Zobrazeno 1 - 10
of 493
pro vyhledávání: '"Morales, Juan M."'
We study decision problems under uncertainty, where the decision-maker has access to $K$ data sources that carry {\em biased} information about the underlying risk factors. The biases are measured by the mismatch between the risk factor distribution
Externí odkaz:
http://arxiv.org/abs/2407.13582
With the ongoing investment in data collection and communication technology in power systems, data-driven optimization has been established as a powerful tool for system operators to handle stochastic system states caused by weather- and behavior-dep
Externí odkaz:
http://arxiv.org/abs/2305.01775
In this paper, we tackle the resolution of chance-constrained problems reformulated via Sample Average Approximation. The resulting data-driven deterministic reformulation takes the form of a large-scale mixed-integer program cursed with Big-Ms. We i
Externí odkaz:
http://arxiv.org/abs/2205.03370
In this paper, we develop a distributionally robust chance-constrained formulation of the Optimal Power Flow problem (OPF) whereby the system operator can leverage contextual information. For this purpose, we exploit an ambiguity set based on probabi
Externí odkaz:
http://arxiv.org/abs/2109.07896
Publikováno v:
Operations Research Perspectives, Vol. 10, 100268 (2023)
We consider a two-stage generation scheduling problem comprising a forward dispatch and a real-time re-dispatch. The former must be conducted facing an uncertain net demand that includes non-dispatchable electricity consumption and renewable power ge
Externí odkaz:
http://arxiv.org/abs/2108.01003
Autor:
Rodriguez Quinteros, Ana C., Soler, Paula, Larroza, Marcela, Morales, Juan M., Gurevitz, Juan M.
Publikováno v:
In Veterinary Parasitology July 2024 329
Publikováno v:
In International Journal of Electrical Power and Energy Systems July 2024 158
In an attempt to speed up the solution of the unit commitment (UC) problem, both machine-learning and optimization-based methods have been proposed to lighten the full UC formulation by removing as many superfluous line-flow constraints as possible.
Externí odkaz:
http://arxiv.org/abs/2104.05746
This paper proposes a polynomial-time algorithm to construct the monotone stepwise curve that minimizes the sum of squared errors with respect to a given cloud of data points. The fitted curve is also constrained on the maximum number of steps it can
Externí odkaz:
http://arxiv.org/abs/2012.03697
We consider stochastic programs conditional on some covariate information, where the only knowledge of the possible relationship between the uncertain parameters and the covariates is reduced to a finite data sample of their joint distribution. By ex
Externí odkaz:
http://arxiv.org/abs/2009.10592