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dc.contributor.authorAnanduta, Wayan Wicak
dc.contributor.authorMaestre Torreblanca, José María
dc.contributor.authorOcampo-Martínez, Carlos
dc.contributor.authorIshii, Hideaki
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2020-04-17T13:14:33Z
dc.date.available2020-04-18T00:26:23Z
dc.date.issued2019-01-01
dc.identifier.citationAnanduta, W. [et al.]. Resilient distributed model predictive control for energy management of interconnected microgrids. "Optimal control applications and methods", 1 Gener 2019, vol. 41, núm. 1, p. 146-169.
dc.identifier.issn0143-2087
dc.identifier.urihttp://hdl.handle.net/2117/183813
dc.description.abstractDistributed 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.
dc.description.sponsorshipFunding information Marie Skłodowska-Curie, Grant/Award Number: 675318; Maria de Maeztu Seal of Excellence to IRI, Grant/Award Number: MDM-2016-0656; Spanish MINECO project, Grant/Award Number: DPI2017-86918-R; Japanese Society for the Promotion of Science Scholarship, Grant/Award Number: PE16048; JST CREST, Grant/Award Number: JPMJCR15K3 and JPMJCR15
dc.format.extent24 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.otherDistributed MPC
dc.subject.otherEconomic dispatch
dc.subject.otherDistributed optimization
dc.subject.otherResilient algorithm
dc.subject.otherMicrogrids
dc.titleResilient distributed model predictive control for energy management of interconnected microgrids
dc.typeArticle
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1002/oca.2534
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Control theory
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/abs/10.1002/oca.2534
dc.rights.accessOpen Access
local.identifier.drac25815265
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/2PE/MDM-2016-0656
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/675318/EU/Innovative controls for renewable sources Integration into smart energy systems/INCITE
local.citation.authorAnanduta, W.; Maestre, J.; Ocampo-Martinez, C.; Ishii, H.
local.citation.publicationNameOptimal control applications and methods
local.citation.volume41
local.citation.number1
local.citation.startingPage146
local.citation.endingPage169


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain