This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose methodology to quickly predict expected tactical descriptions operational solutions (TDOSs). The problem address occurs in context two-stage stochastic programming where second stage is demanding computationally. We aim high speed TDOS associated with problem, cond...

In the power system decarbonization roadmap, novel grid management tools and market mechanisms are fundamental to solving technical problems concerning renewable energy forecast uncertainty. This work proposes a predictive algorithm for procurement of flexibility by operator (SO), which combines SO flexible assets with active reactive short-term markets. The goal is reduce cognitive load human ...