Modeling flexible generator operating regions via chance-constrained stochastic unit commitment
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Modeling flexible generator operating regions via chance-constrained stochastic unit commitment Bismark Singh1 · Bernard Knueven2 · Jean-Paul Watson3 Received: 19 October 2019 / Accepted: 10 April 2020 © The Author(s) 2020
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. Keywords Stochastic optimization · Unit commitment · Power systems operations · Chance constraints · Emergency operations
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Bismark Singh [email protected] Bernard Knueven [email protected] Jean-Paul Watson [email protected]
1
Discrete Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
2
Computational Science Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
3
Center for Applied Scientific Computing, Global Security Directorate Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
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B. Singh et al.
1 Introduction The standard unit commitment (UC) problem for power systems operations involves determining which thermal generators should be scheduled to meet projected demand for power over a given time horizon, while ensuring physical and operational constraints are satisfied. The time horizon is typically one or two days at an hourly resolution. Feasible operating constraints for thermal generators include limits on ramping, minimum up and down times, startup and shutdown ramp rates, and minimum and maximum production levels. The UC problem is extensively studied in the optimization and power systems literature (Anjos and Conejo 2017; Damcı-Kurt et al. 2016; Ostrowski et al. 2015; Padhy 2004; Queyranne and Wolsey 2017; Silbernagl 2016). It can be formulated as a mixed-integer linear program (MILP) and solved with commercial branch-and-cut software packages (Knueven et al. 2020, 2018b; Morales-España et al. 2013; O’Neill 2017). Prior to recent advances in MILP solver technology
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