1 citations · 1 across the 6 of their papers we have counts for
6 papers · 1 filter
A Spatio-Temporal Graph Learning Approach to Real-Time Economic Dispatch with Multi-Transmission-Node DER Aggregation
Zhentong Shao, Jingtao Qin, Xianbang Chen +1
The integration of distributed energy resources (DERs) into wholesale electricity markets, as mandated by FERC Order 2222, imposes new challenges on system operations. To remain co…
Neural Two-Stage Stochastic Volt-VAR Optimization for Three-Phase Unbalanced Distribution Systems with Network Reconfiguration
Zhentong Shao, Jingtao Qin, Nanpeng Yu
The increasing integration of intermittent distributed energy resources (DERs) has introduced significant variability in distribution networks, posing challenges to voltage regulat…
A Neural Column-and-Constraint Generation Method for Solving Two-Stage Stochastic Unit Commitment
Zhentong Shao, Jingtao Qin, Nanpeng Yu
Two-stage stochastic unit commitment (2S-SUC) problems have been widely adopted to manage the uncertainties introduced by high penetrations of intermittent renewable energy resourc…
Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem
Zhentong Shao, Jingtao Qin, Nanpeng Yu
This paper proposes a neural stochastic optimization method for efficiently solving the two-stage stochastic unit commitment (2S-SUC) problem under high-dimensional uncertainty sce…
Solve Large-scale Unit Commitment Problems by Physics-informed Graph Learning
Jingtao Qin, Nanpeng Yu
Unit commitment (UC) problems are typically formulated as mixed-integer programs (MIP) and solved by the branch-and-bound (B&B) scheme. The recent advances in graph neural networks…
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems
Jingtao Qin, Yuanqi Gao, Mikhail Bragin +1
Unit commitment (UC) is a fundamental problem in the day-ahead electricity market, and it is critical to solve UC problems efficiently. Mathematical optimization techniques like dy…