85 citations · 116 across the 22 of their papers we have counts for
30 papers
Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
Salvador Pineda, Juan Pérez-Ruiz, Juan Miguel Morales
The AC optimal power flow (AC-OPF) problem is essential for power system operations, but its non-convex nature makes it challenging to solve. A widely used simplification is the li…
An enhanced heuristic framework for solving the Rank Pricing Problem
Asunción Jiménez-Cordero, Salvador Pineda, Juan Miguel Morales
The Rank Pricing Problem (RPP) is a challenging bilevel optimization problem with binary variables whose objective is to determine the optimal pricing strategy for a set of product…
Practical Framework for Problem-Based Learning in an Introductory Circuit Analysis Course
Sebastian Martin, Salvador Pineda, Juan Perez-Ruiz +2
Introductory courses on electric circuits at undergraduate level are usually presented in quite abstract terms, with questions and problems quite far from practical problems. This…
Tight Big-Ms for Optimal Transmission Switching
Salvador Pineda, Juan Miguel Morales, Álvaro Porras +1
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 cos…
The Cooperative Maximal Covering Location Problem with ordered partial attractions
Concepción Domínguez, Ricardo Gázquez, Juan Miguel Morales +1
The Maximal Covering Location Problem (MCLP) is a classical location problem where a company maximizes the demand covered by placing a given number of facilities, and each demand n…
Learning-Assisted Optimization for Transmission Switching
Salvador Pineda, Juan Miguel Morales, Asunción Jiménez-Cordero
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…
Unifying Chance-Constrained and Robust Optimal Power Flow for Resilient Network Operations
Álvaro Porras, Line Roald, Juan Miguel Morales +1
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, r…
Tight and Compact Sample Average Approximation for Joint Chance-constrained Problems with Applications to Optimal Power Flow
Álvaro Porras, Concepción Domínguez, Juan M. Morales +1
In this paper, we tackle the resolution of chance-constrained problems reformulated via Sample Average Approximation. The resulting data-driven deterministic reformulation takes th…
Prescribing net demand for two-stage electricity generation scheduling
Juan M. Morales, Miguel Á. Muñoz, Salvador Pineda
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 in…
A Functional Data Analysis Approach to Evolution Outlier Detection for Grouped Smart Meters
A. Elías, J. M. Morales, S. Pineda
Smart metering infrastructures collect data almost continuously in the form of fine-grained long time series. These massive data series often have common daily patterns that are re…
Is learning for the unit commitment problem a low-hanging fruit?
S. Pineda, J. M. Morales
The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and black-box tools that have been left…
Learning the price response of active distribution networks for TSO-DSO coordination
Juan Miguel Morales, Salvador Pineda, Yury Dvorkin
The increase in distributed energy resources and flexible electricity consumers has turned TSO-DSO coordination strategies into a challenging problem. Existing decomposition/decent…
Cost-driven Screening of Network Constraints for the Unit Commitment Problem
Álvaro Porras, Salvador Pineda, Juan M. Morales +1
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 formulati…
Warm-starting constraint generation for mixed-integer optimization: A Machine Learning approach
Asunción Jiménez-Cordero, Juan Miguel Morales, Salvador Pineda
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…
An exact dynamic programming approach to segmented isotonic regression
Víctor Bucarey, Martine Labbé, Juan M. Morales +1
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…
A bilevel framework for decision-making under uncertainty with contextual information
Miguel Angel Muñoz, Salvador Pineda, Juan Miguel Morales
In this paper, we propose a novel approach for data-driven decision-making under uncertainty in the presence of contextual information. Given a finite collection of observations of…
A novel embedded min-max approach for feature selection in nonlinear support vector machine classification
Asunción Jiménez-Cordero, Juan Miguel Morales, Salvador Pineda
In recent years, feature selection has become a challenging problem in several machine learning fields, such as classification problems. Support Vector Machine (SVM) is a well-know…
Forecasting the Price-Response of a Pool of Buildings via Homothetic Inverse Optimization
Ricardo Fernández-Blanco, Juan Miguel Morales, Salvador Pineda
This paper focuses on the day-ahead forecasting of the aggregate power of a pool of smart buildings equipped with thermostatically-controlled loads. We first propose the modeling o…
An Efficient Robust Approach to the Day-ahead Operation of an Aggregator of Electric Vehicles
Álvaro Porras, Ricardo Fernández-Blanco, Juan M. Morales +1
The growing use of electric vehicles (EVs) may hinder their integration into the electricity system as well as their efficient operation due to the intrinsic stochasticity associat…
Day-ahead Operation of an Aggregator of Electric Vehicles via Optimization under Uncertainty
Álvaro Porras, Ricardo Fernández-Blanco, Juan Miguel Morales +1
We pose the aggregator's problem as a bilevel model, where the upper level minimizes the total operation costs of the fleet of EVs, while each lower level minimizes the energy avai…
How Can Smart Buildings Be Price-Responsive?
Ricardo Fernández-Blanco, Juan Miguel Morales, Salvador Pineda
The prospective participation of smart buildings in the electricity system is strongly related to the increasing active role of demand-side resources in the electrical grid. In add…
Inverse Optimization with Kernel Regression: Application to the Power Forecasting and Bidding of a Fleet of Electric Vehicles
Ricardo Fernández-Blanco, Juan Miguel Morales, Salvador Pineda +1
This paper considers an aggregator of Electric Vehicles (EVs) who aims to learn the aggregate power of his/her fleet while also participating in the electricity market. The propose…
Feature-driven Improvement of Renewable Energy Forecasting and Trading
Miguel Á. Muñoz, Juan M. Morales, Salvador Pineda
Inspired from recent insights into the common ground of machine learning, optimization and decision-making, this paper proposes an easy-to-implement, but effective procedure to enh…
Data-Driven Screening of Network Constraints for Unit Commitment
Salvador Pineda, Juan Miguel Morales, Asunción Jiménez-Cordero
The transmission-constrained unit commitment (TC-UC) problem is one of the most relevant problems solved by independent system operators for the daily operation of power systems. G…