Solution sensitivity-based scenario reduction for stochastic unit commitment

DOI10.1007/s10287-014-0220-zzbMath1397.90172DBLPjournals/cms/FengR16OpenAlexW2027755357WikidataQ54961352 ScholiaQ54961352MaRDI QIDQ1789567

Yonghan Feng, Sarah M. Ryan

Publication date: 10 October 2018

Published in: Computational Management Science (Search for Journal in Brave)

Full work available at URL: https://lib.dr.iastate.edu/imse_pubs/12

zbMATH Keywords

stochastic programmingunit commitmentscenario reductionvariable generation

Mathematics Subject Classification ID

Stochastic programming (90C15) Deterministic scheduling theory in operations research (90B35) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (6)

Problem-driven scenario clustering in stochastic optimization ⋮ Large-scale unit commitment under uncertainty: an updated literature survey ⋮ Efficient solution selection for two-stage stochastic programs ⋮ Computational Management Science special issue on ``Optimisation methods and applications in the energy sector ⋮ Liner ship bunkering and sailing speed planning with uncertain demand ⋮ Observational data-based quality assessment of scenario generation for stochastic programs

Uses Software

• PySP

Cites Work

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