Computational social science in smart power systems: Reliability, resilience, and restoration

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dc.contributor.authorValinejad, Jaberen
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorYu, Xinghuoen
dc.contributor.authorvan der Wal, C. Natalieen
dc.contributor.authorXu, Yijunen
dc.date.accessioned2024-01-22T14:12:58Zen
dc.date.available2024-01-22T14:12:58Zen
dc.date.issued2023-06-07en
dc.description.abstractSmart grids are typically modelled as cyber–physical power systems, with limited consideration given to the social aspects. Specifically, traditional power system studies tend to overlook the behaviour of stakeholders, such as end‐users. However, the impact of end‐users and their behaviour on power system operation and response to disturbances is significant, particularly with respect to demand response and distributed energy resources. Therefore, it is essential to plan and operate smart grids by taking into account both the technical and social aspects, given the crucial role of active and passive end‐users, as well as the intermittency of renewable energy sources. In order to optimize system efficiency, reliability, and resilience, it is important to consider the level of cooperation, flexibility, and other social features of various stakeholders, including consumers, prosumers, and microgrids. This article aims to address the gaps and challenges associated with modelling social behaviour in power systems, as well as the human‐centred approach for future development and validation of socio‐technical power system models. As the cyber–physical–social system of energy emerges as an important topic, it is imperative to adopt a human‐centred approach in this domain. Considering the significance of computational social science for power system applications, this article proposes a list of research topics that must be addressed to improve the reliability and resilience of power systems in terms of both operation and planning. Solving these problems could have far‐reaching implications for power systems, energy markets, community usage, and energy strategies.en
dc.description.versionPublished versionen
dc.format.extentPages 159-170en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1049/enc2.12087en
dc.identifier.eissn2634-1581en
dc.identifier.issn2634-1581en
dc.identifier.issue3en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117505en
dc.identifier.volume4en
dc.language.isoenen
dc.publisherInstitution of Engineering and Technology (IET)en
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.titleComputational social science in smart power systems: Reliability, resilience, and restorationen
dc.title.serialEnergy Conversion and Economicsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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