Extreme Ray Feasibility Cuts for Unit Commitment with Uncertainty

Chao Li (), Muhong Zhang () and Kory Hedman ()
Additional contact information
Chao Li: Arizona State University, Tempe, Arizona 85281
Muhong Zhang: Amazon.com Services Inc., Seattle, Washington 98109
Kory Hedman: Arizona State University, Tempe, Arizona 85281

INFORMS Journal on Computing, 2021, vol. 33, issue 3, 1037-1055

Abstract: The unit commitment problem with uncertainty is considered one of the most challenging power system scheduling problems. Different stochastic models have been proposed to solve the problem, but such approaches have yet to be applied in industry practice because of computational challenges. In practice, the problem is formulated as a deterministic model with reserve requirements to hedge against uncertainty. However, simply requiring a certain level of reserves cannot ensure power system reliability as the procured reserves may be nondispatchable because of transmission limitations. In this paper, we derive a set of feasibility cuts (constraints) for managing the unit commitment problem with uncertainty. These cuts eliminate unreliable scheduling solutions and reallocate reserves in the power system; they are induced by the extreme rays of a polyhedral dual cone. This paper shows that, with the proposed reformulation, the extreme rays of the dual cone can be characterized by combinatorial selections of transmission lines (arcs) and buses (nodes) of the power system. As a result, the cuts can then be characterized using engineering insights. The unit commitment problem with uncertainty is formulated as a deterministic model with the identified extreme ray feasibility cuts. Test results show that, with the proposed extreme ray feasibility cuts, the problem can be solved more efficiently, and the resulting scheduling decision is also more reliable.

Keywords: mixed integer programming; stochastic programming; Benders decomposition; unit commitment; polyhedral study (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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