Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models
This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which i...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9253
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9253
- Palabra clave:
- Artificial neural networks
Battery energy storage system
Economic dispatch problem
Battery storage
Cost reduction
Data storage equipment
Electric batteries
Electric machine theory
Neural networks
Nonlinear programming
Scheduling
Battery energy storage systems
Economic dispatch problems
Operating condition
Operational periods
Photovoltaic sources
Renewable generators
Short term prediction
Voltage dependent load models
Electric load dispatching
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models |
| title |
Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models |
| spellingShingle |
Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models Artificial neural networks Battery energy storage system Economic dispatch problem Battery storage Cost reduction Data storage equipment Electric batteries Electric machine theory Neural networks Nonlinear programming Scheduling Battery energy storage systems Economic dispatch problems Operating condition Operational periods Photovoltaic sources Renewable generators Short term prediction Voltage dependent load models Electric load dispatching |
| title_short |
Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models |
| title_full |
Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models |
| title_fullStr |
Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models |
| title_full_unstemmed |
Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models |
| title_sort |
Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models |
| dc.subject.keywords.none.fl_str_mv |
Artificial neural networks Battery energy storage system Economic dispatch problem Battery storage Cost reduction Data storage equipment Electric batteries Electric machine theory Neural networks Nonlinear programming Scheduling Battery energy storage systems Economic dispatch problems Operating condition Operational periods Photovoltaic sources Renewable generators Short term prediction Voltage dependent load models Electric load dispatching |
| topic |
Artificial neural networks Battery energy storage system Economic dispatch problem Battery storage Cost reduction Data storage equipment Electric batteries Electric machine theory Neural networks Nonlinear programming Scheduling Battery energy storage systems Economic dispatch problems Operating condition Operational periods Photovoltaic sources Renewable generators Short term prediction Voltage dependent load models Electric load dispatching |
| description |
This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used. © 2019 MDPI AG. All rights reserved. |
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2019 |
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2019 |
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2020-03-26T16:41:28Z |
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2020-03-26T16:41:28Z |
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Artículo |
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publishedVersion |
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Montoya O.D., Gil-González W., Grisales-Norena L., Orozco-Henao C. y Serra F. (2019) Economic dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models. Energies; Vol. 12, Núm. 23 |
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19961073 |
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https://hdl.handle.net/20.500.12585/9253 |
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10.3390/en12234494 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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56919564100 57191493648 55791991200 55488549400 37104976300 |
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Montoya O.D., Gil-González W., Grisales-Norena L., Orozco-Henao C. y Serra F. (2019) Economic dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models. Energies; Vol. 12, Núm. 23 19961073 10.3390/en12234494 Universidad Tecnológica de Bolívar Repositorio UTB 56919564100 57191493648 55791991200 55488549400 37104976300 |
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https://hdl.handle.net/20.500.12585/9253 |
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eng |
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eng |
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Atribución-NoComercial 4.0 Internacional |
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2020-03-26T16:41:28Z2020-03-26T16:41:28Z2019Montoya O.D., Gil-González W., Grisales-Norena L., Orozco-Henao C. y Serra F. (2019) Economic dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models. Energies; Vol. 12, Núm. 2319961073https://hdl.handle.net/20.500.12585/925310.3390/en12234494Universidad Tecnológica de BolívarRepositorio UTB5691956410057191493648557919912005548854940037104976300This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used. © 2019 MDPI AG. All rights reserved.Recurso electrónicoapplication/pdfengMDPI AGhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076239314&doi=10.3390%2fen12234494&partnerID=40&md5=5aad556cf10a550cccf8bae524f3e92dScopus2-s2.0-85076239314Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load modelsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Artificial neural networksBattery energy storage systemEconomic dispatch problemBattery storageCost reductionData storage equipmentElectric batteriesElectric machine theoryNeural networksNonlinear programmingSchedulingBattery energy storage systemsEconomic dispatch problemsOperating conditionOperational periodsPhotovoltaic sourcesRenewable generatorsShort term predictionVoltage dependent load modelsElectric load dispatchingMontoya O.D.Gil-González W.Grisales-Noreña L.F.Orozco-Henao C.Serra F.Mutarraf, M., Terriche, Y., Niazi, K., Vasquez, J., Guerrero, J., Energy storage systems for shipboard microgrids-a review (2018) Energies, 11, p. 3492. , [CrossRef]Hu, J., Shan, Y., Xu, Y., Guerrero, J.M., A coordinated control of hybrid ac/dc microgrids with pv-wind-battery under variable generation and load conditions (2019) Int. 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