Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs
This paper proposes a new hybrid master–slave optimization approach to address the problem of the optimal placement and sizing of distribution static compensators (D-STATCOMs) in electrical distribution grids. The optimal location of the D-STATCOMs is identified by implementing the classical and wel...
- Autores:
-
Montoya, Oscar Danilo
Chamorro, Harold R.
Alvarado-Barrios, Lázaro
Gil-González, Walter
Orozco-Henao, César
- 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:20.500.12585/10343
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/10343
- Palabra clave:
- Annual operational cost minimization
Chu and Beasley genetic algorithm (CBGA)
Daily active and reactive demand curves
Distribution static compensators (D-STATCOMs)
Radial distribution networks
Reactive power compensation
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc/4.0/
| id |
UTB2_83c3d8a8a9c41a43349b386334d54a22 |
|---|---|
| oai_identifier_str |
oai:repositorio.utb.edu.co:20.500.12585/10343 |
| network_acronym_str |
UTB2 |
| network_name_str |
Repositorio Institucional UTB |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs |
| title |
Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs |
| spellingShingle |
Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs Annual operational cost minimization Chu and Beasley genetic algorithm (CBGA) Daily active and reactive demand curves Distribution static compensators (D-STATCOMs) Radial distribution networks Reactive power compensation |
| title_short |
Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs |
| title_full |
Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs |
| title_fullStr |
Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs |
| title_full_unstemmed |
Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs |
| title_sort |
Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs |
| dc.creator.fl_str_mv |
Montoya, Oscar Danilo Chamorro, Harold R. Alvarado-Barrios, Lázaro Gil-González, Walter Orozco-Henao, César |
| dc.contributor.author.none.fl_str_mv |
Montoya, Oscar Danilo Chamorro, Harold R. Alvarado-Barrios, Lázaro Gil-González, Walter Orozco-Henao, César |
| dc.subject.keywords.spa.fl_str_mv |
Annual operational cost minimization Chu and Beasley genetic algorithm (CBGA) Daily active and reactive demand curves Distribution static compensators (D-STATCOMs) Radial distribution networks Reactive power compensation |
| topic |
Annual operational cost minimization Chu and Beasley genetic algorithm (CBGA) Daily active and reactive demand curves Distribution static compensators (D-STATCOMs) Radial distribution networks Reactive power compensation |
| description |
This paper proposes a new hybrid master–slave optimization approach to address the problem of the optimal placement and sizing of distribution static compensators (D-STATCOMs) in electrical distribution grids. The optimal location of the D-STATCOMs is identified by implementing the classical and well-known Chu and Beasley genetic algorithm, which employs an integer codification to select the nodes where these will be installed. To determine the optimal sizes of the D-STATCOMs, a second-order cone programming reformulation of the optimal power flow problem is employed with the aim of minimizing the total costs of the daily energy losses. The objective function considered in this study is the minimization of the annual operative costs associated with energy losses and installation investments in D-STATCOMs. This objective function is subject to classical power balance constraints and device capabilities, which generates a mixed-integer nonlinear programming model that is solved with the proposed genetic-convex strategy. Numerical validations in the 33-node test feeder with radial configuration show the proposed genetic-convex model’s effectiveness to minimize the annual operative costs of the grid when compared with the optimization solvers available in GAMS software. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-07-30T12:19:34Z |
| dc.date.available.none.fl_str_mv |
2021-07-30T12:19:34Z |
| dc.date.issued.none.fl_str_mv |
2021-04-21 |
| dc.date.submitted.none.fl_str_mv |
2021-07-29 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/restrictedAccess |
| dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.identifier.citation.spa.fl_str_mv |
Montoya, O.D.; Chamorro, H.R.; Alvarado-Barrios, L.; Gil-González, W.; Orozco-Henao, C. Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs. Appl. Sci. 2021, 11, 3353. https://doi.org/10.3390/app11083353 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/10343 |
| dc.identifier.doi.none.fl_str_mv |
10.3390/app11083353 |
| 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 |
Montoya, O.D.; Chamorro, H.R.; Alvarado-Barrios, L.; Gil-González, W.; Orozco-Henao, C. Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs. Appl. Sci. 2021, 11, 3353. https://doi.org/10.3390/app11083353 10.3390/app11083353 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/10343 |
| 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/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.cc.*.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.none.fl_str_mv |
15 páginas |
| dc.format.medium.none.fl_str_mv |
Recurso en línea / Electrónico |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.coverage.spatial.none.fl_str_mv |
Colombia, Bolívar |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
| dc.publisher.sede.spa.fl_str_mv |
Campus Tecnológico |
| dc.publisher.discipline.spa.fl_str_mv |
Ingeniería Eléctrica |
| dc.source.spa.fl_str_mv |
Applied Sciences 2021, 11, 3353 |
| institution |
Universidad Tecnológica de Bolívar |
| bitstream.url.fl_str_mv |
https://repositorio.utb.edu.co/bitstream/20.500.12585/10343/1/applsci-11-03353-v2.pdf https://repositorio.utb.edu.co/bitstream/20.500.12585/10343/2/license_rdf https://repositorio.utb.edu.co/bitstream/20.500.12585/10343/3/license.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/10343/4/applsci-11-03353-v2.pdf.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/10343/5/applsci-11-03353-v2.pdf.jpg |
| bitstream.checksum.fl_str_mv |
e6cbf7e83c82fccfdee873c8dc24c8d3 24013099e9e6abb1575dc6ce0855efd5 e20ad307a1c5f3f25af9304a7a7c86b6 f75b884426fed2a452a2f4a961004dc0 b4886eee31b045daf09930a36390710e |
| 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_ |
1814021656401674240 |
| spelling |
Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Chamorro, Harold R.59e2dcd8-f603-4e1f-8459-da694d5a324dAlvarado-Barrios, Lázaro57fdbc12-9b16-4b46-abf4-0ba206be4700Gil-González, Walter1747fed9-7818-4c10-a283-efb3c73ebb27Orozco-Henao, Césarcfb1cfea-5d74-4fc8-8a2e-223843a0e4aaColombia, Bolívar2021-07-30T12:19:34Z2021-07-30T12:19:34Z2021-04-212021-07-29Montoya, O.D.; Chamorro, H.R.; Alvarado-Barrios, L.; Gil-González, W.; Orozco-Henao, C. Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs. Appl. Sci. 2021, 11, 3353. https://doi.org/10.3390/app11083353https://hdl.handle.net/20.500.12585/1034310.3390/app11083353Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis paper proposes a new hybrid master–slave optimization approach to address the problem of the optimal placement and sizing of distribution static compensators (D-STATCOMs) in electrical distribution grids. The optimal location of the D-STATCOMs is identified by implementing the classical and well-known Chu and Beasley genetic algorithm, which employs an integer codification to select the nodes where these will be installed. To determine the optimal sizes of the D-STATCOMs, a second-order cone programming reformulation of the optimal power flow problem is employed with the aim of minimizing the total costs of the daily energy losses. The objective function considered in this study is the minimization of the annual operative costs associated with energy losses and installation investments in D-STATCOMs. This objective function is subject to classical power balance constraints and device capabilities, which generates a mixed-integer nonlinear programming model that is solved with the proposed genetic-convex strategy. Numerical validations in the 33-node test feeder with radial configuration show the proposed genetic-convex model’s effectiveness to minimize the annual operative costs of the grid when compared with the optimization solvers available in GAMS software.Universidad Tecnológica de Bolívar15 páginasRecurso en línea / Electrónicoapplication/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Applied Sciences 2021, 11, 3353Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Annual operational cost minimizationChu and Beasley genetic algorithm (CBGA)Daily active and reactive demand curvesDistribution static compensators (D-STATCOMs)Radial distribution networksReactive power compensationCartagena de IndiasCampus TecnológicoIngeniería EléctricaInvestigadoresGirbau-Llistuella, F.; Díaz-González, F.; Sumper, A.; Gallart-Fernández, R.; Heredero-Peris, D. Smart Grid Architecture for Rural Distribution Networks: Application to a Spanish Pilot Network. Energies 2018, 11, 844Montoya, O.D.; Serra, F.M.; Angelo, C.H.D. On the Efficiency in Electrical Networks with AC and DC Operation Technologies: A Comparative Study at the Distribution Stage. Electronics 2020, 9, 1352Celli, G.; Pilo, F.; Pisano, G.; Cicoria, R.; Iaria, A. Meshed vs. radial MV distribution network in presence of large amount of DG. In Proceedings of the IEEE PES Power Systems Conference and Exposition, New York, NY, USA, 10–13 October 2004.Li, H.; Cui, H.; Li, C. Distribution Network Power Loss Analysis Considering Uncertainties in Distributed Generations. Sustainability 2019, 11, 1311Sharma, A.K.; Murty, V.V.S.N. Analysis of Mesh Distribution Systems Considering Load Models and Load Growth Impact with Loops on System Performance. J. Inst. Eng. India Ser. B 2014, 95, 295–318Gil-González, W.; Montoya, O.D.; Rajagopalan, A.; Grisales-Noreña, L.F.; Hernández, J.C. Optimal Selection and Location of Fixed-Step Capacitor Banks in Distribution Networks Using a Discrete Version of the Vortex Search Algorithm. Energies 2020, 13, 4914Tamilselvan, V.; Jayabarathi, T.; Raghunathan, T.; Yang, X.S. Optimal capacitor placement in radial distribution systems using flower pollination algorithm. Alex. Eng. J. 2018, 57, 2775–2786Riaño, F.E.; Cruz, J.F.; Montoya, O.D.; Chamorro, H.R.; Alvarado-Barrios, L. Reduction of Losses and Operating Costs in Distribution Networks Using a Genetic Algorithm and Mathematical Optimization. Electronics 2021, 10, 419Sirjani, R.; Jordehi, A.R. Optimal placement and sizing of distribution static compensator (D-STATCOM) in electric distribution networks: A review. Renew. Sus. Energ. Rev. 2017, 77, 688–694Marjani, S.R.; Talavat, V.; Galvani, S. Optimal allocation of D-STATCOM and reconfiguration in radial distribution network using MOPSO algorithm in TOPSIS framework. Int. Trans. Electr. Energy Syst. 2018, 29, e2723Stanelyte, D.; Radziukynas, V. Review of Voltage and Reactive Power Control Algorithms in Electrical Distribution Networks. Energies 2019, 13, 58Guo, C.; Zhong, L.; Zhao, J.; Gao, G. Single-Phase Reactive Power Compensation Control for STATCOMs via Unknown System Dynamics Estimation. Math. Probl. Eng. 2020, 2020, 1–9Montoya, O.D.; Gil-González, W.; Hernández, J.C. Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA. Appl. Sci. 2021, 11, 2175Tsai, S.J.S.; Chang, Y. Dynamic and unbalance voltage compensation using STATCOM. In Proceedings of the 2008 IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, USA, 20–24 July 2008Yuvaraj, T.; Ravi, K.; Devabalaji, K. DSTATCOM allocation in distribution networks considering load variations using bat algorithm. Ain Shams Eng. J. 2017, 8, 391–403Saxena, N.K.; Kumar, A. Cost based reactive power participation for voltage control in multi units based isolated hybrid power system. J. Electr. Syst. Inf. Technol. 2016, 3, 442–453Samimi, A.; Golkar, M.A. A Novel Method for Optimal Placement of STATCOM in Distribution Networks Using Sensitivity Analysis by DIgSILENT Software. In Proceedings of the 2011 Asia-Pacific Power and Energy Engineering Conference, Wuhan, China, 25–28 March 2011Tolabi, H.B.; Ali, M.H.; Rizwan, M. Simultaneous Reconfiguration, Optimal Placement of DSTATCOM, and Photovoltaic Array in a Distribution System Based on Fuzzy-ACO Approach. IEEE Trans. Sustain. Energy 2015, 6, 210–218Gupta, A.R.; Kumar, A. Energy Savings Using D-STATCOM Placement in Radial Distribution System. Procedia Comput. Sci. 2015, 70, 558–564Farivar, M.; Low, S.H. Branch Flow Model: Relaxations and Convexification—Part I. IEEE Trans. Power Syst. 2013, 28, 2554–2564Farivar, M.; Low, S.H. Branch Flow Model: Relaxations and Convexification—Part II. IEEE Trans. Power Syst. 2013, 28, 2565–2572Gil-González, W.; Garces, A.; Montoya, O.D.; Hernández, J.C. A Mixed-Integer Convex Model for the Optimal Placement and Sizing of Distributed Generators in Power Distribution Networks. Appl. Sci. 2021, 11, 627Eltved, A.; Dahl, J.; Andersen, M.S. On the robustness and scalability of semidefinite relaxation for optimal power flow problems. Optim. Eng. 2019, 21, 375–392Montoya, O.D.; Gil-González, W.; Orozco-Henao, C. Vortex search and Chu-Beasley genetic algorithms for optimal location and sizing of distributed generators in distribution networks: A novel hybrid approach. Eng. Sci. Technol. Int. J. 2020, 23, 1351–1363Montoya, O.D.; Gil-González, W.; Grisales-Noreña, L.F. Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks. Appl. Sci. 2020, 10, 8616Montoya, O.D.; Gil-González, W. Dynamic active and reactive power compensation in distribution networks with batteries: A day-ahead economic dispatch approach. Comput. Electr. Eng. 2020, 85, 106710Sharma, A.K.; Saxena, A.; Tiwari, R. Optimal Placement of SVC Incorporating Installation Cost. Int. J. Hybrid Inf. Technol. 2016, 9, 289–302http://purl.org/coar/resource_type/c_2df8fbb1ORIGINALapplsci-11-03353-v2.pdfapplsci-11-03353-v2.pdfArtículoapplication/pdf354209https://repositorio.utb.edu.co/bitstream/20.500.12585/10343/1/applsci-11-03353-v2.pdfe6cbf7e83c82fccfdee873c8dc24c8d3MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.utb.edu.co/bitstream/20.500.12585/10343/2/license_rdf24013099e9e6abb1575dc6ce0855efd5MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/10343/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXTapplsci-11-03353-v2.pdf.txtapplsci-11-03353-v2.pdf.txtExtracted texttext/plain47472https://repositorio.utb.edu.co/bitstream/20.500.12585/10343/4/applsci-11-03353-v2.pdf.txtf75b884426fed2a452a2f4a961004dc0MD54THUMBNAILapplsci-11-03353-v2.pdf.jpgapplsci-11-03353-v2.pdf.jpgGenerated Thumbnailimage/jpeg99849https://repositorio.utb.edu.co/bitstream/20.500.12585/10343/5/applsci-11-03353-v2.pdf.jpgb4886eee31b045daf09930a36390710eMD5520.500.12585/10343oai:repositorio.utb.edu.co:20.500.12585/103432023-05-25 10:21:52.759Repositorio Institucional UTBrepositorioutb@utb.edu.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 |