Advanced optimization methods for power systems
Power system planning and operation offers multitudinous opportunities for optimization methods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine both continuous and discrete variables. In the recent years, a number of complementary theoretical advances in addressing such problems have been obtained in the field of applied mathematics. The paper introduces a selection of these advances in the fields of non-convex optimization, in mixed-integer programming, and in optimization under uncertainty. The practical relevance of these developments for power systems planning and operation are discussed, and the opportunities for combining them, together with high-performance computing and big data infrastructures, as well as novel machine learning and randomized algorithms, are highlighted.
© 2014 IEEE.m The authors acknowledge the support of their funding organisms of the present work; the scientific responsibility of the statements of this paper remain with the authors. The work of S. H. Low is supported by NSF, DoE, and SCE. The work of D.K. Molzahn is supported by the Dow Sustainability Fellowship at the University of Michigan. The work of L. Wehenkel is supported by the Belgian Network DYSCO, funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State.