Campus Units
Industrial and Manufacturing Systems Engineering
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
10-2017
Journal or Book Title
Energy Systems
First Page
1
Last Page
26
Research Focus Area(s)
Operations Research
DOI
10.1007/s12667-017-0255-7
Abstract
Probabilistic wind power scenarios constitute a crucial input for stochastic day-ahead unit commitment in power systems with deep penetration of wind generation. To minimize the cost of implemented solutions, the scenario time series of wind power amounts available should accurately represent the stochastic process for available wind power as it is estimated on the day ahead. The high computational demands of stochastic programming motivate a search for ways to evaluate scenarios without extensively simulating the stochastic unit commitment procedure. The statistical reliability of wind power scenario sets can be assessed by approaches extended from ensemble forecast verification. We examine the relationship between the statistical reliability metrics and the results of stochastic unit commitment when implemented solutions encounter the observed available wind power. Lack of uniformity in a mass transportation distance rank histogram can eliminate scenario sets that might lead to either excessive no-load costs of committed units or high penalty costs for violating energy balance when the committed units are dispatched. Event-based metrics can help to predict results of implementing solutions found with the remaining scenario sets.
Copyright Owner
Springer Nature
Copyright Date
2017
Language
en
File Format
application/pdf
Recommended Citation
Sari, Didem and Ryan, Sarah M., "Statistical reliability of wind power scenarios and stochastic unit commitment cost" (2017). Industrial and Manufacturing Systems Engineering Publications. 172.
https://lib.dr.iastate.edu/imse_pubs/172
Comments
This is a manuscript of an article published as Sari, Didem, and Sarah M. Ryan. "Statistical reliability of wind power scenarios and stochastic unit commitment cost." Energy Systems (2017): 1-26.The final publication is available at Springer via 10.1007/s12667-017-0255-7. Posted with permission.