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- Richard Li-Yang Chen, Shervin AhmadBeygi, Amy Mainville Cohn, Damian R. Beil, Amitabh Sinha
- Transportation Science
- 2009

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- Richard Li-Yang Chen, Amy Mainville Cohn, Neng Fan, Ali Pinar
- ArXiv
- 2012

—We consider the problem of designing (or augmenting) an electric power system such that it satisfies the N-k-ε survivability criterion while minimizing total cost. The survivability criterion requires that at least (1 − ε) fraction of the total demand can still be met even if any k (or fewer) of the system components fail. We formulate this problem, taking… (More)

- Richard Li-Yang Chen, Cynthia A. Phillips
- Electronic Notes in Discrete Mathematics
- 2013

- Richard Li-Yang Chen, Neng Fan, Ali Pinar, Jean-Paul Watson
- Annals OR
- 2017

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-ε reliability criterion. This reliability criterion is a generalization of the well-known N-k criterion, and dictates that at least (1 − ε j) fraction of the total system demand must be met… (More)

- Richard Li-Yang Chen, Amy Mainville Cohn, Ali Pinar
- 2011 IEEE Network Science Workshop
- 2011

We consider the problem of designing a network of minimum cost while satisfying a prescribed survivability criterion. The survivability criterion requires that a feasible flow must still exists (i.e. all demands can be satisfied without violating arc capacities) even after the disruption of a subset of the network's arcs. Specifically, we consider the case… (More)

—We consider the problem of designing (or augmenting) an electric power system such that it satisfies the N-k-ε survivability criterion while minimizing total cost. The survivability criterion requires that at least (1 − ε) fraction of the total demand can still be met even if any k (or fewer) of the system components fail. We formulate this problem, taking… (More)

—Stochastic economic dispatch models address uncertainties in forecasts of renewable generation output by considering a finite number of realizations drawn from a stochastic process model, typically via Monte Carlo sampling. Accurate evaluations of expectations or higher-order moments for quantities of interest, e.g., generating cost, can require a… (More)

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