5 papers
DisjunctiveNet: Neural Symbolic Learning via Differentiable Convexified Optimization Layers
Shraman Pal, Can Li
Many learning tasks in science and engineering are characterized by sparse datasets, which limits the effectiveness of purely data-driven approaches. At the same time, these proble…
Self-Supervised Learning of Parametric Approximation for Security-Constrained DC-OPF
Anderson Anrrango, André Quisaguano, Gonzalo E. Constante-Flores +1
This paper introduces a self-supervised learning framework for approximating the Security-Constrained DC Optimal Power Flow (SC-DCOPF) problem using a parametric linear model. The…
A Quadratically-Constrained Convex Approximation for the AC Optimal Power Flow
Gonzalo E. Constante-Flores, Can Li
We introduce a quadratically-constrained approximation (QCAC) of the AC optimal power flow (AC-OPF) problem. Unlike existing approximations like the DC-OPF, our model does not rely…
Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rules
Gonzalo E. Constante-Flores, Hao Chen, Can Li
Deep learning models are increasingly deployed in safety-critical tasks where predictions must satisfy hard constraints, such as physical laws, fairness requirements, or safety lim…
AC-Network-Informed DC Optimal Power Flow for Electricity Markets
Gonzalo E. Constante-Flores, André H. Quisaguano, Antonio J. Conejo +1
This paper presents a parametric quadratic approximation of the AC optimal power flow (AC-OPF) problem for time-sensitive and market-based applications. The parametric approximatio…