Modeling flexible generator operating regions via chance-constrained stochastic unit commitment

Singh B, Knueven B, Watson JP (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Computational Management Science Springer

DOI: 10.1007/s10287-020-00368-3

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.

Authors with CRIS profile

Bismark Singh Economics - Discrete Optimization - Mathematics (EDOM)

Involved external institutions

Sandia National Laboratories US United States (USA) (US) National Renewable Energy Laboratory (NREL) US United States (USA) (US)

How to cite

APA:

Singh, B., Knueven, B., & Watson, J.-P. (2020). Modeling flexible generator operating regions via chance-constrained stochastic unit commitment. Computational Management Science. https://dx.doi.org/10.1007/s10287-020-00368-3

MLA:

Singh, Bismark, Bernard Knueven, and Jean-Paul Watson. "Modeling flexible generator operating regions via chance-constrained stochastic unit commitment." Computational Management Science (2020).

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