Simulation and detection of wind power ramps and identification of their causative atmospheric circulation patterns

The relationship between wind power ramp events and their causative weather systems remains poorly understood, despite its importance to the development of ramp forecasting procedures. Results from previous studies linking ramp events and weather systems have proven difficult to generalize and methodologies used may be difficult to duplicate, especially in cases of measured data scarcity. Accordingly, this paper proposes a flexible methodology for investigating this link between ramps and weather systems in instances of measured data scarcity. A historic wind power time-series is firstly simulated by applying stochastic variations to numeric weather prediction (NWP) reanalysis data. Ramps events are identified within the time-series using a swinging door algorithm. Temporal regularities in ramp statistics are identified as these provide probabilistic insights into ramp occurrences. Finally, ramps are linked to a set of atmospheric circulation archetypes. These archetypes are identified by applying self-organizing maps as a classification procedure to historic NWP data. The proposed methodology is demonstrated through a case study considering a wind farm in South Africa. It is found that mean power and power variability differ significantly as a function of atmospheric circulation, and that thermally driven land-sea breeze interaction can be a primary mechanism for ramp events.