64 citations · 119 across the 11 of their papers we have counts for
10 papers · 1 filter
Improving Feasibility via Fast Autoencoder-Based Projections
Maria Chzhen, Priya L. Donti
Enforcing complex (e.g., nonconvex) operational constraints is a critical challenge in real-world learning and control systems. However, existing methods struggle to efficiently en…
Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels
Khai Nguyen, Petros Ellinas, Anvita Bhagavathula +1
To scale optimization and simulation, prior work has explored training machine-learning surrogates that map problem parameters to solutions inexpensively at inference time. Unfortu…
PF: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations
Ana K. Rivera, Anvita Bhagavathula, Alvaro Carbonero +1
Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under…
FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees
Hoang T. Nguyen, Priya L. Donti
Efficiently solving constrained optimization problems is crucial for numerous real-world applications, yet traditional solvers are often computationally prohibitive for real-time u…
RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations
Enrico Marchesini, Benjamin Donnot, Constance Crozier +7
Reinforcement learning (RL) can provide adaptive and scalable controllers essential for power grid decarbonization. However, RL methods struggle with power grids' complex dynamics,…
Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data
Sheng Chai, Gus Chadney, Charlot Avery +3
Access to granular demand data is essential for the net zero transition; it allows for accurate profiling and active demand management as our reliance on variable renewable generat…