Browsing by Author "Koivisto, Matti Juhani"

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    Classified atmospheric states as operating scenarios in probabilistic power flow analysis for networks with high levels of wind power
    (Elsevier, 2021) Dalton, Amaris; Bekker, Bernard; Koivisto, Matti Juhani
    ENGLISH ABSTRACT: Large-scale atmospheric circulation patterns are the primary drivers of wind power variability on power networks at timescales of hours to days. This paper proposes a methodology that allows power system operators and planners working on networks with high levels of wind generation, to conduct probabilistic power flow (PPF) analyses by defining network ‘operating scenarios’ – i.e. the probability density functions of generators, and correlations between generators representative of a future system state – based on concurrent classified atmospheric states. The most significant contribution made by this paper is in illustrating how PPF operating scenarios derived from clustering historic generation data as a function of a set of classified atmospheric states reduces simulation uncertainty within a PPF analysis. It is anticipated that the proposed methodology may provide network planners with more appropriate operating scenarios for PPF analyses when compared to an unclustered base state, and may assist network operators in converting wind power point-forecasts into probabilistic forecasts whereby the spatial correlations between generators are incorporated. This methodology is illustrated through a case study considering 11 geographically disperse wind generators on the South African transmission network.