Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms
This paper presents a method to find the optimal location, selection, and operation of energy storage systems (ESS- batteries-) and capacitors banks (CB) in distribution systems (DS). A mixed-integer non-linear programming model is proposed to formulate the problem. In this model, the minimization o...
- 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:20.500.12585/9050
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9050
- Palabra clave:
- Capacitor banks
Chu & Beasley genetic algorithm
Energy storage systems
Master-slave algorithm
Optimal power flow
Radial distribution networks
Data storage equipment
Electric energy storage
Electric load flow
Energy dissipation
Genetic algorithms
Integer programming
Location
Nonlinear programming
Numerical methods
Voltage regulators
Capacitor bank
Energy storage systems
Master slave
Optimal power flows
Radial distribution networks
Flow batteries
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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| dc.title.none.fl_str_mv |
Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms |
| title |
Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms |
| spellingShingle |
Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms Capacitor banks Chu & Beasley genetic algorithm Energy storage systems Master-slave algorithm Optimal power flow Radial distribution networks Data storage equipment Electric energy storage Electric load flow Energy dissipation Genetic algorithms Integer programming Location Nonlinear programming Numerical methods Voltage regulators Capacitor bank Energy storage systems Master slave Optimal power flows Radial distribution networks Flow batteries |
| title_short |
Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms |
| title_full |
Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms |
| title_fullStr |
Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms |
| title_full_unstemmed |
Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms |
| title_sort |
Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms |
| dc.subject.keywords.none.fl_str_mv |
Capacitor banks Chu & Beasley genetic algorithm Energy storage systems Master-slave algorithm Optimal power flow Radial distribution networks Data storage equipment Electric energy storage Electric load flow Energy dissipation Genetic algorithms Integer programming Location Nonlinear programming Numerical methods Voltage regulators Capacitor bank Energy storage systems Master slave Optimal power flows Radial distribution networks Flow batteries |
| topic |
Capacitor banks Chu & Beasley genetic algorithm Energy storage systems Master-slave algorithm Optimal power flow Radial distribution networks Data storage equipment Electric energy storage Electric load flow Energy dissipation Genetic algorithms Integer programming Location Nonlinear programming Numerical methods Voltage regulators Capacitor bank Energy storage systems Master slave Optimal power flows Radial distribution networks Flow batteries |
| description |
This paper presents a method to find the optimal location, selection, and operation of energy storage systems (ESS- batteries-) and capacitors banks (CB) in distribution systems (DS). A mixed-integer non-linear programming model is proposed to formulate the problem. In this model, the minimization of energy loss in the DS is selected as an objective function. As constraints are considered: the active and reactive energy balance, voltage regulation, the total number energy storage devices that can be installed into network, as well as the operative bounds associated with the ESS (time of charge-discharge and energy capabilities). Three operating scenarios for the DS are analyzed by adopting the method proposed in this work. The first scenario is an evaluation of the base case (without batteries and CB), in which the initial conditions of the DS are determined. The second scenario considers the location of the ESS composed by redox flow batteries. Finally, the third scenario includes the installation of REDOX flow batteries with CB in parallel to correct operating problems generated by battery charging, and improve their impact on the grid. A master-slave strategy is adopted to solve the problem here discussed, implementing a Chu & Beasley genetic algorithm in both stages as an optimization technique. The proposed method is tested in a 69-node test feeder, where numerical results demonstrate its effectiveness. © 2019 Elsevier Ltd |
| publishDate |
2019 |
| dc.date.issued.none.fl_str_mv |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2020-03-26T16:32:50Z |
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2020-03-26T16:32:50Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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Artículo |
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publishedVersion |
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Journal of Energy Storage; Vol. 25 |
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2352152X |
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https://hdl.handle.net/20.500.12585/9050 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.est.2019.100891 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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55791991200 56919564100 57191493648 |
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Journal of Energy Storage; Vol. 25 2352152X 10.1016/j.est.2019.100891 Universidad Tecnológica de Bolívar Repositorio UTB 55791991200 56919564100 57191493648 |
| url |
https://hdl.handle.net/20.500.12585/9050 |
| dc.language.iso.none.fl_str_mv |
eng |
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eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/restrictedAccess |
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Atribución-NoComercial 4.0 Internacional |
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Recurso electrónico |
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application/pdf |
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Elsevier Ltd |
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Elsevier Ltd |
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2020-03-26T16:32:50Z2020-03-26T16:32:50Z2019Journal of Energy Storage; Vol. 252352152Xhttps://hdl.handle.net/20.500.12585/905010.1016/j.est.2019.100891Universidad Tecnológica de BolívarRepositorio UTB557919912005691956410057191493648This paper presents a method to find the optimal location, selection, and operation of energy storage systems (ESS- batteries-) and capacitors banks (CB) in distribution systems (DS). A mixed-integer non-linear programming model is proposed to formulate the problem. In this model, the minimization of energy loss in the DS is selected as an objective function. As constraints are considered: the active and reactive energy balance, voltage regulation, the total number energy storage devices that can be installed into network, as well as the operative bounds associated with the ESS (time of charge-discharge and energy capabilities). Three operating scenarios for the DS are analyzed by adopting the method proposed in this work. The first scenario is an evaluation of the base case (without batteries and CB), in which the initial conditions of the DS are determined. The second scenario considers the location of the ESS composed by redox flow batteries. Finally, the third scenario includes the installation of REDOX flow batteries with CB in parallel to correct operating problems generated by battery charging, and improve their impact on the grid. A master-slave strategy is adopted to solve the problem here discussed, implementing a Chu & Beasley genetic algorithm in both stages as an optimization technique. The proposed method is tested in a 69-node test feeder, where numerical results demonstrate its effectiveness. © 2019 Elsevier LtdUniversidad Nacional de Colombia, UN 727-2015 Universidad Tecnológica de Pereira, UTP: C2018P020 Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), COLCIENCIAS Department of Science, Information Technology and Innovation, Queensland Government, DSITI P17211This work was supported in part by the Administrative Department of Science, Technology and Innovation of Colombia ( COLCIENCIAS ) through the National Scholarship Program under Grant 727-2015 , in part by Universidad Nacional de Colombia, and Instituto Tecnológico Metropolitano under project P17211, and in part by the Universidad Tecnológica de Bolívar under grant project C2018P020.Recurso electrónicoapplication/pdfengElsevier Ltdhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85070824336&doi=10.1016%2fj.est.2019.100891&partnerID=40&md5=aceaad3c7b8331c512531903381e9477Integration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithmsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Capacitor banksChu & Beasley genetic algorithmEnergy storage systemsMaster-slave algorithmOptimal power flowRadial distribution networksData storage equipmentElectric energy storageElectric load flowEnergy dissipationGenetic algorithmsInteger programmingLocationNonlinear programmingNumerical methodsVoltage regulatorsCapacitor bankEnergy storage systemsMaster slaveOptimal power flowsRadial distribution networksFlow batteriesGrisales-Noreña L.F.Montoya O.D.Gil-González W.Wong, L.A., Ramachandaramurthy, V.K., Taylor, P., Ekanayake, J., Walker, S.L., Padmanaban, S., Review on the optimal placement, sizing and control of an energy storage system in the distribution network (2019) J. 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