Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimization

A convex mathematical model based on second-order cone programming (SOCP) for the optimal operation in direct current microgrids (DCMGs) with high-level penetration of renewable energies and battery energy storage systems (BESSs) is developed in this paper. The SOCP formulation allows converting the...

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Autores:
Gil-González, Walter
Montoya, Oscar Danilo
Grisales-Noreña, Luis Fernando
Cruz-Peragón, Fernando
Alcalá, Gerardo
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/9356
Acceso en línea:
https://hdl.handle.net/20.500.12585/9356
https://www.mdpi.com/1996-1073/13/7/1703
Palabra clave:
Second-order cone programming
Economic dispatch problem
Artificial neural networks;
Battery energy storage system
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc/4.0/
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dc.title.spa.fl_str_mv Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimization
title Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimization
spellingShingle Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimization
Second-order cone programming
Economic dispatch problem
Artificial neural networks;
Battery energy storage system
title_short Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimization
title_full Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimization
title_fullStr Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimization
title_full_unstemmed Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimization
title_sort Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimization
dc.creator.fl_str_mv Gil-González, Walter
Montoya, Oscar Danilo
Grisales-Noreña, Luis Fernando
Cruz-Peragón, Fernando
Alcalá, Gerardo
dc.contributor.author.none.fl_str_mv Gil-González, Walter
Montoya, Oscar Danilo
Grisales-Noreña, Luis Fernando
Cruz-Peragón, Fernando
Alcalá, Gerardo
dc.subject.keywords.spa.fl_str_mv Second-order cone programming
topic Second-order cone programming
Economic dispatch problem
Artificial neural networks;
Battery energy storage system
dc.subject.keywords.none.fl_str_mv Economic dispatch problem
Artificial neural networks;
Battery energy storage system
description A convex mathematical model based on second-order cone programming (SOCP) for the optimal operation in direct current microgrids (DCMGs) with high-level penetration of renewable energies and battery energy storage systems (BESSs) is developed in this paper. The SOCP formulation allows converting the non-convex model of economic dispatch into a convex approach that guarantees the global optimum and has an easy implementation in specialized software, i.e., CVX. This conversion is accomplished by performing a mathematical relaxation to ensure the global optimum in DCMG. The SOCP model includes changeable energy purchase prices in the DCMG operation, which makes it in a suitable formulation to be implemented in real-time operation. An energy short-term forecasting model based on a receding horizon control (RHC) plus an artificial neural network (ANN) is used to forecast primary sources of renewable energy for periods of 0.5h. The proposed mathematical approach is compared to the non-convex model and semidefinite programming (SDP) in three simulation scenarios to validate its accuracy and efficiency
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-08-31T21:37:13Z
dc.date.available.none.fl_str_mv 2020-08-31T21:37:13Z
dc.date.issued.none.fl_str_mv 2020-04-03
dc.date.submitted.none.fl_str_mv 2020-08-31
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dc.type.spa.spa.fl_str_mv Artículo
status_str publishedVersion
dc.identifier.citation.spa.fl_str_mv Gil-González, W.; Montoya, O.D.; Grisales-Noreña, L.F.; Cruz-Peragón, F.; Alcalá, G. Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization. Energies 2020, 13, 1703.
dc.identifier.issn.none.fl_str_mv 1996-1073
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/9356
dc.identifier.url.none.fl_str_mv https://www.mdpi.com/1996-1073/13/7/1703
dc.identifier.doi.none.fl_str_mv 10.3390/en13071703
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio UTB
identifier_str_mv Gil-González, W.; Montoya, O.D.; Grisales-Noreña, L.F.; Cruz-Peragón, F.; Alcalá, G. Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization. Energies 2020, 13, 1703.
1996-1073
10.3390/en13071703
Universidad Tecnológica de Bolívar
Repositorio UTB
url https://hdl.handle.net/20.500.12585/9356
https://www.mdpi.com/1996-1073/13/7/1703
dc.language.iso.spa.fl_str_mv eng
language eng
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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
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eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 15 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.coverage.spatial.none.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 Energies; Vol. 13, Núm. 7 (2020)
institution Universidad Tecnológica de Bolívar
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spelling Gil-González, Walterce1f5078-74c6-4b5c-b56a-784f85e52a08Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Grisales-Noreña, Luis Fernandob2728c9a-1fd6-47c8-b7bc-d95ea0252207Cruz-Peragón, Fernando096faa6a-6c28-48ea-b5ae-37dc9d666644Alcalá, Gerardo04e0a0ab-2e1e-47e9-aa21-c0cb13133579Cartagena de Indias2020-08-31T21:37:13Z2020-08-31T21:37:13Z2020-04-032020-08-31Gil-González, W.; Montoya, O.D.; Grisales-Noreña, L.F.; Cruz-Peragón, F.; Alcalá, G. Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization. Energies 2020, 13, 1703.1996-1073https://hdl.handle.net/20.500.12585/9356https://www.mdpi.com/1996-1073/13/7/170310.3390/en13071703Universidad Tecnológica de BolívarRepositorio UTBA convex mathematical model based on second-order cone programming (SOCP) for the optimal operation in direct current microgrids (DCMGs) with high-level penetration of renewable energies and battery energy storage systems (BESSs) is developed in this paper. The SOCP formulation allows converting the non-convex model of economic dispatch into a convex approach that guarantees the global optimum and has an easy implementation in specialized software, i.e., CVX. This conversion is accomplished by performing a mathematical relaxation to ensure the global optimum in DCMG. The SOCP model includes changeable energy purchase prices in the DCMG operation, which makes it in a suitable formulation to be implemented in real-time operation. An energy short-term forecasting model based on a receding horizon control (RHC) plus an artificial neural network (ANN) is used to forecast primary sources of renewable energy for periods of 0.5h. The proposed mathematical approach is compared to the non-convex model and semidefinite programming (SDP) in three simulation scenarios to validate its accuracy and efficiency15 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Energies; Vol. 13, Núm. 7 (2020)Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimizationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Second-order cone programmingEconomic dispatch problemArtificial neural networks;Battery energy storage systemInvestigadoresCampus TecnológicoIngeniería EléctricaZou, D.; Li, S.; Kong, X.; Ouyang, H.; Li, Z. 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A cost-efficient application of different battery energy storage technologies in microgrids considering load uncertainty. J. Energy Storage 2019, 22, 17–26. [CrossRef]Zheng, Y.; Dong, Z.Y.; Luo, F.J.; Meng, K.; Qiu, J.; Wong, K.P. Optimal allocation of energy storage system for risk mitigation of DISCOs with high renewable penetrations. IEEE Trans. Power Syst. 2013, 29, 212–220. [CrossRef]Lee, G.Y.; Ko, B.S.; Cho, J.; Kim, R.Y. A Distributed Control Method Based on a Voltage Sensitivity Matrix in DC Microgrids with Low-Speed Communication. IEEE Trans. Smart Grid 2019, 10, 3809–3817. [CrossRef]Das, C.K.; Bass, O.; Kothapalli, G.; Mahmoud, T.S.; Habibi, D. Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm. Appl. Energy 2018, 232, 212–228. [CrossRef]Grisales, 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. IEEE Lat. Am. Trans. 2017, 15, 1084–1090. [CrossRef]Montoya-Giraldo, O.D.; Gil-González, W.J.; Garcés-Ruíz, A. Optimal Power Flow for radial and mesh grids using semidefinite programming. Tecno Lógicas 2017, 20, 29–42. [CrossRef]Kim, S.; Kojima, M. Exact solutions of some nonconvex quadratic optimization problems via SDP and SOCP relaxations. Comput. Optim. Appl. 2003, 26, 143–154. [CrossRef]Li, J.; Liu, F.; Wang, Z.; Low, S.H.; Mei, S. Optimal power flow in stand-alone DC microgrids. IEEE Trans. Power Syst. 2018, 33, 5496–5506. [CrossRef]Hindi, H. A tutorial on convex optimization. In Proceedings of the 2004 American Control Conference, Boston, MA, USA, 30 June–2 July 2004; Volume 4, pp. 3252–3265.Alizadeh, F.; Goldfarb, D. Second-order cone programming. Math. Program. 2003, 95, 3–51. [CrossRef]Alzalg, B.M. Stochastic second-order cone programming: Applications models. 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