30 citations · 76 across the 6 of their papers we have counts for
6 papers
Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems
Tianyu Zhao, Xiang Pan, Minghua Chen +1
Ensuring solution feasibility is a key challenge in developing Deep Neural Network (DNN) schemes for solving constrained optimization problems, due to inherent DNN prediction error…
DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility
Tianyu Zhao, Xiang Pan, Minghua Chen +2
Deep Neural Networks (DNNs) approaches for the Optimal Power Flow (OPF) problem received considerable attention recently. A key challenge of these approaches lies in ensuring the f…
DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems
Xiang Pan, Minghua Chen, Tianyu Zhao +1
High percentage penetrations of renewable energy generations introduce significant uncertainty into power systems. It requires grid operators to solve alternative current optimal p…
DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow
Xiang Pan, Tianyu Zhao, Minghua Chen +1
We develop DeepOPF as a Deep Neural Network (DNN) approach for solving security-constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for reliable a…
Efficient and Robust Equilibrium Strategies of Utilities in Day-ahead Market with Load Uncertainty
Tianyu Zhao, Hanling Yi, Minghua Chen +2
We consider the scenario where utilities strategically bid for electricity in the day-ahead market and balance the mismatch between the committed supply and actual demand in th…
DeepOPF: Deep Neural Network for DC Optimal Power Flow
Xiang Pan, Tianyu Zhao, Minghua Chen
We develop DeepOPF as a Deep Neural Network (DNN) approach for solving direct current optimal power flow (DC-OPF) problems. DeepOPF is inspired by the observation that solving DC-O…