Resilient distributed model predictive control for energy management of interconnected microgrids

Distributed energy management of interconnected microgrids that is based on model predictive control (MPC) relies on the cooperation of all agents (microgrids). This paper discusses the case in which some of the agents might perform one type of adversarial actions (attacks) and they do not comply wi...

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Autores: Ananduta, Wicak, Maestre, Jose Maria, Ocampo-Martínez, Carlos, Ishii, Hideaki
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/202301
Acceso en línea:http://hdl.handle.net/10261/202301
Access Level:acceso abierto
Palabra clave:Distributed MPC
Distributed optimization
Economic dispatch
Microgrids
Resilient algorithm
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spelling Resilient distributed model predictive control for energy management of interconnected microgridsAnanduta, WicakMaestre, Jose MariaOcampo-Martínez, CarlosIshii, HideakiDistributed MPCDistributed optimizationEconomic dispatchMicrogridsResilient algorithmDistributed energy management of interconnected microgrids that is based on model predictive control (MPC) relies on the cooperation of all agents (microgrids). This paper discusses the case in which some of the agents might perform one type of adversarial actions (attacks) and they do not comply with the decisions computed by performing a distributed MPC algorithm. In this regard, these agents could obtain a better performance at the cost of degrading the performance of the network as a whole. A resilient distributed method that can deal with such issues is proposed in this paper. The method consists of two parts. The first part is to ensure that the decisions obtained from the algorithm are robustly feasible against most of the attacks with high confidence. In this part, we formulate the economic dispatch problem, taking into account the attacks as a chance-constrained problem, and employ a two-step randomization-based approach to obtain a feasible solution with a predefined level of confidence. The second part consists in the identification and mitigation of the adversarial agents, which utilizes hypothesis testing with Bayesian inference and requires each agent to solve a mixed-integer problem to decide the connections with its neighbors. In addition, an analysis of the decisions computed using the stochastic approach and the outcome of the identification and mitigation method is provided. The performance of the proposed approach is also shown through numerical simulations.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska‐Curie Grant 675318 (INCITE) and has been also supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM‐2016‐0656). Financial support by the Spanish MINECO project DPI2017‐86918‐R and the Japanese Society for the Promotion of Science (scholarship PE16048) is also gratefully acknowledged. The authors also acknowledge JST CREST Grants JPMJCR15K3 and JPMJCR15K5.John Wiley & SonsAgencia Estatal de Investigación (España)European CommissionAgencia Estatal de Investigación (España)Ministerio de Economía y Competitividad (España)Ministerio de Ciencia, Innovación y Universidades (España)Japan Society for the Promotion of ScienceConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2020202020192020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/202301reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#DPI2017-86918-R/AEI/10.13039/501100011033info:eu-repo/grantAgreement/EC/H2020/675318info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MDM-2016-0656info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-86918‐Rhttp://dx.doi.org/10.1002/oca.2534Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2023012026-05-22T06:33:51Z
dc.title.none.fl_str_mv Resilient distributed model predictive control for energy management of interconnected microgrids
title Resilient distributed model predictive control for energy management of interconnected microgrids
spellingShingle Resilient distributed model predictive control for energy management of interconnected microgrids
Ananduta, Wicak
Distributed MPC
Distributed optimization
Economic dispatch
Microgrids
Resilient algorithm
title_short Resilient distributed model predictive control for energy management of interconnected microgrids
title_full Resilient distributed model predictive control for energy management of interconnected microgrids
title_fullStr Resilient distributed model predictive control for energy management of interconnected microgrids
title_full_unstemmed Resilient distributed model predictive control for energy management of interconnected microgrids
title_sort Resilient distributed model predictive control for energy management of interconnected microgrids
dc.creator.none.fl_str_mv Ananduta, Wicak
Maestre, Jose Maria
Ocampo-Martínez, Carlos
Ishii, Hideaki
author Ananduta, Wicak
author_facet Ananduta, Wicak
Maestre, Jose Maria
Ocampo-Martínez, Carlos
Ishii, Hideaki
author_role author
author2 Maestre, Jose Maria
Ocampo-Martínez, Carlos
Ishii, Hideaki
author2_role author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
European Commission
Agencia Estatal de Investigación (España)
Ministerio de Economía y Competitividad (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Japan Society for the Promotion of Science
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Distributed MPC
Distributed optimization
Economic dispatch
Microgrids
Resilient algorithm
topic Distributed MPC
Distributed optimization
Economic dispatch
Microgrids
Resilient algorithm
description Distributed energy management of interconnected microgrids that is based on model predictive control (MPC) relies on the cooperation of all agents (microgrids). This paper discusses the case in which some of the agents might perform one type of adversarial actions (attacks) and they do not comply with the decisions computed by performing a distributed MPC algorithm. In this regard, these agents could obtain a better performance at the cost of degrading the performance of the network as a whole. A resilient distributed method that can deal with such issues is proposed in this paper. The method consists of two parts. The first part is to ensure that the decisions obtained from the algorithm are robustly feasible against most of the attacks with high confidence. In this part, we formulate the economic dispatch problem, taking into account the attacks as a chance-constrained problem, and employ a two-step randomization-based approach to obtain a feasible solution with a predefined level of confidence. The second part consists in the identification and mitigation of the adversarial agents, which utilizes hypothesis testing with Bayesian inference and requires each agent to solve a mixed-integer problem to decide the connections with its neighbors. In addition, an analysis of the decisions computed using the stochastic approach and the outcome of the identification and mitigation method is provided. The performance of the proposed approach is also shown through numerical simulations.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/202301
url http://hdl.handle.net/10261/202301
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
DPI2017-86918-R/AEI/10.13039/501100011033
info:eu-repo/grantAgreement/EC/H2020/675318
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MDM-2016-0656
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-86918‐R
http://dx.doi.org/10.1002/oca.2534
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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