Forecasting Aggregated Wind Power Availability Over Extended Regions
Seminar Room 1, Newton Institute
AbstractThe effective operation of a large power system with significant penetration of wind generation requires as much information as possible about the uncertainty in aggregated wind power output levels close to real-time. In a Bayesian framework, short-term joint posterior predictive distributions for wind speeds are desirable, allowing calculation of the aggregated power output distribution. Given the complex and dynamic spatiotemporal structures associated with wind resource availability, it is clear that the most accurate predictive distributions are obtained from adaptive multivariate statistical models involving the most timely information from across the system's extended area, and utilising a number of meteorological variables. As such, dynamic linear models (DLMs) are presented as a suitable and novel forecasting framework for wind power. This presentation will report on initial results of research into the best DLM model structures for producing such predictive distributions. This work is a collaboration between Heriot-Watt and Lancaster Universities, involving Dr Stan Zachary (Heriot-Watt), and Dr Idris Eckley and Dr Rebecca Killick (Lancaster).
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