Open AccessStatistical metrics for assessing the quality of wind power scenarios for stochastic unit commitment
Author(s) -
Sari Didem,
Lee Youngrok,
Ryan Sarah,
Woodruff David
Publication year - 2016
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.1872
Subject(s) - wind power , histogram , autocorrelation , probabilistic logic , power system simulation , computer science , wind power forecasting , rank (graph theory) , operations research , mathematical optimization , reliability engineering , econometrics , statistics , engineering , power (physics) , mathematics , electric power system , artificial intelligence , physics , quantum mechanics , electrical engineering , combinatorics , image (mathematics)
In power systems with high penetration of wind generation, probabilistic scenarios are generated for use in stochastic formulations of day‐ahead unit commitment problems. To minimize the expected cost, the wind power scenarios should accurately represent the stochastic process for available wind power. We employ some statistical evaluation metrics to assess whether the scenario set possesses desirable properties that are expected to lead to a lower cost in stochastic unit commitment. A new mass transportation distance rank histogram is developed for assessing the reliability of unequally likely scenarios. Energy scores, rank histograms and Brier scores are applied to alternative sets of scenarios that are generated by two very different methods. The mass transportation distance rank histogram is best able to distinguish between sets of scenarios that are more or less calibrated according to their bias, variability and autocorrelation. Copyright © 2015 John Wiley & Sons, Ltd.