Abstract
Driven by a trend towards renewable forms of generation, in particular wind, the nature of power system operation is changing. Systems with high wind penetrations should be capable of managing the uncertainty contained within the wind power forecasts. Stochastic unit commitment with rolling planning and input scenarios, based on wind forecasts, is one way of achieving this. Here, a scenario tree tool is developed which allows forecast error statistics to be altered and facilitates the study of how these statistics impact on unit commitment and system operation. It is shown that the largest individual impact on system operation is from the inclusion of variance and that variance, kurtosis, and skewness together produced the error information with the lowest system cost. Similar impacts for inaccurate error statistics are observed but generalization of these results will need more studies on a range of test systems.
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