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Interval-Partitioned Uncertainty Constrained Robust Dispatch for AC/DC Hybrid Microgrids With Uncontrollable Renewable Generators

This paper proposes an interval-partitioned uncertainty (IPU) constrained robust dispatch model for an islanded AC/DC hybrid microgrid considering the uncertainties of the generation-load power and the operating states of the bidirectional converter. The IPU sets accurately describe the spatiotemporal distributions of the generation-load power and reduce the conservativeness of traditional single-interval uncertainty set-based robust optimization. To coordinate the scheduling of the uncontrollable renewable generators under uncertain conditions, the operating states of these generators were set as the first- or third-level optimal variables of the min-max-min robust model, which was linearized by the big-M method. The tri-level robust optimization with a mixed-integer recourse problem was quickly solved by a nested column-and-constraint generation algorithm. The validity and rationality of the proposed robust models, uncertainty sets, and solving method were confirmed in case studies.
- Sun Yat-sen University China (People's Republic of)
- Sun Yat-sen University China (People's Republic of)
- Southeast University China (People's Republic of)
- Southeast University China (People's Republic of)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).45 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
