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Mostrando ítems 21-30 de 30
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Learning‑assisted optimization for transmission switching
Pineda-Morente, Salvador; Morales-González, Juan Miguel
; Jiménez-Cordero, María Asunción
(Springer, 2024-04)
The design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising ... -
Practical Framework for Problem-Based Learning in an Introductory Circuit Analysis Course
Martín-Rivas, Sebastián; Pineda-Morente, Salvador
; Pérez-Ruiz, Juan
; Alguacil, Natalia; Ruiz-González, Antonio Francisco
(Education Conference (EDUCON) IEEE, 2020-06-25)
Este artículo presentado al Congreso EDUCON y publicado en la Plataforma IEEE Xplore proporciona ejemplos de problemas planteados en contextos reales y prácticos adecuados para implementar el aprendizaje basado en problemas ... -
Prescribing net demand for two-stage electricity generation scheduling
Morales-González, Juan Miguel; Muñoz, Miguel Ángel; Pineda-Morente, Salvador
(Elsevier, 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 ... -
Prescriptive Analytics in Electricity Markets
Muñoz Díaz, Miguel Ángel (UMA Editorial, 2022-11-03)Decision making is critical for any business to survive in a market environment. Examples of decision making tasks are inventory management, resource allocation or portfolio selection. Optimization, understood as the ... -
Tight and Compact Models for Power Systems Operation.
Porras Cabrera, Alvaro (UMA Editorial, 2024)Power systems are among the most complex and colossal engineering structures in modern society, whose operation implies a challenge due to the coordination of multiple generating units to ensure a safe and dependable energy ... -
Tight and Compact Sample Average Approximation for Joint Chance-Constrained Problems with Applications to Optimal Power Flow.
Porras, Álvaro; Domínguez Sánchez, Concepción; Morales-González, Juan Miguel; Pineda-Morente, Salvador
(INFORMS, 2023-08-02)
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 ... -
Tight big-Ms for optimal transmission switching
Pineda-Morente, Salvador; Morales-González, Juan Miguel
; Porras, Álvaro; Domínguez, Concepción (Elsevier, 2024)
This paper addresses the Optimal Transmission Switching (OTS) problem in electricity networks, which aims to find an optimal power grid topology that minimizes system operation costs while satisfying physical and operational ... -
Unifying Chance-Constrained and Robust Optimal Power Flow for Resilient Network Operations.
Uncertainty in renewable energy generation has the potential to adversely impact the operation of electric networks. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chance-constrained ... -
Warm-starting constraint generation for mixed-integer optimization: A Machine Learning approach
Jiménez-Cordero, María Asunción; Morales-González, Juan Miguel
; Pineda-Morente, Salvador
(Elsevier, 2022-10-11)
Mixed Integer Linear Programs (MILP) are well known to be NP-hard (Non-deterministic Polynomial-time hard) problems in general. Even though pure optimization-based methods, such as constraint generation, are guaranteed to ... -
Warm-starting constraint generation for mixed-integer optimization: A Machine Learning approach
Jiménez-Cordero, María Asunción; Morales-González, Juan Miguel
; Pineda-Morente, Salvador
(Elsevier, 2022-10-11)
Mixed Integer Linear Programs (MILP) are well known to be NP-hard (Non-deterministic Polynomial-time hard) problems in general. Even though pure optimization-based methods, such as constraint generation, are guaranteed to ...