Classified atmospheric states as operating scenarios in probabilistic power flow analysis for networks with high levels of wind power

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