4 citations · 15 across the 9 of their papers we have counts for
7 papers · 1 filter
Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow
Seonho Park, Pascal Van Hentenryck
Security-Constrained Optimal Power Flow (SCOPF) plays a crucial role in power grid stability but becomes increasingly complex as systems grow. This paper introduces PDL-SCOPF, a se…
Compact Optimization Learning for AC Optimal Power Flow
Seonho Park, Wenbo Chen, Terrence W. K. Mak +1
This paper reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability is…
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Seonho Park, Wenbo Chen, Dahye Han +2
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased…
Self-Supervised Primal-Dual Learning for Constrained Optimization
Seonho Park, Pascal Van Hentenryck
This paper studies how to train machine-learning models that directly approximate the optimal solutions of constrained optimization problems. This is an empirical risk minimization…
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
Wenbo Chen, Seonho Park, Mathieu Tanneau +1
The Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO) to clear real-time energy markets while ensuring relia…
Deep Data Density Estimation through Donsker-Varadhan Representation
Seonho Park, Panos M. Pardalos
Estimating the data density is one of the challenging problems in deep learning. In this paper, we present a simple yet effective method for estimating the data density using a dee…