17 citations · 28 across the 7 of their papers we have counts for
7 papers
Trustworthiness Layer for Foundation Models in Power Systems: Application to N-k Contingency Screening
Antonio Alcántara, Spyros Chatzivasileiadis
We propose a model-agnostic trustworthiness layer that equips any foundation model (FM) for power systems with statistically valid prediction intervals. The layer offers two calibr…
Optimal placement of wind farms via quantile constraint learning
Wenxiu Feng, Antonio Alcántara, Carlos Ruiz
Wind farm placement arranges the size and the location of multiple wind farms within a given region. The power output is highly related to the wind speed on spatial and temporal le…
Leveraging Neural Networks to Optimize Heliostat Field Aiming Strategies in Concentrating Solar Power Tower Plants
Antonio Alcántara, Pablo Diaz-Cachinero, Alberto Sánchez-González +1
Concentrating Solar Power Tower (CSPT) plants rely on heliostat fields to focus sunlight onto a central receiver. Although simple aiming strategies, such as directing all heliostat…
A Quantile Neural Network Framework for Two-stage Stochastic Optimization
Antonio Alcántara, Carlos Ruiz, Calvin Tsay
Two-stage stochastic programming is a popular framework for optimization under uncertainty, where decision variables are split between first-stage decisions, and second-stage (or r…
A Neural Network-Based Distributional Constraint Learning Methodology for Mixed-Integer Stochastic Optimization
Antonio Alcántara, Carlos Ruiz
The use of machine learning methods helps to improve decision making in different fields. In particular, the idea of bridging predictions (machine learning models) and prescription…
On data-driven chance constraint learning for mixed-integer optimization problems
Antonio Alcántara, Carlos Ruiz
When dealing with real-world optimization problems, decision-makers usually face high levels of uncertainty associated with partial information, unknown parameters, or complex rela…
Optimal day-ahead offering strategy for large producers based on market price response learning
António Alcántara, Carlos Ruiz
In day-ahead electricity markets based on uniform marginal pricing, small variations in the offering and bidding curves may substantially modify the resulting market outcomes. In t…