Journal article

BELTISTOS : a robust interior point method for large-scale optimal power flow problems

  • Kardoš, Juraj Institute of Computing (CI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Kourounis, Drosos NEPLAN AG, Küsnacht Zürich, Switzerland
  • Schenk, Olaf ORCID Institute of Computing (CI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Zimmerman, Ray Charles H. Dyson School of Applied Economics and Management, Cornell University, NY, USA
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  • 2022
Published in:
  • Electric power systems research. - 2022, vol. 212, p. 108613
English 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.
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Language
  • English
Classification
Computer science and technology
License
CC BY
Open access status
hybrid
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Persistent URL
https://n2t.net/ark:/12658/srd1325627
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