Kardoš, Juraj Kourounis, Drosos Schenk, Olaf Zimmerman, Ray 2022 Optimal power flow (OPF) problems are ubiquitous for daily power grid operations and planning. These optimal control problems are nonlinear, non-convex, and computationally demanding for large power networks especially for OPF problems defined over a large number of time periods, which are commonly intertemporally coupled due to constraints associated with energy storage devices. A robust interior point optimization library BELTISTOS is proposed, which allows fast and accurate solutions to single-period OPF problems and significantly accelerates the solution of multi-period OPF problems via the aid of structure-exploiting algorithms. Adhering to high reporting standards for replicable and reliable analysis, BELTISTOS is compared with interior point optimizers within the software package MATPOWER and evaluated using large scale power networks with up to 193,000 buses and problems spanning up to 4800 time periods. application/pdf https://n2t.net/ark:/12658/srd1325627 https://susi.usi.ch/global/documents/325627 https://susi.usi.ch/documents/325627/files/Schenk_2022_Else_epsr.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.epsr.2022.108613 info:eu-repo/semantics/altIdentifier/ark/12658/srd1325627 info:eu-repo/semantics/openAccess CC BY Electric power systems research. - 2022, vol. 212, p. 108613 Interior point methods Optimal power flow Multi-period optimal power flow Optimization software Structure-exploiting algorithms Performance profiling info:eu-repo/classification/udc/004 BELTISTOS : a robust interior point method for large-scale optimal power flow problems http://purl.org/coar/resource_type/c_6501