Modeling Flexible Generator Operating Regions via Chance- constrained Stochastic Unit Commitment

Singh, Bismark; Knueven, Bernard; Watson, Jean -Paul

doi:10.1007/s10287-020-00368-3

Modeling Flexible Generator Operating Regions via Chance- constrained Stochastic Unit Commitment

Journal Article · Sat Jul 11 00:00:00 EDT 2020 · Computational Management Science

DOI:https://doi.org/10.1007/s10287-020-00368-3· OSTI ID:1497004

Singh, Bismark ^[1]; Knueven, Bernard ^[1]; Watson, Jean -Paul ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Here, we introduce a novel chance-constrained stochastic unit commitment model to address uncertainty in renewables' production uncertainty in power systems operation. 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 in-frequently 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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1497004

Alternate ID(s):: OSTI ID: 1639591

Report Number(s):: SAND--2019-1222J; 672155

Journal Information:: Computational Management Science, Journal Name: Computational Management Science Vol. 17; ISSN 1619-697X

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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