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dc.contributor.authorPorras, Álvaro
dc.contributor.authorRoald, Line
dc.contributor.authorMorales-González, Juan Miguel 
dc.contributor.authorPineda-Morente, Salvador 
dc.date.accessioned2024-08-28T11:58:19Z
dc.date.available2024-08-28T11:58:19Z
dc.date.issued2024
dc.identifier.citationÁ. Porras, L. Roald, J. M. Morales and S. Pineda, "Unifying Chance-Constrained and Robust Optimal Power Flow for Resilient Network Operations," in IEEE Transactions on Control of Network Systems, doi: 10.1109/TCNS.2024.3432188.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/32460
dc.descriptionPolítica de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/37938 (accepted version, pathway c)es_ES
dc.description.abstractUncertainty in renewable energy generation has the potential to adversely impact the operation of electric networks. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chance-constrained programming to robust optimization. However, these approaches either tend to be conservative or leave the system vulnerable to low probability, high impact uncertainty realizations. To address this issue, we propose a new formulation for stochastic optimal power flow that explicitly distinguishes between “normal operation”, in which automatic generation control (AGC) is sufficient to guarantee system security, and “adverse operation”, in which the system operator is required to take additional actions, e.g., manual reserve deployment. The new formulation has been compared with the classical ones in a case study on the IEEE-118 and IEEE-300 bus systems. We observe that our consideration of extreme scenarios enables solutions that are more secure than typical chance-constrained formulations, yet less costly than solutions that guarantee robust feasibility with only AGC.es_ES
dc.description.sponsorshipMinisterio de Ciencia e Innovación de España (AEI/10.13039/501100011033): proyectos PID2020-115460GB-I00 y PID2023-148291NB-I00, y ayuda FPU19/03053. Consejo Europeo de Investigaciones Científicas (ERC), programa Horizonte 2020 de la UE, ayuda No 755705. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program (USA): Contract Number DE-AC02-06CH11347. Universidad de Málaga SCBI, Centro de Supercomputación.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectOptimización matemáticaes_ES
dc.subjectRecursos energéticos renovableses_ES
dc.subjectIngeniería eléctrica - Matemáticases_ES
dc.subject.otherOptimal power flowes_ES
dc.subject.otherChance constraintses_ES
dc.subject.otherAutomatic generation controles_ES
dc.subject.otherManual adjustmentes_ES
dc.subject.otherWind poweres_ES
dc.titleUnifying Chance-Constrained and Robust Optimal Power Flow for Resilient Network Operations.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroFacultad de Cienciases_ES
dc.identifier.doi10.1109/TCNS.2024.3432188
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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