Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288451 
Year of Publication: 
2020
Citation: 
[Journal:] Computational Management Science [ISSN:] 1619-6988 [Volume:] 17 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2020 [Pages:] 309-326
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
We introduce a novel chance-constrained stochastic unit commitment model to address uncertainty in renewables’ production in operations of power systems. For most thermal generators, underlying technical constraints that are universally treated as “hard” by deterministic unit commitment models are in fact based on engineering judgments, such that system operators can periodically request operation outside these limits in non-nominal situations, e.g., to ensure reliability. We incorporate this practical consideration into a chance-constrained stochastic unit commitment model, specifically by infrequently allowing minor deviations from the minimum and maximum thermal generator power output levels. We demonstrate that an extensive form of our model is computationally tractable for medium-sized power systems given modest numbers of scenarios for renewables’ production. We show that the model is able to potentially save significant annual production costs by allowing infrequent and controlled violation of the traditionally hard bounds imposed on thermal generator production limits. Finally, we conduct a sensitivity analysis of optimal solutions to our model under two restricted regimes and observe similar qualitative results.
Subjects: 
Stochastic optimization
Unit commitment
Power systems operations
Chance constraints
Emergency operations
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.