Show simple item record

dc.contributor.authorMolina Martin, Federico
dc.contributor.authorMontoya, Oscar Danilo
dc.contributor.authorGrisales Noreña, Luis Fernando
dc.contributor.authorHernández, Jesus C.
dc.contributor.authorRamírez Vanegas, Carlos A.
dc.date.accessioned2021-07-31T14:43:52Z
dc.date.available2021-07-31T14:43:52Z
dc.date.issued2021-04-22
dc.date.submitted2021-07-30
dc.identifier.citationMolina-Martin, F.; Montoya, O.D.; Grisales-Noreña, 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 2021, 10, 1002. https://doi.org/10.3390/electronics10091002spa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/10348
dc.description.abstractThe problem of the optimal operation of battery energy storage systems (BESSs) in AC grids is addressed in this paper from the point of view of multi-objective optimization. A nonlinear programming (NLP) model is presented to minimize the total emissions of contaminant gasses to the atmosphere and costs of daily energy losses simultaneously, considering the AC grid complete model. The BESSs are modeled with their linear relation between the state-of-charge and the active power injection/absorption. The Pareto front for the multi-objective optimization NLP model is reached through the general algebraic modeling system, i.e., GAMS, implementing the pondered optimization approach using weighting factors for each objective function. Numerical results in the IEEE 33-bus and IEEE 69-node test feeders demonstrate the multi-objective nature of this optimization problem and the multiple possibilities that allow the grid operators to carry out an efficient operation of their distribution networks when BESS and renewable energy resources are introduced.spa
dc.description.sponsorshipUniversidad Tecnológica de Bolívarspa
dc.format.extent21 páginas
dc.format.mediumRecurso en línea / Electrónico
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.sourceElectronics 2021, 10, 1002spa
dc.titleSimultaneous Minimization of Energy Losses and Greenhouse Gas Emissions in AC Distribution Networks Using BESSspa
dcterms.bibliographicCitationValencia, A.; Hincapie, R.A.; Gallego, R.A. Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks. J. Energy Storage 2021, 34, 102158spa
dcterms.bibliographicCitationSoroudi, A. Power System Optimization Modeling in GAMS; Springer International Publishing: Berlin/Heidelberg, Germany, 2017.spa
dcterms.bibliographicCitationGonzalez, W.J.G.; Bocanegra, S.Y.; Serra, F.M.; Bueno-López, M.; Magaldi, G.L. Control Methods for Single-phase Voltage Supply with VSCs to Feed Nonlinear Loads in Rural Areas. Trans. Energy Syst. Eng. Appl. 2020, 1, 33–47spa
dcterms.bibliographicCitationRaugei, M.; Peluso, A.; Leccisi, E.; Fthenakis, V. Life-Cycle Carbon Emissions and Energy Return on Investment for 80% Domestic Renewable Electricity with Battery Storage in California (U.S.A.). Energies 2020, 13, 3934spa
dcterms.bibliographicCitationGong, Z.; Chau, S.; Trescases, O. Quantifying the GHG Reduction versus Battery Size in Diesel Buses with Electrified HVAC. In Proceedings of the 2020 IEEE Transportation Electrification Conference & Expo (ITEC), Chicago, IL, USA, 23–26 June 2020spa
dcterms.bibliographicCitationGrisales-Noreña, L.; 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 2020, 29, 101488spa
dcterms.bibliographicCitationWeniger, J.; Tjaden, T.; Quaschning, V. Sizing of Residential PV Battery Systems. Energy Procedia 2014, 46, 78–87spa
dcterms.bibliographicCitationSubramaniam, U.; Vavilapalli, S.; Padmanaban, S.; Blaabjerg, F.; Holm-Nielsen, J.B.; Almakhles, D. A Hybrid PV-Battery System for ON-Grid and OFF-Grid Applications—Controller-In-Loop Simulation Validation. Energies 2020, 13, 755spa
dcterms.bibliographicCitationZhu, Y.; Liu, C.; Wang, B.; Sun, K. Damping control for a target oscillation mode using battery energy storage. J. Mod. Power Syst. Clean Energy 2018, 6, 833–845spa
dcterms.bibliographicCitationKisacikoglu, M.C.; Ozpineci, B.; Tolbert, L.M. Effects of V2G reactive power compensation on the component selection in an EV or PHEV bidirectional charger. In Proceedings of the 2010 IEEE Energy Conversion Congress and Exposition, Atlanta, GA, USA, 12–16 September 2010.spa
dcterms.bibliographicCitationMazza, A.; Mirtaheri, H.; Chicco, G.; Russo, A.; Fantino, M. Location and Sizing of Battery Energy Storage Units in Low Voltage Distribution Networks. Energies 2019, 13, 52spa
dcterms.bibliographicCitationWang, Z.; Zhong, J.; Chen, D.; Lu, Y.; Men, K. A multi-period optimal power flow model including battery energy storage. In Proceedings of the 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, Canada, 21–25 July 2013spa
dcterms.bibliographicCitationAghaei, J.; Bozorgavari, S.A.; Pirouzi, S.; Farahmand, H.; Korpås, M. Flexibility Planning of Distributed Battery Energy Storage Systems in Smart Distribution Networks. Iran. J. Sci. Technol. Trans. Electr. Eng. 2019, 44, 1105–1121spa
dcterms.bibliographicCitationDas, C.K.; Bass, O.; Kothapalli, G.; Mahmoud, T.S.; Habibi, D. Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality. Renew. Sustain. Energy Rev. 2018, 91, 1205–1230.spa
dcterms.bibliographicCitationHeine, P.; Hellman, H.P.; Pihkala, A.; Siilin, K. Battery Energy Storage for Distribution System—Case Helsinki. In Proceedings of the 2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM), Kärdla, Estonia, 12–15 June 2019.spa
dcterms.bibliographicCitationAlmehizia, A.A.; Al-Ismail, F.S.; Alohali, N.S.; Al-Shammari, M.M. Assessment of battery storage utilization in distribution feeders. Energy Transit. 2020, 4, 101–112spa
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. 2017, 15, 890–900spa
dcterms.bibliographicCitationLuna, A.C.; Diaz, N.L.; Andrade, F.; Graells, M.; Guerrero, J.M.; Vasquez, J.C. Economic power dispatch of distributed generators in a grid-connected microgrid. In Proceedings of the 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia), Seoul, Korea, 1–5 June 2015.spa
dcterms.bibliographicCitationFarivar, M.; Low, S.H. Branch Flow Model: Relaxations and Convexification—Part I. IEEE Trans. Power Syst. 2013, 28, 2554–2564spa
dcterms.bibliographicCitationMora, C.A.; Montoya, O.D.; Trujillo, E.R. Mixed-Integer Programming Model for Transmission Network Expansion Planning with Battery Energy Storage Systems (BESS). Energies 2020, 13, 4386.spa
dcterms.bibliographicCitationGrisales-Noreña, L.; Montoya, O.D.; Gil-González, W. Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms. J. Energy Storage 2019, 25, 100891spa
dcterms.bibliographicCitationMolzahn, D.K. Identifying and Characterizing Non-Convexities in Feasible Spaces of Optimal Power Flow Problems. IEEE Trans. Circuits Syst. II Express Briefs 2018, 65, 672–676spa
dcterms.bibliographicCitationBerglund, F.; Zaferanlouei, S.; Korpås, M.; Uhlen, K. Optimal Operation of Battery Storage for a Subscribed Capacity-Based Power Tariff Prosumer—A Norwegian Case Study. Energies 2019, 12, 4450.spa
dcterms.bibliographicCitationDenholm, P.; Sioshansi, R. The value of compressed air energy storage with wind in transmission-constrained electric power systems. Energy Policy 2009, 37, 3149–3158spa
dcterms.bibliographicCitationMazaheri, H.; Abbaspour, A.; Fotuhi-Firuzabad, M.; Farzin, H.; Moeini-Aghtaie, M. Investigating the impacts of energy storage systems on transmission expansion planning. In Proceedings of the 2017 Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran, 2–4 May 2017spa
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. 2020, 85, 106710.spa
dcterms.bibliographicCitationMontoya, 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, 1352spa
dcterms.bibliographicCitationZia, M.F.; Elbouchikhi, E.; Benbouzid, M. Optimal operational planning of scalable DC microgrid with demand response, islanding, and battery degradation cost considerations. Appl. Energy 2019, 237, 695–707spa
dcterms.bibliographicCitationChoi, J.; Park, W.K.; Lee, I.W. Economic Dispatch of Multiple Energy Storage Systems Under Different Characteristics. Energy Procedia 2017, 141, 216–221spa
dcterms.bibliographicCitationFarivar, M.; Low, S.H. Branch Flow Model: Relaxations and Convexification—Part II. IEEE Trans. Power Syst. 2013, 28, 2565–2572spa
dcterms.bibliographicCitationMontoya, O.D.; Gil-González, W.; Hernández, J.C. Optimal Selection and Location of BESS Systems in Medium-Voltage Rural Distribution Networks for Minimizing Greenhouse Gas Emissions. Electronics 2020, 9, 2097spa
dcterms.bibliographicCitationDe Oliveira, L.S.; Saramago, S.F.P. Multiobjective optimization techniques applied to engineering problems. J. Braz. Soc. Mech. Sci. Eng. 2010, 32, 94–105.spa
dcterms.bibliographicCitationEmmerich, M.T.M.; Deutz, A.H. A tutorial on multiobjective optimization: Fundamentals and evolutionary methods. Nat. Comput. 2018, 17, 585–609spa
dcterms.bibliographicCitationLópez-Lezama, J.M. Optimal location of distributed generation in distribution systems using a model of nonlineal whole mixed programming. Tecnura 2011, 15, 101–110spa
dcterms.bibliographicCitationAyodele, T.R.; Ogunjuyigbe, A.S.O.; Akinola, O.O. Optimal Location, Sizing, and Appropriate Technology Selection of Distributed Generators for Minimizing Power Loss Using Genetic Algorithm. J. Renew. Energy 2015, 2015, 832917spa
dcterms.bibliographicCitationBabu, P.V.; Singh, S. Optimal Placement of DG in Distribution Network for Power Loss Minimization Using NLP & PLS Technique. Energy Procedia 2016, 90, 441–454.spa
dcterms.bibliographicCitationMontoya, O.D.; Gil-González, W.; Grisales-Noreña, 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. 2020, 11, 409–418spa
dcterms.bibliographicCitationGil-González, W.; Montoya, O.D.; Grisales-Noreña, L.F.; Perea-Moreno, A.J.; Hernandez-Escobedo, Q. Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves. Sustainability 2020, 12, 2983.spa
dcterms.bibliographicCitationPorkar, S.; Poure, P.; Abbaspour-Tehrani-fard, A.; Saadate, S. A new framework for large distribution system optimal planning in a competitive electricity market. In Proceedings of the 2010 IEEE International Energy Conference, Manama, Bahrain, 18–22 December 2010spa
dcterms.bibliographicCitationSiahi, M.; Porkar, S.; Abbaspour-Tehrani-Fard, A.; Poure, P.; Saadate, S. Competitive distribution system planning model integration of dg, interruptible load and voltage regulator devices. Iran. J. Sci. Technol. Trans. Eng. 2010, 34, 619–635spa
dcterms.bibliographicCitationKazmi, S.; Shahzad, M.; Shin, D. Multi-Objective Planning Techniques in Distribution Networks: A Composite Review. Energies 2017, 10, 208spa
dcterms.bibliographicCitationSoleymani, S.; Mozafari, B.; Kamarposhti, M. Optimal capacitor placement for power loss reduction and voltage stability enhancement in distribution systems. Trakia J. Sci. 2014, 12, 425–430spa
dcterms.bibliographicCitationAman, M.; Jasmon, G.; Bakar, A.; Mokhlis, H.; Karimi, M. Optimum shunt capacitor placement in distribution system—A review and comparative study. Renew. Sustain. Energy Rev. 2014, 30, 429–439.spa
dcterms.bibliographicCitationThang, V.V.; Minh, N.D. Optimal Allocation and Sizing of Capacitors for Distribution Systems Reinforcement Based on Minimum Life Cycle Cost and Considering Uncertainties. Open Electr. Electron. Eng. J. 2017, 11, 165–176spa
dcterms.bibliographicCitationNaghiloo, A.; Abbaspour, M.; Mohammadi-Ivatloo, B.; Bakhtari, K. GAMS based approach for optimal design and sizing of a pressure retarded osmosis power plant in Bahmanshir river of Iran. Renew. Sustain. Energy Rev. 2015, 52, 1559–1565spa
dcterms.bibliographicCitationAnsari, A.; Abbaspour, M. Modelling and economic evaluation of pressure-retarded osmosis power plant case study: Iran. Int. J. Ambient Energy 2017, 40, 69–81spa
dcterms.bibliographicCitationTouati, K.; Tadeo, F. Green energy generation by pressure retarded osmosis: State of the art and technical advancement—review. Int. J. Green Energy 2016, 14, 337–360spa
dcterms.bibliographicCitationUlanicki, B.; Bounds, P.L.M.; Rance, J.P. Using a GAMS Modelling Environment to Solve Network Scheduling Problems. Meas. Control 1999, 32, 110–115spa
dcterms.bibliographicCitationTin-Loi, F. A GAMS model for the plastic limit analysis of plane frames. Appl. Math. Model. 1993, 17, 595–602spa
dcterms.bibliographicCitationCastillo, E.; Gonejo, A.J.; Pedregal, P.; Garciá, R.; Alguacil, N. Building and Solving Mathematical Programming Models in Engineering and Science; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2001spa
dcterms.bibliographicCitationAndrei, N. Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology; Springer International Publishing: Berlin/Heidelberg, Germany, 2017spa
dcterms.bibliographicCitationChen, S.; Gooi, H.; Wang, M. Solar radiation forecast based on fuzzy logic and neural networks. Renew. Energy 2013, 60, 195–201spa
dcterms.bibliographicCitationKim, J.; Moon, J.; Hwang, E.; Kang, P. Recurrent inception convolution neural network for multi short-term load forecasting. Energy Build. 2019, 194, 328–341spa
dcterms.bibliographicCitationYang, X.; Xu, M.; Xu, S.; Han, X. Day-ahead forecasting of photovoltaic output power with similar cloud space fusion based on incomplete historical data mining. Appl. Energy 2017, 206, 683–696.spa
dcterms.bibliographicCitationGil-González, W.; Montoya, O.D.; Holguín, E.; Garces, A.; Grisales-Noreña, L.F. Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model. J. Energy Storage 2019, 21, 1–8spa
dcterms.bibliographicCitationOu, G.; Murphey, Y.L. Multi-class pattern classification using neural networks. Pattern Recognit. 2007, 40, 4–18spa
dcterms.bibliographicCitationYang, S.; Ting, T.; Man, K.; Guan, S.U. Investigation of Neural Networks for Function Approximation. Procedia Comput. Sci. 2013, 17, 586–594spa
dcterms.bibliographicCitationTambouratzis, G.; Tambouratzis, T.; Tambouratzis, D. Clustering with artificial neural networks and traditional techniques. Int. J. Intell. Syst. 2003, 18, 405–428.spa
dcterms.bibliographicCitationTealab, A. Time series forecasting using artificial neural networks methodologies: A systematic review. Future Comput. Inform. J. 2018, 3, 334–340spa
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.doi10.3390/electronics10091002
dc.subject.keywordsEnergy storage with batteriespa
dc.subject.keywordsDistribution networksspa
dc.subject.keywordsEconomic dispatch approachspa
dc.subject.keywordsEnergy purchasing costsspa
dc.subject.keywordsMathematical programmingspa
dc.subject.keywordsMulti-objective optimizationspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.ccAtribución-NoComercial 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
dc.publisher.sedeCampus Tecnológicospa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.publisher.disciplineIngeniería Eléctricaspa


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

http://creativecommons.org/licenses/by-nc/4.0/
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc/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.