Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization
This paper presents a metaheuristic optimization technique named back hole optimization (BHO) for solving the problem of optimal dimensioning of distributed generation in radial distribution networks. This problem is formulated as a conventional optimal power flow problem in ac power grids. A master...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8858
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8858
- Palabra clave:
- Black-hole optimization
Complex domain formulation
Distributed generation
Optimal power flow
Acoustic generators
Distributed power generation
Electric load flow
Gravitation
MATLAB
Particle swarm optimization (PSO)
Stars
Black holes
Complex domains
Distributed generators
Meta-heuristic optimization techniques
Optimal power flow problem
Optimal power flows
Optimization techniques
Radial distribution networks
Electric power transmission networks
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| id |
UTB2_185f383019322c8f7f965a0a7bae0fee |
|---|---|
| oai_identifier_str |
oai:repositorio.utb.edu.co:20.500.12585/8858 |
| network_acronym_str |
UTB2 |
| network_name_str |
Repositorio Institucional UTB |
| repository_id_str |
|
| dc.title.none.fl_str_mv |
Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization |
| title |
Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization |
| spellingShingle |
Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization Black-hole optimization Complex domain formulation Distributed generation Optimal power flow Acoustic generators Distributed power generation Electric load flow Gravitation MATLAB Particle swarm optimization (PSO) Stars Black holes Complex domains Distributed generators Meta-heuristic optimization techniques Optimal power flow problem Optimal power flows Optimization techniques Radial distribution networks Electric power transmission networks |
| title_short |
Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization |
| title_full |
Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization |
| title_fullStr |
Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization |
| title_full_unstemmed |
Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization |
| title_sort |
Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization |
| dc.subject.keywords.none.fl_str_mv |
Black-hole optimization Complex domain formulation Distributed generation Optimal power flow Acoustic generators Distributed power generation Electric load flow Gravitation MATLAB Particle swarm optimization (PSO) Stars Black holes Complex domains Distributed generators Meta-heuristic optimization techniques Optimal power flow problem Optimal power flows Optimization techniques Radial distribution networks Electric power transmission networks |
| topic |
Black-hole optimization Complex domain formulation Distributed generation Optimal power flow Acoustic generators Distributed power generation Electric load flow Gravitation MATLAB Particle swarm optimization (PSO) Stars Black holes Complex domains Distributed generators Meta-heuristic optimization techniques Optimal power flow problem Optimal power flows Optimization techniques Radial distribution networks Electric power transmission networks |
| description |
This paper presents a metaheuristic optimization technique named back hole optimization (BHO) for solving the problem of optimal dimensioning of distributed generation in radial distribution networks. This problem is formulated as a conventional optimal power flow problem in ac power grids. A master-slave methodology is proposed to solve this optimization problem. In the master stage the BHO technique decides the power output of each distributed generator (DG), while slave stage is responsible for solving the resulting power flow problem via classical sweep backward/forward technique. As comparison methods, classical particle swarm optimization as well as interior point methods are used. Two classical test systems with radial topologiesy and 33 and 69 nodes are used for numerical validations by using the MATLAB programming environment. Simulation results show the quality of the proposed optimization technique for power losses reduction in comparison with large-scale used optimization approaches available in specialized literature. © 2018 IEEE. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2020-03-26T16:32:31Z |
| dc.date.available.none.fl_str_mv |
2020-03-26T16:32:31Z |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_c94f |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
| dc.type.hasversion.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.spa.none.fl_str_mv |
Conferencia |
| status_str |
publishedVersion |
| dc.identifier.citation.none.fl_str_mv |
2018 IEEE 9th Power, Instrumentation and Measurement Meeting, EPIM 2018 |
| dc.identifier.isbn.none.fl_str_mv |
9781538678428 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/8858 |
| dc.identifier.doi.none.fl_str_mv |
10.1109/EPIM.2018.8756354 |
| dc.identifier.instname.none.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.none.fl_str_mv |
Repositorio UTB |
| dc.identifier.orcid.none.fl_str_mv |
56919564100 57210170020 55791991200 57205565936 22836502400 |
| identifier_str_mv |
2018 IEEE 9th Power, Instrumentation and Measurement Meeting, EPIM 2018 9781538678428 10.1109/EPIM.2018.8756354 Universidad Tecnológica de Bolívar Repositorio UTB 56919564100 57210170020 55791991200 57205565936 22836502400 |
| url |
https://hdl.handle.net/20.500.12585/8858 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.conferencedate.none.fl_str_mv |
14 November 2018 through 16 November 2018 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_16ec |
| dc.rights.uri.none.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/restrictedAccess |
| dc.rights.cc.none.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_16ec |
| eu_rights_str_mv |
restrictedAccess |
| dc.format.medium.none.fl_str_mv |
Recurso electrónico |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers Inc. |
| publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers Inc. |
| dc.source.none.fl_str_mv |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069788404&doi=10.1109%2fEPIM.2018.8756354&partnerID=40&md5=7a6f28d2ce047f5214b68e1d84f5372e |
| institution |
Universidad Tecnológica de Bolívar |
| dc.source.event.none.fl_str_mv |
9th IEEE Power, Instrumentation and Measurement Meeting, EPIM 2018 |
| bitstream.url.fl_str_mv |
https://repositorio.utb.edu.co/bitstream/20.500.12585/8858/1/MiniProdInv.png |
| bitstream.checksum.fl_str_mv |
0cb0f101a8d16897fb46fc914d3d7043 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional UTB |
| repository.mail.fl_str_mv |
repositorioutb@utb.edu.co |
| _version_ |
1814021581227163648 |
| spelling |
2020-03-26T16:32:31Z2020-03-26T16:32:31Z20182018 IEEE 9th Power, Instrumentation and Measurement Meeting, EPIM 20189781538678428https://hdl.handle.net/20.500.12585/885810.1109/EPIM.2018.8756354Universidad Tecnológica de BolívarRepositorio UTB5691956410057210170020557919912005720556593622836502400This paper presents a metaheuristic optimization technique named back hole optimization (BHO) for solving the problem of optimal dimensioning of distributed generation in radial distribution networks. This problem is formulated as a conventional optimal power flow problem in ac power grids. A master-slave methodology is proposed to solve this optimization problem. In the master stage the BHO technique decides the power output of each distributed generator (DG), while slave stage is responsible for solving the resulting power flow problem via classical sweep backward/forward technique. As comparison methods, classical particle swarm optimization as well as interior point methods are used. Two classical test systems with radial topologiesy and 33 and 69 nodes are used for numerical validations by using the MATLAB programming environment. Simulation results show the quality of the proposed optimization technique for power losses reduction in comparison with large-scale used optimization approaches available in specialized literature. © 2018 IEEE.Universidad Nacional de Colombia, UN Departamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIAS Department of Science, Information Technology and Innovation, Queensland Government, DSITI UNAL-ITM-39823/P17211 Universidad Tecnológica de Pereira, UTPFINANCIAL SUPPORT This work was supported by the Administrative Department of Science, Technology and Innovation of Colombia (COLCIENCIAS) through the National Scholarship Program, calling contest 727-2015, the PhD program in Engineering of la Universidad Tecnológica de Pereira, and the Univer-sidad Nacional de Colombia and the Instituto Tecnológico Metropolitano under the project UNAL-ITM-39823/P17211.Recurso electrónicoapplication/pdfengInstitute of Electrical and Electronics Engineers Inc.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85069788404&doi=10.1109%2fEPIM.2018.8756354&partnerID=40&md5=7a6f28d2ce047f5214b68e1d84f5372e9th IEEE Power, Instrumentation and Measurement Meeting, EPIM 2018Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimizationinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fBlack-hole optimizationComplex domain formulationDistributed generationOptimal power flowAcoustic generatorsDistributed power generationElectric load flowGravitationMATLABParticle swarm optimization (PSO)StarsBlack holesComplex domainsDistributed generatorsMeta-heuristic optimization techniquesOptimal power flow problemOptimal power flowsOptimization techniquesRadial distribution networksElectric power transmission networks14 November 2018 through 16 November 2018Montoya O.D.Garrido Arévalo, Víctor ManuelGrisales-Noreña L.F.González-Montoya D.Ramos-Paja C.A.Montoya, O.D., Grajales, A., Garces, A., Castro, C.A., Distribution systems operation considering energy storage devices and distributed generation (2017) IEEE Lat. Am. Trans., 15 (5), pp. 890-900. , MayGrisales, 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 Lat. Am. Trans., 15 (6), pp. 1084-1090. , JuneGrisales-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 (1018), pp. 1-27. , febGrisales, L.F., Montoya, O.D., Grajales, A., Hincapie, R.A., Granada, M., Optimal planning and operation of distribution systems considering distributed energy resources and automatic reclosers (2018) IEEE Lat. Am. Trans., 16 (1), pp. 126-134. , JanBouchekara, H., Optimal power flow using black-hole-based optimization approach (2014) Appl. Soft Comput., 24, pp. 879-888Montoya-Giraldo, O.D., Gil-González, W.J., Garcés-Ruiz, A., Optimal Power Flow for radial and mesh grids using semidefinite programming (2017) Tecnologicas, 20 (40). , decGarces, A., A quadratic approximation for the optimal power flow in power distribution systems (2016) Electr. Power Syst. Res., 130, pp. 222-229Baradar, M., Hesamzadeh, M.R., Ac power flow representation in conic format (2015) IEEE Trans. Power Syst., 30 (1), pp. 546-547. , JanBakirtzis, A.G., Biskas, P.N., Zoumas, C.E., Petridis, V., Optimal power flow by enhanced genetic algorithm (2002) IEEE Trans. Power Syst., 17 (2), pp. 229-236. , MayYan, W., Liu, F., Chung, C.Y., Wong, K.P., A hybrid genetic algorithm-interior point method for optimal reactive power flow (2006) IEEE Trans. Power Syst., 21 (3), pp. 1163-1169. , AugVlachogiannis, J.G., Hatziargyriou, N.D., Lee, K.Y., Ant colony system-based algorithm for constrained load flow problem (2005) IEEE Trans. Power Syst., 20 (3), pp. 1241-1249. , AugLuo, J., Shi, L., Ni, Y., A solution of optimal power flow incorporating wind generation and power grid uncertainties (2018) IEEE Access, 6, pp. 19681-19690Esmin, A.A.A., Lambert-Torres, G., De Souza, A.C.Z., A hybrid particle swarm optimization applied to loss power minimization (2005) IEEE Trans. Power Syst., 20 (2), pp. 859-866. , MayOnate-Yumbla, P.E., Ramirez, J.M., Coello-Coello, C.A., Optimal Power Flow Subject to Security Constraints Solved with a Particle Swarm Optimizer (2008) IEEE Trans. Power Syst., 23 (1), pp. 33-40. , FebAttia, A.-F., Sehiemy, R.A.E., Hasanien, H.M., Optimal power flow solution in power systems using a novel sine-cosine algorithm (2018) Int. J. Electr. Power Energy Syst., 99, pp. 331-343Algabalawy, M.A., Abdelaziz, A.Y., Mekhamer, S.F., Aleem, S.H.A., Considerations on optimal design of hybrid power generation systems using whale and sine cosine optimization algorithms (2018) J. Electr. Syst. Inf. Technol.Lenin, K., Reddy, B.R., Suryakalavathi, M., Hybrid Tabu searchsimulated annealing method to solve optimal reactive power problem (2016) Int. J. Electr. Power Energy Syst., 82, pp. 87-91Roa-Sepulveda, C., Pavez-Lazo, B., A solution to the optimal power flow using simulated annealing (2003) Int. J. Electr. Power Energy Syst., 25 (1), pp. 47-57Chang, G.W., Chu, S.Y., Wang, H.L., An improved backward/-forward sweep load flow algorithm for radial distribution systems (2007) IEEE Transactions on Power Systems, 22 (2), pp. 882-884. , MayPiotrowski, A.P., Napiorkowski, J.J., Rowinski, P.M., How novel is the novel black hole optimization approach? (2014) Information Sciences, 267, pp. 191-200Kaur, S., Kumbhar, G., Sharma, J., A MINLP technique for optimal placement of multiple DG units in distribution systems (2014) International Journal of Electrical Power & Energy Systems, 63, pp. 609-617http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8858/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8858oai:repositorio.utb.edu.co:20.500.12585/88582023-05-26 10:05:26.913Repositorio Institucional UTBrepositorioutb@utb.edu.co |