Listar por autor "Morales-González, Juan Miguel"
Mostrando ítems 21-34 de 34
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Learning-based state estimation in distribution systems with limited real-time measurements
Gómez de la Varga, J.; Pineda-Morente, Salvador
; Morales-González, Juan Miguel; Porras, A. (Elsevier, 2025-04)
The task of state estimation in active distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates. As a result, distribution systems are essentially unobservable ... -
Learning-based State Estimation in Distribution Systems with Limited Real-Time Measurements.
Gómez de la Varga, J.; Pineda-Morente, Salvador; Morales-González, Juan Miguel; Porras, Álvaro (2024-01)
The task of state estimation in active distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates. As a result, distribution systems are essentially unobservable ... -
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 ... -
Partition-based distributionally robust optimization via optimal transport with order cone constraints
Esteban-Pérez, Adrián; Morales-González, Juan Miguel (Springer, 2021)
In this paper we wish to tackle stochastic programs affected by ambiguity about the probability law that governs their uncertain parameters. Using optimal transport theory, we construct an ambiguity set that exploits the ... -
Predicting the electricity demand response via data-driven inverse optimization
Morales-González, Juan Miguel; Saez-Gallego, Javier (2018-07-06)
A method to predict the aggregate demand of a cluster of price-responsive consumers of electricity is discussed in this presentation. The price-response of the aggregation is modeled by an optimization problem whose ... -
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
(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 ... -
Theory and applications of Distributionally Robust Optimization with side data
(UMA Editorial, 2022)Nowadays, a large amount of varied data is being generated which, when made available to the decision maker, constitutes a valuable resource in optimization problems. These data, however, are not free from uncertainty ... -
Tight and Compact Models for Power Systems Operation.
(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 ...