Data-driven optimization for seismic-resilient power network planning
dc.article.number | 106628 | |
dc.catalogador | jwg | |
dc.contributor.author | Oneto Schiappacasse, Alfredo Ernesto | |
dc.contributor.author | Lorca Gálvez, Álvaro Hugo | |
dc.contributor.author | Ferrario, Elisa | |
dc.contributor.author | Poulos Campbell, Alan John | |
dc.contributor.author | Llera Martin, Juan Carlos de la | |
dc.contributor.author | Negrete Pincetic, Matías Alejandro | |
dc.date.accessioned | 2025-04-16T22:27:20Z | |
dc.date.available | 2025-04-16T22:27:20Z | |
dc.date.issued | 2024 | |
dc.description.abstract | Many regions of the planet are exposed to seismic hazards that can cause devastating consequences on power systems. Due to these systems’ crucial role, the evaluation and planning for their safe and reliable operation are paramount. This paper develops a novel data-driven optimization framework to assess the power network’s seismic resilience and plan cost-effective investments for its enhancement. Under a robust optimization scheme, an earthquake attacker–defender model finds the worst-case realization of random earthquake network contingencies within an uncertainty set defined with a large number of scenarios generated by state-of-the-art engineering methods. Moreover, data-driven stochastic-robust optimization is employed in a two-stage seismic-resilient power network planning model, leveraging multiple seismic sources’ distributional information. Transmission line expansions and siting and sizing of battery energy storage systems are decided in the first stage, while the second stage decides operational variables. Experiments on a 281-node Chilean power system provide insights for seismic-resilient planning and demonstrate the efficiency of the proposed approach. | |
dc.fechaingreso.objetodigital | 2025-04-16 | |
dc.format.extent | 13 páginas | |
dc.fuente.origen | ORCID | |
dc.identifier.doi | 10.1016/j.cor.2024.106628 | |
dc.identifier.eissn | 1873-765X | |
dc.identifier.issn | 0305-0548 | |
dc.identifier.uri | https://doi.org/10.1016/j.cor.2024.106628 | |
dc.identifier.uri | https://repositorio.uc.cl/handle/11534/103346 | |
dc.identifier.wosid | WoS_ID: 001218201400001 | |
dc.information.autoruc | Escuela de Ingeniería; Oneto Schiappacasse, Alfredo Ernesto; S/I; 245300 | |
dc.information.autoruc | Escuela de Ingeniería; Lorca Gálvez, Álvaro Hugo; 0000-0002-9864-0932; 148348 | |
dc.information.autoruc | Escuela de Ingeniería; Poulos Campbell, Alan John; S/I; 177702 | |
dc.information.autoruc | Escuela de Ingeniería; Llera Martin, Juan Carlos de la; 0000-0002-9064-0938; 53086 | |
dc.information.autoruc | Escuela de Ingeniería; Negrete Pincetic, Matías Alejandro; S/I; 13212 | |
dc.language.iso | en | |
dc.revista | Computers & Operations Research | |
dc.rights.license | CC BY-NC-ND Atribución-NonComercial-NoDerivadas Internacional 4.0 | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | OR in energy | |
dc.subject | Data-driven optimization | |
dc.subject | Robust optimization | |
dc.subject | Power systems resilience | |
dc.subject | Seismic hazards | |
dc.subject.ddc | 620 | |
dc.subject.dewey | Ingeniería | es_ES |
dc.title | Data-driven optimization for seismic-resilient power network planning | |
dc.type | artículo | |
dc.volumen | 166 | |
sipa.codpersvinculados | 245300 | |
sipa.codpersvinculados | 148348 | |
sipa.codpersvinculados | 177702 | |
sipa.codpersvinculados | 53086 | |
sipa.codpersvinculados | 13212 | |
sipa.index | WOS | |
sipa.trazabilidad | ORCID;2024-03-25 |
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