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

Bismark Singh (bismark.singh***at***fau.de)
Bernard Knueven (bknueve***at***sandia.gov)
Jean-Paul Watson (jwatson***at***sandia.gov)

Abstract: 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 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.

Keywords: Stochastic optimization · Unit commitment · Power systems operations · Chance constraints · Emergency operations

Category 1: Stochastic Programming

Citation: in review

Download: [PDF]

Entry Submitted: 02/04/2019
Entry Accepted: 02/04/2019
Entry Last Modified: 07/10/2019

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