 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5 doi: 10.1109/5.940290Machol, B., Rizk, S. Economic value of U.S. fossil fuel electricity health impacts (2013) Environment International, 52, pp. 75-80. Cited 90 times. www.elsevier.com/locate/envint doi: 10.1016/j.envint.2012.03.003Zhang, N., Zhou, P., Choi, Y. Energy efficiency, CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea: A meta-frontier non-radial directional distance functionanalysis (2013) Energy Policy, 56, pp. 653-662. Cited 276 times. doi: 10.1016/j.enpol.2013.01.033Muñoz, A.A.R., Grisales-Noreña, L.F., Montano, J., Montoya, O.D., Giral-Ramírez, D.A. Optimal power dispatch of distributed generators in direct current networks using a master–slave methodology that combines the salp swarm algorithm and the successive approximation method (2021) Electronics (Switzerland), 10 (22), art. no. 2837. Cited 6 times. https://www.mdpi.com/2079-9292/10/22/2837/pdf doi: 10.3390/electronics10222837Rosales-Muñoz, A.A., Grisales-Noreña, L.F., Montano, J., Montoya, O.D., Perea-Moreno, A.-J. Application of the multiverse optimization method to solve the optimal power flow problem in direct current electrical networks (2021) Sustainability (Switzerland), 13 (16), art. no. 8703. Cited 11 times. https://www.mdpi.com/2071-1050/13/16/8703/pdf doi: 10.3390/su13168703Grisales-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, L.F., Restrepo Cuestas, B.J. Ubicación y dimensionamiento de generación distribuida: Una revisión (2017) Cienc. Ing. Neogranadina, 27, pp. 157-176. Cited 15 times.Grisales-Noreña, L.F., Ramos-Paja, C.A., Gonzalez-Montoya, D., Alcalá, G., Hernandez-Escobedo, Q. Energy management in PV based microgrids designed for the Universidad Nacional de Colombia (2020) Sustainability (Switzerland), 12 (3), art. no. 1219. Cited 17 times. https://res.mdpi.com/d_attachment/sustainability/sustainability-12-01219/article_deploy/sustainability-12-01219-v2.pdf doi: 10.3390/su12031219Franck, C.M. HVDC circuit breakers: A review identifying future research needs (2011) IEEE Transactions on Power Delivery, 26 (2), art. no. 5686894, pp. 998-1007. Cited 899 times. doi: 10.1109/TPWRD.2010.2095889Ocampo-Toro, J.A., Garzon-Rivera, O.D., Grisales-Noreña, L.F., Montoya-Giraldo, O.D., Gil-González, W. Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm (2021) Arabian Journal for Science and Engineering, 46 (10), pp. 9995-10006. Cited 4 times. https://link.springer.com/journal/13369 doi: 10.1007/s13369-021-05831-0Grisales, L.F., Grajales, A., Montoya, O.D., Hincapie, R.A., Granada, M., Castro, C.A. Optimal location, sizing and operation of energy storage in distribution systems using multi-objective approach (2017) IEEE Latin America Transactions, 15 (6), art. no. 7932696, pp. 1084-1090. Cited 24 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9907 doi: 10.1109/TLA.2017.7932696Montoya, O.D., Grisales-Noreña, L.F., González-Montoya, D., Ramos-Paja, C.A., Garces, A. Linear power flow formulation for low-voltage DC power grids (2018) Electric Power Systems Research, Part A 163, pp. 375-381. Cited 79 times. doi: 10.1016/j.epsr.2018.07.003Dommel, H.W., Tinney, W.F. Optimal Power Flow Solutions (Open Access) (1968) IEEE Transactions on Power Apparatus and Systems, PAS-87 (10), pp. 1866-1876. Cited 1270 times. doi: 10.1109/TPAS.1968.292150Alsac, O., Stott, B. Optimal load flow with steady-state security (Open Access) (1974) IEEE Transactions on Power Apparatus and Systems, PAS-93 (3), pp. 745-751. Cited 642 times. doi: 10.1109/TPAS.1974.293972Ali, E.S., Abd Elazim, S.M., Abdelaziz, A.Y. Ant Lion Optimization Algorithm for renewable Distributed Generations (2016) Energy, Part 1 116, pp. 445-458. Cited 213 times. www.elsevier.com/inca/publications/store/4/8/3/ doi: 10.1016/j.energy.2016.09.104Montoya, O.D., Molina-Cabrera, A., Chamorro, H.R., Alvarado-Barrios, L., Rivas-Trujillo, E. A hybrid approach based on socp and the discrete version of the sca for optimal placement and sizing dgs in ac distribution networks (Open Access) (2021) Electronics (Switzerland), 10 (1), art. no. 26, pp. 1-19. Cited 12 times. https://www.mdpi.com/2079-9292/10/1/26/pdf doi: 10.3390/electronics10010026Bernal-Romero, D.L., Montoya, O.D., Arias-Londoño, A. Solution of the optimal reactive power flow problem using a discrete-continuous cbga implemented in the digsilent programming language (2021) Computers, 10 (11), art. no. 151. Cited 8 times. https://www.mdpi.com/2073-431X/10/11/151/pdf doi: 10.3390/computers10110151Lashkar Ara, A., Kazemi, A., Gahramani, S., Behshad, M. Optimal reactive power flow using multi-objective mathematical programming (Open Access) (2012) Scientia Iranica, 19 (6), pp. 1829-1836. Cited 48 times. scientiairanica.sharif.edu doi: 10.1016/j.scient.2012.07.010Bhattacharya, A., Chattopadhyay, P.K. Solution of optimal reactive power flow using biogeography-based optimization (2010) Int. J. Electr. Electron. Eng, 4, pp. 568-576. Cited 92 times.Ayan, K., Kiliç, U. Artificial bee colony algorithm solution for optimal reactive power flow (Open Access) (2012) Applied Soft Computing Journal, 12 (5), pp. 1477-1482. Cited 170 times. doi: 10.1016/j.asoc.2012.01.006Gutiérrez, D., Villa, W.M., López-Lezama, J.M. Optimal reactive power dispatch by means of particle swarm optimization (Open Access) (2017) Informacion Tecnologica, 28 (5), pp. 215-224. Cited 8 times. http://www.scielo.cl/pdf/infotec/v28n5/art20.pdf doi: 10.4067/S0718-07642017000500020Mahmoud, K., Yorino, N., Ahmed, A. Optimal Distributed Generation Allocation in Distribution Systems for Loss Minimization (2016) IEEE Transactions on Power Systems, 31 (2), art. no. 7091050, pp. 960-969. Cited 287 times. doi: 10.1109/TPWRS.2015.2418333Mosbah, M., Zine, R., Arif, S., Mohammedi, R.D., Bacha, S. Optimal Power Flow for Transmission System with Photovoltaic Based DG Using Biogeography-Based Optimization (2019) Proceedings of 2018 3rd International Conference on Electrical Sciences and Technologies in Maghreb, CISTEM 2018, art. no. 8613610. Cited 9 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8593337 ISBN: 978-153864987-9 doi: 10.1109/CISTEM.2018.8613610Montoya, O.D., Giral-Ramirez, D.A., Grisales-Norena, L.F. Black hole optimizer for the optimal power injection in distribution networks using DG (2021) Journal of Physics: Conference Series, 2135 (1), art. no. 012010. Cited 2 times. http://iopscience.iop.org/journal/1742-6596 doi: 10.1088/1742-6596/2135/1/012010Wang, P., Wang, W., Xu, D. Optimal sizing of distributed generations in DC microgrids with comprehensive consideration of system operation modes and operation targets (2018) IEEE Access, 6, pp. 31129-31140. Cited 52 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 doi: 10.1109/ACCESS.2018.2842119Manrique, M.L., Montoya, O.D., Garrido, V.M., Grisales-Noreña, L.F., Gil-González, W. Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generators (2019) Communications in Computer and Information Science, 1052, pp. 28-39. Cited 10 times. http://www.springer.com/series/7899 ISBN: 978-303031018-9 doi: 10.1007/978-3-030-31019-6_3Mirjalili, S., Mirjalili, S.M., Hatamlou, A. Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (2016) Neural Computing and Applications, 27 (2), pp. 495-513. Cited 1634 times. http://link.springer.com/journal/521 doi: 10.1007/s00521-015-1870-7Trivedi, I.N., Jangir, P., Jangir, N., Parmar, S.A., Bhoye, M., Kumar, A. Voltage stability enhancement and voltage deviation minimization using multi-verse optimizer algorithm (2016) Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2016, art. no. 7530136. Cited 19 times. ISBN: 978-150901277-0 doi: 10.1109/ICCPCT.2016.7530136Ceylan, O. Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters (Open Access) (2021) Neural Computing and Applications, 33 (6), pp. 1935-1950. Cited 7 times. http://link.springer.com/journal/521 doi: 10.1007/s00521-020-05062-8Kumar, A., Suhag, S. Multiverse optimized fuzzy-PID controller with a derivative filter for load frequency control of multisource hydrothermal power system (2017) Turkish Journal of Electrical Engineering and Computer Sciences, 25 (5), pp. 4187-4199. Cited 17 times. http://journals.tubitak.gov.tr/elektrik/issues/elk-17-25-5/elk-25-5-56-1612-176.pdf doi: 10.3906/elk-1612-176Sulaiman, M., Ahmad, S., Iqbal, J., Khan, A., Khan, R. Optimal operation of the hybrid electricity generation system using multiverse optimization algorithm (Open Access) (2019) Computational Intelligence and Neuroscience, 2019, art. no. 6192980. Cited 22 times. http://www.hindawi.com/journals/cin doi: 10.1155/2019/6192980Montoya, O.D., Garrido, V.M., Gil-Gonzalez, W., Grisales-Norena, L.F. Power Flow Analysis in DC Grids: Two Alternative Numerical Methods (2019) IEEE Transactions on Circuits and Systems II: Express Briefs, 66 (11), art. no. 8606244, pp. 1865-1869. Cited 59 times. http://www.ieee-cas.org doi: 10.1109/TCSII.2019.2891640Linde, A., Linde, D., Mezhlumian, A. From the big bang theory to the theory of a stationary universe (1994) Physical Review D, 49 (4), pp. 1783-1826. Cited 435 times. doi: 10.1103/PhysRevD.49.1783Schaefer, H.F. (1996) The Big Bang, Stephen Hawking, and God Lincoln Christian College and Seminary: Sídney, AustraliaSingh, S. (2005) The Origin of the Universe. Cited 3 times. Harper Perennial: Meghalaya, IndiaKhoury, J., Ovrut, B.A., Steinhardt, P.J., Turok, N. Ekpyrotic universe: Colliding branes and the origin of the hot big bang (2001) Physical Review D, 64 (12), art. no. 123522. Cited 1193 times. https://journals.aps.org/prd/issuesWallace, D. The Emergent Multiverse: Quantum Theory According to the Everett Interpretation (Open Access) (2012) The Emergent Multiverse: Quantum Theory According to the Everett Interpretation, 9780199546961, pp. 1-560. Cited 363 times. http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780199546961.001.0001/acprof-9780199546961 ISBN: 978-019174141-8; 978-019954696-1 doi: 10.1093/acprof:oso/9780199546961.001.0001Carr, B., Ellis, G. Universe or multiverse? (Open Access) (2008) Astronomy and Geophysics, 49 (2), pp. 2.29-2.33. Cited 32 times. http://astrogeo.oxfordjournals.org/ doi: 10.1111/j.1468-4004.2008.49229.xEllis, G.F. Does the multiverse really exist? (2011) Sci. Am, 305, pp. 38-43. Cited 33 times.Weinberg, S. (2005) Living in the multiverse. Cited 83 times. arXiv arXiv:hep-th/0511037Boesgaard, A.M., Steigman, G. Big Bang nucleosynthesis: Theories and observations (1985) Annu. Rev. Astron. Astrophys, 23, pp. 319-378. Cited 396 times.Rothstein, B. (2009) Anti-Corruption: A Big-Bang Theory. Cited 23 times. QoG Working Paper; University of Gothenburg: Gothenburg, SwedenBarrau, A., Martineau, K., Moulin, F. A status report on the phenomenology of black holes in loop quantum gravity: Evaporation, tunneling to white holes, dark matter and gravitational waves (Open Access) (2018) Universe, 4 (10), art. no. 102. Cited 32 times. https://www.mdpi.com/2218-1997/4/10/102/pdf doi: 10.3390/universe4100102Wald, R.M., Ramaswamy, S. Particle production by white holes (1980) Physical Review D, 21 (10), pp. 2736-2741. Cited 20 times. doi: 10.1103/PhysRevD.21.2736Bekenstein, J.D. Black holes and information theory (2004) Contemporary Physics, 45 (1), pp. 31-43. Cited 73 times. doi: 10.1080/00107510310001632523Krylov, V. Acoustic black holes: Recent developments in the theory and applications (2014) IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 61 (8), art. no. 6863850, pp. 1296-1306. Cited 93 times. doi: 10.1109/TUFFC.2014.3036Chandrasekhar, S., Chandrasekhar, S. (1998) The Mathematical THEORY of black Holes, 69. Cited 3528 times. Oxford University Press: Oxford, UKYoum, D. Black holes and solitons in string theory (1999) Physics Report, 316 (1-3), pp. 1-232. Cited 152 times. http://www.elsevier.com/locate/physrep doi: 10.1016/S0370-1573(99)00037-XPage, D.N., Hawking, S.W. Gamma rays from primordial black holes (1976) Astrophys. J, 206, pp. 1-7. Cited 256 times.Kanti, P., Kleihaus, B., Kunz, J. Wormholes in dilatonic Einstein-Gauss-Bonnet theory (Open Access) (2011) Physical Review Letters, 107 (27), art. no. 271101. Cited 197 times. http://oai.aps.org/filefetch?identifier=10.1103/PhysRevLett.107.271101&component=fulltext&description=markup&format=xml doi: 10.1103/PhysRevLett.107.271101Safonova, M., Torres, D.F., Romero, G.E. Microlensing by natural wormholes: Theory and simulations (Open Access) (2002) Physical Review D, 65 (2), art. no. 023001. Cited 101 times. https://journals.aps.org/prd/issues doi: 10.1103/PhysRevD.65.023001Arkani-Hamed, N., Orgera, J., Polchinski, J. Euclidean wormholes in string theory (2007) Journal of High Energy Physics, 2007 (12), art. no. 018. Cited 88 times. doi: 10.1088/1126-6708/2007/12/018Agnese, A.G., La Camera, M. Wormholes in the Brans-Dicke theory of gravitation (1995) Physical Review D, 51 (4), pp. 2011-2013. Cited 164 times. doi: 10.1103/PhysRevD.51.2011Lipowski, A., Lipowska, D. Roulette-wheel selection via stochastic acceptance (Open Access) (2012) Physica A: Statistical Mechanics and its Applications, 391 (6), pp. 2193-2196. Cited 477 times. http://www.journals.elsevier.com/physica-a-statistical-mechanics-and-its-applications/ doi: 10.1016/j.physa.2011.12.004Montoya, O.D., Gil-González, W. On the numerical analysis based on successive approximations for power flow problems in AC distribution systems (Open Access) (2020) Electric Power Systems Research, 187, art. no. 106454. Cited 39 times. https://www.journals.elsevier.com/electric-power-systems-research doi: 10.1016/j.epsr.2020.106454Kennedy, James, Eberhart, Russell Particle swarm optimization (Open Access) (1995) IEEE International Conference on Neural Networks - Conference Proceedings, 4, pp. 1942-1948. Cited 52422 times.Abualigah, L., Diabat, A. A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments (Open Access) (2021) Cluster Computing, 24 (1), pp. 205-223. Cited 146 times. https://link.springer.com/journal/volumesAndIssues/10586 doi: 10.1007/s10586-020-03075-5Hatamlou, A. Black hole: A new heuristic optimization approach for data clustering (Open Access) (2013) Information Sciences, 222, pp. 175-184. Cited 901 times. doi: 10.1016/j.ins.2012.08.023Chelouah, R., Siarry, P. Continuous genetic algorithm designed for the global optimization of multimodal functions (Open Access) (2000) Journal of Heuristics, 6 (2), pp. 191-213. Cited 250 times. doi: 10.1023/A:1009626110229Montoya, O.D., Garrid, V.M., Grisales-Noreña, L.F., González-Montoya, D., Ramos-Paja, C.A. Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization (2018) 2018 IEEE 9th Power, Instrumentation and Measurement Meeting, EPIM 2018, art. no. 8756354. Cited 2 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8753126 ISBN: 978-153867842-8 doi: 10.1109/EPIM.2018.8756354http://purl.org/coar/resource_type/c_6501ORIGINALelectronics-11-01287-v2.pdfelectronics-11-01287-v2.pdfapplication/pdf1074483https://repositorio.utb.edu.co/bitstream/20.500.12585/12277/1/electronics-11-01287-v2.pdf260b0788a99caca1e92a21a339701408MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/12277/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12277/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXTelectronics-11-01287-v2.pdf.txtelectronics-11-01287-v2.pdf.txtExtracted texttext/plain96958https://repositorio.utb.edu.co/bitstream/20.500.12585/12277/4/electronics-11-01287-v2.pdf.txt9fdf3cbcaf46302d847f1c5899a06fabMD54THUMBNAILelectronics-11-01287-v2.pdf.jpgelectronics-11-01287-v2.pdf.jpgGenerated Thumbnailimage/jpeg7907https://repositorio.utb.edu.co/bitstream/20.500.12585/12277/5/electronics-11-01287-v2.pdf.jpga6ef9166b97c8dc8d56869b45b8d1340MD5520.500.12585/12277oai:repositorio.utb.edu.co:20.500.12585/122772023-07-22 00:17:53.132Repositorio Institucional UTBrepositorioutb@utb.edu.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 |