Power system planning and operation offers multitudinous opportunities for optimizationmethods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine both continuous...
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ISBN:
(纸本)9788393580132
Power system planning and operation offers multitudinous opportunities for optimizationmethods. 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.
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