2,362 research outputs found
Contingency-Constrained Unit Commitment with Post-Contingency Corrective Recourse
We consider the problem of minimizing costs in the generation unit commitment
problem, a cornerstone in electric power system operations, while enforcing an
N-k-e reliability criterion. This reliability criterion is a generalization of
the well-known - criterion, and dictates that at least
fraction of the total system demand must be met following the failures of
or fewer system components. We refer to this problem as the
Contingency-Constrained Unit Commitment problem, or CCUC. We present a
mixed-integer programming formulation of the CCUC that accounts for both
transmission and generation element failures. We propose novel cutting plane
algorithms that avoid the need to explicitly consider an exponential number of
contingencies. Computational studies are performed on several IEEE test systems
and a simplified model of the Western US interconnection network, which
demonstrate the effectiveness of our proposed methods relative to current
state-of-the-art
Contingency-Constrained Unit Commitment With Intervening Time for System Adjustments
The N-1-1 contingency criterion considers the con- secutive loss of two
components in a power system, with intervening time for system adjustments. In
this paper, we consider the problem of optimizing generation unit commitment
(UC) while ensuring N-1-1 security. Due to the coupling of time periods
associated with consecutive component losses, the resulting problem is a very
large-scale mixed-integer linear optimization model. For efficient solution, we
introduce a novel branch-and-cut algorithm using a temporally decomposed
bilevel separation oracle. The model and algorithm are assessed using multiple
IEEE test systems, and a comprehensive analysis is performed to compare system
performances across different contingency criteria. Computational results
demonstrate the value of considering intervening time for system adjustments in
terms of total cost and system robustness.Comment: 8 pages, 5 figure
Computing confidence intervals on solution costs for stochastic grid generation expansion problems.
A range of core operations and planning problems for the national electrical grid are naturally formulated and solved as stochastic programming problems, which minimize expected costs subject to a range of uncertain outcomes relating to, for example, uncertain demands or generator output. A critical decision issue relating to such stochastic programs is: How many scenarios are required to ensure a specific error bound on the solution cost? Scenarios are the key mechanism used to sample from the uncertainty space, and the number of scenarios drives computational difficultly. We explore this question in the context of a long-term grid generation expansion problem, using a bounding procedure introduced by Mak, Morton, and Wood. We discuss experimental results using problem formulations independently minimizing expected cost and down-side risk. Our results indicate that we can use a surprisingly small number of scenarios to yield tight error bounds in the case of expected cost minimization, which has key practical implications. In contrast, error bounds in the case of risk minimization are significantly larger, suggesting more research is required in this area in order to achieve rigorous solutions for decision makers
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