Mostrar el registro sencillo del ítem

dc.contributor.authorMontoya, Oscar Danilo
dc.contributor.authorArias‑Londoño, Andrés
dc.contributor.authorGarrido Arévalo, Víctor Manuel
dc.contributor.authorGil-González, Walter
dc.contributor.authorGrisales-Noreña, Luis Fernando
dc.date.accessioned2022-01-28T20:41:17Z
dc.date.available2022-01-28T20:41:17Z
dc.date.issued2021-11-07
dc.date.submitted2022-01-28
dc.identifier.citationMontoya Giraldo, Oscar & Arias-Londoño, Andrés & Garrido, Victor & Gil González, Walter & Grisales-Noreña, Luis. (2021). A quadratic convex approximation for optimal operation of battery energy storage systems in DC distribution networks. Energy Systems. 10.1007/s12667-021-00495-z.spa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/10430
dc.description.abstractThis paper proposes a quadratic convex model for optimal operation of battery energy storage systems in a direct current (DC) network that approximates the original nonlinear non-convex one. The proposed quadratic convex model uses Taylor’s series expansion to transform the product between voltage variables in the power balance equations into a linear combination of them. Numerical simulations in the general algebraic modeling system (GAMS) for both models show small diferences in the daily energy losses, which are lower than 3.00%. The main advantage of the proposed quadratic model is that its optimal solution is achievable with interior point methods guaranteeing its uniqueness (convexity properties of the solution space and objective function), which is not possible to guarantee with the exact nonlinear non-convex model. The 30-bus DC test feeder with four distributed generators (with power generation forecast via artifcial neural networks with errors lower than 1% between real and predicted generation curves) and three batteries is used to validate the proposed convex and exact models. Numerical results obtained by GAMS show the efectiveness of the proposed quadratic convex model for diferent simulation scenarios tested, which was confrmed by the CVX tool for convex optimization in MATLABspa
dc.format.extent22 Páginas
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceEnergy Systemsspa
dc.titleA quadratic convex approximation for optimal operation of battery energy storage systems in DC distribution networksspa
dcterms.bibliographicCitationAlharbi, T., Bhattacharya, K.: Optimal scheduling of energy resources and management of loads in isolated/islanded microgrids. Can. J. Electr. Comput. Eng. 40(4), 284–294 (2017)spa
dcterms.bibliographicCitationSchoendienst, T., Vokkarane, V.M.: Reducing greenhouse gas emissions with power source-aware multidomain multilayer networks. IEEE Syst. J. 11(2), 673–683 (2017)spa
dcterms.bibliographicCitation. Bogaerts, M., Cirhigiri, L., Robinson, I., Rodkin, M., Hajjar, R., Junior, C.C., Newton, P.: Climate change mitigation through intensifed pasture management: estimating greenhouse gas emissions on cattle farms in the Brazilian Amazon. J. Clean. Prod. 162, 1539–1550 (2017)spa
dcterms.bibliographicCitationCrosson, P., Shalloo, L., O’Brien, D., Lanigan, G., Foley, P., Boland, T., and Kenny, D.: A review of whole farm systems models of greenhouse gas emissions from beef and dairy cattle production systems, Anim. Feed Sci. Technol. 166–167, 29–45 (2011). Special Issue: Greenhouse Gases in Animal Agriculture—Finding a Balance between Food and Emissionsspa
dcterms.bibliographicCitationThaker, S., Oni, A.O., Gemechu, E., Kumar, A.: Evaluating energy and greenhouse gas emission footprints of thermal energy storage systems for concentrated solar power applications. J. Energy Storage 26, 100992 (2019)spa
dcterms.bibliographicCitation. Karmaker, A.K., Rahman, M.M., Hossain, M.A., Ahmed, M.R.: Exploration and corrective measures of greenhouse gas emission from fossil fuel power stations for Bangladesh. J. Clean. Prod. 244, 118645 (2020)spa
dcterms.bibliographicCitation. Bauer, A., Menrad, K.: Standing up for the Paris Agreement: do global climate targets infuence individuals’ greenhouse gas emissions? Environ. Sci. Policy 99, 72–79 (2019)spa
dcterms.bibliographicCitationFerreira, A., Pinheiro, M.D., de Brito, J., Mateus, R.: Decarbonizing strategies of the retail sector following the Paris Agreement. Energy Policy 135, 110999 (2019)spa
dcterms.bibliographicCitationMontoya, O.D., Gil-González, W., Grisales-Norena, L., Orozco-Henao, C., Serra, F.: Economic dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models. Energies 12(23), 4494 (2019)spa
dcterms.bibliographicCitationYan, Q., Zhang, B., Kezunovic, M.: Optimized operational cost reduction for an EV charging station integrated with battery energy storage and PV generation. IEEE Trans. Smart Grid 10(2), 2096– 2106 (2019)spa
dcterms.bibliographicCitationAdefarati, T., Bansal, R.C., John Justo, J.: Techno-economic analysis of a PV-wind-battery-diesel standalone power system in a remote area. J. Eng. 2017(13), 740–744 (2017)spa
dcterms.bibliographicCitationNaumann, M., Karl, R.C., Truong, C.N., Jossen, A., Hesse, H.C.: Lithium-ion battery cost analysis in PV-household application. Energy Procedia 73, 37–47 (2015). 9th International Renewable Energy Storage Conference, IRES 2015spa
dcterms.bibliographicCitationWentker, M., Greenwood, M., Leker, J.: A bottom-up approach to lithium-ion battery cost modeling with a focus on cathode active materials. Energies 12(3), 504 (2019)spa
dcterms.bibliographicCitationMontoya, O.D., Grajales, A., Garces, A., Castro, C.A.: Distribution systems operation considering energy storage devices and distributed generation. IEEE Lat. Am. Trans. 15(5), 890–900 (2017)spa
dcterms.bibliographicCitationWang, Z., Du, J., Zhang, M., Yu, J., Liu, H., Chai, X., Yang, B., Zhu, C., Xu, J.: Continuous preparation of high performance fexible asymmetric supercapacitor with a very fast, low-cost, simple and scalable electrochemical co-deposition method. J. Power Sources 437, 226827 (2019)spa
dcterms.bibliographicCitationKrishan, O., Suhag, S.: Grid-independent PV system hybridization with fuel cell-battery/supercapacitor: optimum sizing and comparative techno-economic analysis. Sustain. Energy Technol. Assess. 37, 100625 (2020)spa
dcterms.bibliographicCitationTeyber, R., Rowe, A.: Superconducting magnet design for magnetic liquefers using total cost minimization. Cryogenics 99, 114–122 (2019)spa
dcterms.bibliographicCitationGil-González, W., Montoya, O.D., Garces, A.: Control of a SMES for mitigating subsynchronous oscillations in power systems: a PBC-PI approach. J. Energy Storage 20, 163–172 (2018)spa
dcterms.bibliographicCitationKale, V., Secanell, M.: A comparative study between optimal metal and composite rotors for fywheel energy storage systems. Energy Rep. 4, 576–585 (2018)spa
dcterms.bibliographicCitationRamli, M.A., Hiendro, A., Twaha, S.: Economic analysis of PV/diesel hybrid system with fywheel energy storage. Renew. Energy 78, 398–405 (2015)spa
dcterms.bibliographicCitationGil-González, W., Montoya, O.D., Holguín, E., Garces, A., Grisales-Norena, L.F.: Economic dispatch of energy storage systems in dc microgrids employing a semidefnite programming model. J. Energy Storage 21, 1–8 (2019)spa
dcterms.bibliographicCitationVazquez, L., Majanne, Y., Castro, M., Luukkanen, J., Hohmeyer, O., Vilaragut, M., Diaz, D.: Energy system planning towards renewable power system: energy matrix change in Cuba by 2030 IFAC-PapersOnLine 51(28), 522–527 (2018). 10th IFAC Symposium on Control of Power and Energy Systems CPES 2018spa
dcterms.bibliographicCitationCepeda, C., Orozco-Henao, C., Percybrooks, W., Pulgarín-Rivera, J.D., Montoya, O.D., GilGonzález, W., Vélez, J.C.: Intelligent fault detection system for microgrids. Energies 13(5), 1223 (2020)spa
dcterms.bibliographicCitationJiang, Q., Xue, M., Geng, G.: Energy management of microgrid in grid-connected and stand-alone modes. IEEE Trans. Power Syst. 28(3), 3380–3389 (2013)spa
dcterms.bibliographicCitationMontoya, O.D., Grisales-Norena, L.F., Gil-González, W., Alcalá, G., Hernandez-Escobedo, Q.: Optimal location and sizing of PV sources in DC networks for minimizing greenhouse emissions in diesel generators. Symmetry 12(2), 322 (2020)spa
dcterms.bibliographicCitationMesso, T., Luhtala, R., Roinila, T., de Jong, E., Scharrenberg, R., Caldognetto, T., Mattavelli, P., Sun, Y., Fabian, A.: Using high-bandwidth voltage amplifer to emulate grid-following inverter for AC microgrid dynamics studies. Energies 12(3), 379 (2019)spa
dcterms.bibliographicCitationGrisales-Norena, 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. Sustainability 12(3), 1219 (2020)spa
dcterms.bibliographicCitation. Xia, Y., Wei, W., Yu, M., Wang, X., Peng, Y.: Power management for a hybrid AC/DC microgrid with multiple subgrids. IEEE Trans. Power Electron. 33(4), 3520–3533 (2018)spa
dcterms.bibliographicCitationMontoya, O.D.: A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks. Eng. Sci. Technol. Int. J. 23(3), 527–533 (2020)spa
dcterms.bibliographicCitationLi, J., Liu, F., Wang, Z., Low, S.H., Mei, S.: Optimal power fow in stand-alone DC microgrids. IEEE Trans. Power Syst. 33(5), 5496–5506 (2018)spa
dcterms.bibliographicCitation. Iskender, I., Genc, N.: Power electronic converters in DC microgrid. In: Power Systems, pp. 115– 137. Springer International Publishing (2019)spa
dcterms.bibliographicCitation. Biczel, P.: Power electronic converters in DC microgrid. In: Compatibility in Power Electronics, May 2007, pp. 1–6 (2007)spa
dcterms.bibliographicCitationMurillo-Yarce, D., Garcés-Ruiz, A., Escobar-Mejía, A.: Passivity-based control for DC-microgrids with constant power terminals in island mode operation. Revista Facultad de Ingeniería Universidad de Antioquia 86, 32–39 (2018)spa
dcterms.bibliographicCitationMahmoodi, M., Shamsi, P., Fahimi, B.: Economic dispatch of a hybrid microgrid with distributed energy storage. IEEE Trans. Smart Grid 6(6), 2607–2614 (2015)spa
dcterms.bibliographicCitationDominguez-Jimenez, J., Montoya, O., Campillo, J., Gil-González, W.: Economic dispatch in dc microgrids considering diferent battery technologies: a benchmark study. In: 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), vol. 4, pp. 1–6. IEEE (2020)spa
dcterms.bibliographicCitationGrisales-Norena, L.F., Montoya, O.D., Ramos-Paja, C.A.: An energy management system for optimal operation of bss in dc distributed generation environments based on a parallel PSO algorithm. J. Energy Storage 29, 101488 (2020)spa
dcterms.bibliographicCitationMontoya, O.D., Gil-González, W., Garces, A.: Optimal power fow on DC microgrids: a quadratic convex approximation. IEEE Trans. Circuits Syst. II 66(6), 1018–1022 (2018)spa
dcterms.bibliographicCitationMolina-Martin, F., Montoya, O.D., Grisales-Norena, L.F., Hernández, J.C., Ramírez-Vanegas, C.A.: Simultaneous minimization of energy losses and greenhouse gas emissions in AC distribution networks using BESS. Electronics 10(9), 1002 (2021)spa
dcterms.bibliographicCitationGarcés, A., Montoya, O.-D.: A potential function for the power fow in DC microgrids: an analysis of the uniqueness and existence of the solution and convergence of the algorithms. J. Control Autom. Electr. Syst. 30(5), 794–801 (2019)spa
dcterms.bibliographicCitationMontoya, O.D., Grisales-na, L., González-Montoya, D., Ramos-Paja, C., Garces, A.: Linear power fow formulation for low-voltage DC power grids. Electr. Power Syst. Res. 163, 375–381 (2018)spa
dcterms.bibliographicCitationGarces, A.: Uniqueness of the power fow solutions in low voltage direct current grids. Electr. Power Syst. Res. 151, 149–153 (2017)spa
dcterms.bibliographicCitationliliana and Napitupulu, T.A.: Artifcial neural network application in gross domestic product forecasting: an Indonesia case. In: 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies. IEEE (2010)spa
dcterms.bibliographicCitationTümer, A.E., Akkuş, A.: Forecasting gross domestic product per capita using artifcial neural networks with non-economical parameters. Phys. A Stat. Mech. Appl. 512, 468–473 (2018)spa
dcterms.bibliographicCitationJung, D.-H., Kim, H.-J., Kim, J.Y., Lee, T.S., Park, S.H.: Model predictive control via output feedback neural network for improved multi-window greenhouse ventilation control. Sensors 20(6), 1756 (2020)spa
dcterms.bibliographicCitationFente, D.N., Singh, D.K.: Weather forecasting using artifcial neural network. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE (2018)spa
dcterms.bibliographicCitationAntanasijević, D.Z., Ristić, M., Perić-Grujić, A.A., Pocajt, V.V.: Forecasting GHG emissions using an optimized artifcial neural network model based on correlation and principal component analysis. Int. J. Greenh. Gas Control 20, 244–253 (2014)spa
dcterms.bibliographicCitationYu, L., Wang, S., Lai, K.K.: Foreign-Exchange-Rate Forecasting With Artifcial Neural Networks. Springer US, New York (2007)spa
dcterms.bibliographicCitationMontoya, 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. 85, 106710 (2020)spa
dcterms.bibliographicCitationKocer, M.C., Cengiz, C., Gezer, M., Gunes, D., Cinar, M.A., Alboyaci, B., Onen, A.: Assessment of battery storage technologies for a Turkish power network. Sustainability 11(13), 3669 (2019)spa
dcterms.bibliographicCitationWang, P., Ang, W., Xu, D.: Optimal sizing of distributed generations in DC microgrids with comprehensive consideration of system operation modes and operation targets. IEEE Access 6, 31129– 31140 (2018)spa
dcterms.bibliographicCitationData, s.s.r. time series of solar radiation data. Available online http://www.soda-pro.com/. Accessed 5 July 2019spa
dcterms.bibliographicCitationMontoya, O.D., Grajales, A., Garces, A., Castro, C.A.: Distribution systems operation considering energy storage devices and distributed generation. IEEE Lat. Am. Trans. 15(5), 890–900 (2017spa
dcterms.bibliographicCitationGiraldo, O.D.M.: Solving a classical optimization problem using gams optimizer package: economic dispatch problem implementation. Ingeniería y Ciencia 13(26), 39–63 (2017)spa
dcterms.bibliographicCitationMontoya, O.D., Gil-González, W., Grisales-Norena, L.: An exact MINLP model for optimal location and sizing of DGs in distribution networks: a general algebraic modeling system approach. Ain Shams Eng. J. 11(2), 409–418 (2020)spa
dcterms.bibliographicCitationTartibu, L., Sun, B., Kaunda, M.: Multi-objective optimization of the stack of a thermoacoustic engine using GAMS. Appl. Soft Comput. 28, 30–43 (2015)spa
dcterms.bibliographicCitationSkworcow, P., Paluszczyszyn, D., Ulanicki, B., Rudek, R., Belrain, T.: Optimisation of pump and valve schedules in complex large-scale water distribution systems using GAMS modelling language. Procedia Eng. 70, 1566–1574. 12th International Conference on Computing and Control for the Water Industry, CCWI2013 (2014)spa
dcterms.bibliographicCitationPintér, J.D.: Nonlinear optimization with GAMS /LGO. J. Glob. Optim. 38(1), 79–101 (2006)spa
datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.hasversioninfo:eu-repo/semantics/restrictedAccessspa
dc.identifier.doi10.1007/s12667-021-00495-z
dc.subject.keywordsBattery energy storage systemsspa
dc.subject.keywordsQuadratic convex approximationspa
dc.subject.keywordsEconomic dispatchspa
dc.subject.keywordsTaylor’s series expansionspa
dc.subject.keywordsDirect current distribution networksspa
dc.subject.keywordsArtifcial neural networksspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa
dc.subject.armarcLEMB
dc.type.spahttp://purl.org/coar/resource_type/c_2df8fbb1spa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_2df8fbb1spa


Ficheros en el ítem

Thumbnail
Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

http://creativecommons.org/licenses/by-nc-nd/4.0/
http://creativecommons.org/licenses/by-nc-nd/4.0/

Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.