3 citations · 4 across the 4 of their papers we have counts for
6 papers
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…
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…
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…
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…
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…